Descriptor: "PARAMETER-ESTIMATION" - Searchworks@Jio Institute Digital Library Search Results

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173 results on '"PARAMETER-ESTIMATION"'

1. Data-efficient surrogate modeling of thermodynamic equilibria using Sobolev training, data augmentation and adaptive sampling.

Author: Winz, Joschka and Engell, Sebastian
Subjects: *DATA augmentation, *THERMODYNAMIC equilibrium, *PHASE equilibrium, *PROCESS optimization, *MACHINE learning
Abstract: Modern thermodynamic models, such as the PC-SAFT equation of state, are very accurate but also computationally intensive, which limits their applicability to process design optimization, for example. Surrogate models, which can be evaluated quickly, can be used to approximate the thermodynamic equilibria. However, this requires many data points from the flash calculation routine. In this paper, we investigate three approaches to reduce the number of samples and thus the effort needed to train the surrogate models. First, Sobolev training is used, where the surrogate model is trained not only on the output values, but also on derivative information. Second, data augmentation along the tie lines in LLE systems is proposed to generate samples without additional flash calculations. Third, adaptive sampling is revisited with a novel quality criterion. It is shown that the combination of these techniques can be used to significantly reduce the number of samples required. • An extensive neural network hyperparameter screening for pT-flash surrogate modeling is conducted. • Sobolev training and data augmentation are introduced to reduce the necessary number of samples. • Adaptive sampling is revisited and combined with Sobolev training and data augmentation for improved accuracy. [ABSTRACT FROM AUTHOR]
Published: 2024
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2. Multi-physics study on double-diffusion convective flow in an inverted T-shaped porous enclosure: ANN-based parametric estimation.

Author: Kumar, Sumant, Murthy, S.V.S.S.N.V.G. Krishna, Kumar, B.V. Rathish, and Parmar, Deepika
Subjects: *CONVECTIVE flow, *BUOYANCY, *HEAT radiation & absorption, *MASS transfer, *POROUS materials
Abstract: This study explored the multi-physics effects (chemical reaction, magnetic field, etc.) on double-diffusion natural convection within an inverted T-shaped porous enclosure saturated with hybrid nanofluid. A penalty FEM is employed for numerical simulation, whereas an ANN-based approach was proposed for efficient parameter estimation. Results show a significant reduction in computational cost using the ANN method. Streamlines, isotherms, and heat/mass transfer rates are analyzed for various flow parameters, which reveals that the higher buoyancy forces enhance convection while stronger magnetic fields suppress it. The influence of other flow parameters is complex and depends on specific parameter values. • A nonlinear mathematical model is proposed for investigating the multiphysics interactions in double-diffusive flow. • Developed nonlinear mathematical model is numerically simulated through penalty finite element approach. • Set of flow parameters identified for better convective fluid flow, heat, and mass transport characteristics. • The proposed ANN-based model efficiently predicts and saves computational time for parametric estimation. [ABSTRACT FROM AUTHOR]
Published: 2024
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3. Measuring Unipolar Traits With Continuous Response Items: Some Methodological and Substantive Developments

Author: Universitat Rovira i Virgili, Ferrando, PJ; Morales-Vives, F; Hernández-Dorado, A, Universitat Rovira i Virgili, and Ferrando, PJ; Morales-Vives, F; Hernández-Dorado, A
Abstract: In recent years, some models for binary and graded format responses have been proposed to assess unipolar variables or “quasi-traits.” These studies have mainly focused on clinical variables that have traditionally been treated as bipolar traits. In the present study, we have made a proposal for unipolar traits measured with continuous response items. The proposed log-logistic continuous unipolar model (LL-C) is remarkably simple and is more similar to the original binary formulation than the graded extensions, which is an advantage. Furthermore, considering that irrational, extreme, or polarizing beliefs could be another domain of unipolar variables, we have applied this proposal to an empirical example of superstitious beliefs. The results suggest that, in certain cases, the standard linear model can be a good approximation to the LL-C model in terms of parameter estimation and goodness of fit, but not trait estimates and their accuracy. The results also show the importance of considering the unipolar nature of this kind of trait when predicting criterion variables, since the validity results were clearly different.
Published: 2024

4. Thermo-magnetic radiative flow in porous enclosure with deep-learning parameter estimation.

Author: Kumar, Sumant, Murthy, S.V.S.S.N.V.G. Krishna, Kumar, B.V. Rathish, and Parmar, Deepika
Subjects: *RADIATIVE flow, *HEAT convection, *PARAMETER estimation, *RAYLEIGH number, *BUOYANCY, *HEAT radiation & absorption, *NATURAL heat convection, *ENTROPY
Abstract: This study numerically investigates convective heat transfer, fluid flow, and entropy generation within an inverted T-shaped porous enclosure filled with a hybrid nanofluid. The interplay between thermal radiation and a magnetic field is considered, as these factors significantly influence thermal acquisition processes in renewable and thermal engineering applications, particularly solar power collectors with the selected physical domain. By analyzing various combinations of flow parameters, this work aims to identify favorable conditions for maximizing heat transfer efficiency in such systems. A Darcy–Forchheimer–Brinkman-based mathematical model has been incorporated and numerically simulated using the penalty finite element method (FEM). Moreover, the variation of fluid flow, isotherms, and entropy generation is thoroughly analyzed for the variation of non-dimensional permeability parameter (Darcy number, D a), buoyancy force (Rayleigh number, R a), magnetic field (Hartmann number, H a), porosity value (ϵ), and thermal radiation parameter (R d). The comprehensive result demonstrates that increasing values of R a , D a , ϵ , and R d intensify entropy generation, convective heat, and fluid flow phenomena, whereas the increasing value of H a substantially weakens these phenomena. The thermal efficiency of the model attends at the lower buoyancy forces (R a ≤ 1 0 5 ) for each flow parameter (except for H a). However, the increasing magnetic forces (H a) substantially improve the thermal efficiency of the developed model. Additionally, this study employs a feed-forward neural network (FNN) based deep learning (DL) approach to predict the parametric influence on convective heat transport rate (N u m ), total entropy generation rate (S t o t), and total Bejan number due to heat transport irreversibility (B e θ , t o t ) based on the minimally supplied experimental data for FNN training process. The trained FNN model predicts the results very quickly with 98.6% accuracy compared to FEM results, demonstrating its efficiency in approximating the parametric influence of complex mathematical model results. Moreover, this approach significantly reduces computational costs and efforts compared to traditional numerical methods, making it a valuable tool for forecasting the influence of an unknown set of flow parameters in complex mathematical models. [Display omitted] • Analyzes multiphysics interactions within a solar power collector using a nonlinear mathematical model. • Identifies flow parameter combinations for maximizing thermal efficiency of the developed model. • FNN model predicts thermal efficient model with 98.6% accuracy under various flow parameters. • FNN approach significantly reduces computational cost compared to traditional methods. [ABSTRACT FROM AUTHOR]
Published: 2024
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5. Analysis of Gap Gene Regulation in a 3D Organism-Scale Model of the Drosophila melanogaster Embryo

Author: Hengenius, James B., Gribskov, Michael, Rundell, Ann E., Fowlkes, Charless C., and Umulis, David M.
Subjects: larval cuticle, zygotic loci, positional information, parameter-estimation, morphogen gradient, pattern-formation, bicoid protein, hunchback, segmentation, expression
Abstract: The axial bodyplan of Drosophila melanogaster is determined during a process called morphogenesis. Shortly after fertilization, maternal bicoid mRNA is translated into Bicoid (Bcd). This protein establishes a spatially graded morphogen distribution along the anterior-posterior (AP) axis of the embryo. Bcd initiates AP axis determination by triggering expression of gap genes that subsequently regulate each other's expression to form a precisely controlled spatial distribution of gene products. Reaction-diffusion models of gap gene expression on a 1D domain have previously been used to infer complex genetic regulatory network (GRN) interactions by optimizing model parameters with respect to 1D gap gene expression data. Here we construct a finite element reaction-diffusion model with a realistic 3D geometry fit to full 3D gap gene expression data. Though gap gene products exhibit dorsal-ventral asymmetries, we discover that previously inferred gap GRNs yield qualitatively correct AP distributions on the 3D domain only when DV-symmetric initial conditions are employed. Model patterning loses qualitative agreement with experimental data when we incorporate a realistic DV-asymmetric distribution of Bcd. Further, we find that geometry alone is insufficient to account for DV-asymmetries in the final gap gene distribution. Additional GRN optimization confirms that the 3D model remains sensitive to GRN parameter perturbations. Finally, we find that incorporation of 3D data in simulation and optimization does not constrain the search space or improve optimization results.
Published: 2011

6. Inverse modeling of cloud-aerosol interactions - Part 1: Detailed response surface analysis

Author: Partridge, D. G, Vrugt, J. A, Tunved, P., Ekman, A. M. L, Gorea, D., and Sorooshian, A.
Subjects: rainfall-runoff models, size distribution, parameter-estimation, global optimization, hydraulic-properties, droplet activation, hydrologic-models, data assimilation, calibration data, ccn activation
Abstract: New methodologies are required to probe the sensitivity of parameters describing cloud droplet activation. This paper presents an inverse modeling-based method for exploring cloud-aerosol interactions via response surfaces. The objective function, containing the difference between the measured and model predicted cloud droplet size distribution is studied in a two-dimensional framework, and presented for pseudo-adiabatic cloud parcel model parameters that are pair-wise selected. From this response surface analysis it is shown that the susceptibility of cloud droplet size distribution to variations in different aerosol physiochemical parameters is highly dependent on the aerosol environment and meteorological conditions. In general the cloud droplet size distribution is most susceptible to changes in the up-draft velocity. A shift towards an increase in the importance of chemistry for the cloud nucleating ability of particles is shown to exist somewhere between marine average and rural continental aerosol regimes. We also use these response surfaces to explore the feasibility of inverse modeling to determine cloud-aerosol interactions. It is shown that the "cloud-aerosol" inverse problem is particularly difficult to solve due to significant parameter interaction, presence of multiple regions of attraction, numerous local optima, and considerable parameter insensitivity. The identifiability of the model parameters will be dependent on the choice of the objective function. Sensitivity analysis is performed to investigate the location of the information content within the calibration data to confirm that our choice of objective function maximizes information retrieval from the cloud droplet size distribution. Cloud parcel models that employ a moving-centre based calculation of the cloud droplet size distribution pose additional difficulties when applying automatic search algorithms for studying cloud-aerosol interactions. To aid future studies, an increased resolution of the region of the size spectrum associated with droplet activation within cloud parcel models, or further development of fixed-sectional cloud models would be beneficial. Despite these improvements, it is demonstrated that powerful search algorithms remain necessary to efficiently explore the parameter space and successfully solve the cloud-aerosol inverse problem.
Published: 2011

7. The Comparison of Estimation Methods for the Four-Parameter Logistic Item Response Theory Model

Author: Ömür Kaya Kalkan
Subjects: Quasi-Monte-Carlo, Statistics and Probability, Robbins-Monro Algorithm, Applied Mathematics, Sample-Size, 4PL IRT model, Maximum-Likelihood-Estimation, Irt Models, Education, Quasi-Monte Carlo EM, Mirt, expectation-maximization, Parameter-Estimation, Metropolis-Hastings Robbins-Monro, upper asymptote, Ability, Unidimensionality
Abstract: The four-parameter logistic (4PL) Item Response Theory (IRT) model has recently been reconsidered in the literature due to the advances in the statistical modeling software and the recent developments in the estimation of the 4PL IRT model parameters. The current simulation study evaluated the performance of expectation-maximization (EM), Quasi-Monte Carlo EM (QMCEM), and Metropolis-Hastings Robbins-Monro (MH-RM) estimation methods for the item parameters in the 4PL IRT model under the manipulated study conditions, including the number of factors, the correlation between factors, and test length. The results indicated that there was no method to be recommended as the best one among the three estimation algorithms for the estimation of 4PL item parameters accurately across all study conditions. However, using the MH-RM algorithm for 4PL model item parameter estimation can be suggested when the number of factors is 2 or 3. In addition, it may be advised to prefer long test lengths rather than shorter test lengths (n = 24), as three algorithms provide better item parameter estimates at long test lengths (n = 48).
Published: 2022

8. Overcoming the modeling bottleneck: A methodology for dynamic gray-box modeling with optimized training data.

Author: Winz, Joschka, Fromme, Florian, and Engell, Sebastian
Subjects: *DYNAMIC models, *DIFFERENTIAL-algebraic equations, *MACHINE learning, *PROCESS optimization, *PARAMETER estimation, *REAL-time control
Abstract: For model-based control and real-time optimization, the existence of sufficiently reliable process models is a prerequisite. The development of such models often is the bottleneck in developing advanced solutions and particularly challenging in cases where the underlying physico-chemical phenomena are not fully understood. When process data is available, machine learning (ML) approaches can be used to build data-based models but the validity of such models is limited to the conditions for which sufficiently rich training data was available. To combine the flexibility of ML-models and the reliability of purely mechanistic models, gray-box models can be applied. In this work we consider gray-box models in which ML-submodels are embedded into differential–algebraic equations that have been derived from first principles. To identify the ML-submodels is challenging as no explicit input/output data for the submodels is available. Therefore, suitable training data must first be estimated from the available data. We propose a systematic methodology for developing gray-box models in a sequence of steps, employing specific regularizations and numerical techniques. We apply the methodology to two case studies: a distillation column with a reaction occurring in the rectifying section, and a fermentation process of a sporulating bacterium. The value of the fermentation model is demonstrated by its use in dynamic process optimization. [Display omitted] • Method to identify dynamic gray-box models systematically rather than by trial and error. • Optimization-based training data estimation with subsequent full dynamic parameter estimation. • Regularization with modified Huber loss function. • Successful application to a distillation column and a fermentation process. [ABSTRACT FROM AUTHOR]
Published: 2023
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9. Meaningful change definitions

Author: Gjalt-Jorn Peters, Stefan L. K. Gruijters, Section Applied Social Psychology, RS: FPN WSP II, Section General Psychology, RS-Research Line General psychology (part of IIESB program), Section Methodology & Statistics, and RS-Research Line Methodology & statistics (part of IIESB program)
Subjects: meaningful change definitions, Operations research, ACCURACY, Psychological intervention, bepress|Social and Behavioral Sciences|Psychology|Quantitative Psychology, Plan (drawing), Effect size, PsyArXiv|Social and Behavioral Sciences|Health Psychology, 03 medical and health sciences, 0302 clinical medicine, STATISTICAL POWER, practical significance, PSYCHOLOGICAL-RESEARCH, smallest effect size of interest, Humans, 030212 general & internal medicine, Study planning, Applied Psychology, intervention research, HEALTH-STATUS, 030505 public health, Data Collection, sample size planning, Public Health, Environmental and Occupational Health, General Medicine, General Chemistry, DIFFERENCE, Test (assessment), PsyArXiv|Social and Behavioral Sciences, Intervention (law), bepress|Social and Behavioral Sciences|Psychology|Health Psychology, Sample size determination, Research Design, Sample Size, Intervention research, bepress|Social and Behavioral Sciences, A priori and a posteriori, PsyArXiv|Social and Behavioral Sciences|Quantitative Methods, DISPLAY, 0305 other medical science, Psychology, PARAMETER-ESTIMATION
Abstract: Experimental tests of interventions need to have sufficient sample size to constitute a robust test of the intervention's effectiveness with reasonable precision and power. To estimate the required sample size adequately, researchers are required to specify an effect size. But what effect size should be used to plan the required sample size? Various inroads into selecting the a priori effect size have been suggested in the literature-including using conventions, prior research, and theoretical or practical importance. In this paper, we first discuss problems with some of the proposed methods of selecting the effect size for study planning. We then lay out a method for intervention researchers that provides a way out of many of these problems. The proposed method requires setting a meaningful change definition, it is specifically suited for applied researchers interested in planning tests of intervention effectiveness. We provide a hands-on walk through of the method and provide easy-to-use R functions to implement it.
Published: 2022

10. Addressing the relative degree restriction in nonlinear adaptive observers: A high-gain observer approach

Author: Universitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial, Universitat Politècnica de Catalunya. SAC - Sistemes Avançats de Control, Cecilia Piñol, Andreu, Costa Castelló, Ramon, Universitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial, Universitat Politècnica de Catalunya. SAC - Sistemes Avançats de Control, Cecilia Piñol, Andreu, and Costa Castelló, Ramon
Abstract: The design of adaptive observers is a common approach for the joint state and parameter-estimation problem. Nonetheless, there are still some obstacles that have to be solved to improve the design of adaptive observers and extend its implementability to a larger class of systems. First, the separation of the state-estimation and the parameter-estimation requires a relative degree one or zero between some known signal and the parameters to be estimated. Second, standard stability proofs for adaptive observers cannot be easily extended to consider the unavoidable presence of sensor noise and unmodelled system uncertainty. Consequently, on the one hand, this work proposed a methodology to relax the relative degree condition through the use of a high-gain observer that will be coupled with the adaptive observer. On the other hand, the stability and performance of the proposed observer scheme will be analysed by the use of a strict Lyapunov function based on the Mazenc construction, which allows to have provable convergence and to study the effect of sensor noise and model uncertainty through common Lyapunov theory. Finally, the proposed approach is validated in a compartmental epidemiology model., Peer Reviewed, Postprint (author's final draft)
Published: 2022

11. Behind the scenes of streamflow model performance

Author: L. J. E. Bouaziz, F. Fenicia, G. Thirel, T. de Boer-Euser, J. Buitink, C. C. Brauer, J. De Niel, B. J. Dewals, G. Drogue, B. Grelier, L. A. Melsen, S. Moustakas, J. Nossent, F. Pereira, E. Sprokkereef, J. Stam, A. H. Weerts, P. Willems, H. H. G. Savenije, M. Hrachowitz, Delft University of Technology (TU Delft), Department Catchment and Urban Hydrology, Deltares, Boussinesqweg 1, 2629 HV Delft, the Netherlands, Swiss Federal Insitute of Aquatic Science and Technology [Dübendorf] (EAWAG), Hydrosystèmes continentaux anthropisés : ressources, risques, restauration (UR HYCAR), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Wageningen University and Research [Wageningen] (WUR), Catholic University of Leuven - Katholieke Universiteit Leuven (KU Leuven), Université de Liège, Centre de Recherche en Géographie (LOTERR), Université de Lorraine (UL), Vrije Universiteit Brussel [Bruxelles] (VUB), Flanders Hydraulics Research, Ministry of Infrastructure and Water Management, Zuiderwagenplein 2, 8224 AD Lelystad, the Netherlands, EU-FP6 project UERRA, and Copernicus Climate ChangeService and the data providers in the ECA&D project
Subjects: 010504 meteorology & atmospheric sciences, 0208 environmental biotechnology, SENSITIVITY-ANALYSIS, 02 engineering and technology, Structural basin, SOIL-MOISTURE, Hydrology and Quantitative Water Management, Atmospheric sciences, 01 natural sciences, lcsh:Technology, lcsh:TD1-1066, Streamflow, Life Science, Geosciences, Multidisciplinary, PART 2, [SDU.STU.HY]Sciences of the Universe [physics]/Earth Sciences/Hydrology, lcsh:Environmental technology. Sanitary engineering, [SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces, environment, Water content, RAINFALL-RUNOFF MODEL, lcsh:Environmental sciences, 0105 earth and related environmental sciences, lcsh:GE1-350, Science & Technology, WIMEK, INTERCOMPARISON PROJECT, lcsh:T, lcsh:Geography. Anthropology. Recreation, Geology, DISTRIBUTED HYDROLOGICAL MODELS, [SHS.GEO]Humanities and Social Sciences/Geography, CLIMATE-CHANGE IMPACT, Snow, [SDE.ES]Environmental Sciences/Environmental and Society, 020801 environmental engineering, Current (stream), Water resources, Variable (computer science), lcsh:G, 13. Climate action, Physical Sciences, SATELLITE DATA, Water Resources, SIMULATOR WALRUS, Environmental science, Interception, PARAMETER-ESTIMATION, Hydrologie en Kwantitatief Waterbeheer
Abstract: Streamflow is often the only variable used to evaluate hydrological models. In a previous international comparison study, eight research groups followed an identical protocol to calibrate 12 hydrological models using observed streamflow of catchments within the Meuse basin. In the current study, we quantify the differences in five states and fluxes of these 12 process-based models with similar streamflow performance, in a systematic and comprehensive way. Next, we assess model behavior plausibility by ranking the models for a set of criteria using streamflow and remote-sensing data of evaporation, snow cover, soil moisture and total storage anomalies. We found substantial dissimilarities between models for annual interception and seasonal evaporation rates, the annual number of days with water stored as snow, the mean annual maximum snow storage and the size of the root-zone storage capacity. These differences in internal process representation imply that these models cannot all simultaneously be close to reality. Modeled annual evaporation rates are consistent with Global Land Evaporation Amsterdam Model (GLEAM) estimates. However, there is a large uncertainty in modeled and remote-sensing annual interception. Substantial differences are also found between Moderate Resolution Imaging Spectroradiometer (MODIS) and modeled number of days with snow storage. Models with relatively small root-zone storage capacities and without root water uptake reduction under dry conditions tend to have an empty root-zone storage for several days each summer, while this is not suggested by remote-sensing data of evaporation, soil moisture and vegetation indices. On the other hand, models with relatively large root-zone storage capacities tend to overestimate very dry total storage anomalies of the Gravity Recovery and Climate Experiment (GRACE). None of the models is systematically consistent with the information available from all different (remote-sensing) data sources. Yet we did not reject models given the uncertainties in these data sources and their changing relevance for the system under investigation.
Published: 2021

12. Addressing the relative degree restriction in nonlinear adaptive observers: A high-gain observer approach

Author: Andreu Cecilia, Ramon Costa-Castelló, Ministerio de Economía y Competitividad (España), Ministerio de Ciencia, Innovación y Universidades (España), Agencia Estatal de Investigación (España), Consejo Superior de Investigaciones Científicas (España), Universitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial, and Universitat Politècnica de Catalunya. SAC - Sistemes Avançats de Control
Subjects: Robust estimation, Parameter-estimation, Informàtica::Automàtica i control [Àrees temàtiques de la UPC], Observers (Control theory), Computer Networks and Communications, Control and Systems Engineering, Applied Mathematics, Signal Processing, Strict Lyapunov functions, Adaptive systems, Nonlinear observer design, Compartmental model, Observadors (Teoria de control)
Abstract: The design of adaptive observers is a common approach for the joint state and parameter-estimation problem. Nonetheless, there are still some obstacles that have to be solved to improve the design of adaptive observers and extend its implementability to a larger class of systems. First, the separation of the state-estimation and the parameter-estimation requires a relative degree one or zero between some known signal and the parameters to be estimated. Second, standard stability proofs for adaptive observers cannot be easily extended to consider the unavoidable presence of sensor noise and unmodeled system uncertainty. Consequently, on the one hand, this work proposed a methodology to relax the relative degree condition through the use of a high-gain observer that will be coupled with the adaptive observer. On the other hand, the stability and performance of the proposed observer scheme will be analyzed by the use of a strict Lyapunov function based on the Mazenc construction, which allows to have provable convergence and to study the effect of sensor noise and model uncertainty through common Lyapunov theory. Finally, the proposed approach is validated in a compartmental epidemiology model., This work has been partially funded by the Spanish State Research Agency through the María de Maeztu Seal of Excellence to IRI (MDM-2016-0656) and by the project DOVELAR (ref. RTI2018-096001-B-C32).
Published: 2022

13. The comparison of the dimensionality results provided by the automated item selection procedure and DETECT analysis

Author: Ezgi MOR DİRLİK and Seval KARTAL
Subjects: procedure, Nonparametric Approach, Mokken scaling DETECT, Mokken Scale, Ocean Engineering, Automated item selection, Multidimensionality, Dimensionality, Automated item selection procedure, Non parametric item response theory, Non-parametric item, Index, response theory, Parameter-Estimation, Education and Educational Research, Eğitim, Eğitim Araştırmaları, Unidimensionality
Abstract: The dimensionality is one of the most investigated concepts in the psychological assessment, and there are many ways to determine the dimensionality of a measured construct. The Automated Item Selection Procedure (AISP) and the DETECT are non-parametric methods aiming to determine the factorial structure of a data set. In the current study, dimensionality results provided by the two methods were compared based on the original factorial structure defined by the scale developers. For the comparison of the two methods, the data was obtained by implementing a scale measuring academic dishonesty levels of bachelor students. The scale was conducted on junior students studying at a public and a private university. The dataset was analyzed by using the AISP and DETECT analyses. The “mokken” and “sirt” packages on the R program were utilized for the AISP and DETECT analyses, respectively. The similarities and differences between the findings provided by the methods were analyzed depending on the original factor structure of the scale verified by the scale developers.
Published: 2022

14. Generalized Gamma-Laguerre polynomial chaos to model random bending of wearable antennas

Author: Hendrik Rogier
Subjects: Technology and Engineering, DESIGN, Statistical analysis, TEXTILE ANTENNAS, Microstrip antennas, EXPANSION, Electrical and Electronic Engineering, Random variables, UNCERTAINTY QUANTIFICATION, flexible electronics, PARAMETER-ESTIMATION
Abstract: A novel generalized Gamma-Laguerre polynomial chaos expansion is proposed to account for the effect of random variations in lower-bounded design parameters on antenna performance. After fitting a shifted generalized Gamma distribution to data sets of such random variables, a predistorted polynomial chaos expansion is generated based on a set of orthogonal generalized Laguerre polynomials. The new statistical methodology is applied to assess the random change in resonance frequency when bending a wearable antenna around different parts of the human body, such as a leg, an arm and a head. For different data sets, an excellent statistical fit is found to both the estimated probability density function of the bending radius and the resulting statistical distribution of the resonance frequency, while requiring up to 480 times fewer sample evaluations.
Published: 2022

15. Dark Energy Survey Year 3 results: Cosmology with moments of weak lensing mass maps

Author: Gatti, M., Jain, B., Chang, C., Raveri, M., Zürcher, D., Secco, L., Whiteway, L., Jeffrey, N., Doux, C., Kacprzak, T., Bacon, D., Fosalba, P., Alarcon, A., Amon, A., Bechtol, K., Becker, M., Bernstein, G., Blazek, J., Campos, A., Choi, A., Davis, C., Derose, J., Dodelson, S., Elsner, F., Elvin-Poole, J., Everett, S., Ferte, A., Gruen, D., Harrison, I., Huterer, D., Jarvis, M., Krause, E., Leget, P. F., Lemos, P., Maccrann, N., Mccullough, J., Muir, J., Myles, J., Navarro, A., Pandey, S., Prat, J., Rollins, R. P., Roodman, A., Sanchez, C., Sheldon, E., Shin, T., Troxel, M., Tutusaus, I., Yin, B., Aguena, M., Allam, S., Andrade-Oliveira, F., Annis, J., Bertin, E., Brooks, D., Burke, D. L., Carnero Rosell, A., Carrasco Kind, M., Carretero, J., Cawthon, R., Costanzi, M., da Costa, L. N., Pereira, M. E. S., De Vicente, J., Desai, S., Diehl, H. T., Dietrich, J. P., Doel, P., Drlica-Wagner, A., Eckert, K., Evrard, A. E., Ferrero, I., García-Bellido, J., Gaztanaga, E., Giannantonio, T., Gruendl, R. A., Gschwend, J., Gutierrez, G., Hinton, S. R., Hollowood, D. L., Honscheid, K., James, D. J., Kuehn, K., Kuropatkin, N., Lahav, O., Lidman, C., Maia, M. A. G., Marshall, J. L., Melchior, P., Menanteau, F., Miquel, R., Morgan, R., Palmese, A., Paz-Chinchón, F., Pieres, A., Plazas Malagón, A. A., Reil, K., Rodriguez-Monroyv, M., Romer, A. K., Sanchez, E., Schubnell, M., Serrano, S., Sevilla-Noarbe, I., Smith, M., Soares-Santos, M., Suchyta, E., Tarle, G., Thomas, D., To, C., Varga, T. N., DES Collaboration, Gatti, M., Jain, B., Chang, C., Raveri, M., Zürcher, D., Secco, L., Whiteway, L., Jeffrey, N., Doux, C., Kacprzak, T., Bacon, D., Fosalba, P., Alarcon, A., Amon, A., Bechtol, K., Becker, M., Bernstein, G., Blazek, J., Campos, A., Choi, A., Davis, C., Derose, J., Dodelson, S., Elsner, F., Elvin-Poole, J., Everett, S., Ferte, A., Gruen, D., Harrison, I., Huterer, D., Jarvis, M., Krause, E., Leget, P. F., Lemos, P., Maccrann, N., Mccullough, J., Muir, J., Myles, J., Navarro, A., Pandey, S., Prat, J., Rollins, R. P., Roodman, A., Sanchez, C., Sheldon, E., Shin, T., Troxel, M., Tutusaus, I., Yin, B., Aguena, M., Allam, S., Andrade-Oliveira, F., Annis, J., Bertin, E., Brooks, D., Burke, D. L., Carnero Rosell, A., Carrasco Kind, M., Carretero, J., Cawthon, R., Costanzi, M., da Costa, L. N., Pereira, M. E. S., De Vicente, J., Desai, S., Diehl, H. T., Dietrich, J. P., Doel, P., Drlica-Wagner, A., Eckert, K., Evrard, A. E., Ferrero, I., García-Bellido, J., Gaztanaga, E., Giannantonio, T., Gruendl, R. A., Gschwend, J., Gutierrez, G., Hinton, S. R., Hollowood, D. L., Honscheid, K., James, D. J., Kuehn, K., Kuropatkin, N., Lahav, O., Lidman, C., Maia, M. A. G., Marshall, J. L., Melchior, P., Menanteau, F., Miquel, R., Morgan, R., Palmese, A., Paz-Chinchón, F., Pieres, A., Plazas Malagón, A. A., Reil, K., Rodriguez-Monroyv, M., Romer, A. K., Sanchez, E., Schubnell, M., Serrano, S., Sevilla-Noarbe, I., Smith, M., Soares-Santos, M., Suchyta, E., Tarle, G., Thomas, D., To, C., Varga, T. N., Des, Collaboration, UAM. Departamento de Física Teórica, Ministerio de Ciencia, Innovación y Universidades (España), European Research Council, European Commission, Agencia Estatal de Investigación (España), Generalitat de Catalunya, Ministerio de Economía y Competitividad (España), Department of Energy (US), National Aeronautics and Space Administration (US), and National Science Foundation (US)
Subjects: Gravitational Lensing, data-compression, inference, Cosmology and Nongalactic Astrophysics (astro-ph.CO), Física, FOS: Physical sciences, Astrophysics::Cosmology and Extragalactic Astrophysics, parameter-estimation, universe, Dark Energy, Astrophysic, cosmic shear 2-point, model predictions, galaxy, simulations, Weak, peak statistics, constraints, Cosmology and Nongalactic Astrophysics, Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract: DES Collaboration: M. Gatti et al., We present a cosmological analysis using the second and third moments of the weak lensing mass (convergence) maps from the first three years of data (Y3) data of the Dark Energy Survey. The survey spans an effective area of 4139 square degrees and uses the images of over 100 million galaxies to reconstruct the convergence field. The second moment of the convergence as a function of smoothing scale contains information similar to standard shear 2-point statistics. The third moment, or the skewness, contains additional non-Gaussian information. The data is analyzed in the context of the ΛCDM model, varying five cosmological parameters and 19 nuisance parameters modeling astrophysical and measurement systematics. Our modeling of the observables is completely analytical, and has been tested with simulations in our previous methodology study. We obtain a 1.7% measurement of the amplitude of fluctuations parameter S8≡σ8(Ωm/0.3)0.5=0.784±0.013. The measurements are shown to be internally consistent across redshift bins, angular scales, and between second and third moments. In particular, the measured third moment is consistent with the expectation of gravitational clustering under the ΛCDM model. The addition of the third moment improves the constraints on S8 and Ωm by ∼15% and ∼25% compared to an analysis that only uses second moments. We compare our results with Planck constraints from the cosmic microwave background, finding a 2.2–2.8σ tension in the full parameter space, depending on the combination of moments considered. The third moment, independently, is in 2.8σ tension with Planck, and thus provides a cross-check on the analyses of 2-point correlations., Funding for the DES Projects has been provided by the U.S. Department of Energy, the U.S. National Science Foundation, the Ministry of Science and Education of Spain, the Science and Technology Facilities Council of the United Kingdom, the Higher Education Funding Council for England, the National Center for Supercomputing Applications at the University of Illinois at Urbana-Champaign, the Kavli Institute of Cosmological Physics at the University of Chicago, the Center for Cosmology and Astro-Particle Physics at the Ohio State University, the Mitchell Institute for Fundamental Physics and Astronomy at Texas A&M University, Financiadora de Estudos e Projetos, Fundação Carlos Chagas Filho de Amparo a Pesquisa do Estado do Rio de Janeiro, Conselho Nacional de Desenvolvimento Científico e Tecnológico and the Ministerio da Ciência, Tecnologia e Inovação, the Deutsche Forschungsgemeinschaft and the Collaborating Institutions in the Dark Energy Survey. The Collaborating Institutions are Argonne National Laboratory, the University of California at Santa Cruz, the University of Cambridge, Centro de Investigaciones Energeticas, Medioambientales y Tecnológicas-Madrid, the University of Chicago, University College London, the DES-Brazil Consortium, the University of Edinburgh, the Eidgenössische Technische Hochschule (ETH) Zürich, Fermi National Accelerator Laboratory, the University of Illinois at Urbana-Champaign, the Institut de Ciencies de l’Espai (IEEC/CSIC), the Institut de Física d’Altes Energies, Lawrence Berkeley National Laboratory, the Ludwig-Maximilians Universität München and the associated Excellence Cluster Universe, the University of Michigan, NSF’s NOIRLab, the University of Nottingham, The Ohio State University, the University of Pennsylvania, the University of Portsmouth, SLAC National Accelerator Laboratory, Stanford University, the University of Sussex, Texas A&M University, and the OzDES Membership Consortium. Based in part on observations at Cerro Tololo Inter-American Observatory at NSF’s NOIRLab (NOIRLab Prop. ID 2012B-0001; PI: J. Frieman), which is managed by the Association of Universities for Research in Astronomy (AURA) under a cooperative agreement with the National Science Foundation. The DES data management system is supported by the National Science Foundation under Grants No. AST-1138766 and No. AST-1536171. The DES participants from Spanish institutions are partially supported by MICINN under Grants No. ESP2017-89838, No. PGC2018-094773, No. PGC2018-102021, No. SEV-2016-0588, No. SEV-2016-0597, and No. MDM-2015-0509, some of which include ERDF funds from the European Union. I. F. A. E. is partially funded by the CERCA program of the Generalitat de Catalunya. Research leading to these results has received funding from the European Research Council under the European Union’s Seventh Framework Program (FP7/2007-2013) including ERC Grant agreements No. 240672, No. 291329, and No. 306478. We acknowledge support from the Brazilian Instituto Nacional de Ciência e Tecnologia (INCT) do e-Universo (CNPq Grant No. 465376/2014-2). This manuscript has been authored by Fermi Research Alliance, LLC under Contract No. DEAC02-07CH11359 with the U.S. Department of Energy, Office of Science, Office of High Energy Physics.
Published: 2022

16. Pre-merger Localization of Compact-binary Mergers with Third-generation Observatories

Author: Nitz, Alexander H., Dal Canton, Tito, Nitz, Alexander H., and Dal Canton, Tito
Abstract: We present the prospects for the pre-merger detection and localization of binary neutron star mergers with third-generation gravitational-wave (GW) observatories. We consider a wide variety of GW networks that may be operating in the 2030s and beyond; these networks include up to two Cosmic Explorer (CE) sites, the Einstein Telescope (ET), and continued observation with the existing second-generation ground-based detectors. For a fiducial local merger rate of 300 Gpc-3 yr-1, we find that the ET on its own is able to detect six and two sources per year at 5 and 30 minutes before merger, respectively, while providing a localization of <10 deg2. A single CE would detect but be unable to localize sources on its own. A two-detector CE network, however, would detect 22 and 0.4 mergers per year using the same criteria. A full three-detector network with the operation of dual CEs and the ET would allow for <1 deg2 source localization at 5 minutes before merger for ∼seven sources per year. Given the dramatic increase in localization and detection capabilities, third-generation observatories will enable the regular observation of the prompt emission of mergers by a broad array of observatories including gamma-ray, X-ray, and optical telescopes. Moreover, sub-degree localizations minutes before merger, combined with narrow-field-of-view high-energy telescopes, could strongly constrain the high-energy pre-merger emission models proposed in the last decade.
Published: 2021

17. Analysing point patterns on networks — A review

Author: Baddeley, Adrian, Nair, Gopalan, Rakshit, Suman, McSwiggan, Greg, Davies, Tilman, Baddeley, Adrian, Nair, Gopalan, Rakshit, Suman, McSwiggan, Greg, and Davies, Tilman
Abstract: We review recent research on statistical methods for analysing spatial patterns of points on a network of lines, such as road accident locations along a road network. Due to geometrical complexities, the analysis of such data is extremely challenging, and we describe several common methodological errors. The intrinsic lack of homogeneity in a network militates against the traditional methods of spatial statistics based on stationary processes. Topics include kernel density estimation, relative risk estimation, parametric and non-parametric modelling of intensity, second-order analysis using the K-function and pair correlation function, and point process model construction. An important message is that the choice of distance metric on the network is pivotal in the theoretical development and in the analysis of real data. Challenges for statistical computation are discussed and open-source software is provided.
Published: 2021

18. Power System Dynamic State Estimation: Motivations, Definitions, Methodologies, and Future Work

Author: Marcos Netto, Abhinav Kumar Singh, Bikash C. Pal, A. P. Sakis Meliopoulos, Innocent Kamwa, Antonio Gomez-Exposito, Vladimir Terzija, Ali Abur, Zhenyu Huang, Junbo Zhao, Lamine Mili, and Junjian Qi
Subjects: Technology, Computer science, 020209 energy, Reliability (computer networking), NONLINEAR CONTROL, Energy Engineering and Power Technology, 02 engineering and technology, Electric power system, Engineering, power system dynamics, power system protection, ADAPTIVE PROTECTION, 0202 electrical engineering, electronic engineering, information engineering, synchrophasor measurements, tracking state estimation (TSE), bad data, Electrical and Electronic Engineering, Resilience (network), Dynamic state estimation (DSE), power system stability and control, Science & Technology, Energy, IDENTIFICATION, Estimation theory, PMU DATA, Engineering, Electrical & Electronic, robust estimation, Kalman filter, PERFORMANCE, static state estimation (SSE), Industrial engineering, forecasting-aided state estimation (FASE), 0906 Electrical and Electronic Engineering, UNSCENTED KALMAN FILTER, State (computer science), Power engineering, parameter estimation, Power-system protection, Kalman filtering, PARAMETER-ESTIMATION
Abstract: This paper summarizes the technical activities of the Task Force on Power System Dynamic State and Parameter Estimation. This Task Force was established by the IEEE Working Group on State Estimation Algorithms to investigate the added benefits of dynamic state and parameter estimation for the enhancement of the reliability, security, and resilience of electric power systems. The motivations and engineering values of dynamic state estimation (DSE) are discussed in detail. Then, a set of potential applications that will rely on DSE is presented and discussed. Furthermore, a unified framework is proposed to clarify the important concepts related to DSE, forecasting-aided state estimation, tracking state estimation, and static state estimation. An overview of the current progress in DSE and dynamic parameter estimation is provided. The paper also provides future research needs and directions for the power engineering community.
Published: 2019

19. An auto-deployed model-based fault detection and diagnosis approach for Air Handling Units using BIM and Modelica

Author: Ando Andriamamonjy, Dirk Saelens, and Ralf Klein
Subjects: Technology, Engineering, Civil, Modelica, Computer science, 020209 energy, Model based method, COLLABORATIVE DESIGN, 0211 other engineering and technologies, GENETIC ALGORITHM, 02 engineering and technology, Fault detection and isolation, Damper, Engineering, SYSTEMS, Fault Detection and Diagnosis (FDD), 021105 building & construction, 0202 electrical engineering, electronic engineering, information engineering, METHODOLOGIES, STRATEGY, OPTIMIZATION, Civil and Structural Engineering, computer.programming_language, Industry Foundation Class (IFC), Science & Technology, BUILDING ENERGY, Estimation theory, business.industry, PROGNOSTICS, Building Information Model (BIM), Building and Construction, Python (programming language), Reliability engineering, Building information modeling, Control and Systems Engineering, Software deployment, SIMULATION, Construction & Building Technology, Prognostics, business, computer, PARAMETER-ESTIMATION
Abstract: © 2018 Elsevier B.V. The Air Handling Unit (AHU) is one of the most energy consuming devices in building systems. Fault Detection and Diagnosis (FDD) methods integrated into AHUs can help to ensure that they comply with the intended design, and their efficiency is maintained throughout the entire operational stage of the building. Nonetheless, the implementation and deployment of FDDs at the operational stage require an extensive effort. Especially, FDD approaches that rely on first principle models (model-based FDD) need to be manually implemented, and the information necessary for this process is scattered between several exchange formats and files, thus making it time-consuming, error-prone and subject to modellers’ poor judgment. This study aims at facilitating and partially automating the implementation and deployment of model-based FDD. An automated tool-chain that combines a BIM (Building Information Model)-to-BEPS (Building Energy Performance Simulation) tool with a model-based FDD approach is developed. The contribution of this paper lies in the extension of an existing BIM to Modelica BEPS method with an automated calibration approach and a novel model-based FDD. These three elements are integrated in a framework (implemented using Python) to reduce experts’ involvement in FDD implementation and deployment. The developed model-based FDD combines a parity relation procedure for fault detection and profile identification for fault diagnosis. The latter uses the robust multi-objective optimisation algorithm NSGA-2. An error is detected when the difference between prediction and measured data over a specific time window is superior to a predefined threshold. The origin of the error is subsequently identified by estimating the profile of the different controllable components’ control signal. The developed tool-chain was applied to an actual AHU as well as on several numerical scenarios to identify typical AHU faults such as faulty dampers, valves and sensors. This study shows that the developed model-based FDD approach can identify some of the most common faults in AHUs, but more importantly that BIM can facilitate the deployment of model-based FDD in building systems. ispartof: Automation in Construction vol:96 pages:508-526 status: published
Published: 2018

20. TEMPERED PARETO-TYPE MODELLING USING WEIBULL DISTRIBUTIONS

Author: Jan Beirlant, José Carlos Araujo-Acuna, and Hansjörg Albrecher
Subjects: Mathematics, Interdisciplinary Applications, Economics and Econometrics, Economics, Statistics & Probability, Social Sciences, Mathematics - Statistics Theory, Statistics Theory (math.ST), 01 natural sciences, 010104 statistics & probability, Accounting, Business & Economics, tail estimation, 0502 economics and business, Range (statistics), Econometrics, FOS: Mathematics, 0101 mathematics, Weibull tempering, Weibull distribution, Mathematics, TAIL, 050208 finance, Science & Technology, Estimation theory, 05 social sciences, Pareto principle, Estimator, Liability insurance, Function (mathematics), Social Sciences, Mathematical Methods, heavy tails, Physical Sciences, peaks-over-threshold, Finance, Mathematical Methods In Social Sciences, Quantile, PARAMETER-ESTIMATION
Abstract: In various applications of heavy-tail modelling, the assumed Pareto behaviour is tempered ultimately in the range of the largest data. In insurance applications, claim payments are influenced by claim management and claims may, for instance, be subject to a higher level of inspection at highest damage levels leading to weaker tails than apparent from modal claims. Generalizing earlier results of Meerschaert et al. (2012) and Raschke (2020), in this paper we consider tempering of a Pareto-type distribution with a general Weibull distribution in a peaks-over-threshold approach. This requires to modulate the tempering parameters as a function of the chosen threshold. Modelling such a tempering effect is important in order to avoid overestimation of risk measures such as the value-at-risk at high quantiles. We use a pseudo maximum likelihood approach to estimate the model parameters and consider the estimation of extreme quantiles. We derive basic asymptotic results for the estimators, give illustrations with simulation experiments and apply the developed techniques to fire and liability insurance data, providing insight into the relevance of the tempering component in heavy-tail modelling.
Published: 2021

21. Impact of Retrofitting and Item Ordering on DIF

Author: Lokman AKBAY
Subjects: dina model, LC8-6691, Computer science, cognitive diagnosis models, retrofitting, Cdm, booklet affect, DINA model, Special aspects of education, Differential item functioning, Inferences, Education, Reliability engineering, Social, Parameter-Estimation, differential item functioning,DINA model,retrofitting,booklet affect,cognitive diagnosis models, Developmental and Educational Psychology, differential item functioning, Retrofitting, Diagnostic Classification Models, Sosyal
Abstract: Richer diagnostic information about examinees’ cognitive strength and weaknesses are obtained from cognitively diagnostic assessments (CDA) when a proper cognitive diagnosis model (CDM) is used for response data analysis. To do so, researchers state that a preset cognitive model specifying the underlying hypotheses about response data structure is needed. However, many real data CDM applications are adds-on to simulation studies and retrofitted to data obtained from non-CDAs. Such a procedure is referred to as retrofitting, and fitting CDMs to traditional test data is not uncommon. To deal with a major validity concern of item/test bias in CDAs, some recent DIF detection techniques compatible with various CDMs have been proposed. This study employs several DIF detection techniques developed based on CTT, IRT, and CDM frameworks and compares the results to understand the extent to which DIF flagging behavior of items is affected by retrofitting. A secondary purpose of this study is to gather evidence about test booklet effects (i.e., item ordering) on items’ psychometric properties through DIF analyses. Results indicated severe DIF flagging prevalence differences for items across DIF detection techniques employing Wald test, Raju’s area measures, and Mantel-Haenzsel statistics. The largest numbers of DIF cases were observed when the data were retrofitted to a CDM. The results further revealed that an item might be flagged as DIF in one booklet, whereas it might not be flagged in another.
Published: 2021

22. GWTC-2: compact binary coalescences observed by LIGO and Virgo during the first half of the third observing run

Author: Abbott R.a, Abbott T.D.b, Abraham S.c, Acernese F.d, e, Ackley K.f, Adams A.g, Adams C.h, Adhikari R.X.a, Adya V.B.i, Affeldt C.j, k, Agathos M.l, m, Agatsuma K.n, Aggarwal N.o, Aguiar O.D.p, Aiello L.q, r, Ain A.s, t, Ajith P.u, Akcay S.m, v, Allen G.w, Allocca A.s, Altin P.A.i, Amato A.x, Anand S.a, Ananyeva A.a, Anderson S.B.a, Anderson W.G.y, Angelova S.V.z, Ansoldi S.aa, Antelis J.M.ac, Antier S.ad, Appert S.a, Arai K.a, Araya M.C.a, Areeda J.S.ae, Arène M.ad, Arnaud N.af, Aronson S.M.ah, Arun K.G.ai, Asali Y.aj, Ascenzi S.q, Ashton G.f, Aston S.M.h, Astone P.al, Aubin F.am, Aufmuth P.j, Aultoneal K.ac, Austin C.b, Avendano V.an, Babak S.ad, Badaracco F.q, Bader M.K.M.ao, Bae S.ap, Baer A.M.g, Bagnasco S.aq, Baird J.ad, Ball M.ar, Ballardin G.ag, Ballmer S.W.as, Bals A.ac, Balsamo A.g, Baltus G.at, Banagiri S.au, Bankar D.c, Bankar R.S.c, Barayoga J.C.a, Barbieri C.av, Barish B.C.a, Barker D.ay, Barneo P.az, Barnum S.ba, Barone F.e, Barr B.bc, Barsotti L.ba, Barsuglia M.ad, Barta D.bd, Bartlett J.ay, Bartos I.ah, Bassiri R.be, Basti A.s, Bawaj M.bf, Bayley J.C.bc, Bazzan M.bh, Becher B.R.bj, Bécsy B.bk, Bedakihale V.M.bl, Bejger M.bm, Belahcene I.af, Beniwal D.bn, Benjamin M.G.ac, Bennett T.F.bo, Bentley J.D.n, Bergamin F.j, Berger B.K.be, Bergmann G.j, Bernuzzi S.m, Berry C.P.L.o, Bersanetti D.bp, Bertolini A.ao, Betzwieser J.h, Bhandare R.bq, Bhandari A.V.c, Bhattacharjee D.br, Bidler J.ae, Bilenko I.A.bs, Billingsley G.a, Birney R.bt, Birnholtz O.bu, Biscans S.a, Bischi M.bv, Biscoveanu S.ba, Bisht A.j, Bitossi M.s, Bizouard M.-A.bx, Blackburn J.K.a, Blackman J.by, Blair C.D.bz, Blair D.G.bz, Blair R.M.ay, Blanch O.ca, Bobba F.cb, Bode N.j, Boer M.bx, Boetzel Y.cd, Bogaert G.bx, Boldrini M.al, Bondu F.cf, Bonilla E.be, Bonnand R.am, Booker P.j, Boom B.A.ao, Bork R.a, Boschi V.s, Bose S.c, Bossilkov V.bz, Boudart V.at, Bouffanais Y.bh, Bozzi A.ag, Bradaschia C.s, Brady P.R.y, Bramley A.h, Branchesi M.q, Brau J.E.ar, Breschi M.m, Briant T.cg, Briggs J.H.bc, Brighenti F.bv, Brillet A.bx, Brinkmann M.j, Brockill P.y, Brooks A.F.a, Brooks J.ag, Brown D.D.bn, Brunett S.a, Bruno G.ch, Bruntz R.g, Buikema A.ba, Bulik T.ci, Bulten H.J.ao, Buonanno A.ck, Buscicchio R.n, Buskulic D.am, Byer R.L.be, Cabero M.j, Cadonati L.cm, Caesar M.cn, Cagnoli G.x, Cahillane C.a, Calderón Bustillo J.f, Callaghan J.D.bc, Callister T.A.co, Calloni E.e, Camp J.B.cq, Canepa M.bp, Cannon K.C.cs, Cao H.bn, Cao J.ct, Carapella G.cb, Carbognani F.ag, Carney M.F.o, Carpinelli M.cu, Carullo G.s, Carver T.L.cw, Casanueva Diaz J.ag, Casentini C.ak, Caudill S.ao, Cavaglià M.br, Cavalier F.af, Cavalieri R.ag, Cella G.s, Cerdá-Durán P.cy, Cesarini E.ak, Chaibi W.bx, Chakravarti K.c, Chan C.-L.cz, Chan C.cs, Chandra K.da, Chanial P.ag, Chao S.db, Charlton P.dc, Chase E.A.o, Chassande-Mottin E.ad, Chatterjee D.y, Chattopadhyay D.dd, Chaturvedi M.bq, Chatziioannou K.co, Chen A.cz, Chen H.Y.de, Chen X.bz, Chen Y.by, Cheng H.-P.ah, Cheong C.K.cz, Chia H.Y.ah, Chiadini F.cc, Chierici R.dg, Chincarini A.bp, Chiummo A.ag, Cho G.dh, Cho H.S.di, Cho M.cl, Choate S.cn, Christensen N.bx, Chu Q.bz, Chua S.cg, Chung K.W.dj, Chung S.bz, Ciani G.bh, Ciecielag P.bm, Cie?lar M.bm, Cifaldi M.ak, Ciobanu A.A.bn, Ciolfi R.bi, Cipriano F.bx, Cirone A.bp, Clara F.ay, Clark E.N.dl, Clark J.A.cm, Clarke L.dm, Clearwater P.dn, Clesse S.ch, Cleva F.bx, Coccia E.q, Cohadon P.-F.cg, Cohen D.E.af, Colleoni M.do, Collette C.G.dp, Collins C.n, Colpi M.av, Constancio M., Jr.p, Conti L.bi, Cooper S.J.n, Corban P.h, Corbitt T.R.b, Cordero-Carrión I.dq, Corezzi S.bf, Corley K.R.aj, Cornish N.bk, Corre D.af, Corsi A.dr, Cortese S.ag, Costa C.A.p, Cotesta R.ck, Coughlin M.W.a, Coughlin S.B.o, Coulon J.-P.bx, Countryman S.T.aj, Cousins B.ds, Couvares P.a, Covas P.B.do, Coward D.M.bz, Cowart M.J.h, Coyne D.C.a, Coyne R.dt, Creighton J.D.E.y, Creighton T.D.du, Croquette M.cg, Crowder S.G.dv, Cudell J.R.at, Cullen T.J.b, Cumming A.bc, Cummings R.bc, Cunningham L.bc, Cuoco E.ag, Cury?o M.ci, Canton T.D.af, Dálya G.dx, Dana A.be, Daneshgaranbajastani L.M.bo, D'Angelo B.bp, Danila B.dy, Danilishin S.L.dz, D'Antonio S.ak, Danzmann K.j, Darsow-Fromm C.ea, Dasgupta A.bl, Datrier L.E.H.bc, Dattilo V.ag, Dave I.bq, Davier M.af, Davies G.S.eb, Davis D.a, Daw E.J.ec, Dean R.cn, Debra D.be, Deenadayalan M.c, Degallaix J.ed, De Laurentis M.e, Deléglise S.cg, Del Favero V.ee, De Lillo F.ch, De Lillo N.bc, Del Pozzo W.s, Demarchi L.M.o, De Matteis F.ak, D'Emilio V.cw, Demos N.ba, Denker T.j, Dent T.eb, Depasse A.ch, De Pietri R.ef, De Rosa R.e, De Rossi C.ag, Desalvo R.cc, De Varona O.j, Dhurandhar S.c, Díaz M.C.du, Diaz-Ortiz M., Jr.ah, Didio N.A.as, Dietrich T.ao, Di Fiore L.e, Difronzo C.n, Di Giorgio C.cb, Di Giovanni F.cy, Di Giovanni M.ei, Di Girolamo T.e, Di Lieto A.s, Ding B.dp, Di Pace S.al, Di Palma I.al, Di Renzo F.s, Divakarla A.K.ah, Dmitriev A.n, Doctor Z.ar, D'Onofrio L.e, Donovan F.ba, Dooley K.L.cw, Doravari S.c, Dorrington I.cw, Downes T.P.y, Drago M.q, Driggers J.C.ay, Du Z.ct, Ducoin J.-G.af, Dupej P.bc, Durante O.cb, D'Urso D.cu, Duverne P.-A.af, Dwyer S.E.ay, Easter P.J.f, Eddolls G.bc, Edelman B.ar, Edo T.B.ec, Edy O.ek, Effler A.h, Eichholz J.i, Eikenberry S.S.ah, Eisenmann M.am, Eisenstein R.A.ba, Ejlli A.cw, Errico L.e, Essick R.C.de, Estellés H.do, Estevez D.am, Etienne Z.B.el, Etzel T.a, Evans M.ba, Evans T.M.h, Ewing B.E.ds, Fafone V.q, Fair H.as, Fairhurst S.cw, Fan X.ct, Farah A.M.de, Farinon S.bp, Farr B.ar, Farr W.M.co, Fauchon-Jones E.J.cw, Favata M.an, Fays M.at, Fazio M.en, Feicht J.a, Fejer M.M.be, Feng F.ad, Fenyvesi E.bd, Ferguson D.L.cm, Fernandez-Galiana A.ba, Ferrante I.s, Ferreira T.A.p, Fidecaro F.s, Figura P.ci, Fiori I.ag, Fiorucci D.q, Fishbach M.de, Fisher R.P.g, Fishner J.M.ba, Fittipaldi R.cc, Fitz-Axen M.au, Fiumara V.cc, Flaminio R.am, Floden E.au, Flynn E.ae, Fong H.cs, Font J.A.cy, Forsyth P.W.F.i, Fournier J.-D.bx, Frasca S.al, Frasconi F.s, Frei Z.dx, Freise A.n, Frey R.ar, Frey V.af, Fritschel P.ba, Frolov V.V.h, Fronzé G.G.aq, Fulda P.ah, Fyffe M.h, Gabbard H.A.bc, Gadre B.U.ck, Gaebel S.M.n, Gair J.R.ck, Gais J.cz, Galaudage S.f, Gamba R.m, Ganapathy D.ba, Ganguly A.u, Gaonkar S.G.c, Garaventa B.bp, García-Quirós C.do, Garufi F.e, Gateley B.ay, Gaudio S.ac, Gayathri V.ah, Gemme G.bp, Gennai A.s, George D.w, George J.bq, George R.N.et, Gergely L.dy, Ghonge S.cm, Ghosh A.ck, Ghosh A.ao, Ghosh S.y, Giacomazzo B.av, Giacoppo L.al, Giaime J.A.b, h, Giardina K.D.h, Gibson D.R.bt, Gier C.z, Gill K.aj, Giri P.s, Glanzer J.b, Gleckl A.E.ae, Godwin P.ds, Goetz E.ex, Goetz R.ah, Gohlke N.j, Goncharov B.f, González G.b, Gopakumar A.ey, Gossan S.E.a, Gosselin M.s, Gouaty R.am, Grace B.i, Grado A.e, Granata M.ed, Granata V.cb, Grant A.bc, Gras S.ba, Grassia P.a, Gray C.ay, Gray R.bc, Greco G.bv, Green A.C.ah, Green R.cw, Gretarsson E.M.ac, Griggs H.L.cm, Grignani G.bf, Grimaldi A.ei, Grimes E.ac, Grimm S.J.q, Grote H.cw, Grunewald S.ck, Gruning P.af, Guerrero J.G.ae, Guidi G.M.bv, Guimaraes A.R.b, Guixé G.az, Gulati H.K.bl, Guo Y.ao, Gupta A.a, Gupta A.ds, Gupta P.ao, Gustafson E.K.a, Gustafson R.fb, Guzman F.dl, Haegel L.ad, Halim O.q, Hall E.D.ba, Hamilton E.Z.cw, Hammond G.bc, Haney M.cd, Hanke M.M.j, Hanks J.ay, Hanna C.ds, Hannam M.D.cw, Hannuksela O.A.cz, Hannuksela O.ao, Hansen H.ay, Hansen T.J.ac, Hanson J.h, Harder T.bx, Hardwick T.b, Haris K.u, Harms J.q, Harry G.M.fc, Harry I.W.ek, Hartwig D.ea, Hasskew R.K.h, Haster C.-J.ba, Haughian K.bc, Hayes F.J.bc, Healy J.ee, Heidmann A.cg, Heintze M.C.h, Heinze J.j, Heinzel J.fd, Heitmann H.bx, Hellman F.fe, Hello P.af, Helmling-Cornell A.F.ar, Hemming G.ag, Hendry M.bc, Heng I.S.bc, Hennes E.ao, Hennig J.j, Hennig M.H.j, Hernandez Vivanco F.f, Heurs M.j, Hild S.dz, Hill P.z, Hines A.S.dl, Hochheim S.j, Hofgard E.be, Hofman D.ed, Hohmann J.N.ea, Holgado A.M.w, Holland N.A.i, Hollows I.J.ec, Holmes Z.J.bn, Holt K.h, Holz D.E.de, Hopkins P.cw, Horst C.y, Hough J.bc, Howell E.J.bz, Hoy C.G.cw, Hoyland D.n, Huang Y.ba, Hübner M.T.f, Huddart A.D.dm, Huerta E.A.w, Hughey B.ac, Hui V.am, Husa S.do, Huttner S.H.bc, Hutzler B.M.b, Huxford R.ds, Huynh-Dinh T.h, Idzkowski B.ci, Iess A.ak, Imperato S.o, Inchauspe H.ah, Ingram C.bn, Intini G.al, Isi M.ba, Iyer B.R.u, Jaberianhamedan V.bz, Jacqmin T.cg, Jadhav S.J.ff, Jadhav S.P.c, James A.L.cw, Jani K.cm, Janssens K.fg, Janthalur N.N.ff, Jaranowski P.fh, Jariwala D.ah, Jaume R.do, Jenkins A.C.dj, Jeunon M.au, Jiang J.ah, Johns G.R.g, Johnson-Mcdaniel N.K.l, Jones A.W.n, Jones D.I.fi, Jones J.D.ay, Jones P.n, Jones R.bc, Jonker R.J.G.ao, Ju L.bz, Junker J.j, Kalaghatgi C.V.cw, Kalogera V.o, Kamai B.a, Kandhasamy S.c, Kang G.ap, Kanner J.B.a, Kapadia S.J.u, Kapasi D.P.i, Karathanasis C.ca, Karki S.br, Kashyap R.ds, Kasprzack M.a, Kastaun W.j, Katsanevas S.ag, Katsavounidis E.ba, Katzman W.h, Kawabe K.ay, Kéfélian F.bx, Keitel D.do, Key J.S.fj, Khadka S.be, Khalili F.Y.bs, Khan I.q, Khan S.cw, Khazanov E.A.fk, Khetan N.q, Khursheed M.bq, Kijbunchoo N.i, Kim C.fl, Kim G.J.cm, Kim J.C.fm, Kim K.fn, Kim W.S.fo, Kim Y.-M.fp, Kimball C.o, King P.J.ay, Kinley-Hanlon M.bc, Kirchhoff R.j, Kissel J.S.ay, Kleybolte L.ea, Klimenko S.ah, Knowles T.D.el, Knyazev E.ba, Koch P.j, Koehlenbeck S.M.j, Koekoek G.ao, Koley S.ao, Kolstein M.ca, Komori K.ba, Kondrashov V.a, Kontos A.bj, Koper N.j, Korobko M.ea, Korth W.Z.a, Kovalam M.bz, Kozak D.B.a, Krämer C.j, Kringel V.j, Krishnendu N.V.j, Królak A.fr, Kuehn G.j, Kumar A.ff, Kumar P.ft, Kumar R.ay, Kumar R.bl, Kuns K.ba, Kwang S.y, Lackey B.D.ck, Laghi D.s, Lalande E.fu, Lam T.L.cz, Lamberts A.bx, Landry M.ay, Lane B.B.ba, Lang R.N.ba, Lange J.ee, Lantz B.be, Lanza R.K.ba, La Rosa I.am, Lartaux-Vollard A.af, Lasky P.D.f, Laxen M.h, Lazzarini A.a, Lazzaro C.bh, Leaci P.al, Leavey S.j, Lecoeuche Y.K.ay, Lee H.M.fn, Lee H.W.fm, Lee J.dh, Lee K.be, Lehmann J.j, Leon E.ae, Leroy N.af, Letendre N.am, Levin Y.f, Li A.a, Li J.ct, Li K.J.L.cz, Li T.G.F.cz, Li X.by, Linde F.ao, Linker S.D.bo, Linley J.N.bc, Littenberg T.B.fx, Liu J.j, Liu X.y, Llorens-Monteagudo M.cy, Lo R.K.L.a, Lockwood A.fy, London L.T.ba, Longo A.fz, Lorenzini M.ak, Loriette V.gb, Lormand M.h, Losurdo G.s, Lough J.D.j, Lousto C.O.ee, Lovelace G.ae, Lück H.j, Lumaca D.ak, Lundgren A.P.ek, Ma Y.by, Macas R.cw, Macinnis M.ba, Macleod D.M.cw, Macmillan I.A.O.a, Macquet A.bx, Magaña Hernandez I.y, Magaña-Sandoval F.ah, Magazzù C.s, Magee R.M.ds, Majorana E.al, Maksimovic I.gb, Maliakal S.a, Malik A.bq, Man N.bx, Mandic V.au, Mangano V.al, Mansell G.L.ay, Manske M.y, Mantovani M.ag, Mapelli M.bh, Marchesoni F.bf, Marion F.am, Márka S.aj, Márka Z.aj, Markakis C.l, Markosyan A.S.be, Markowitz A.a, Maros E.a, Marquina A.dq, Marsat S.ad, Martelli F.bv, Martin I.W.bc, Martin R.M.an, Martinez M.ca, Martinez V.x, Martynov D.V.n, Masalehdan H.ea, Mason K.ba, Massera E.ec, Masserot A.am, Massinger T.J.ba, Masso-Reid M.bc, Mastrogiovanni S.ad, Matas A.ck, Mateu-Lucena M.do, Matichard F.a, Matiushechkina M.j, Mavalvala N.ba, Maynard E.b, McCann J.J.bz, McCarthy R.ay, McClelland D.E.i, McCormick S.h, McCuller L.ba, McGuire S.C.gd, McIsaac C.ek, McIver J.ex, McManus D.J.i, McRae T.i, McWilliams S.T.el, Meacher D.y, Meadors G.D.f, Mehmet M.j, Mehta A.K.ck, Melatos A.dn, Melchor D.A.ae, Mendell G.ay, Menendez-Vazquez A.ca, Mercer R.A.y, Mereni L.ed, Merfeld K.ar, Merilh E.L.ay, Merritt J.D.ar, Merzougui M.bx, Meshkov S.a, Messenger C.bc, Messick C.et, Metzdorff R.cg, Meyers P.M.dn, Meylahn F.j, Mhaske A.c, Miani A.ei, Miao H.n, Michaloliakos I.ah, Michel C.ed, Middleton H.dn, Milano L.e, Miller A.L.ah, Millhouse M.dn, Mills J.C.cw, Milotti E.ab, Milovich-Goff M.C.bo, Minazzoli O.bx, Minenkov Y.ak, Mir L.M.ca, Mishkin A.ah, Mishra C.gg, Mistry T.ec, Mitra S.c, Mitrofanov V.P.bs, Mitselmakher G.ah, Mittleman R.ba, Mo G.ba, Mogushi K.br, Mohapatra S.R.P.ba, Mohite S.R.y, Molina I.ae, Molina-Ruiz M.fe, Mondin M.bo, Montani M.bv, Moore C.J.n, Moraru D.ay, Morawski F.bm, Moreno G.ay, Morisaki S.cs, Mours B.gh, Mow-Lowry C.M.n, Mozzon S.ek, Muciaccia F.al, Mukherjee A.bc, Mukherjee D.ds, Mukherjee S.du, Mukherjee S.bl, Mukund N.j, Mullavey A.h, Munch J.bn, Muñiz E.A.as, Murray P.G.bc, Nadji S.L.j, Nagar A.aq, Nardecchia I.ak, Naticchioni L.al, Nayak R.K.gk, Neil B.F.bz, Neilson J.cc, Nelemans G.gl, Nelson T.J.N.h, Nery M.j, Neunzert A.fj, Nitz A.H.j, Ng K.Y.ba, Ng S.bn, Nguyen C.ad, Nguyen P.ar, Nguyen T.ba, Nichols S.A.b, Nissanke S.ao, Nocera F.ag, Noh M.ex, North C.cw, Nothard D.gm, Nuttall L.K.ek, Oberling J.ay, O'Brien B.D.ah, O'Dell J.dm, Oganesyan G.q, Ogin G.H.gn, Oh J.J.fo, Oh S.H.fo, Ohme F.j, Ohta H.cs, Okada M.A.p, Olivetto C.ag, Oppermann P.j, Oram R.J.h, O'Reilly B.h, Ormiston R.G.au, Ortega L.F.ah, O'Shaughnessy R.ee, Ossokine S.ck, Osthelder C.a, Ottaway D.J.bn, Overmier H.h, Owen B.J.dr, Pace A.E.ds, Pagano G.s, Page M.A.bz, Pagliaroli G.q, Pai A.da, Pai S.A.bq, Palamos J.R.ar, Palashov O.fk, Palomba C.al, Pan H.db, Panda P.K.ff, Pang T.H.ao, Pankow C.o, Pannarale F.al, Pant B.C.bq, Paoletti F.s, Paoli A.ag, Paolone A.al, Parker W.h, Pascucci D.ao, Pasqualetti A.ag, Passaquieti R.s, Passuello D.s, Patel M.g, Patricelli B.s, Payne E.f, Pechsiri T.C.ah, Pedraza M.a, Pegoraro M.bi, Pele A.h, Penn S.gp, Perego A.ei, Perez C.J.ay, Périgois C.am, Perreca A.ei, Perriès S.dg, Petermann J.ea, Petterson D.a, Pfeiffer H.P.ck, Pham K.A.au, Phukon K.S.c, Piccinni O.J.al, Pichot M.bx, Piendibene M.s, Piergiovanni F.bv, Pierini L.al, Pierro V.cc, Pillant G.ag, Pilo F.s, Pinard L.ed, Pinto I.M.cc, Piotrzkowski K.ch, Pirello M.ay, Pitkin M.gq, Placidi E.ce, Plastino W.fz, Pluchar C.dl, Poggiani R.s, Polini E.am, Pong D.Y.T.cz, Ponrathnam S.c, Popolizio P.ag, Porter E.K.ad, Poverman A.bj, Powell J.dd, Pracchia M.am, Prajapati A.K.bl, Prasai K.be, Prasanna R.ff, Pratten G.n, Prestegard T.y, Principe M.cc, Prodi G.A.ej, Prokhorov L.n, Prosposito P.ak, Prudenzi L.ck, Puecher A.ao, Punturo M.bf, Puosi F.s, Puppo P.al, Pürrer M.ck, Qi H.cw, Quetschke V.du, Quinonez P.J.ac, Quitzow-James R.br, Raab F.J.ay, Raaijmakers G.ao, Radkins H.ay, Radulesco N.bx, Raffai P.dx, Rafferty H.gs, Rail S.X.fu, Raja S.bq, Rajan C.bq, Rajbhandari B.dr, Rakhmanov M.du, Ramirez K.E.du, Ramirez T.D.ae, Ramos-Buades A.do, Rana J.ds, Rao K.o, Rapagnani P.al, Rapol U.D.gt, Ratto B.ac, Raymond V.cw, Razzano M.s, Read J.ae, Regimbau T.am, Rei L.bp, Reid S.z, Reitze D.H.a, Rettegno P.aq, Ricci F.al, Richardson C.J.ac, Richardson J.W.a, Richardson L.dl, Ricker P.M.w, Riemenschneider G.aq, Riles K.fb, Rizzo M.o, Robertson N.A.a, Robinet F.af, Rocchi A.ak, Rocha J.A.ae, Rodriguez S.ae, Rodriguez-Soto R.D.ac, Rolland L.am, Rollins J.G.a, Roma V.J.ar, Romanelli M.cf, Romano R.d, Romel C.L.ay, Romero A.ca, Romero-Shaw I.M.f, Romie J.H.h, Ronchini S.q, Rose C.A.y, Rose D.ae, Rose K.gm, Rosell M.J.B.et, Rosi?ska D.ci, Rosofsky S.G.w, Ross M.P.fy, Rowan S.bc, Rowlinson S.J.n, Roy S.c, Roy S.gv, Ruggi P.ag, Ryan K.ay, Sachdev S.ds, Sadecki T.ay, Sadiq J.ee, Sakellariadou M.dj, Salafia O.S.av, Salconi L.ag, Saleem M.ai, Samajdar A.ao, Sanchez E.J.a, Sanchez J.H.ae, Sanchez L.E.a, Sanchis-Gual N.gw, Sanders J.R.gx, Sandles L.cw, Santiago K.A.an, Santos E.bx, Saravanan T.R.c, Sarin N.f, Sassolas B.ed, Sathyaprakash B.S.cw, Sauter O.am, Savage R.L.ay, Savant V.c, Sawant D.da, Sayah S.ed, Schaetzl D.a, Schale P.ar, Scheel M.by, Scheuer J.o, Schindler-Tyka A.ah, Schmidt P.n, Schnabel R.ea, Schofield R.M.S.ar, Schönbeck A.ea, Schreiber E.j, Schulte B.W.j, Schutz B.F.j, Schwarm O.gn, Schwartz E.cw, Scott J.bc, Scott S.M.i, Seglar-Arroyo M.am, Seidel E.w, Sellers D.h, Sengupta A.S.gv, Sennett N.ck, Sentenac D.ag, Sequino V.e, Sergeev A.fk, Setyawati Y.j, Shaffer T.ay, Shahriar M.S.o, Sharifi S.b, Sharma A.q, Sharma P.bq, Shawhan P.cl, Shen H.w, Shikauchi M.cs, Shink R.fu, Shoemaker D.H.ba, Shoemaker D.M.cm, Shukla K.fe, Shyamsundar S.bq, Sieniawska M.bm, Sigg D.ay, Singer L.P.cq, Singh D.ds, Singh N.ci, Singha A.dz, Singhal A.q, Sintes A.M.do, Sipala V.cu, Skliris V.cw, Slagmolen B.J.J.i, Slaven-Blair T.J.bz, Smetana J.n, Smith J.R.ae, Smith R.J.E.f, Somala S.N.gy, Son E.J.fo, Soni K.c, Soni S.b, Sorazu B.bc, Sordini V.dg, Sorrentino F.bp, Sorrentino N.s, Soulard R.bx, Souradeep T.c, Sowell E.dr, Spencer A.P.bc, Spera M.o, Srivastava A.K.bl, Srivastava V.as, Staats K.o, Stachie C.bx, Steer D.A.ad, Steinhoff J.ck, Steinke M.j, Steinlechner J.bc, Steinlechner S.dz, Steinmeyer D.j, Stevenson S.P.dd, Stolle-Mcallister G.gm, Stops D.J.n, Stover M.gm, Strain K.A.bc, Stratta G.bw, Strunk A.ay, Sturani R.ha, Stuver A.L.cn, Südbeck J.ea, Sudhagar S.c, Sudhir V.ba, Suh H.G.y, Summerscales T.Z.hb, Sun H.bz, Sun L.a, Sunil S.bl, Sur A.bm, Suresh J.cs, Sutton P.J.cw, Swinkels B.L.ao, Szczepa?czyk M.J.ah, Tacca M.ao, Tait S.C.bc, Talbot C.f, Tanasijczuk A.J.ch, Tanner D.B.ah, Tao D.a, Tapia A.ae, Tapia San Martin E.N.ao, Tasson J.D.fd, Taylor R.a, Tenorio R.do, Terkowski L.ea, Thirugnanasambandam M.P.c, Thomas L.M.n, Thomas M.h, Thomas P.ay, Thompson J.E.cw, Thondapu S.R.bq, Thorne K.A.h, Thrane E.f, Tiwari S.cd, Tiwari S.ey, Tiwari V.cw, Toland K.bc, Tolley A.E.ek, Tonelli M.s, Tornasi Z.bc, Torres-Forné A.ck, Torrie C.I.a, E Melo I.T.cu, Töyrä D.i, Tran A.T.dv, Trapananti A.bf, Travasso F.bf, Traylor G.h, Tringali M.C.ci, Tripathee A.fb, Trovato A.ad, Trudeau R.J.a, Tsai D.S.db, Tsang K.W.ao, Tse M.ba, Tso R.by, Tsukada L.cs, Tsuna D.cs, Tsutsui T.cs, Turconi M.bx, Ubhi A.S.n, Udall R.P.cm, Ueno K.cs, Ugolini D.gs, Unnikrishnan C.S.ey, Urban A.L.b, Usman S.A.de, Utina A.C.dz, Vahlbruch H.j, Vajente G.a, Vajpeyi A.f, Valdes G.b, Valentini M.ei, Valsan V.y, Van Bakel N.ao, Van Beuzekom M.ao, Van Den Brand J.F.J.ao, Van Den Broeck C.ao, Vander-Hyde D.C.as, Van Der Schaaf L.ao, Van Heijningen J.V.bz, Vardaro M.ao, Vargas A.F.dn, Varma V.by, Vass S.a, Vasúth M.bd, Vecchio A.n, Vedovato G.bi, Veitch J.bc, Veitch P.J.bn, Venkateswara K.fy, Venneberg J.j, Venugopalan G.a, Verkindt D.am, Verma Y.bq, Veske D.aj, Vetrano F.bv, Viceré A.bv, Viets A.D.hd, Vijaykumar A.u, Villa-Ortega V.eb, Vinet J.-Y.bx, Vitale S.ba, Vo T.as, Vocca H.bf, Vorvick C.ay, Vyatchanin S.P.bs, Wade A.R.i, Wade L.E.gm, Wade M.gm, Walet R.C.ao, Walker M.g, Wallace G.S.z, Wallace L.a, Walsh S.y, Wang J.Z.fb, Wang S.w, Wang W.H.du, Wang Y.F.cz, Ward R.L.i, Warner J.ay, Was M.am, Washington N.Y.a, Watchi J.dp, Weaver B.ay, Wei L.j, Weinert M.j, Weinstein A.J.a, Weiss R.ba, Wellmann F.j, Wen L.bz, Weßels P.j, Westhouse J.W.ac, Wette K.i, Whelan J.T.ee, White D.D.ae, White L.V.as, Whiting B.F.ah, Whittle C.ba, Wilken D.M.j, Williams D.bc, Williams M.J.bc, Williamson A.R.ek, Willis J.L.a, Willke B.j, Wilson D.J.dl, Wimmer M.H.j, Winkler W.j, Wipf C.C.a, Woan G.bc, Woehler J.j, Wofford J.K.ee, Wong I.C.F.cz, Wrangel J.j, Wright J.L.bc, Wu D.S.j, Wysocki D.M.ee, Xiao L.a, Yamamoto H.a, Yang L.en, Yang Y.ah, Yang Z.au, Yap M.J.i, Yeeles D.W.cw, Yoon A.g, Yu H.by, Yu H.ba, Yuen S.H.R.cz, Zadro?ny A.fs, Zanolin M.ac, Zelenova T.ag, Zendri J.-P.bi, Zevin M.o, Zhang J.bz, Zhang L.a, Zhang R.ah, Zhang T.n, Zhao C.bz, Zhao G.dp, Zheng Y.br, Zhou M.o, Zhou Z.o, Zhu X.J.f, Zimmerman A.B.et, Zlochower Y.ee, Zucker M.E.a, Zweizig J.a, Astrophysique Relativiste Théories Expériences Métrologie Instrumentation Signaux (ARTEMIS), Université Nice Sophia Antipolis (... - 2019) (UNS), COMUE Université Côte d'Azur (2015 - 2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015 - 2019) (COMUE UCA)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire de la Côte d'Azur, COMUE Université Côte d'Azur (2015 - 2019) (COMUE UCA)-Université Côte d'Azur (UCA)-Université Côte d'Azur (UCA)-Centre National de la Recherche Scientifique (CNRS), Abbott, R., Abbott, T. 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A., Prokhorov, L., Prosposito, P., Prudenzi, L., Puecher, A., Punturo, M., Puosi, F., Puppo, P., Pürrer, M., Qi, H., Quetschke, V., Quinonez, P. J., Quitzow-James, R., Raab, F. J., Raaijmakers, G., Radkins, H., Radulesco, N., Raffai, P., Rafferty, H., Rail, S. X., Raja, S., Rajan, C., Rajbhandari, B., Rakhmanov, M., Ramirez, K. E., Ramirez, T. D., Ramos-Buades, A., Rana, J., Rao, K., Rapagnani, P., Rapol, U. D., Ratto, B., Raymond, V., Razzano, M., Read, J., Regimbau, T., Rei, L., Reid, S., Reitze, D. H., Rettegno, P., Ricci, F., Richardson, C. J., Richardson, J. W., Richardson, L., Ricker, P. M., Riemenschneider, G., Riles, K., Rizzo, M., Robertson, N. A., Robinet, F., Rocchi, A., Rocha, J. A., Rodriguez, S., Rodriguez-Soto, R. D., Rolland, L., Rollins, J. G., Roma, V. J., Romanelli, M., Romano, R., Romel, C. L., Romero, A., Romero-Shaw, I. M., Romie, J. H., Ronchini, S., Rose, C. A., Rose, D., Rose, K., Rosell, M. J. B., Rosińska, D., Rosofsky, S. G., Ross, M. P., Rowan, S., Rowlinson, S. J., Roy, Santosh, Roy, Soumen, Ruggi, P., Ryan, K., Sachdev, S., Sadecki, T., Sadiq, J., Sakellariadou, M., Salafia, O. S., Salconi, L., Saleem, M., Samajdar, A., Sanchez, E. J., Sanchez, J. H., Sanchez, L. E., Sanchis-Gual, N., Sanders, J. R., Sandles, L., Santiago, K. A., Santos, E., Saravanan, T. R., Sarin, N., Sassolas, B., Sathyaprakash, B. S., Sauter, O., Savage, R. L., Savant, V., Sawant, D., Sayah, S., Schaetzl, D., Schale, P., Scheel, M., Scheuer, J., Schindler-Tyka, A., Schmidt, P., Schnabel, R., Schofield, R. M. S., Schönbeck, A., Schreiber, E., Schulte, B. W., Schutz, B. F., Schwarm, O., Schwartz, E., Scott, J., Scott, S. M., Seglar-Arroyo, M., Seidel, E., Sellers, D., Sengupta, A. S., Sennett, N., Sentenac, D., Sequino, V., Sergeev, A., Setyawati, Y., Shaffer, T., Shahriar, M. S., Sharifi, S., Sharma, A., Sharma, P., Shawhan, P., Shen, H., Shikauchi, M., Shink, R., Shoemaker, D. H., Shoemaker, D. M., Shukla, K., Shyamsundar, S., Sieniawska, M., Sigg, D., Singer, L. P., Singh, D., Singh, N., Singha, A., Singhal, A., Sintes, A. M., Sipala, V., Skliris, V., Slagmolen, B. J. J., Slaven-Blair, T. J., Smetana, J., Smith, J. R., Smith, R. J. E., Somala, S. N., Son, E. J., Soni, K., Soni, S., Sorazu, B., Sordini, V., Sorrentino, F., Sorrentino, N., Soulard, R., Souradeep, T., Sowell, E., Spencer, A. P., Spera, M., Srivastava, A. K., Srivastava, V., Staats, K., Stachie, C., Steer, D. A., Steinhoff, J., Steinke, M., Steinlechner, J., Steinlechner, S., Steinmeyer, D., Stevenson, S. P., Stolle-McAllister, G., Stops, D. J., Stover, M., Strain, K. A., Stratta, G., Strunk, A., Sturani, R., Stuver, A. L., Südbeck, J., Sudhagar, S., Sudhir, V., Suh, H. G., Summerscales, T. Z., Sun, H., Sun, L., Sunil, S., Sur, A., Suresh, J., Sutton, P. J., Swinkels, B. L., Szczepańczyk, M. J., Tacca, M., Tait, S. C., Talbot, C., Tanasijczuk, A. J., Tanner, D. B., Tao, D., Tapia, A., Tapia San Martin, E. N., Tasson, J. D., Taylor, R., Tenorio, R., Terkowski, L., Thirugnanasambandam, M. P., Thomas, L. M., Thomas, M., Thomas, P., Thompson, J. E., Thondapu, S. R., Thorne, K. A., Thrane, E., Tiwari, Shubhanshu, Tiwari, Srishti, Tiwari, V., Toland, K., Tolley, A. E., Tonelli, M., Tornasi, Z., Torres-Forné, A., Torrie, C. I., e Melo, I. Tosta, Töyrä, D., Tran, A. T., Trapananti, A., Travasso, F., Traylor, G., Tringali, M. C., Tripathee, A., Trovato, A., Trudeau, R. J., Tsai, D. S., Tsang, K. W., Tse, M., Tso, R., Tsukada, L., Tsuna, D., Tsutsui, T., Turconi, M., Ubhi, A. S., Udall, R. P., Ueno, K., Ugolini, D., Unnikrishnan, C. S., Urban, A. L., Usman, S. A., Utina, A. C., Vahlbruch, H., Vajente, G., Vajpeyi, A., Valdes, G., Valentini, M., Valsan, V., van Bakel, N., van Beuzekom, M., van den Brand, J. F. J., Van Den Broeck, C., Vander-Hyde, D. C., van der Schaaf, L., van Heijningen, J. V., Vardaro, M., Vargas, A. F., Varma, V., Vass, S., Vasúth, M., Vecchio, A., Vedovato, G., Veitch, J., Veitch, P. J., Venkateswara, K., Venneberg, J., Venugopalan, G., Verkindt, D., Verma, Y., Veske, D., Vetrano, F., Viceré, A., Viets, A. D., Vijaykumar, A., Villa-Ortega, V., Vinet, J. -Y., Vitale, S., Vo, T., Vocca, H., Vorvick, C., Vyatchanin, S. P., Wade, A. R., Wade, L. E., Wade, M., Walet, R. C., Walker, M., Wallace, G. S., Wallace, L., Walsh, S., Wang, J. Z., Wang, S., Wang, W. H., Wang, Y. F., Ward, R. L., Warner, J., Was, M., Washington, N. Y., Watchi, J., Weaver, B., Wei, L., Weinert, M., Weinstein, A. J., Weiss, R., Wellmann, F., Wen, L., Weßels, P., Westhouse, J. W., Wette, K., Whelan, J. T., White, D. D., White, L. V., Whiting, B. F., Whittle, C., Wilken, D. M., Williams, D., Williams, M. J., Williamson, A. R., Willis, J. L., Willke, B., Wilson, D. J., Wimmer, M. H., Winkler, W., Wipf, C. C., Woan, G., Woehler, J., Wofford, J. K., Wong, I. C. F., Wrangel, J., Wright, J. L., Wu, D. S., Wysocki, D. M., Xiao, L., Yamamoto, H., Yang, L., Yang, Y., Yang, Z., Yap, M. J., Yeeles, D. W., Yoon, A., Yu, Hang, Yu, Haocun, Yuen, S. H. R., Zadrożny, A., Zanolin, M., Zelenova, T., Zendri, J. -P., Zevin, M., Zhang, J., Zhang, L., Zhang, R., Zhang, T., Zhao, C., Zhao, G., Zheng, Y., Zhou, M., Zhou, Z., Zhu, X. J., Zimmerman, A. B., Zlochower, Y., Zucker, M. E., Zweizig, J., Université Côte d'Azur (UCA)-COMUE Université Côte d'Azur (2015 - 2019) (COMUE UCA)-Université Côte d'Azur (UCA)-Centre National de la Recherche Scientifique (CNRS), Grav. waves and fundamental physics, RS: FSE Grav. waves and fundamental physics, RS: FSE MSP, LIGO Scientific Collaboration, Virgo Collaboration, Centre National de la Recherche Scientifique (CNRS)-Observatoire de la Côte d'Azur, Université Côte d'Azur (UCA)-COMUE Université Côte d'Azur (2015 - 2019) (COMUE UCA)-Université Côte d'Azur (UCA)-COMUE Université Côte d'Azur (2015 - 2019) (COMUE UCA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Nice Sophia Antipolis (... - 2019) (UNS), COMUE Université Côte d'Azur (2015 - 2019) (COMUE UCA), Abbott, R, Abbott, T, Abraham, S, Acernese, F, Ackley, K, Adams, A, Adams, C, Adhikari, R, Adya, V, Affeldt, C, Agathos, M, Agatsuma, K, Aggarwal, N, Aguiar, O, Aiello, L, Ain, A, Ajith, P, Akcay, S, Allen, G, Allocca, A, Altin, P, Amato, A, Anand, S, Ananyeva, A, Anderson, S, Anderson, W, Angelova, S, Ansoldi, S, Antelis, J, Antier, S, Appert, S, Arai, K, Araya, M, Areeda, J, Arene, M, Arnaud, N, Aronson, S, Arun, K, Asali, Y, Ascenzi, S, Ashton, G, Aston, S, Astone, P, Aubin, F, Aufmuth, P, Aultoneal, K, Austin, C, Avendano, V, Babak, S, Badaracco, F, Bader, M, Bae, S, Baer, A, Bagnasco, S, Baird, J, Ball, M, Ballardin, G, Ballmer, S, Bals, A, Balsamo, A, Baltus, G, Banagiri, S, Bankar, D, Bankar, R, Barayoga, J, Barbieri, C, Barish, B, Barker, D, Barneo, P, Barnum, S, Barone, F, Barr, B, Barsotti, L, Barsuglia, M, Barta, D, Bartlett, J, Bartos, I, Bassiri, R, Basti, A, Bawaj, M, Bayley, J, Bazzan, M, Becher, B, Becsy, B, Bedakihale, V, Bejger, M, Belahcene, I, Beniwal, D, Benjamin, M, Bennett, T, Bentley, J, Bergamin, F, Berger, B, Bergmann, G, Bernuzzi, S, Berry, C, Bersanetti, D, Bertolini, A, Betzwieser, J, Bhandare, R, Bhandari, A, Bhattacharjee, D, Bidler, J, Bilenko, I, Billingsley, G, Birney, R, Birnholtz, O, Biscans, S, Bischi, M, Biscoveanu, S, Bisht, A, Bitossi, M, Bizouard, M, Blackburn, J, Blackman, J, Blair, C, Blair, D, Blair, R, Blanch, O, Bobba, F, Bode, N, Boer, M, Boetzel, Y, Bogaert, G, Boldrini, M, Bondu, F, Bonilla, E, Bonnand, R, Booker, P, Boom, B, Bork, R, Boschi, V, Bose, S, Bossilkov, V, Boudart, V, Bouffanais, Y, Bozzi, A, Bradaschia, C, Brady, P, Bramley, A, Branchesi, M, Brau, J, Breschi, M, Briant, T, Briggs, J, Brighenti, F, Brillet, A, Brinkmann, M, Brockill, P, Brooks, A, Brooks, J, Brown, D, Brunett, S, Bruno, G, Bruntz, R, Buikema, A, Bulik, T, Bulten, H, Buonanno, A, Buscicchio, R, Buskulic, D, Byer, R, Cabero, M, Cadonati, L, Caesar, M, Cagnoli, G, Cahillane, C, Calderon Bustillo, J, Callaghan, J, Callister, T, Calloni, E, Camp, J, Canepa, M, Cannon, K, Cao, H, Cao, J, Carapella, G, Carbognani, F, Carney, M, Carpinelli, M, Carullo, G, Carver, T, Casanueva Diaz, J, Casentini, C, Caudill, S, Cavaglia, M, Cavalier, F, Cavalieri, R, Cella, G, Cerda-Duran, P, Cesarini, E, Chaibi, W, Chakravarti, K, Chan, C, Chandra, K, Chanial, P, Chao, S, Charlton, P, Chase, E, Chassande-Mottin, E, Chatterjee, D, Chattopadhyay, D, Chaturvedi, M, Chatziioannou, K, Chen, A, Chen, H, Chen, X, Chen, Y, Cheng, H, Cheong, C, Chia, H, Chiadini, F, Chierici, R, Chincarini, A, Chiummo, A, Cho, G, Cho, H, Cho, M, Choate, S, Christensen, N, Chu, Q, Chua, S, Chung, K, Chung, S, Ciani, G, Ciecielag, P, Cieslar, M, Cifaldi, M, Ciobanu, A, Ciolfi, R, Cipriano, F, Cirone, A, Clara, F, Clark, E, Clark, J, Clarke, L, Clearwater, P, Clesse, S, Cleva, F, Coccia, E, Cohadon, P, Cohen, D, Colleoni, M, Collette, C, Collins, C, Colpi, M, Constancio, M, Conti, L, Cooper, S, Corban, P, Corbitt, T, Cordero-Carrion, I, Corezzi, S, Corley, K, Cornish, N, Corre, D, Corsi, A, Cortese, S, Costa, C, Cotesta, R, Coughlin, M, Coughlin, S, Coulon, J, Countryman, S, Cousins, B, Couvares, P, Covas, P, Coward, D, Cowart, M, Coyne, D, Coyne, R, Creighton, J, Creighton, T, Croquette, M, Crowder, S, Cudell, J, Cullen, T, Cumming, A, Cummings, R, Cunningham, L, Cuoco, E, Curylo, M, Canton, T, Dalya, G, Dana, A, Daneshgaranbajastani, L, D'Angelo, B, Danila, B, Danilishin, S, D'Antonio, S, Danzmann, K, Darsow-Fromm, C, Dasgupta, A, Datrier, L, Dattilo, V, Dave, I, Davier, M, Davies, G, Davis, D, Daw, E, Dean, R, Debra, D, Deenadayalan, M, Degallaix, J, De Laurentis, M, Deleglise, S, Del Favero, V, De Lillo, F, De Lillo, N, Del Pozzo, W, Demarchi, L, De Matteis, F, D'Emilio, V, Demos, N, Denker, T, Dent, T, Depasse, A, De Pietri, R, De Rosa, R, De Rossi, C, Desalvo, R, De Varona, O, Dhurandhar, S, Diaz, M, Diaz-Ortiz, M, Didio, N, Dietrich, T, Di Fiore, L, Difronzo, C, Di Giorgio, C, Di Giovanni, F, Di Giovanni, M, Di Girolamo, T, Di Lieto, A, Ding, B, Di Pace, S, Di Palma, I, Di Renzo, F, Divakarla, A, Dmitriev, A, Doctor, Z, D'Onofrio, L, Donovan, F, Dooley, K, Doravari, S, Dorrington, I, Downes, T, Drago, M, Driggers, J, Du, Z, Ducoin, J, Dupej, P, Durante, O, D'Urso, D, Duverne, P, Dwyer, S, Easter, P, Eddolls, G, Edelman, B, Edo, T, Edy, O, Effler, A, Eichholz, J, Eikenberry, S, Eisenmann, M, Eisenstein, R, Ejlli, A, Errico, L, Essick, R, Estelles, H, Estevez, D, Etienne, Z, Etzel, T, Evans, M, Evans, T, Ewing, B, Fafone, V, Fair, H, Fairhurst, S, Fan, X, Farah, A, Farinon, S, Farr, B, Farr, W, Fauchon-Jones, E, Favata, M, Fays, M, Fazio, M, Feicht, J, Fejer, M, Feng, F, Fenyvesi, E, Ferguson, D, Fernandez-Galiana, A, Ferrante, I, Ferreira, T, Fidecaro, F, Figura, P, Fiori, I, Fiorucci, D, Fishbach, M, Fisher, R, Fishner, J, Fittipaldi, R, Fitz-Axen, M, Fiumara, V, Flaminio, R, Floden, E, Flynn, E, Fong, H, Font, J, Forsyth, P, Fournier, J, Frasca, S, Frasconi, F, Frei, Z, Freise, A, Frey, R, Frey, V, Fritschel, P, Frolov, V, Fronze, G, Fulda, P, Fyffe, M, Gabbard, H, Gadre, B, Gaebel, S, Gair, J, Gais, J, Galaudage, S, Gamba, R, Ganapathy, D, Ganguly, A, Gaonkar, S, Garaventa, B, Garcia-Quiros, C, Garufi, F, Gateley, B, Gaudio, S, Gayathri, V, Gemme, G, Gennai, A, George, D, George, J, George, R, Gergely, L, Ghonge, S, Ghosh, A, Ghosh, S, Giacomazzo, B, Giacoppo, L, Giaime, J, Giardina, K, Gibson, D, Gier, C, Gill, K, Giri, P, Glanzer, J, Gleckl, A, Godwin, P, Goetz, E, Goetz, R, Gohlke, N, Goncharov, B, Gonzalez, G, Gopakumar, A, Gossan, S, Gosselin, M, Gouaty, R, Grace, B, Grado, A, Granata, M, Granata, V, Grant, A, Gras, S, Grassia, P, Gray, C, Gray, R, Greco, G, Green, A, Green, R, Gretarsson, E, Griggs, H, Grignani, G, Grimaldi, A, Grimes, E, Grimm, S, Grote, H, Grunewald, S, Gruning, P, Guerrero, J, Guidi, G, Guimaraes, A, Guixe, G, Gulati, H, Guo, Y, Gupta, A, Gupta, P, Gustafson, E, Gustafson, R, Guzman, F, Haegel, L, Halim, O, Hall, E, Hamilton, E, Hammond, G, Haney, M, Hanke, M, Hanks, J, Hanna, C, Hannam, M, Hannuksela, O, Hansen, H, Hansen, T, Hanson, J, Harder, T, Hardwick, T, Haris, K, Harms, J, Harry, G, Harry, I, Hartwig, D, Hasskew, R, Haster, C, Haughian, K, Hayes, F, Healy, J, Heidmann, A, Heintze, M, Heinze, J, Heinzel, J, Heitmann, H, Hellman, F, Hello, P, Helmling-Cornell, A, Hemming, G, Hendry, M, Heng, I, Hennes, E, Hennig, J, Hennig, M, Hernandez Vivanco, F, Heurs, M, Hild, S, Hill, P, Hines, A, Hochheim, S, Hofgard, E, Hofman, D, Hohmann, J, Holgado, A, Holland, N, Hollows, I, Holmes, Z, Holt, K, Holz, D, Hopkins, P, Horst, C, Hough, J, Howell, E, Hoy, C, Hoyland, D, Huang, Y, Hubner, M, Huddart, A, Huerta, E, Hughey, B, Hui, V, Husa, S, Huttner, S, Hutzler, B, Huxford, R, Huynh-Dinh, T, Idzkowski, B, Iess, A, Imperato, S, Inchauspe, H, Ingram, C, Intini, G, Isi, M, Iyer, B, Jaberianhamedan, V, Jacqmin, T, Jadhav, S, James, A, Jani, K, Janssens, K, Janthalur, N, Jaranowski, P, Jariwala, D, Jaume, R, Jenkins, A, Jeunon, M, Jiang, J, Johns, G, Johnson-Mcdaniel, N, Jones, A, Jones, D, Jones, J, Jones, P, Jones, R, Jonker, R, Ju, L, Junker, J, Kalaghatgi, C, Kalogera, V, Kamai, B, Kandhasamy, S, Kang, G, Kanner, J, Kapadia, S, Kapasi, D, Karathanasis, C, Karki, S, Kashyap, R, Kasprzack, M, Kastaun, W, Katsanevas, S, Katsavounidis, E, Katzman, W, Kawabe, K, Kefelian, F, Keitel, D, Key, J, Khadka, S, Khalili, F, Khan, I, Khan, S, Khazanov, E, Khetan, N, Khursheed, M, Kijbunchoo, N, Kim, C, Kim, G, Kim, J, Kim, K, Kim, W, Kim, Y, Kimball, C, King, P, Kinley-Hanlon, M, Kirchhoff, R, Kissel, J, Kleybolte, L, Klimenko, S, Knowles, T, Knyazev, E, Koch, P, Koehlenbeck, S, Koekoek, G, Koley, S, Kolstein, M, Komori, K, Kondrashov, V, Kontos, A, Koper, N, Korobko, M, Korth, W, Kovalam, M, Kozak, D, Kramer, C, Kringel, V, Krishnendu, N, Krolak, A, Kuehn, G, Kumar, A, Kumar, P, Kumar, R, Kuns, K, Kwang, S, Lackey, B, Laghi, D, Lalande, E, Lam, T, Lamberts, A, Landry, M, Lane, B, Lang, R, Lange, J, Lantz, B, Lanza, R, La Rosa, I, Lartaux-Vollard, A, Lasky, P, Laxen, M, Lazzarini, A, Lazzaro, C, Leaci, P, Leavey, S, Lecoeuche, Y, Lee, H, Lee, J, Lee, K, Lehmann, J, Leon, E, Leroy, N, Letendre, N, Levin, Y, Li, A, Li, J, Li, K, Li, T, Li, X, Linde, F, Linker, S, Linley, J, Littenberg, T, Liu, J, Liu, X, Llorens-Monteagudo, M, Lo, R, Lockwood, A, London, L, Longo, A, Lorenzini, M, Loriette, V, Lormand, M, Losurdo, G, Lough, J, Lousto, C, Lovelace, G, Luck, H, Lumaca, D, Lundgren, A, Ma, Y, Macas, R, Macinnis, M, Macleod, D, Macmillan, I, Macquet, A, Magana Hernandez, I, Magana-Sandoval, F, Magazzu, C, Magee, R, Majorana, E, Maksimovic, I, Maliakal, S, Malik, A, Man, N, Mandic, V, Mangano, V, Mansell, G, Manske, M, Mantovani, M, Mapelli, M, Marchesoni, F, Marion, F, Marka, S, Marka, Z, Markakis, C, Markosyan, A, Markowitz, A, Maros, E, Marquina, A, Marsat, S, Martelli, F, Martin, I, Martin, R, Martinez, M, Martinez, V, Martynov, D, Masalehdan, H, Mason, K, Massera, E, Masserot, A, Massinger, T, Masso-Reid, M, Mastrogiovanni, S, Matas, A, Mateu-Lucena, M, Matichard, F, Matiushechkina, M, Mavalvala, N, Maynard, E, Mccann, J, Mccarthy, R, Mcclelland, D, Mccormick, S, Mcculler, L, Mcguire, S, Mcisaac, C, Mciver, J, Mcmanus, D, Mcrae, T, Mcwilliams, S, Meacher, D, Meadors, G, Mehmet, M, Mehta, A, Melatos, A, Melchor, D, Mendell, G, Menendez-Vazquez, A, Mercer, R, Mereni, L, Merfeld, K, Merilh, E, Merritt, J, Merzougui, M, Meshkov, S, Messenger, C, Messick, C, Metzdorff, R, Meyers, P, Meylahn, F, Mhaske, A, Miani, A, Miao, H, Michaloliakos, I, Michel, C, Middleton, H, Milano, L, Miller, A, Millhouse, M, Mills, J, Milotti, E, Milovich-Goff, M, Minazzoli, O, Minenkov, Y, Mir, L, Mishkin, A, Mishra, C, Mistry, T, Mitra, S, Mitrofanov, V, Mitselmakher, G, Mittleman, R, Mo, G, Mogushi, K, Mohapatra, S, Mohite, S, Molina, I, Molina-Ruiz, M, Mondin, M, Montani, M, Moore, C, Moraru, D, Morawski, F, Moreno, G, Morisaki, S, Mours, B, Mow-Lowry, C, Mozzon, S, Muciaccia, F, Mukherjee, A, Mukherjee, D, Mukherjee, S, Mukund, N, Mullavey, A, Munch, J, Muniz, E, Murray, P, Nadji, S, Nagar, A, Nardecchia, I, Naticchioni, L, Nayak, R, Neil, B, Neilson, J, Nelemans, G, Nelson, T, Nery, M, Neunzert, A, Nitz, A, Ng, K, Ng, S, Nguyen, C, Nguyen, P, Nguyen, T, Nichols, S, Nissanke, S, Nocera, F, Noh, M, North, C, Nothard, D, Nuttall, L, Oberling, J, O'Brien, B, O'Dell, J, Oganesyan, G, Ogin, G, Oh, J, Oh, S, Ohme, F, Ohta, H, Okada, M, Olivetto, C, Oppermann, P, Oram, R, O'Reilly, B, Ormiston, R, Ortega, L, O'Shaughnessy, R, Ossokine, S, Osthelder, C, Ottaway, D, Overmier, H, Owen, B, Pace, A, Pagano, G, Page, M, Pagliaroli, G, Pai, A, Pai, S, Palamos, J, Palashov, O, Palomba, C, Pan, H, Panda, P, Pang, T, Pankow, C, Pannarale, F, Pant, B, Paoletti, F, Paoli, A, Paolone, A, Parker, W, Pascucci, D, Pasqualetti, A, Passaquieti, R, Passuello, D, Patel, M, Patricelli, B, Payne, E, Pechsiri, T, Pedraza, M, Pegoraro, M, Pele, A, Penn, S, Perego, A, Perez, C, Perigois, C, Perreca, A, Perries, S, Petermann, J, Petterson, D, Pfeiffer, H, Pham, K, Phukon, K, Piccinni, O, Pichot, M, Piendibene, M, Piergiovanni, F, Pierini, L, Pierro, V, Pillant, G, Pilo, F, Pinard, L, Pinto, I, Piotrzkowski, K, Pirello, M, Pitkin, M, Placidi, E, Plastino, W, Pluchar, C, Poggiani, R, Polini, E, Pong, D, Ponrathnam, S, Popolizio, P, Porter, E, Poverman, A, Powell, J, Pracchia, M, Prajapati, A, Prasai, K, Prasanna, R, Pratten, G, Prestegard, T, Principe, M, Prodi, G, Prokhorov, L, Prosposito, P, Prudenzi, L, Puecher, A, Punturo, M, Puosi, F, Puppo, P, Purrer, M, Qi, H, Quetschke, V, Quinonez, P, Quitzow-James, R, Raab, F, Raaijmakers, G, Radkins, H, Radulesco, N, Raffai, P, Rafferty, H, Rail, S, Raja, S, Rajan, C, Rajbhandari, B, Rakhmanov, M, Ramirez, K, Ramirez, T, Ramos-Buades, A, Rana, J, Rao, K, Rapagnani, P, Rapol, U, Ratto, B, Raymond, V, Razzano, M, Read, J, Regimbau, T, Rei, L, Reid, S, Reitze, D, Rettegno, P, Ricci, F, Richardson, C, Richardson, J, Richardson, L, Ricker, P, Riemenschneider, G, Riles, K, Rizzo, M, Robertson, N, Robinet, F, Rocchi, A, Rocha, J, Rodriguez, S, Rodriguez-Soto, R, Rolland, L, Rollins, J, Roma, V, Romanelli, M, Romano, R, Romel, C, Romero, A, Romero-Shaw, I, Romie, J, Ronchini, S, Rose, C, Rose, D, Rose, K, Rosell, M, Rosinska, D, Rosofsky, S, Ross, M, Rowan, S, Rowlinson, S, Roy, S, Ruggi, P, Ryan, K, Sachdev, S, Sadecki, T, Sadiq, J, Sakellariadou, M, Salafia, O, Salconi, L, Saleem, M, Samajdar, A, Sanchez, E, Sanchez, J, Sanchez, L, Sanchis-Gual, N, Sanders, J, Sandles, L, Santiago, K, Santos, E, Saravanan, T, Sarin, N, Sassolas, B, Sathyaprakash, B, Sauter, O, Savage, R, Savant, V, Sawant, D, Sayah, S, Schaetzl, D, Schale, P, Scheel, M, Scheuer, J, Schindler-Tyka, A, Schmidt, P, Schnabel, R, Schofield, R, Schonbeck, A, Schreiber, E, Schulte, B, Schutz, B, Schwarm, O, Schwartz, E, Scott, J, Scott, S, Seglar-Arroyo, M, Seidel, E, Sellers, D, Sengupta, A, Sennett, N, Sentenac, D, Sequino, V, Sergeev, A, Setyawati, Y, Shaffer, T, Shahriar, M, Sharifi, S, Sharma, A, Sharma, P, Shawhan, P, Shen, H, Shikauchi, M, Shink, R, Shoemaker, D, Shukla, K, Shyamsundar, S, Sieniawska, M, Sigg, D, Singer, L, Singh, D, Singh, N, Singha, A, Singhal, A, Sintes, A, Sipala, V, Skliris, V, Slagmolen, B, Slaven-Blair, T, Smetana, J, Smith, J, Smith, R, Somala, S, Son, E, Soni, K, Soni, S, Sorazu, B, Sordini, V, Sorrentino, F, Sorrentino, N, Soulard, R, Souradeep, T, Sowell, E, Spencer, A, Spera, M, Srivastava, A, Srivastava, V, Staats, K, Stachie, C, Steer, D, Steinhoff, J, Steinke, M, Steinlechner, J, Steinlechner, S, Steinmeyer, D, Stevenson, S, Stolle-Mcallister, G, Stops, D, Stover, M, Strain, K, Stratta, G, Strunk, A, Sturani, R, Stuver, A, Sudbeck, J, Sudhagar, S, Sudhir, V, Suh, H, Summerscales, T, Sun, H, Sun, L, Sunil, S, Sur, A, Suresh, J, Sutton, P, Swinkels, B, Szczepanczyk, M, Tacca, M, Tait, S, Talbot, C, Tanasijczuk, A, Tanner, D, Tao, D, Tapia, A, Tapia San Martin, E, Tasson, J, Taylor, R, Tenorio, R, Terkowski, L, Thirugnanasambandam, M, Thomas, L, Thomas, M, Thomas, P, Thompson, J, Thondapu, S, Thorne, K, Thrane, E, Tiwari, S, Tiwari, V, Toland, K, Tolley, A, Tonelli, M, Tornasi, Z, Torres-Forne, A, Torrie, C, E Melo, I, Toyra, D, Tran, A, Trapananti, A, Travasso, F, Traylor, G, Tringali, M, Tripathee, A, Trovato, A, Trudeau, R, Tsai, D, Tsang, K, Tse, M, Tso, R, Tsukada, L, Tsuna, D, Tsutsui, T, Turconi, M, Ubhi, A, Udall, R, Ueno, K, Ugolini, D, Unnikrishnan, C, Urban, A, Usman, S, Utina, A, Vahlbruch, H, Vajente, G, Vajpeyi, A, Valdes, G, Valentini, M, Valsan, V, Van Bakel, N, Van Beuzekom, M, Van Den Brand, J, Van Den Broeck, C, Vander-Hyde, D, Van Der Schaaf, L, Van Heijningen, J, Vardaro, M, Vargas, A, Varma, V, Vass, S, Vasuth, M, Vecchio, A, Vedovato, G, Veitch, J, Veitch, P, Venkateswara, K, Venneberg, J, Venugopalan, G, Verkindt, D, Verma, Y, Veske, D, Vetrano, F, Vicere, A, Viets, A, Vijaykumar, A, Villa-Ortega, V, Vinet, J, Vitale, S, Vo, T, Vocca, H, Vorvick, C, Vyatchanin, S, Wade, A, Wade, L, Wade, M, Walet, R, Walker, M, Wallace, G, Wallace, L, Walsh, S, Wang, J, Wang, S, Wang, W, Wang, Y, Ward, R, Warner, J, Was, M, Washington, N, Watchi, J, Weaver, B, Wei, L, Weinert, M, Weinstein, A, Weiss, R, Wellmann, F, Wen, L, Wessels, P, Westhouse, J, Wette, K, Whelan, J, White, D, White, L, Whiting, B, Whittle, C, Wilken, D, Williams, D, Williams, M, Williamson, A, Willis, J, Willke, B, Wilson, D, Wimmer, M, Winkler, W, Wipf, C, Woan, G, Woehler, J, Wofford, J, Wong, I, Wrangel, J, Wright, J, Wu, D, Wysocki, D, Xiao, L, Yamamoto, H, Yang, L, Yang, Y, Yang, Z, Yap, M, Yeeles, D, Yoon, A, Yu, H, Yuen, S, Zadrozny, A, Zanolin, M, Zelenova, T, Zendri, J, Zevin, M, Zhang, J, Zhang, L, Zhang, R, Zhang, T, Zhao, C, Zhao, G, Zheng, Y, Zhou, M, Zhou, Z, Zhu, X, Zimmerman, A, Zlochower, Y, Zucker, M, Zweizig, J, Spectroscopies optiques des matériaux verres, amorphes et à nanoparticules (SOPRANO), Institut Lumière Matière [Villeurbanne] (ILM), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Centre National de la Recherche Scientifique (CNRS), AstroParticule et Cosmologie (APC (UMR_7164)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Observatoire de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-Université de Paris (UP), Laboratoire de Physique des 2 Infinis Irène Joliot-Curie (IJCLab), Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Laboratoire d'Annecy de Physique des Particules (LAPP), Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS), Université Côte d'Azur (UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Université Côte d'Azur (UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Nice Sophia Antipolis (... - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA), Observatoire de la Côte d'Azur (OCA), Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS), Institut des Fonctions Optiques pour les Technologies de l'informatiON (Institut FOTON), Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-École Nationale Supérieure des Sciences Appliquées et de Technologie (ENSSAT)-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Laboratoire Kastler Brossel (LKB [Collège de France]), École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-Fédération de recherche du Département de physique de l'Ecole Normale Supérieure - ENS Paris (FRDPENS), Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)-Collège de France (CdF (institution)), Institut de Physique des 2 Infinis de Lyon (IP2I Lyon), Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Université Claude Bernard Lyon 1 (UCBL), Laboratoire des matériaux avancés (LMA), Centre National de la Recherche Scientifique (CNRS)-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon, Ecole Superieure de Physique et de Chimie Industrielles de la Ville de Paris (ESPCI Paris), Université Paris sciences et lettres (PSL), Institut Pluridisciplinaire Hubert Curien (IPHC), Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Université de Strasbourg (UNISTRA)-Centre National de la Recherche Scientifique (CNRS), Institut des Hautes Etudes Scientifiques (IHES), IHES, LIGO Scientific, Virgo, Arene, M., Becsy, B., Calderon Bustillo, J., Cavaglia, M., Cerda-Duran, P., Cieslar, M., Cordero-Carrion, I., Curylo, M., Canton, T. D., Dalya, G., D'Angelo, B., D'Antonio, S., Deleglise, S., D'Emilio, V., De Varona, O., Diaz, M. C., D'Onofrio, L., D'Urso, D., Estelles, H., Fronze, G. G., Garcia-Quiros, C., Ghosh, A., Gonzalez, G., Guixe, G., Gupta, A., Hubner, M. T., Johnson-Mcdaniel, N. K., Kefelian, F., Kramer, C., Krolak, A., Kumar, R., Luck, H., Magana Hernandez, I., Magana-Sandoval, F., Magazzu, C., Marka, S., Marka, Z., Mir, L. M., Mukherjee, A., Mukherjee, S., Muniz, E. A., O'Brien, B. D., O'Dell, J., O'Reilly, B., O'Shaughnessy, R., Perigois, C., Perries, S., Purrer, M., Rosinska, D., Roy, S., Schonbeck, A., Stolle-Mcallister, G., Sudbeck, J., Szczepanczyk, M. J., Tiwari, S., Torres-Forne, A., E Melo, I. T., Toyra, D., Van Bakel, N., Van Beuzekom, M., Van Den Brand, J. F. J., Van Der Schaaf, L., Van Heijningen, J. V., Vasuth, M., Vicere, A., Wessels, P., Yu, H., Zadrozny, A., The LIGO Scientific Collaboration, The Virgo Collaboration, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité), Université Nice Sophia Antipolis (1965 - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire de la Côte d'Azur, COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Université Côte d'Azur (UCA)-Université Côte d'Azur (UCA)-Centre National de la Recherche Scientifique (CNRS), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-École Nationale Supérieure des Sciences Appliquées et de Technologie (ENSSAT)-Centre National de la Recherche Scientifique (CNRS), Fédération de recherche du Département de physique de l'Ecole Normale Supérieure - ENS Paris (FRDPENS), École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-Collège de France (CdF (institution))-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Université de Lyon-Université de Lyon-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Centre National de la Recherche Scientifique (CNRS), Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Centre National de la Recherche Scientifique (CNRS), Institut des Hautes Études Scientifiques (IHES), (Astro)-Particles Physics, LIGO Sci Collaboration, Faculty of Science, Gravitation and Astroparticle Physics Amsterdam, Other Research IHEF (IoP, FNWI), Astroparticle Physics (IHEF, IoP, FNWI), and IoP (FNWI)
Subjects: neutron star: binary, IMPACT, Astronomy, Binary number, General Physics and Astronomy, Astrophysics, Coalescence, Ray coordinates, binary systems, black holes, false alarm rate, mass ratio, near-real time, neutron stars, ray coordinates, total mass, 01 natural sciences, General Relativity and Quantum Cosmology, Gravitation, Cosmology & Astrophysics, GRAVITATIONAL-WAVES, 010305 fluids & plasmas, Cosmology & Astrophysics, Gravitational Waves, LIGO, Virgo, CBC, O3, EQUATION, LIGO, Gravitational wave detection, Gravitational wave sources, QC, High Energy Astrophysical Phenomena (astro-ph.HE), Physics, BILBY, Settore FIS/03, black hole: spin, Black holes, Gravitational effects, BAYESIAN-INFERENCE, COMPACT OBJECTS, SPINMASS, False alarm rate, STATE, Binary systems, Physical Sciences, [PHYS.GRQC]Physics [physics]/General Relativity and Quantum Cosmology [gr-qc], Gravitational wave source, Astrophysics - High Energy Astrophysical Phenomena, asymmetry, CBC, Gravitation, BLACK-HOLE MERGERS, PARAMETER-ESTIMATION, GW_HIGHLIGHT, data analysis method, Astrophysics::High Energy Astrophysical Phenomena, Physics, Multidisciplinary, O3, FOS: Physical sciences, General Relativity and Quantum Cosmology (gr-qc), Astrophysics::Cosmology and Extragalactic Astrophysics, Gravitational Wave Transient Catalog 2, Gravity waves, Physics and Astronomy(all), gravitational radiation: direct detection, 1ST, Neutron stars, binary: coalescence, Binary black hole, SEARCH, 0103 physical sciences, ddc:530, 010306 general physics, STFC, MASS-DISTRIBUTION, Gravitational Waves, Science & Technology, Gravitational wave, Virgo, Gamma rays, gravitational radiation, RCUK, black hole: mass, Mass ratio, Near-real time, binary: compact, Stars, gravitational radiation detector, Redshift, detector: sensitivity, Black hole, Neutron star, wave: model, VIRGO, black hole: binary, Physics and Astronomy, [SDU]Sciences of the Universe [physics], gravitational radiation: emission, Dewey Decimal Classification::500 | Naturwissenschaften::530 | Physik, [PHYS.ASTR]Physics [physics]/Astrophysics [astro-ph], Total mass
Abstract: We report on gravitational wave discoveries from compact binary coalescences detected by Advanced LIGO and Advanced Virgo in the first half of the third observing run (O3a) between 1 April 2019 15:00 UTC and 1 October 2019 15:00. By imposing a false-alarm-rate threshold of two per year in each of the four search pipelines that constitute our search, we present 39 candidate gravitational wave events. At this threshold, we expect a contamination fraction of less than 10%. Of these, 26 candidate events were reported previously in near real-time through GCN Notices and Circulars; 13 are reported here for the first time. The catalog contains events whose sources are black hole binary mergers up to a redshift of ~0.8, as well as events whose components could not be unambiguously identified as black holes or neutron stars. For the latter group, we are unable to determine the nature based on estimates of the component masses and spins from gravitational wave data alone. The range of candidate events which are unambiguously identified as binary black holes (both objects $\geq 3~M_\odot$) is increased compared to GWTC-1, with total masses from $\sim 14~M_\odot$ for GW190924_021846 to $\sim 150~M_\odot$ for GW190521. For the first time, this catalog includes binary systems with significantly asymmetric mass ratios, which had not been observed in data taken before April 2019. We also find that 11 of the 39 events detected since April 2019 have positive effective inspiral spins under our default prior (at 90% credibility), while none exhibit negative effective inspiral spin. Given the increased sensitivity of Advanced LIGO and Advanced Virgo, the detection of 39 candidate events in ~26 weeks of data (~1.5 per week) is consistent with GWTC-1., Comment: This version updates with minor revisions to typographical errors. We would also like to call attention to the updated parameter estimation samples data release here: https://dcc.ligo.org/LIGO-P2000223/public
Published: 2021

23. Uncertainty in model‐based treatment decision support: applied to aortic valve stenosis

Author: Frans N. van de Vosse, Roel Meiburg, Marcel C. M. Rutten, Wouter Huberts, Cardiovascular Biomechanics, Eindhoven MedTech Innovation Center, EAISI Health, Biomedische Technologie, and RS: Carim - H07 Cardiovascular System Dynamics
Subjects: Monte Carlo Markov chain, Computer science, medicine.medical_treatment, FLOW, 02 engineering and technology, 030204 cardiovascular system & hematology, 0302 clinical medicine, Valve replacement, patient specific, Polynomial chaos, Estimation theory, Applied Mathematics, Uncertainty, STATE, Markov Chains, prediction uncertainty, Treatment Outcome, Computational Theory and Mathematics, Modeling and Simulation, Aortic valve stenosis, Aortic Valve, symbols, parameter estimation, PARAMETER-ESTIMATION, 0206 medical engineering, INDEXES, Biomedical Engineering, PRESSURE, unscented Kalman filter, 03 medical and health sciences, symbols.namesake, DATA ASSIMILATION, Control theory, medicine, Research Article ‐ Applications, Humans, Computer Simulation, Molecular Biology, Research Article ‐ Application, HYPERTENSION, GLOBAL SENSITIVITY-ANALYSIS, aortic stenosis, Markov chain Monte Carlo, Kalman filter, Aortic Valve Stenosis, medicine.disease, 020601 biomedical engineering, Confidence interval, Test case, IMPLANTATION, Software
Abstract: Patient outcome in trans‐aortic valve implantation (TAVI) therapy partly relies on a patient's haemodynamic properties that cannot be determined from current diagnostic methods alone. In this study, we predict changes in haemodynamic parameters (as a part of patient outcome) after valve replacement treatment in aortic stenosis patients. A framework to incorporate uncertainty in patient‐specific model predictions for decision support is presented. A 0D lumped parameter model including the left ventricle, a stenotic valve and systemic circulatory system has been developed, based on models published earlier. The unscented Kalman filter (UKF) is used to optimize model input parameters to fit measured data pre‐intervention. After optimization, the valve treatment is simulated by significantly reducing valve resistance. Uncertain model parameters are then propagated using a polynomial chaos expansion approach. To test the proposed framework, three in silico test cases are developed with clinically feasible measurements. Quality and availability of simulated measured patient data are decreased in each case. The UKF approach is compared to a Monte Carlo Markov Chain (MCMC) approach, a well‐known approach in modelling predictions with uncertainty. Both methods show increased confidence intervals as measurement quality decreases. By considering three in silico test‐cases we were able to show that the proposed framework is able to incorporate optimization uncertainty in model predictions and is faster and the MCMC approach, although it is more sensitive to noise in flow measurements. To conclude, this work shows that the proposed framework is ready to be applied to real patient data., In this study, we aim to provide a framework to predict changes in haemodynamic parameters (as a part of patient outcome) after valve replacement treatment in aortic stenosis. The framework involves uncertain input parameter optimization using an unscented Kalman filter approach. Valve replacement treatment is then simulated, and uncertainties are propagated via polynomial chaos expansions. The framework is able to capture measurement quality in prediction confidence intervals and is ready to be applied to patient data. Novelty Statement We developed a lumped parameter model to predict changes in haemodynamic parameters after valve replacement therapy in aortic stenosisWe showed a data assimilation approach (Unscented Kalman Filter) is suitable for parameter optimization for this model based on available clinical data, with estimated optimization uncertaintyWe expanded parameter optimization via Unscented Kalman Filtering via sensitivity‐based weightingWe coupled uncertain parameter optimization with a polynomial chaos expansion approach to propagate uncertain parameters in treatment prediction
Published: 2020

24. Necessary conditions for algorithmic tuning of weather prediction models using OpenIFS as an example

Author: Vladimir Shemyakin, Madeleine Ekblom, Lauri Tuppi, Pirkka Ollinaho, Heikki Järvinen, INAR Physics, and Institute for Atmospheric and Earth System Research (INAR)
Subjects: 010504 meteorology & atmospheric sciences, Degree (graph theory), Estimation theory, Computer science, lcsh:QE1-996.5, 0207 environmental engineering, Structure (category theory), Process (computing), 02 engineering and technology, State (functional analysis), Gauge (firearms), 01 natural sciences, 114 Physical sciences, 010305 fluids & plasmas, lcsh:Geology, 13. Climate action, 0103 physical sciences, Convergence (routing), Applied mathematics, Convergence tests, 020701 environmental engineering, OPTIMIZATION, 0105 earth and related environmental sciences, PARAMETER-ESTIMATION
Abstract: Algorithmic model tuning is a promising approach to yield the best possible performance of multiscale multi-phase atmospheric models once the model structure is fixed. We are curious about to what degree one can trust the algorithmic tuning process. We approach the problem by studying the convergence of this process in a semi-realistic case. Let us denote M(x0;θd) as the default model, where x0 and θd are the initial state and default model parameter vectors, respectively. A necessary condition for an algorithmic tuning process to converge in a fully-realistic case is that the default model is recovered if the tuning process is initialised with perturbed model parameters θ and the default model forecasts are used as pseudo-observations. In this paper we study the circumstances where this condition is valid by carrying out a large set of convergence tests using two different tuning methods and the OpenIFS model. These tests are interpreted as guidelines for algorithmic model tuning applications.The results of this study can be used as recipe for maximising efficiency of algorithmic tuning. In the convergence tests, maximised efficiency was reached with using ensemble initial conditions, cost function that covers entire model domain, short forecast length and medium-sized ensembles.
Published: 2020

25. Development of robust extended Kalman filter and moving window estimator for simultaneous state and parameter/disturbance estimation

Author: Lorenz T. Biegler, Sachin C. Patwardhan, and Jayaram Valluru
Subjects: 0209 industrial biotechnology, DATA RECONCILIATION, Optimization problem, Computer science, Bayesian probability, Simultaneous state and parameter estimation, 02 engineering and technology, M-estimators, GROSS-ERROR-DETECTION, Industrial and Manufacturing Engineering, Extended Kalman filter, 020901 industrial engineering & automation, Measurement gross errors, 020401 chemical engineering, Control theory, Robustness (computer science), 0204 chemical engineering, MAXIMUM-LIKELIHOOD, OPTIMIZATION, Estimation theory, Estimator, Kalman filter, Moving window estimator, Computer Science Applications, Robust estimation, Control and Systems Engineering, Modeling and Simulation, NAVIGATION, Outlier, MODEL-PREDICTIVE CONTROL, FAULT-DIAGNOSIS, DYNAMIC-SYSTEMS, PARAMETER-ESTIMATION
Abstract: Simultaneous occurrence of gross errors (outliers/biases/drifts) in the measured signals, and drifting disturbances/parameter variations affecting the system dynamics can lead to biased state estimates, and, in turn, can lead to deterioration in the performance of model-based monitoring and control schemes. In this work, robust recursive and moving window based Bayesian state and parameter estimators are developed that are robust w.r.t. gross errors in the measurements and can simultaneously estimate non-additive unmeasured disturbance/parameter variations. Using Bayes' rule, the update step of Kalman filter (KF) is recast as an optimization problem. The optimization is then modified by replacing the likelihood term in the objective function with cost function defined by an M-estimator. The M-estimators considered in this work are Huber's Fair function and Hampel's redescending estimator. The reformulated KF is then used as a basis for reformulating extended Kalman filter (EKF). This re-formulated EKF is then used for developing robust simultaneous state and parameter estimation schemes. In particular, a robust version of recently proposed moving window based state and parameter estimator [1] has been developed. The resulting formulation can be viewed as a hybrid approach, in which the gross errors in the measurements are dealt with in a passive manner, with an active elimination of model plant mismatch by estimating unmeasured disturbance/parameter variations simultaneously. The efficacy of the proposed robust state and parameter estimators is demonstrated by conducting simulation studies and experimental studies. Analysis of the simulation and experimental results reveal that the proposed robust recursive and moving window based state and parameter estimators significantly reduce or completely nullify the effect of gross errors on the state estimates while simultaneously estimating drifting unmeasured disturbances/parameters. The simulation study also underscores the importance of simultaneous estimation of unmeasured disturbances/parameters while achieving robustness using the M-estimators. Moreover, Hampel's redescending estimator is found to be a better choice of M-estimator than the popular Huber's Fair function, as the redescending estimator can completely nullify the effect of gross errors on the state and parameter estimates. (C) 2018 Elsevier Ltd. All rights reserved.
Published: 2018

26. Parameter-estimation and algorithm-selection based United-Judgment for image steganalysis.

Author: Lu, Jicang, Liu, Fenlin, Luo, Xiangyang, and Yang, Chunfang
Subjects: DENSITY functionals, CHARACTERISTIC functions, DISCRETE cosine transforms, WAVELETS (Mathematics), ALGORITHMS, JPEG (Image coding standard)
Abstract: In order to synthetically utilize multiple steganalytic algorithms, and further improve the detection accuracy and enhance detection reliability, United-Judgment methods are researched and analyzed in this paper. According to the performance of each algorithm, United-Judgment methods for both blind and specific steganalysis are proposed based on parameter-estimation and algorithm-selection. Experiments are carried out for the former with seven typical blind detections and the latter one with five typical spatial domain steganalytic methods. Experimental results show that the proposed methods can synthetically utilize the existing multiple algorithms effectively, and achieve more reliable detection. [ABSTRACT FROM AUTHOR]
Published: 2012
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27. Mapping forest successional stages in the Brazilian Amazon using forest heights derived from TanDEM-X SAR interferometry

Author: Heiko Balzter, Matteo Pardini, Kevin Tansey, Igor Rizaev, Polyanna da Conceição Bispo, Luciana Spinelli Araujo, Konstantinos Papathanassiou, Dominik Rains, Florian Kugler, Maiza Nara dos Santos, João Roberto dos Santos, POLYANNA DA CONCEICAO BISPO, University of Leicester, MATTEO PARDINI, German Aerospace Center, KONSTANTINUS P PAPATHANASSIOU, German Aerospace Center, FLORIAN KUGLER, German Aerospace Center, HEIKO BALTZER, Ghent University, DOMINIK RAINS, University of Leicester, JOAO ROBERTO DOS SANTOS, INPE, IGOR G RIZAEV, University of Bristol, KEVIN TANSEY, University of Leicester, MAIZA FERREIRA DA SILVA, SGE, and LUCIANA SPINELLI DE ARAUJO, CNPMA.
Subjects: 010504 meteorology & atmospheric sciences, Tropical forests, 0208 environmental biotechnology, Soil Science, Context (language use), 02 engineering and technology, 01 natural sciences, Synthetic Aperture Radar, BAND, CLASSIFICATION, POLINSAR, TanDEM-X, Floresta Tropical, Interferometric synthetic aperture radar, Tropical vegetation, POL-INSAR, Mapa, INVERSION, Computers in Earth Sciences, Digital elevation model, SECONDARY FORESTS, 0105 earth and related environmental sciences, Remote sensing, geography, geography.geographical_feature_category, Radar, Height, Synthetic Aperture, Geology, synthetic aperture radar, 15. Life on land, Old-growth forest, 020801 environmental engineering, Forest height, Lidar, Forest succession, Interferometry, Successional stages, Earth and Environmental Sciences, Spatial ecology, Environmental science, Secondary forest, TROPICAL-FOREST, INTEGRATION, ABOVEGROUND BIOMASS, PARAMETER-ESTIMATION, Sensoriamento Remoto
Abstract: Knowledge of the spatial patterns of successional stages (i.e., primary and secondary forest) in tropical forests allows to monitor forest preservation, mortality and regeneration in relation to natural and anthropogenic disturbances. Different successional stages have also different capabilities of re-establishing carbon stocks. Therefore, a successful discrimination of successional stages over wide areas can lead to an improved quantification of above ground biomass and carbon stocks. The reduction of the mapping uncertainties is especially a challenge due to high heterogeneity of the tropical vegetation. In this framework, the development of innovative remote sensing approaches is required. Forests (top) height (and its spatial distribution) are an important structural parameter that can be used to differentiate between different successional stages, and can be provided by Interferometric Synthetic Aperture Radar (InSAR) acquisitions. In this context, this paper investigates the potential of forest heights estimated from TanDEM-X InSAR data and a LiDAR digital terrain model (DTM) for separating successional stages (primary or old growth and secondary forest at different stages of succession) by means of a maximum likelihood classification. The study was carried out in the region of the Tapajós National Forest (Pará, Brazil) in the Amazon biome. The forest heights for three years (2012, 2013 and 2016) were estimated from a single-polarization in bistatic mode using InSAR model-based inversion techniques aided by the LiDAR digital terrain model. The validation of the TanDEM-X forest heights with independent LiDAR H100 datasets was carried out in the location of seven field inventory plots (measuring 50?×?50?m, equivalent to 0.25?ha), also allowing for the validation of the LiDAR datasets against the field data. The validation of the estimated heights showed a high correlation (r?=?0.93) and a low uncertainty (RMSE?=?3?m). The information about the successional stages and forest heights from field datasets was used to select training samples in the LiDAR and TanDEM-X forest heights to classify successional stages with a maximum likelihood classifier. The identification of different stages of forest succession based on TanDEM-X forest heights was possible with an overall accuracy of about 80%. Made available in DSpace on 2019-11-27T18:14:02Z (GMT). No. of bitstreams: 1 ARAUJOSPINELLIMappingForest2019.pdf: 3208805 bytes, checksum: 7902ab1e9e1d3da159d912e43abb2a28 (MD5) Previous issue date: 2019
Published: 2019

28. The Debiased Spatial Whittle Likelihood

Author: Arthur P. Guillaumin, Adam M. Sykulski, Sofia C. Olhede, and Frederik J. Simons
Subjects: Statistics and Probability, FOS: Computer and information sciences, Statistics & Probability, aliasing, MODELS, TIME-SERIES, Machine Learning (stat.ML), Statistics - Applications, Statistics - Computation, Methodology (stat.ME), missing data, Statistics - Machine Learning, 0102 Applied Mathematics, STATIONARY PROCESS, 1403 Econometrics, Applications (stat.AP), COVARIANCE, stat.AP, Computation (stat.CO), Statistics - Methodology, Whittle likelihood, stat.CO, Science & Technology, 0104 Statistics, stat.ML, stat.ME, Physical Sciences, irregular boundaries, random fields, Statistics, Probability and Uncertainty, Mathematics, PARAMETER-ESTIMATION
Abstract: We provide a computationally and statistically efficient method for estimating the parameters of a stochastic covariance model observed on a regular spatial grid in any number of dimensions. Our proposed method, which we call the Debiased Spatial Whittle likelihood, makes important corrections to the well-known Whittle likelihood to account for large sources of bias caused by boundary effects and aliasing. We generalize the approach to flexibly allow for significant volumes of missing data including those with lower-dimensional substructure, and for irregular sampling boundaries. We build a theoretical framework under relatively weak assumptions which ensures consistency and asymptotic normality in numerous practical settings including missing data and non-Gaussian processes. We also extend our consistency results to multivariate processes. We provide detailed implementation guidelines which ensure the estimation procedure can be conducted in O(nlogn) operations, where n is the number of points of the encapsulating rectangular grid, thus keeping the computational scalability of Fourier and Whittle-based methods for large data sets. We validate our procedure over a range of simulated and realworld settings, and compare with state-of-the-art alternatives, demonstrating the enduring practical appeal of Fourier-based methods, provided they are corrected by the procedures developed in this paper.
Published: 2019

29. A hybrid stochastic rainfall model that reproduces some important rainfall characteristics at hourly to yearly timescales

Author: Christian Onof, Jeongha Park, and Dongkyun Kim
Subjects: Environmental Engineering, 010504 meteorology & atmospheric sciences, CASCADE, 0208 environmental biotechnology, Climate change, TIME-SERIES, 02 engineering and technology, Atmospheric sciences, Poisson distribution, lcsh:Technology, 01 natural sciences, lcsh:TD1-1066, 0905 Civil Engineering, law.invention, symbols.namesake, law, Intermittency, Precipitation, Autoregressive integrated moving average, lcsh:Environmental technology. Sanitary engineering, Geosciences, Multidisciplinary, Extreme value theory, lcsh:Environmental sciences, 0105 earth and related environmental sciences, lcsh:GE1-350, Science & Technology, CLIMATE-CHANGE, lcsh:T, Estimation theory, lcsh:Geography. Anthropology. Recreation, Geology, WEATHER GENERATOR, POINT PROCESS MODELS, 020801 environmental engineering, DISAGGREGATION, 0907 Environmental Engineering, lcsh:G, PRECIPITATION, Physical Sciences, SIMULATION, symbols, Water Resources, Environmental science, RECTANGULAR PULSE MODEL, Maxima, 0406 Physical Geography and Environmental Geoscience, PARAMETER-ESTIMATION
Abstract: A novel approach to stochastic rainfall generation that can reproduce various statistical characteristics of observed rainfall at hourly to yearly timescales is presented. The model uses a seasonal autoregressive integrated moving average (SARIMA) model to generate monthly rainfall. Then, it downscales the generated monthly rainfall to the hourly aggregation level using the Modified Bartlett–Lewis Rectangular Pulse (MBLRP) model, a type of Poisson cluster rainfall model. Here, the MBLRP model is carefully calibrated such that it can reproduce the sub-daily statistical properties of observed rainfall. This was achieved by first generating a set of fine-scale rainfall statistics reflecting the complex correlation structure between rainfall mean, variance, auto-covariance, and proportion of dry periods, and then coupling it to the generated monthly rainfall, which were used as the basis of the MBLRP parameterization. The approach was tested on 34 gauges located in the Midwest to the east coast of the continental United States with a variety of rainfall characteristics. The results of the test suggest that our hybrid model accurately reproduces the first- to the third-order statistics as well as the intermittency properties from the hourly to the annual timescales, and the statistical behaviour of monthly maxima and extreme values of the observed rainfall were reproduced well.
Published: 2019

30. Soil hydraulic properties determined by inverse modeling of drip infiltrometer experiments extended with pedotransfer functions

Author: Ali Mehmandoost Kotlar, Quirijn de Jong van Lier, Lis Wollesen de Jonge, Ioannis Varvaris, Bo V. Iversen, and Per Moldrup
Subjects: lcsh:GE1-350, PEDOLOGIA, INFILTRATION METHOD, TENSION, FLOW, lcsh:QE1-996.5, Soil Science, Inverse, Soil science, EVAPORATION METHOD, lcsh:Geology, Pedotransfer function, Environmental science, Infiltrometer, CONDUCTIVITY, lcsh:Environmental sciences, PARAMETER-ESTIMATION
Abstract: A transient flow experiment using automated drip infiltrometers (ADIs) was performed on soil columns (about 6 dm3) large enough to incorporate macropore flow effects. We investigated to what extent the estimated soil hydraulic parameters obtained from inverse modeling of these experiments are reliable. A machine learning based pedotransfer function (PTF) for prediction of water content at −1, −10, and −158 m pressure head was developed. Sensitivity analysis of the van Genuchten parameters (residual and saturated water content qr and qs, fitting parameters α, n, and l, and saturated hydraulic conductivity Ks) in soils of sandy, silty, and clayey textures showed that the temporal variation of pressure heads in ADI scenarios was not sensitive to qr and qs. The other parameters were accurately estimated from numerically synthesized data. The uniqueness of the estimated parameters did not change when a bias, representing experimental error, was added to the data set. In actual columns, using the temporal and spatial pressure head data from the ADIs and the water contents in the drier range predicted by the developed PTF resulted in a precise estimation of the van Genuchten parameters. Not including the PTF water contents resulted in non-uniquely estimated van Genuchten parameters.
Published: 2019

31. Properties of the Binary Neutron Star Merger GW170817

Author: Abbott, B. P, Abbott, R, Abbott, T. D, Acernese, F, Ackley, K, Adams, C, Adams, T, Addesso, P, Adhikari, R. X, Adya, V. B, Affeldt, C, Agarwal, B, Agathos, M, Agatsuma, K, Aggarwal, N, Aguiar, O. D, Aiello, L, Ain, A, Ajith, P, Allen, B, Allen, G, Allocca, A, Aloy, M. A, Altin, P. A, Amato, A, Ananyeva, A, Anderson, S. B, Anderson, W. G, Angelova, V, S, Antier, S, Appert, S, Arai, K, Araya, M. C, Areeda, J. S, Arene, M, Arnaud, N, Arun, K. G, Ascenzi, S, Ashton, G, Ast, M, Aston, S. M, Astone, P, Atallah, D, Aubin, F, Aufmuth, P, Aulbert, C, Aultoneal, K, Austin, C, Avila-Alvarez, A, Babak, S, Bacon, P, Badaracco, F, Bader, M. K. M, Bae, S, Baker, P. T, Baldaccini, F, Ballardin, G, Ballmer, S. W, Banagiri, S, Barayoga, J. C, Barclay, S. E, Barish, B. C, Barker, D, Barkett, K, Barnum, S, Barone, F, Barr, B, Barsotti, L, Barsuglia, M, Barta, D, Bartlett, J, Bartos, I and Bassiri, R, Basti, A, Batch, J. C, Bawaj, M, Bayley, J. C, Bazzan, M, Becsy, B, Beer, C, Bejger, M, Belahcene, I and Bell, A. S., Beniwal, D, Bensch, M, Berger, B. K, Bergmann, G, Bernuzzi, S, Bero, J. J, Berry, C. P. L, Bersanetti, D, Bertolini, A, Betzwieser, J, Bhandare, R, Bilenko, I. A, Bilgili, S. A, Billingsley, G, Billman, C. R, Birch, J, Birney, R, Birnholtz, O, Biscans, S, Biscoveanu, S, Bisht, A, Bitossi, M, Bizouard, M. A, Blackburn, J. K, Blackman, J, Blair, C. D, Blair, D. G, Blair, R. M, Bloemen, S, Bock, O, Bode, N, Boer, M, Boetzel, Y, Bogaert, G, Bohe, A, Bondu, F, Bonilla, E, Bonnand, R, Booker, P, Boom, B. A, Booth, C. D, Bork, R, Boschi, V and Bose, Bossie, K, Bossilkov, V and Bosveld, J, Bouffanais, Y, Bozzi, A, Bradaschia, C, Brady, P. R, Bramley, A, Branchesi, M, Brau, J. E, Briant, T, Brighenti, F, Brillet, A, Brinkmann, M, Brisson, V and Brockill, P, Brooks, A. F, Brown, D. D, Brunett, S, Buchanan, C. C, Buikema, A, Bulik, T, Bulten, H. J, Buonanno, A, Buskulic, D, Buy, C, Byer, R. L, Cabero, M, Cadonati, L, Cagnoli, G, Cahillane, C, Bustillo, Caldron and Callister, J., T. A., Calloni, E, Camp, J. B, Canepa, M, Canizares, P, Cannon, K. C, Cao, H, Cao, J, Capano, C. D, Capocasa, E, Carbognani, F, Caride, S, Carney, M. F, Carullo, G, Diaz, Casanueva and Casentini, J., C, Caudill, S, Cavaglia, M, Cavalier, F, Cavalieri, R, Cella, G, Cepeda, C. B, Cerda-Duran, P, Cerretani, G, Cesarini, E, Chaibi, O, Chamberlin, S. J, Chan, M, Chao, S, Charlton, P, Chase, E, Chassande-Mottin, E, Chatterjee, D, Chatziioannou, K, Cheeseboro, B. D, Chen, H. Y, Chen, X, Chen, Y, Cheng, H-P and Chia, H. Y., Chincarini, A, Chiummo, A, Chmiel, T, Cho, H. S, Cho, M, Chow, J. H, Christensen, N, Chu, Q, Chua, A. J. K, Chua, S, Chung, K. W, Chung, S, Ciani, G, Ciobanu, A. A, Ciolfi, R, Cipriano, F, Cirelli, C. E, Cirone, A, Clara, F, Clark, J. A, Clearwater, P, Cleva, F, Cocchieri, C, Coccia, E, Cohadon, P-F and Cohen, Colla, A, Collette, C. G, Collins, C, Cominsky, L. R, Constancio, M, Conti, L, Cooper, S. J, Corban, P, Corbitt, T. R, Cordero-Carrion, I and Corley, K. R., Cornish, N, Corsi, A, Cortese, S, Costa, C. A, Cotesta, R, Coughlin, M. W, Coughlin, S. B, Coulon, J-P and Countryman, S. T., Couvares, P, Covas, P. B, Cowan, E. E, Coward, D. M, Cowart, M. J, Coyne, D. C, Coyne, R, Creighton, J. D. E, Creighton, T. D, Cripe, J, Crowder, S. G, Cullen, T. J, Cumming, A, Cunningham, L, Cuoco, E, Dal Canton, T, Dalya, G, Danilishin, S. L, D'Antonio, S, Danzmann, K, Dasgupta, A, Da Silva Costa, C. F, Dattilo, V and Dave, I and Davier, M, Davis, D, Daw, E. J, Day, B, Debra, D, Deenadayalan, M, Degallaix, J, De Laurentis, M, Deleglise, S, Del Pozzo, W, Demos, N, Denker, T, Dent, T, De Pietri, R, Derby, J, Dergachev, V and De Rosa, De Rossi, C, Desalvo, R, de Varona, O, Dhurandhar, S, Diaz, M. C, Dietrich, T, Di Fiore, L, Di Giovanni, M, Di Girolamo, T, Di Lieto, A, Ding, B, Di Pace, S, Palma, Di, I and Di Renzo, F, Dmitriev, A, Doctor, Z, Dolique, V and Donovan, Dooley, K. L, Doravari, S, Dorrington, I and Alvarez, Dovale and Downes, M., T. P., Drago, M, Dreissigacker, C, Driggers, J. C, Du, Z, Dudi, R, Dupej, P, Dwyer, S. E, Easter, P. J, Edo, T. B, Edwards, M. C, Effler, A, Eggenstein, H-B and Ehrens, Eichhol, J, Eikenberry, S. S, Eisenmann, M, Eisenstein, R. A, Essick, R. C, Estelles, H, Estevez, D, Etienne, Z. B, Etzel, T, Evans, M, Evans, T. M, Fafone, V and Fair, H, Fairhurst, S, Fan, X, Farinon, S, Farr, B, Farr, W. M, Fauchon-Jones, E. J, Favata, M, Fays, M, Fee, C, Fehrmann, H, Feicht, J, Fejer, M. M, Feng, F, Fernandez-Galiana, A, Ferrante, I and Ferreira, E. C., Ferrini, F, Fidecaro, F, Fiori, I and Fiorucci, Fishbach, M, Fisher, R. P, Fishner, J. M, Fitz-Axen, M, Flaminio, R, Fletcher, M, Fong, H, Font, J. A, Forsyth, P. W. F, Forsyth, S. S, Fournier, J-D and Frasca, Frasconi, F, Frei, Z, Freise, A, Frey, R, Frey, V and Fritschel, Frolov, V. V, Fulda, P, Fyffe, M, Gabbard, H. A, Gadre, B. U, Gaebel, S. M, Gair, J. R, Gammaitoni, L, Ganija, M. R, Gaonkar, S. G, Garcia, A, Garcia-Quiros, C, Garufi, F, Gateley, B, Gaudio, S, Gaur, G, Gayathri, V and Gemme, G, Genin, E, Gennai, A, George, D, George, J, Gergely, L, Germain, V and Ghonge, Ghosh, Abhirup and Ghosh, Archisman and Ghosh, Giacomazzo, B, Giaime, J. A, Giardina, K. D, Giazotto, A, Gill, K, Giordano, G, Glover, L, Goetz, E, Goetz, R, Goncharov, B, Gonzalez, G, Castro, Gonzalez and Gopakumar, J. M., A, Gorodetsky, M. L, Gossan, S. E, Gosselin, M, Gouaty, R, Grado, A, Graef, C, Granata, M, Grant, A, Gras, S, Gray, C, Greco, G, Green, A. C, Green, R, Gretarsson, E. M, Groot, P, Grote, H, Grunewald, S, Gruning, P, Guidi, G. M, Gulati, H. K, Guo, X, Gupta, A, Gupta, M. K, Gushwa, K. E, Gustafson, E. K, Gustafson, R, Halim, O, Hall, B. R, Hall, E. D, Hamilton, E. Z, Hamilton, H. F, Hammond, G, Haney, M, Hanke, M. M, Hanks, J, Hanna, C, Hannam, M. D, Hannuksela, O. A, Hanson, J, Hardwick, T, Harms, J, Harry, G. M, Harry, I. W, Hart, M. J, Haster, C-J and Haughian, K, Healy, J, Heidmann, A, Heintze, M. C, Heitmann, H, Hello, P, Hemming, G, Hendry, M, Heng, I. S, Hennig, J, Heptonstall, A. W, Hernandez, F. J, Heurs, M, Hild, S, Hinderer, T, Hoak, D, Hochheim, S, Hofman, D, Holland, N. A, Holtz, K, Holz, D. E, Hopkins, P, Horst, C, Hough, J, Houston, E. A, Howell, E. J, Hreibi, A, Huerta, E. A, Huet, D, Hughey, B, Hulko, M, Husa, S, Huttner, S. H, Huynh-Dinh, T, Iess, A, Indik, N, Ingram, C, Inta, R, Intini, G, Isa, H. N, Isac, J-M and Isi, Iyer, B. R, Izumi, K, Jacqmin, T, Jani, K, Jaranowski, P, Johnson, D. S, Johnson, W. W, Jones, I, Jones, R, Jonker, R. J. G, Ju, L, Junker, J, Kalaghatgi, Kalogera, V and Kamai, B, Kandhasamy, S, Kang, G, Kanner, J. B, Kapadia, S. J, Karki, S, Karvinen, K. S, Kasprzack, M, Kastaun, W, Katolik, M, Katsanevas, S, Katsavounidis, E, Katzman, W, Kaufer, S, Kawabe, K, Keerthana, N, Kefelian, F, Keitel, D, Kemball, A. J, Kennedy, R, Key, J. S, Khalili, F. Y, Khamesra, B, Khan, H, Khan, I and Khan, Khan, Z, Khazanov, E. A, Kijbunchoo, N, Kim, Chunglee and Kim, J. C., Kim, K, Kim, W, Kim, W. S, Y-M and King, E. J., King, P. J, Kinley-Hanlon, M, Kirchhoff, R, Kissel, J. S, Kleybolte, L, Klimenko, S, Knowles, T. D, Koch, P, Koehlenbeck, S. M, Koley, S, Kondrashov, V and Kontos, Korobko, M, Korth, W. Z, Kowalska, I and Kozak, D. B., Kraemer, C, Kringel, V and Krishnan, Krolak, A, Kuehn, G, Kumar, P, Kumar, R, Kumar, S, Kuo, L, Kutynia, A, Kwang, S, Lackey, B. D, Lai, K. H, Landry, M, Landry, P, Lang, R. N, Lange, J, Lantz, B, Lanza, R. K, Lartaux-Vollard, A, Lasky, P. D, Laxen, M, Lazzarini, A, Lazzaro, C, Leaci, P, Leavey, S, Lee, C. H, Lee, H. K, Lee, H. M, Lee, H. W, Lee, K, Lehmann, J, Lenon, A, Leonardi, M, Leroy, N, Letendre, N, Levin, Y, Li, J, T. G. F, Li, Li, X, Linker, S. D, Littenberg, T. B, Liu, J, Liu, X, R. K. L, Lo, Lockerbie, N. A, London, L. T, Longo, A, Lorenzini, M, Loriette, V and Lormand, Losurdo, G, Lough, J. D, Lousto, C. O, Lovelace, G, Lueck, H, Lumaca, D, Lundgren, A. P, Lynch, R, Ma, Y, Macas, R, Macfoy, S, Machenschalk, B, Macinnis, M, Macleod, D. M, Hernandez, Magana and Magana-Sandoval, I., Zertuche, Magana and Magee, L., R. M., Majorana, E, Maksimovic, I and Man, Mandic, V and Mangano, V and Mansell, G. L., Manske, M, Mantovani, M, Marchesoni, F, Marion, F, Marka, S, Marka, Z, Markakis, C, Markosyan, A. S, Markowitz, A, Maros, E, Marquina, A, Martelli, F, Martellini, L, Martin, I. W, Martin, R. M, Martynov, Mason, K, Massera, E, Masserot, A, Massinger, T. J, Masso-Reid, M, Mastrogiovanni, S, Matas, A, Matichard, F, Matone, L, Mavalvala, N, Mazumder, N, Mccann, J. J, Mccarthy, R, Mcclelland, D. E, Mccormick, S, Mcculler, L, Mcguire, S. C, Mciver, J, Mcmanus, D. J, Mcrae, T, Mcwilliams, S. T, Meacher, D, Meadors, G. D, Mehmet, M, Meidam, J, Mejuto-Villa, E, Melatos, A, Mendell, G, Mendoza-Gandara, D, Mercer, R. A, Mereni, L, Merilh, E. L, Merzougui, M, Meshkov, S, Messenger, C, Messick, C, Metzdorff, R, Meyers, P. M, Miao, H, Michel, C, Middleton, H, Mikhailov, E. E, Milano, L, Miller, A. L, Miller, A, Miller, B. B, Miller, J, Millhouse, M, Mills, J, Milovich-Goff, M. C, Minazzoli, O, Minenkov, Y, Ming, J, Mishra, C, Mitra, S, Mitrofanov, V. P, Mitselmakher, G, Mittleman, R, Moffa, D, Mogushi, K, Mohan, M, Mohapatra, S. R. P, Montani, M, Moore, C. J, Moraru, D, Moreno, G, Morisaki, S, Mours, B, Mow-Lowry, C. M, Mueller, G, Muir, A. W, Mukherjee, Arunava and Mukherjee, Mukherjee, S, Mukund, N, Mullavey, A, Munch, J, Muniz, E. A, Muratore, M, Murray, P. G, Nagar, A, Napier, K, Nardecchia, I and Naticchioni, L, Nayak, R. K, Neilson, J, Nelemans, G, Nelson, T. J. N, Nery, M, Neunzert, A, Nevin, L, Newport, J. M, K. Y, Ng, Ng, S, Nguyen, P, Nguyen, T. T, Nichols, D, Nielsen, A. B, Nissanke, S, Nitz, A, Nocera, F, Nolting, D, North, C, Nuttall, L. K, Obergaulinger, M, Oberling, J, O'Brien, B. D, O'Dea, G. D, Ogin, G. H, J. J, Oh, S. H, Oh, Ohme, F, Ohta, H, Okada, M. A, Oliver, M, Oppermann, P, Oram, Richard J, O'Reilly, B, Ormiston, R, Ortega, L. F, O'Shaughnessy, R, Ossokine, S, Ottaway, D. J, Overmier, H, Owen, B. J, Pace, A. E, Pagano, G, Page, J, Page, M. A, Pai, A, Pai, S. A, Palamos, J. R, Palashov, O, Palomba, C, Pal-Singh, A, Pan, Howard and Pan, Huang-Wei and Pang, Pang, P. T. H, Pankow, C, Pannarale, F, Pant, B. C, Paoletti, F, Paoli, A, Papa, M. A, Parida, A, Parker, W, Pascucci, D, Pasqualetti, A, Passaquieti, R, Passuello, D, Patil, M, Patricelli, B, Pearlstone, B. L, Pedersen, C, Pedraza, M, Pedurand, R, Pekowsky, L, Pele, A, Penn, S, Perez, C. J, Perreca, A, Perri, L. M, Pfeiffer, H. P, Phelps, M, Phukon, K. S, Piccinni, O. J, Pichot, M, Piergiovanni, F, Pierro, V and Pillant, Pinard, L, Pinto, I. M, Pirello, M, Pitkin, M, Poggiani, R, Popolizio, P, Porter, E. K, Possenti, L, Post, A, Powell, J, Prasad, J, Pratt, J. W. W, Pratten, G, Predoi, V and Prestegard, T, Principe, M, Privitera, S, Prodi, G. A, Prokhorov, L. G, Puncken, O, Punturo, M, Puppo, P, Prrer, M, Qi, H, Quetschke, V and Quintero, E. A., Quitzow-James, R, Raab, F. J, Rabeling, D. S, Radkins, H, Raffai, P, Raja, S, Rajan, C, Rajbhandari, B, Rakhmanov, M, Ramirez, K. E, Ramos-Buades, A, Rana, Javed and Rapagnani, Raymond, V and Razzano, Read, J, Regimbau, T, Rei, L, Reid, S, Reitze, D. H, Ren, W, Ricci, F, Ricker, P. M, Riemenschneider, G, Riles, K, Rizzo, M, Robertson, N. A, Robie, R, Robinet, F, Robson, T, Rocchi, A, Rolland, L, Rollins, J. G, Roma, V. J, Romano, R, Romel, C. L, Romie, J. H, Rosinska, D, Ross, M. P, Rowan, S, Ruediger, A, Ruggi, P, Rutins, G, Ryan, K, Sachdev, S, Sadecki, T, Sakellariadou, M, Salconi, L, Saleem, M, Salemi, F, Samajdar, A, Sammut, L, Sampson, L. M, Sanchez, E. J, Sanchez, L. E, Sanchis-Gual, N, Sandberg, V and Sanders, J. R., Sarin, N, Sassolas, B, Sathyaprakash, B. S, Saulson, P. R, Sauter, O, Savage, R. L, Sawadsky, A, Schale, P, Scheel, M, Scheuer, J, Schmidt, P, Schnabel, R, Schofield, R. M. S, Schoenbeck, A, Schreiber, E, Schuette, D, Schulte, B. W, Schutz, B. F, Schwalbe, S. G, Scott, J, Scott, S. M, Seidel, E, Sellers, D, Sengupta, A. S, Sentenac, D, Sequino, V and Sergeev, Setyawati, Y, Shaddock, D. A, Shaffer, T. J, Shah, A. A, Shahriar, M. S, Shaner, M. B, Shao, L, Shapiro, B, Shawhan, P, Shen, H, Shoemaker, D. H, Shoemaker, D. M, Siellez, K, Siemens, X, Sieniawska, M, Sigg, D, Silva, A. D, Singer, L. P, Singh, A, Singhal, A, Sintes, A. M, Slagmolen, B. J. J, Slaven-Blair, T. J, Smith, B, Smith, J. R, Smith, R. J. E, Somala, S, Son, E. J, Sorazu, B, Sorrentino, F, Souradeep, T, Spencer, A. P, Srivastava, A. K, Staats, K, Steinke, M, Steinlechner, J, Steinlechner, S, Steinmeyer, D, Steltner, B, Stevenson, S. P, Stocks, D, Stone, R, Stops, D. J, Strain, K. A, Stratta, G, Strigin, S. E, Strunk, A, Sturani, R, Stuver, A. L, Summerscales, T. Z, Sun, L, Sunil, S, Suresh, J, Sutton, P. J, Swinkels, B. L, Szczepanczyk, M. J, Tacca, M, Tait, S. C, Talbot, C, Talukder, D, Tanner, D. B, Tapai, M, Taracchini, A, Tasson, J. D, Taylor, J. A, Taylor, R, Tewari, Theeg, T, Thies, F, Thomas, E. G, Thomas, M, Thomas, P, Thorne, K. A, Thrane, E, Tiwari, S, Tiwari, V and Tokmakov, Toland, K, Tonelli, M, Tornasi, Z, Torres-Forne, A, Torrie, Toeyrae, D, Travasso, F, Traylor, G, Trinastic, J, Tringali, M. C, Trozzo, L, Tsang, K. W, Tse, M, Tso, R, Tsuna, D, Tsukada, L, Tuyenbayev, D, Ueno, K, Ugolini, D, Urban, A. L, Usman, S. A, Vahlbruch, H, Vajente, G, Valdes, G, van Bakel, N, van Beuzekom, M, van den Brand, J. F. J, van den Broeck, C, vander-Hyde, D. C, van der Schaaf, L, Van, Heijningen, van Veggel, A. A, Vardaro, M, Varma, V, Vass, S, Vasuth, M, Vecchio, A, Vedovato, G, Veitch, J, Veitch, P. J, Venkateswara, K, Venugopalan, G, Verkindt, D, Vetrano, F, Vicere, A, Viets, A. D, Vinciguerra, S, Vine, D. J, Vinet, J-Y and Vitale, Vo, T, Vocca, H, Vorvick, C, Vyatchanin, S. P, Wade, A. R, Wade, L. E, Wade, M, Walet, R, Walker, M, Wallace, L, Walsh, S, Wang, G, Wang, H, Wang, J. Z, Wang, W. H, Wang, Y. F, Ward, R. L, Warner, J, Was, M, Watchi, J, Weaver, B, Wei, L-W and Weinert, Weinstein, A. J, Weiss, R, Wellmann, F, Wen, L, Wessel, E. K, Wessels, P, Westerweck, J, Wette, K, Whelan, J. T, Whiting, B. F, Whittle, C, Wilken, D, Williams, D, Williams, R. D, Williamson, A. R, Willis, J. L, Willke, B, Wimmer, M. H, Winkler, W, Wipf, C. C, Wittel, H, Woan, G, Woehler, J, Wofford, J. K, Wong, W. K, Worden, J, Wright, J. L, D. S, Wu, Wysocki, D. M, Xiao, S, Yam, W, Yamamoto, H, Yancey, C. C, Yang, L, Yap, M. J, Yazback, M, Hang and Yu, Haocun and Yvert, Zadrozny, A, Zanolin, M, Zelenova, T, Zendri, J-P and Zevin, Zhang, J, Zhang, L, Zhang, M, Zhang, T, Zhang, Y-H and Zhao, Zhou, M, Zhou, Z, Zhu, S. J, Zhu, X. J, Zimmerman, A. B, Zlochower, Y, Zucker, M. E, Zweizig, J, LIGO Sci Collaboration and Virgo Collaboration, Abbott, B, Abbott, R, Abbott, T, Acernese, F, Ackley, K, Adams, C, Adams, T, Addesso, P, Adhikari, R, Adya, V, Affeldt, C, Agarwal, B, Agathos, M, Agatsuma, K, Aggarwal, N, Aguiar, O, Aiello, L, Ain, A, Ajith, P, Allen, B, Allen, G, Allocca, A, Aloy, M, Altin, P, Amato, A, Ananyeva, A, Anderson, S, Anderson, W, Angelova, S, Antier, S, Appert, S, Arai, K, Araya, M, Areeda, J, Arène, M, Arnaud, N, Arun, K, Ascenzi, S, Ashton, G, Ast, M, Aston, S, Astone, P, Atallah, D, Aubin, F, Aufmuth, P, Aulbert, C, Aultoneal, K, Austin, C, Avila-Alvarez, A, Babak, S, Bacon, P, Badaracco, F, Bader, M, Bae, S, Baker, P, Baldaccini, F, Ballardin, G, Ballmer, S, Banagiri, S, Barayoga, J, Barclay, S, Barish, B, Barker, D, Barkett, K, Barnum, S, Barone, F, Barr, B, Barsotti, L, Barsuglia, M, Barta, D, Bartlett, J, Bartos, I, Bassiri, R, Basti, A, Batch, J, Bawaj, M, Bayley, J, Bazzan, M, Bécsy, B, Beer, C, Bejger, M, Belahcene, I, Bell, A, Beniwal, D, Bensch, M, Berger, B, Bergmann, G, Bernuzzi, S, Bero, J, Berry, C, Bersanetti, D, Bertolini, A, Betzwieser, J, Bhandare, R, Bilenko, I, Bilgili, S, Billingsley, G, Billman, C, Birch, J, Birney, R, Birnholtz, O, Biscans, S, Biscoveanu, S, Bisht, A, Bitossi, M, Bizouard, M, Blackburn, J, Blackman, J, Blair, C, Blair, D, Blair, R, Bloemen, S, Bock, O, Bode, N, Boer, M, Boetzel, Y, Bogaert, G, Bohe, A, Bondu, F, Bonilla, E, Bonnand, R, Booker, P, Boom, B, Booth, C, Bork, R, Boschi, V, Bose, S, Bossie, K, Bossilkov, V, Bosveld, J, Bouffanais, Y, Bozzi, A, Bradaschia, C, Brady, P, Bramley, A, Branchesi, M, Brau, J, Briant, T, Brighenti, F, Brillet, A, Brinkmann, M, Brisson, V, Brockill, P, Brooks, A, Brown, D, Brunett, S, Buchanan, C, Buikema, A, Bulik, T, Bulten, H, Buonanno, A, Buskulic, D, Buy, C, Byer, R, Cabero, M, Cadonati, L, Cagnoli, G, Cahillane, C, Bustillo, J, Callister, T, Calloni, E, Camp, J, Canepa, M, Canizares, P, Cannon, K, Cao, H, Cao, J, Capano, C, Capocasa, E, Carbognani, F, Caride, S, Carney, M, Carullo, G, Diaz, J, Casentini, C, Caudill, S, Cavaglià, M, Cavalier, F, Cavalieri, R, Cella, G, Cepeda, C, Cerdá-Durán, P, Cerretani, G, Cesarini, E, Chaibi, O, Chamberlin, S, Chan, M, Chao, S, Charlton, P, Chase, E, Chassande-Mottin, E, Chatterjee, D, Chatziioannou, K, Cheeseboro, B, Chen, H, Chen, X, Chen, Y, Cheng, H, Chia, H, Chincarini, A, Chiummo, A, Chmiel, T, Cho, H, Cho, M, Chow, J, Christensen, N, Chu, Q, Chua, A, Chua, S, Chung, K, Chung, S, Ciani, G, Ciobanu, A, Ciolfi, R, Cipriano, F, Cirelli, C, Cirone, A, Clara, F, Clark, J, Clearwater, P, Cleva, F, Cocchieri, C, Coccia, E, Cohadon, P, Cohen, D, Colla, A, Collette, C, Collins, C, Cominsky, L, Constancio, M, Conti, L, Cooper, S, Corban, P, Corbitt, T, Cordero-Carrión, I, Corley, K, Cornish, N, Corsi, A, Cortese, S, Costa, C, Cotesta, R, Coughlin, M, Coughlin, S, Coulon, J, Countryman, S, Couvares, P, Covas, P, Cowan, E, Coward, D, Cowart, M, Coyne, D, Coyne, R, Creighton, J, Creighton, T, Cripe, J, Crowder, S, Cullen, T, Cumming, A, Cunningham, L, Cuoco, E, Canton, T, Dálya, G, Danilishin, S, D'Antonio, S, Danzmann, K, Dasgupta, A, Dattilo, V, Dave, I, Davier, M, Davis, D, Daw, E, Day, B, Debra, D, Deenadayalan, M, Degallaix, J, De Laurentis, M, Deléglise, S, Del Pozzo, W, Demos, N, Denker, T, Dent, T, De Pietri, R, Derby, J, Dergachev, V, De Rosa, R, De Rossi, C, Desalvo, R, De Varona, O, Dhurandhar, S, Díaz, M, Dietrich, T, Di Fiore, L, Di Giovanni, M, Di Girolamo, T, Di Lieto, A, Ding, B, Di Pace, S, Di Palma, I, Di Renzo, F, Dmitriev, A, Doctor, Z, Dolique, V, Donovan, F, Dooley, K, Doravari, S, Dorrington, I, Álvarez, M, Downes, T, Drago, M, Dreissigacker, C, Driggers, J, Du, Z, Dudi, R, Dupej, P, Dwyer, S, Easter, P, Edo, T, Edwards, M, Effler, A, Eggenstein, H, Ehrens, P, Eichholz, J, Eikenberry, S, Eisenmann, M, Eisenstein, R, Essick, R, Estelles, H, Estevez, D, Etienne, Z, Etzel, T, Evans, M, Evans, T, Fafone, V, Fair, H, Fairhurst, S, Fan, X, Farinon, S, Farr, B, Farr, W, Fauchon-Jones, E, Favata, M, Fays, M, Fee, C, Fehrmann, H, Feicht, J, Fejer, M, Feng, F, Fernandez-Galiana, A, Ferrante, I, Ferreira, E, Ferrini, F, Fidecaro, F, Fiori, I, Fiorucci, D, Fishbach, M, Fisher, R, Fishner, J, Fitz-Axen, M, Flaminio, R, Fletcher, M, Fong, H, Font, J, Forsyth, P, Forsyth, S, Fournier, J, Frasca, S, Frasconi, F, Frei, Z, Freise, A, Frey, R, Frey, V, Fritschel, P, Frolov, V, Fulda, P, Fyffe, M, Gabbard, H, Gadre, B, Gaebel, S, Gair, J, Gammaitoni, L, Ganija, M, Gaonkar, S, Garcia, A, García-Quirós, C, Garufi, F, Gateley, B, Gaudio, S, Gaur, G, Gayathri, V, Gemme, G, Genin, E, Gennai, A, George, D, George, J, Gergely, L, Germain, V, Ghonge, S, Ghosh, A, Ghosh, S, Giacomazzo, B, Giaime, J, Giardina, K, Giazotto, A, Gill, K, Giordano, G, Glover, L, Goetz, E, Goetz, R, Goncharov, B, González, G, Castro, J, Gopakumar, A, Gorodetsky, M, Gossan, S, Gosselin, M, Gouaty, R, Grado, A, Graef, C, Granata, M, Grant, A, Gras, S, Gray, C, Greco, G, Green, A, Green, R, Gretarsson, E, Groot, P, Grote, H, Grunewald, S, Gruning, P, Guidi, G, Gulati, H, Guo, X, Gupta, A, Gupta, M, Gushwa, K, Gustafson, E, Gustafson, R, Halim, O, Hall, B, Hall, E, Hamilton, E, Hamilton, H, Hammond, G, Haney, M, Hanke, M, Hanks, J, Hanna, C, Hannam, M, Hannuksela, O, Hanson, J, Hardwick, T, Harms, J, Harry, G, Harry, I, Hart, M, Haster, C, Haughian, K, Healy, J, Heidmann, A, Heintze, M, Heitmann, H, Hello, P, Hemming, G, Hendry, M, Heng, I, Hennig, J, Heptonstall, A, Hernandez, F, Heurs, M, Hild, S, Hinderer, T, Hoak, D, Hochheim, S, Hofman, D, Holland, N, Holt, K, Holz, D, Hopkins, P, Horst, C, Hough, J, Houston, E, Howell, E, Hreibi, A, Huerta, E, Huet, D, Hughey, B, Hulko, M, Husa, S, Huttner, S, Huynh-Dinh, T, Iess, A, Indik, N, Ingram, C, Inta, R, Intini, G, Isa, H, Isac, J, Isi, M, Iyer, B, Izumi, K, Jacqmin, T, Jani, K, Jaranowski, P, Johnson, D, Johnson, W, Jones, D, Jones, R, Jonker, R, Ju, L, Junker, J, Kalaghatgi, C, Kalogera, V, Kamai, B, Kandhasamy, S, Kang, G, Kanner, J, Kapadia, S, Karki, S, Karvinen, K, Kasprzack, M, Kastaun, W, Katolik, M, Katsanevas, S, Katsavounidis, E, Katzman, W, Kaufer, S, Kawabe, K, Keerthana, N, Kéfélian, F, Keitel, D, Kemball, A, Kennedy, R, Key, J, Khalili, F, Khamesra, B, Khan, H, Khan, I, Khan, S, Khan, Z, Khazanov, E, Kijbunchoo, N, Kim, C, Kim, J, Kim, K, Kim, W, Kim, Y, King, E, King, P, Kinley-Hanlon, M, Kirchhoff, R, Kissel, J, Kleybolte, L, Klimenko, S, Knowles, T, Koch, P, Koehlenbeck, S, Koley, S, Kondrashov, V, Kontos, A, Korobko, M, Korth, W, Kowalska, I, Kozak, D, Krämer, C, Kringel, V, Krishnan, B, Królak, A, Kuehn, G, Kumar, P, Kumar, R, Kumar, S, Kuo, L, Kutynia, A, Kwang, S, Lackey, B, Lai, K, Landry, M, Landry, P, Lang, R, Lange, J, Lantz, B, Lanza, R, Lartaux-Vollard, A, Lasky, P, Laxen, M, Lazzarini, A, Lazzaro, C, Leaci, P, Leavey, S, Lee, C, Lee, H, Lee, K, Lehmann, J, Lenon, A, Leonardi, M, Leroy, N, Letendre, N, Levin, Y, Li, J, Li, T, Li, X, Linker, S, Littenberg, T, Liu, J, Liu, X, Lo, R, Lockerbie, N, London, L, Longo, A, Lorenzini, M, Loriette, V, Lormand, M, Losurdo, G, Lough, J, Lousto, C, Lovelace, G, Lück, H, Lumaca, D, Lundgren, A, Lynch, R, Ma, Y, Macas, R, Macfoy, S, Machenschalk, B, Macinnis, M, Macleod, D, Hernandez, I, Magaña-Sandoval, F, Zertuche, L, Magee, R, Majorana, E, Maksimovic, I, Man, N, Mandic, V, Mangano, V, Mansell, G, Manske, M, Mantovani, M, Marchesoni, F, Marion, F, Márka, S, Márka, Z, Markakis, C, Markosyan, A, Markowitz, A, Maros, E, Marquina, A, Martelli, F, Martellini, L, Martin, I, Martin, R, Martynov, D, Mason, K, Massera, E, Masserot, A, Massinger, T, Masso-Reid, M, Mastrogiovanni, S, Matas, A, Matichard, F, Matone, L, Mavalvala, N, Mazumder, N, Mccann, J, Mccarthy, R, Mcclelland, D, Mccormick, S, Mcculler, L, Mcguire, S, Mciver, J, Mcmanus, D, Mcrae, T, Mcwilliams, S, Meacher, D, Meadors, G, Mehmet, M, Meidam, J, Mejuto-Villa, E, Melatos, A, Mendell, G, Mendoza-Gandara, D, Mercer, R, Mereni, L, Merilh, E, Merzougui, M, Meshkov, S, Messenger, C, Messick, C, Metzdorff, R, Meyers, P, Miao, H, Michel, C, Middleton, H, Mikhailov, E, Milano, L, Miller, A, Miller, B, Miller, J, Millhouse, M, Mills, J, Milovich-Goff, M, Minazzoli, O, Minenkov, Y, Ming, J, Mishra, C, Mitra, S, Mitrofanov, V, Mitselmakher, G, Mittleman, R, Moffa, D, Mogushi, K, Mohan, M, Mohapatra, S, Montani, M, Moore, C, Moraru, D, Moreno, G, Morisaki, S, Mours, B, Mow-Lowry, C, Mueller, G, Muir, A, Mukherjee, A, Mukherjee, D, Mukherjee, S, Mukund, N, Mullavey, A, Munch, J, Muñiz, E, Muratore, M, Murray, P, Nagar, A, Napier, K, Nardecchia, I, Naticchioni, L, Nayak, R, Neilson, J, Nelemans, G, Nelson, T, Nery, M, Neunzert, A, Nevin, L, Newport, J, Ng, K, Ng, S, Nguyen, P, Nguyen, T, Nichols, D, Nielsen, A, Nissanke, S, Nitz, A, Nocera, F, Nolting, D, North, C, Nuttall, L, Obergaulinger, M, Oberling, J, O'Brien, B, O'Dea, G, Ogin, G, Oh, J, Oh, S, Ohme, F, Ohta, H, Okada, M, Oliver, M, Oppermann, P, Oram, R, O'Reilly, B, Ormiston, R, Ortega, L, O'Shaughnessy, R, Ossokine, S, Ottaway, D, Overmier, H, Owen, B, Pace, A, Pagano, G, Page, J, Page, M, Pai, A, Pai, S, Palamos, J, Palashov, O, Palomba, C, Pal-Singh, A, Pan, H, Pang, B, Pang, P, Pankow, C, Pannarale, F, Pant, B, Paoletti, F, Paoli, A, Papa, M, Parida, A, Parker, W, Pascucci, D, Pasqualetti, A, Passaquieti, R, Passuello, D, Patil, M, Patricelli, B, Pearlstone, B, Pedersen, C, Pedraza, M, Pedurand, R, Pekowsky, L, Pele, A, Penn, S, Perez, C, Perreca, A, Perri, L, Pfeiffer, H, Phelps, M, Phukon, K, Piccinni, O, Pichot, M, Piergiovanni, F, Pierro, V, Pillant, G, Pinard, L, Pinto, I, Pirello, M, Pitkin, M, Poggiani, R, Popolizio, P, Porter, E, Possenti, L, Post, A, Powell, J, Prasad, J, Pratt, J, Pratten, G, Predoi, V, Prestegard, T, Principe, M, Privitera, S, Prodi, G, Prokhorov, L, Puncken, O, Punturo, M, Puppo, P, Pürrer, M, Qi, H, Quetschke, V, Quintero, E, Quitzow-James, R, Raab, F, Rabeling, D, Radkins, H, Raffai, P, Raja, S, Rajan, C, 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P., Abbott, R., Abbott, T. D., Acernese, F., Ackley, K., Adams, C., Adams, T., Addesso, P., Adhikari, R. X., Adya, V. B., Affeldt, C., Agarwal, B., Agathos, M., Agatsuma, K., Aggarwal, N., Aguiar, O. D., Aiello, L., Ain, A., Ajith, P., Allen, B., Allen, G., Allocca, A., Aloy, M. A., Altin, P. A., Amato, A., Ananyeva, A., Anderson, S. B., Anderson, W. G., Angelova, S. V., Antier, S., Appert, S., Arai, K., Araya, M. C., Areeda, J. S., Arène, M., Arnaud, N., Arun, K. G., Ascenzi, S., Ashton, G., Ast, M., Aston, S. M., Astone, P., Atallah, D. V., Aubin, F., Aufmuth, P., Aulbert, C., Aultoneal, K., Austin, C., Avila-Alvarez, A., Babak, S., Bacon, P., Badaracco, F., Bader, M. K. M., Bae, S., Baker, P. T., Baldaccini, F., Ballardin, G., Ballmer, S. W., Banagiri, S., Barayoga, J. C., Barclay, S. E., Barish, B. C., Barker, D., Barkett, K., Barnum, S., Barone, F., Barr, B., Barsotti, L., Barsuglia, M., Barta, D., Bartlett, J., Bartos, I., Bassiri, R., Basti, A., Batch, J. C., Bawaj, M., Bayley, J. C., Bazzan, M., Bécsy, B., Beer, C., Bejger, M., Belahcene, I., Bell, A. S., Beniwal, D., Bensch, M., Berger, B. K., Bergmann, G., Bernuzzi, S., Bero, J. J., Berry, C. P. L., Bersanetti, D., Bertolini, A., Betzwieser, J., Bhandare, R., Bilenko, I. A., Bilgili, S. A., Billingsley, G., Billman, C. R., Birch, J., Birney, R., Birnholtz, O., Biscans, S., Biscoveanu, S., Bisht, A., Bitossi, M., Bizouard, M. A., Blackburn, J. K., Blackman, J., Blair, C. D., Blair, D. G., Blair, R. M., Bloemen, S., Bock, O., Bode, N., Boer, M., Boetzel, Y., Bogaert, G., Bohe, A., Bondu, F., Bonilla, E., Bonnand, R., Booker, P., Boom, B. A., Booth, C. D., Bork, R., Boschi, V., Bose, S., Bossie, K., Bossilkov, V., Bosveld, J., Bouffanais, Y., Bozzi, A., Bradaschia, C., Brady, P. R., Bramley, A., Branchesi, M., Brau, J. E., Briant, T., Brighenti, F., Brillet, A., Brinkmann, M., Brisson, V., Brockill, P., Brooks, A. F., Brown, D. D., Brunett, S., Buchanan, C. C., Buikema, A., Bulik, T., Bulten, H. J., Buonanno, A., Buskulic, D., Buy, C., Byer, R. L., Cabero, M., Cadonati, L., Cagnoli, G., Cahillane, C., Bustillo, J. Calderón, Callister, T. A., Calloni, E., Camp, J. B., Canepa, M., Canizares, P., Cannon, K. C., Cao, H., Cao, J., Capano, C. D., Capocasa, E., Carbognani, F., Caride, S., Carney, M. F., Carullo, G., Diaz, J. Casanueva, Casentini, C., Caudill, S., Cavaglià, M., Cavalier, F., Cavalieri, R., Cella, G., Cepeda, C. B., Cerdá-Durán, P., Cerretani, G., Cesarini, E., Chaibi, O., Chamberlin, S. J., Chan, M., Chao, S., Charlton, P., Chase, E., Chassande-Mottin, E., Chatterjee, D., Chatziioannou, K., Cheeseboro, B. D., Chen, H. Y., Chen, X., Chen, Y., Cheng, H. -P., Chia, H. Y., Chincarini, A., Chiummo, A., Chmiel, T., Cho, H. S., Cho, M., Chow, J. H., Christensen, N., Chu, Q., Chua, A. J. K., Chua, S., Chung, K. W., Chung, S., Ciani, G., Ciobanu, A. A., Ciolfi, R., Cipriano, F., Cirelli, C. E., Cirone, A., Clara, F., Clark, J. 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Gonzalez, Gopakumar, A., Gorodetsky, M. L., Gossan, S. E., Gosselin, M., Gouaty, R., Grado, A., Graef, C., Granata, M., Grant, A., Gras, S., Gray, C., Greco, G., Green, A. C., Green, R., Gretarsson, E. M., Groot, P., Grote, H., Grunewald, S., Gruning, P., Guidi, G. M., Gulati, H. K., Guo, X., Gupta, A., Gupta, M. K., Gushwa, K. E., Gustafson, E. K., Gustafson, R., Halim, O., Hall, B. R., Hall, E. D., Hamilton, E. Z., Hamilton, H. F., Hammond, G., Haney, M., Hanke, M. M., Hanks, J., Hanna, C., Hannam, M. D., Hannuksela, O. A., Hanson, J., Hardwick, T., Harms, J., Harry, G. M., Harry, I. W., Hart, M. J., Haster, C. -J., Haughian, K., Healy, J., Heidmann, A., Heintze, M. C., Heitmann, H., Hello, P., Hemming, G., Hendry, M., Heng, I. S., Hennig, J., Heptonstall, A. W., Hernandez, F. J., Heurs, M., Hild, S., Hinderer, T., Hoak, D., Hochheim, S., Hofman, D., Holland, N. A., Holt, K., Holz, D. E., Hopkins, P., Horst, C., Hough, J., Houston, E. A., Howell, E. J., Hreibi, A., Huerta, E. A., Huet, D., Hughey, B., Hulko, M., Husa, S., Huttner, S. H., Huynh-Dinh, T., Iess, A., Indik, N., Ingram, C., Inta, R., Intini, G., Isa, H. N., Isac, J. -M., Isi, M., Iyer, B. R., Izumi, K., Jacqmin, T., Jani, K., Jaranowski, P., Johnson, D. S., Johnson, W. W., Jones, D. I., Jones, R., Jonker, R. J. G., Ju, L., Junker, J., Kalaghatgi, C. V., Kalogera, V., Kamai, B., Kandhasamy, S., Kang, G., Kanner, J. B., Kapadia, S. J., Karki, S., Karvinen, K. S., Kasprzack, M., Kastaun, W., Katolik, M., Katsanevas, S., Katsavounidis, E., Katzman, W., Kaufer, S., Kawabe, K., Keerthana, N. V., Kéfélian, F., Keitel, D., Kemball, A. J., Kennedy, R., Key, J. S., Khalili, F. Y., Khamesra, B., Khan, H., Khan, I., Khan, S., Khan, Z., Khazanov, E. A., Kijbunchoo, N., Kim, Chunglee, Kim, J. C., Kim, K., Kim, W., Kim, W. S., Kim, Y. -M., King, E. J., King, P. J., Kinley-Hanlon, M., Kirchhoff, R., Kissel, J. S., Kleybolte, L., Klimenko, S., Knowles, T. D., Koch, P., Koehlenbeck, S. M., Koley, S., Kondrashov, V., Kontos, A., Korobko, M., Korth, W. Z., Kowalska, I., Kozak, D. B., Krämer, C., Kringel, V., Krishnan, B., Królak, A., Kuehn, G., Kumar, P., Kumar, R., Kumar, S., Kuo, L., Kutynia, A., Kwang, S., Lackey, B. D., Lai, K. H., Landry, M., Landry, P., Lang, R. N., Lange, J., Lantz, B., Lanza, R. K., Lartaux-Vollard, A., Lasky, P. D., Laxen, M., Lazzarini, A., Lazzaro, C., Leaci, P., Leavey, S., Lee, C. H., Lee, H. K., Lee, H. M., Lee, H. W., Lee, K., Lehmann, J., Lenon, A., Leonardi, M., Leroy, N., Letendre, N., Levin, Y., Li, J., Li, T. G. F., Li, X., Linker, S. D., Littenberg, T. B., Liu, J., Liu, X., Lo, R. K. L., Lockerbie, N. A., London, L. T., Longo, A., Lorenzini, M., Loriette, V., Lormand, M., Losurdo, G., Lough, J. D., Lousto, C. O., Lovelace, G., Lück, H., Lumaca, D., Lundgren, A. P., Lynch, R., Ma, Y., Macas, R., Macfoy, S., Machenschalk, B., Macinnis, M., Macleod, D. M., Hernandez, I. Magaña, Magaña-Sandoval, F., Zertuche, L. Magaña, Magee, R. M., Majorana, E., Maksimovic, I., Man, N., Mandic, V., Mangano, V., Mansell, G. L., Manske, M., Mantovani, M., Marchesoni, F., Marion, F., Márka, S., Márka, Z., Markakis, C., Markosyan, A. S., Markowitz, A., Maros, E., Marquina, A., Martelli, F., Martellini, L., Martin, I. W., Martin, R. M., Martynov, D. V., Mason, K., Massera, E., Masserot, A., Massinger, T. J., Masso-Reid, M., Mastrogiovanni, S., Matas, A., Matichard, F., Matone, L., Mavalvala, N., Mazumder, N., Mccann, J. J., Mccarthy, R., Mcclelland, D. E., Mccormick, S., Mcculler, L., Mcguire, S. C., Mciver, J., Mcmanus, D. J., Mcrae, T., Mcwilliams, S. T., Meacher, D., Meadors, G. D., Mehmet, M., Meidam, J., Mejuto-Villa, E., Melatos, A., Mendell, G., Mendoza-Gandara, D., Mercer, R. A., Mereni, L., Merilh, E. L., Merzougui, M., Meshkov, S., Messenger, C., Messick, C., Metzdorff, R., Meyers, P. M., Miao, H., Michel, C., Middleton, H., Mikhailov, E. E., Milano, L., Miller, A. L., Miller, A., Miller, B. B., Miller, J., Millhouse, M., Mills, J., Milovich-Goff, M. C., Minazzoli, O., Minenkov, Y., Ming, J., Mishra, C., Mitra, S., Mitrofanov, V. P., Mitselmakher, G., Mittleman, R., Moffa, D., Mogushi, K., Mohan, M., Mohapatra, S. R. P., Montani, M., Moore, C. J., Moraru, D., Moreno, G., Morisaki, S., Mours, B., Mow-Lowry, C. M., Mueller, G., Muir, A. W., Mukherjee, Arunava, Mukherjee, D., Mukherjee, S., Mukund, N., Mullavey, A., Munch, J., Muñiz, E. A., Muratore, M., Murray, P. G., Nagar, A., Napier, K., Nardecchia, I., Naticchioni, L., Nayak, R. K., Neilson, J., Nelemans, G., Nelson, T. J. N., Nery, M., Neunzert, A., Nevin, L., Newport, J. M., Ng, K. Y., Ng, S., Nguyen, P., Nguyen, T. T., Nichols, D., Nielsen, A. B., Nissanke, S., Nitz, A., Nocera, F., Nolting, D., North, C., Nuttall, L. K., Obergaulinger, M., Oberling, J., O’Brien, B. D., O’Dea, G. D., Ogin, G. H., Oh, J. J., Oh, S. H., Ohme, F., Ohta, H., Okada, M. A., Oliver, M., Oppermann, P., Oram, Richard J., O’Reilly, B., Ormiston, R., Ortega, L. F., O’Shaughnessy, R., Ossokine, S., Ottaway, D. J., Overmier, H., Owen, B. J., Pace, A. E., Pagano, G., Page, J., Page, M. A., Pai, A., Pai, S. A., Palamos, J. R., Palashov, O., Palomba, C., Pal-Singh, A., Pan, Howard, Pan, Huang-Wei, Pang, B., Pang, P. T. H., Pankow, C., Pannarale, F., Pant, B. C., Paoletti, F., Paoli, A., Papa, M. A., Parida, A., Parker, W., Pascucci, D., Pasqualetti, A., Passaquieti, R., Passuello, D., Patil, M., Patricelli, B., Pearlstone, B. L., Pedersen, C., Pedraza, M., Pedurand, R., Pekowsky, L., Pele, A., Penn, S., Perez, C. J., Perreca, A., Perri, L. M., Pfeiffer, H. P., Phelps, M., Phukon, K. S., Piccinni, O. J., Pichot, M., Piergiovanni, F., Pierro, V., Pillant, G., Pinard, L., Pinto, I. M., Pirello, M., Pitkin, M., Poggiani, R., Popolizio, P., Porter, E. K., Possenti, L., Post, A., Powell, J., Prasad, J., Pratt, J. W. W., Pratten, G., Predoi, V., Prestegard, T., Principe, M., Privitera, S., Prodi, G. A., Prokhorov, L. G., Puncken, O., Punturo, M., Puppo, P., Pürrer, M., Qi, H., Quetschke, V., Quintero, E. A., Quitzow-James, R., Raab, F. J., Rabeling, D. S., Radkins, H., Raffai, P., Raja, S., Rajan, C., Rajbhandari, B., Rakhmanov, M., Ramirez, K. E., Ramos-Buades, A., Rana, Javed, Rapagnani, P., Raymond, V., Razzano, M., Read, J., Regimbau, T., Rei, L., Reid, S., Reitze, D. H., Ren, W., Ricci, F., Ricker, P. M., Riemenschneider, G., Riles, K., Rizzo, M., Robertson, N. A., Robie, R., Robinet, F., Robson, T., Rocchi, A., Rolland, L., Rollins, J. G., Roma, V. J., Romano, R., Romel, C. L., Romie, J. H., Rosińska, D., Ross, M. P., Rowan, S., Rüdiger, A., Ruggi, P., Rutins, G., Ryan, K., Sachdev, S., Sadecki, T., Sakellariadou, M., Salconi, L., Saleem, M., Salemi, F., Samajdar, A., Sammut, L., Sampson, L. M., Sanchez, E. J., Sanchez, L. E., Sanchis-Gual, N., Sandberg, V., Sanders, J. R., Sarin, N., Sassolas, B., Sathyaprakash, B. S., Saulson, P. R., Sauter, O., Savage, R. L., Sawadsky, A., Schale, P., Scheel, M., Scheuer, J., Schmidt, P., Schnabel, R., Schofield, R. M. S., Schönbeck, A., Schreiber, E., Schuette, D., Schulte, B. W., Schutz, B. F., Schwalbe, S. G., Scott, J., Scott, S. M., Seidel, E., Sellers, D., Sengupta, A. S., Sentenac, D., Sequino, V., Sergeev, A., Setyawati, Y., Shaddock, D. A., Shaffer, T. J., Shah, A. A., Shahriar, M. S., Shaner, M. B., Shao, L., Shapiro, B., Shawhan, P., Shen, H., Shoemaker, D. H., Shoemaker, D. M., Siellez, K., Siemens, X., Sieniawska, M., Sigg, D., Silva, A. D., Singer, L. P., Singh, A., Singhal, A., Sintes, A. M., Slagmolen, B. J. J., Slaven-Blair, T. J., Smith, B., Smith, J. R., Smith, R. J. E., Somala, S., Son, E. J., Sorazu, B., Sorrentino, F., Souradeep, T., Spencer, A. P., Srivastava, A. K., Staats, K., Steinke, M., Steinlechner, J., Steinlechner, S., Steinmeyer, D., Steltner, B., Stevenson, S. P., Stocks, D., Stone, R., Stops, D. J., Strain, K. A., Stratta, G., Strigin, S. E., Strunk, A., Sturani, R., Stuver, A. L., Summerscales, T. Z., Sun, L., Sunil, S., Suresh, J., Sutton, P. J., Swinkels, B. L., Szczepańczyk, M. J., Tacca, M., Tait, S. C., Talbot, C., Talukder, D., Tanner, D. B., Tápai, M., Taracchini, A., Tasson, J. D., Taylor, J. A., Taylor, R., Tewari, S. V., Theeg, T., Thies, F., Thomas, E. G., Thomas, M., Thomas, P., Thorne, K. A., Thrane, E., Tiwari, S., Tiwari, V., Tokmakov, K. V., Toland, K., Tonelli, M., Tornasi, Z., Torres-Forné, A., Torrie, C. I., Töyrä, D., Travasso, F., Traylor, G., Trinastic, J., Tringali, M. C., Trozzo, L., Tsang, K. W., Tse, M., Tso, R., Tsuna, D., Tsukada, L., Tuyenbayev, D., Ueno, K., Ugolini, D., Urban, A. L., Usman, S. A., Vahlbruch, H., Vajente, G., Valdes, G., van Bakel, N., van Beuzekom, M., van den Brand, J. F. J., Van Den Broeck, C., Vander-Hyde, D. C., van der Schaaf, L., van Heijningen, J. V., van Veggel, A. A., Vardaro, M., Varma, V., Vass, S., Vasúth, M., Vecchio, A., Vedovato, G., Veitch, J., Veitch, P. J., Venkateswara, K., Venugopalan, G., Verkindt, D., Vetrano, F., Viceré, A., Viets, A. D., Vinciguerra, S., Vine, D. J., Vinet, J. -Y., Vitale, S., Vo, T., Vocca, H., Vorvick, C., Vyatchanin, S. P., Wade, A. R., Wade, L. E., Wade, M., Walet, R., Walker, M., Wallace, L., Walsh, S., Wang, G., Wang, H., Wang, J. Z., Wang, W. H., Wang, Y. F., Ward, R. L., Warner, J., Was, M., Watchi, J., Weaver, B., Wei, L. -W., Weinert, M., Weinstein, A. J., Weiss, R., Wellmann, F., Wen, L., Wessel, E. K., Weßels, P., Westerweck, J., Wette, K., Whelan, J. T., Whiting, B. F., Whittle, C., Wilken, D., Williams, D., Williams, R. D., Williamson, A. R., Willis, J. L., Willke, B., Wimmer, M. H., Winkler, W., Wipf, C. C., Wittel, H., Woan, G., Woehler, J., Wofford, J. K., Wong, W. K., Worden, J., Wright, J. L., Wu, D. S., Wysocki, D. M., Xiao, S., Yam, W., Yamamoto, H., Yancey, C. C., Yang, L., Yap, M. J., Yazback, M., Yu, Hang, Yu, Haocun, Yvert, M., Zadrożny, A., Zanolin, M., Zelenova, T., Zendri, J. -P., Zevin, M., Zhang, J., Zhang, L., Zhang, M., Zhang, T., Zhang, Y. -H., Zhao, C., Zhou, M., Zhou, Z., Zhu, S. J., Zhu, X. J., Zimmerman, A. B., Zlochower, Y., Zucker, M. E., Zweizig, J., The LIGO Scientific Collaboration, The Virgo Collaboration, Astroparticle Physics (IHEF, IoP, FNWI), LIGO Scientific Collaboration, and Virgo Collaboration
Subjects: Astrofísica, Gravitació, neutron star: binary, Astronomy, General Physics and Astronomy, Binary number, Astrophysics, ELECTROMAGNETIC COUNTERPART, spin, 01 natural sciences, General Relativity and Quantum Cosmology, GRAVITATIONAL-WAVES, localization, 010305 fluids & plasmas, Gravitational wave detectors, EQUATION, energy: density, LIGO, GEO600, QC, astro-ph.HE, Settore FIS/01, Physics, High Energy Astrophysical Phenomena (astro-ph.HE), GAMMA-RAY BURSTS, Settore FIS/05, Equations of state, Gravitational effects, Gravitational-wave signals, Deformability parameter, Amplitude, Physical Sciences, Physical effects, INSPIRALING COMPACT BINARIES, [PHYS.GRQC]Physics [physics]/General Relativity and Quantum Cosmology [gr-qc], Spectral energy density, Astrophysics - High Energy Astrophysical Phenomena, PARAMETER-ESTIMATION, Binary neutron stars, data analysis method, gr-qc, QC1-999, Physics, Multidisciplinary, FOS: Physical sciences, General Relativity and Quantum Cosmology (gr-qc), Astrophysics::Cosmology and Extragalactic Astrophysics, Gravity waves, Bayesian, Gravimeters, electromagnetic field: production, Physics and Astronomy (all), galaxy: binary, 0103 physical sciences, ddc:530, SDG 7 - Affordable and Clean Energy, 010306 general physics, gravitational radiation: frequency, STFC, Astrophysics::Galaxy Astrophysics, equation of state, LIGHT CURVES, Equation of state, Science & Technology, Spins, gravitational radiation, RCUK, Spectral density, KILONOVA, TRANSIENTS, binary: compact, Stars, Galaxy, gravitational radiation detector, Neutron star, VIRGO, Physics and Astronomy, gravitational radiation: emission, RADIATION, Bayesian Analysis, Dewey Decimal Classification::500 | Naturwissenschaften::530 | Physik, [PHYS.ASTR]Physics [physics]/Astrophysics [astro-ph]
Abstract: On August 17, 2017, the Advanced LIGO and Advanced Virgo gravitational-wave detectors observed a low-mass compact binary inspiral. The initial sky localization of the source of the gravitational-wave signal, GW170817, allowed electromagnetic observatories to identify NGC 4993 as the host galaxy. In this work, we improve initial estimates of the binary's properties, including component masses, spins, and tidal parameters, using the known source location, improved modeling, and recalibrated Virgo data. We extend the range of gravitational-wave frequencies considered down to 23 Hz, compared to 30 Hz in the initial analysis. We also compare results inferred using several signal models, which are more accurate and incorporate additional physical effects as compared to the initial analysis. We improve the localization of the gravitational-wave source to a 90% credible region of $16~\mathrm{deg}^2$. We find tighter constraints on the masses, spins, and tidal parameters, and continue to find no evidence for nonzero component spins. The component masses are inferred to lie between 1.00 and 1.89 $M_\odot$ when allowing for large component spins, and to lie between 1.16 and 1.60 $M_\odot$ (with a total mass $2.73^{+0.04}_{-0.01} \, M_\odot$) when the spins are restricted to be within the range observed in Galactic binary neutron stars. Under minimal assumptions about the nature of the compact objects, our constraints for the tidal deformability parameter $\tilde \Lambda$ are $(0,630)$ when we allow for large component spins, and $300^{+420}_{-230}$ (using a 90% highest posterior density interval) when restricting the magnitude of the component spins, ruling out several equation-of-state models at the 90% credible level. Finally, with LIGO and GEO600 data, we use a Bayesian analysis to place upper limits on the amplitude and spectral energy density of a possible post-merger signal. (Abridged), Comment: 32 pages, 15 figures. Version 3 is the final published version; results are unchanged compared to previous versions. Data behind the figures, including posterior samples, are available at dcc.ligo.org/LIGO-P1800061/public. The gravitational wave strain data for this event are available at the LIGO Open Science Center, gw-openscience.org/events/GW170817
Published: 2019

32. Comparing design of experiments and optimal experimental design techniques for modelling the microbial growth rate under static environmental conditions

Author: Jan Van Impe, Simen Akkermans, and Philippe Nimmegeers
Subjects: Central composite design, Computer science, PH, Model prediction, Models, Biological, Microbiology, 03 medical and health sciences, symbols.namesake, 0404 agricultural biotechnology, 0302 clinical medicine, FOOD, Cell density, SQUARE-ROOT MODEL, Biology, TEMPERATURE, Science & Technology, Bacteria, Predictive microbiology, Secondary model, Design of experiments, Model prediction uncertainty, 04 agricultural and veterinary sciences, Function (mathematics), Factorial experiment, 040401 food science, Experimental design, BACTERIAL-GROWTH, D-criterion, Kinetics, Chemistry, Biotechnology & Applied Microbiology, Research Design, Food Science & Technology, Jacobian matrix and determinant, 030221 ophthalmology & optometry, symbols, Biochemical engineering, Engineering sciences. Technology, Life Sciences & Biomedicine, Mathematics, Food Science, PARAMETER-ESTIMATION
Abstract: Building secondary models that describe the growth rate as a function of multiple environmental conditions is often very labour intensive and costly. As such, the current research aims to assist in decreasing the required experimental effort by studying the efficacy of both design of experiments (DOE) and optimal experimental designs (OED) techniques. This is the first research in predictive microbiology (i) to make a comparison of these techniques based on the (relative) model prediction uncertainty of the obtained models and (ii) to compare OED criteria for the design of experiments with static (instead of dynamic) environmental conditions. A comparison of the DOE techniques demonstrated that the inscribed central composite design and full factorial design were most suitable. Five conventional and two tailor made OED criteria were tested. The commonly used D-criterion performed best out of the conventional designs and almost equally well as the best of the dedicated criteria. Moreover, the modelling results of the D-criterion were less dependent on the experimental variability and differences in the microbial response than the two selected DOE techniques. Finally, it was proven that solving the optimisation of the D-criterion can be made more efficient by considering the sensitivities of the growth rate relative to its value as Jacobian matrix instead of the sensitivities of the cell density measurements. ispartof: FOOD MICROBIOLOGY vol:76 pages:504-512 ispartof: location:England status: published
Published: 2018

33. Testing Unemployment Theories: A Multivariate Long Memory Approach

Author: Luis A. Gil-Alana, Yuliya Lovcha, and Guglielmo Maria Caporale
Subjects: Multivariate statistics, Economics, media_common.quotation_subject, Social Sciences, unemployment rate, multivariate long memory, fractional integration, jel:C22, jel:E24, Persistence, Business & Economics, Structural Breaks, Rates, 0502 economics and business, Econometrics, Mean reversion, Fractional-Integration, 050207 economics, Netherlands, 050205 econometrics, media_common, Wage Dynamics, Series (mathematics), Hysteresis, 05 social sciences, Null (mathematics), Univariate, jel:C32, Unemployment rate, Degree (music), Fractional integration, Parameter-Estimation, Hysteresis (economics), Multivariate long memory, Unemployment, Time-Series, Unit Roots, Unit root, General Economics, Econometrics and Finance
Abstract: This paper investigates the empirical relevance of different unemployment theories in three major economies, namely the UK, the US and Japan, by estimating the degree of dependence in the unemployment series. Both univariate and multivariate long memory methods are used. The results vary depending on whether the former or the latter approach is followed. Specifically, when taking a univariate approach, the unit root null cannot be rejected in case of the UK and Japanese unemployment series, and some degree of mean reversion (d < 1) is found in the case of the US unemployment rate. When applying multivariate methods instead, higher orders of integration are still found for the UK and Japanese series, but the NAIRU hypothesis cannot be rejected in the case of the US.
Published: 2016

34. Quantum-limited time-frequency estimation through mode-selective photon measurement

Author: Donohue, J. M., Ansari, V., Řeháček, Jaroslav, Hradil, Zdenek, Stoklasa, Bohumil, Paúr, Martin, Sánchez Soto, Luis Lorenzo, Silberhorn, C., Donohue, J. M., Ansari, V., Řeháček, Jaroslav, Hradil, Zdenek, Stoklasa, Bohumil, Paúr, Martin, Sánchez Soto, Luis Lorenzo, and Silberhorn, C.
Abstract: We thank K. Bonsma-Fisher, O. DiMatteo, J. Gil-López, M. Allgaier, C. Fabre, N. Treps, and B. Brecht for fruitful discussions. This research has received funding from the European Unions (EU) Horizon 2020 research and innovation program under Grant Agreement No. 665148, the Grant Agency of the Czech Republic (Grant No. 18-04291S), Palacký University (Grant No. IGA-PrF-2018-003), and the Spanish MINECO (Grant No. FIS2015-67963-P). J. M. D. gratefully acknowledges support from Natural Sciences and Engineering Research Council of Canada., By projecting onto complex optical mode profiles, it is possible to estimate arbitrarily small separations between objects with quantum-limited precision, free of uncertainty arising from overlapping intensity profiles. Here we extend these techniques to the time-frequency domain using mode-selective sum-frequency generation with shaped ultrafast pulses. We experimentally resolve temporal and spectral separations between incoherent mixtures of single-photon level signals ten times smaller than their optical bandwidths with a tenfold improvement in precision over the intensity-only Cramer-Rao bound., Unión Europea. H2020, Ministerio de Economía y Competitividad (MINECO), Grant Agency of the Czech Republic, Palacky University, Depto. de Óptica, Fac. de Ciencias Físicas, TRUE, pub
Published: 2018

35. Forecast And Analysis Of The Cosmological Redshift Drift

Author: Física teórica e historia de la ciencia, Fisika teorikoa eta zientziaren historia, Lazcoz Sáez, Ruth, Leanizbarrutia Alonso, Iker, Salzano, Vincenzo, Física teórica e historia de la ciencia, Fisika teorikoa eta zientziaren historia, Lazcoz Sáez, Ruth, Leanizbarrutia Alonso, Iker, and Salzano, Vincenzo
Abstract: The cosmological redshift drift could lead to the next step in high-precision cosmic geometric observations, becoming a direct and irrefutable test for cosmic acceleration. In order to test the viability and possible properties of this effect, also called Sandage-Loeb (SL) test, we generate a model-independent mock data set in order to compare its constraining power with that of the future mock data sets of Type Ia Supernovae (SNe) and Baryon Acoustic Oscillations (BAO). The performance of those data sets is analyzed by testing several cosmological models with the Markov chain Monte Carlo (MCMC) method, both independently as well as combining all data sets. Final results show that, in general, SL data sets allow for remarkable constraints on the matter density parameter today Omega(m) on every tested model, showing also a great complementarity with SNe and BAO data regarding dark energy parameters.
Published: 2018

36. Scaling, similarity, and the fourth paradigm for hydrology

Author: Niko E. C. Verhoest, Ross Woods, Christa D. Peters-Lidard, Kevin O. Achieng, Tim van Emmerik, Luis Samaniego, Martyn P. Clark, Remko Uijlenhoet, and Trenton E. Franz
Subjects: WATERSHED THERMODYNAMICS, Computer science, SPATIAL VARIABILITY, 0208 environmental biotechnology, SUBSURFACE HETEROGENEITY, 02 engineering and technology, Universal law, Hydrology and Quantitative Water Management, lcsh:Technology, Article, lcsh:TD1-1066, REPRESENTATIVE ELEMENTARY AREA, Computational simulation, ComputerApplications_MISCELLANEOUS, Similarity (psychology), Life Science, UNIFYING FRAMEWORK, lcsh:Environmental technology. Sanitary engineering, Scaling, lcsh:Environmental sciences, Hydrology, lcsh:GE1-350, WIMEK, GENERAL-CIRCULATION MODELS, lcsh:T, lcsh:Geography. Anthropology. Recreation, Mutual information, LAND-SURFACE, 020801 environmental engineering, lcsh:G, Earth and Environmental Sciences, General Circulation Model, State (computer science), STOCHASTIC APPROACH, SOIL-MOISTURE PRODUCTS, PARAMETER-ESTIMATION, Hydrologie en Kwantitatief Waterbeheer
Abstract: In this synthesis paper addressing hydrologic scaling and similarity, we posit that roadblocks in the search for universal laws of hydrology are hindered by our focus on computational simulation (the third paradigm) and assert that it is time for hydrology to embrace a fourth paradigm of data-intensive science. Advances in information-based hydrologic science, coupled with an explosion of hydrologic data and advances in parameter estimation and modeling, have laid the foundation for a data-driven framework for scrutinizing hydrological scaling and similarity hypotheses. We summarize important scaling and similarity concepts (hypotheses) that require testing; describe a mutual information framework for testing these hypotheses; describe boundary condition, state, flux, and parameter data requirements across scales to support testing these hypotheses; and discuss some challenges to overcome while pursuing the fourth hydrological paradigm. We call upon the hydrologic sciences community to develop a focused effort towards adopting the fourth paradigm and apply this to outstanding challenges in scaling and similarity.
Published: 2018

37. Bayesian optimisation for likelihood-free cosmological inference

Author: Florent Leclercq
Subjects: FOS: Computer and information sciences, Cosmology and Nongalactic Astrophysics (astro-ph.CO), Bayesian probability, FOS: Physical sciences, Inference, Astronomy & Astrophysics, Bayesian inference, COMPUTATION, Statistics - Applications, 01 natural sciences, Physics, Particles & Fields, DATA-COMPRESSION, 0103 physical sciences, POWER-SPECTRUM INFERENCE, Applications (stat.AP), Instrumentation and Methods for Astrophysics (astro-ph.IM), 010303 astronomy & astrophysics, stat.AP, Parametric statistics, Physics, Science & Technology, 010308 nuclear & particles physics, Bayesian optimization, Statistics::Computation, Parametric model, Physical Sciences, COVARIANCE MATRICES, astro-ph.CO, Approximate Bayesian computation, Astrophysics - Instrumentation and Methods for Astrophysics, Likelihood function, Algorithm, Astrophysics - Cosmology and Nongalactic Astrophysics, PARAMETER-ESTIMATION, astro-ph.IM
Abstract: Many cosmological models have only a finite number of parameters of interest, but a very expensive data-generating process and an intractable likelihood function. We address the problem of performing likelihood-free Bayesian inference from such black-box simulation-based models, under the constraint of a very limited simulation budget (typically a few thousand). To do so, we adopt an approach based on the likelihood of an alternative parametric model. Conventional approaches to approximate Bayesian computation such as likelihood-free rejection sampling are impractical for the considered problem, due to the lack of knowledge about how the parameters affect the discrepancy between observed and simulated data. As a response, we make use of a strategy previously developed in the machine learning literature (Bayesian optimisation for likelihood-free inference, BOLFI), which combines Gaussian process regression of the discrepancy to build a surrogate surface with Bayesian optimisation to actively acquire training data. We extend the method by deriving an acquisition function tailored for the purpose of minimising the expected uncertainty in the approximate posterior density, in the parametric approach. The resulting algorithm is applied to the problems of summarising Gaussian signals and inferring cosmological parameters from the Joint Lightcurve Analysis supernovae data. We show that the number of required simulations is reduced by several orders of magnitude, and that the proposed acquisition function produces more accurate posterior approximations, as compared to common strategies., Comment: 16+9 pages, 12 figures. Matches PRD published version after minor modifications
Published: 2018

38. An extension of the RiMAX multipath estimation algorithm for ultra-wideband channel modeling

Author: David Plets, Brecht Hanssens, Martine Lienard, Luc Martens, Emmeric Tanghe, Claude Oestges, Wout Joseph, Davy P. Gaillot, Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 (IEMN), Centrale Lille-Institut supérieur de l'électronique et du numérique (ISEN)-Université de Valenciennes et du Hainaut-Cambrésis (UVHC)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF), Télécommunication, Interférences et Compatibilité Electromagnétique (IEMN-TELICE), Centrale Lille-Institut supérieur de l'électronique et du numérique (ISEN)-Université de Valenciennes et du Hainaut-Cambrésis (UVHC)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-Centrale Lille-Institut supérieur de l'électronique et du numérique (ISEN)-Université de Valenciennes et du Hainaut-Cambrésis (UVHC)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF), Department of Information Technology (INTEC), Universiteit Gent = Ghent University (UGENT), Télécommunication, Interférences et Compatibilité Electromagnétique - IEMN (TELICE - IEMN), Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-JUNIA (JUNIA), Université catholique de Lille (UCL)-Université catholique de Lille (UCL)-Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-JUNIA (JUNIA), Université catholique de Lille (UCL)-Université catholique de Lille (UCL), Institute of Information and Communication Technologies, Electronics and Applied Mathematics (ICTEAM), Université Catholique de Louvain = Catholic University of Louvain (UCL), and The author would also like to express his gratitude to Dr. Rein Houthooft, M. Sc., for his many insights, fruitful discussions, and shedding brightening lights on this subject.Funding: Brecht Hanssens is funded by the Agency for Innovation and Entrepreneurship. Emmeric Tanghe is a Post-Doctoral fellow of the Research Foundation Flanders (FWO).This work was carried out in the framework of COST Action CA15104 IRACON, and the Excellence of Science (EOS) project MUlti-SErvice WIreless NETwork (MUSEWINET). This research was supported by the FWO project G027714N.Availability of data and materialsThe data can be made available for scientific purposes only by sending a request via e-mail to Brecht.Hanssens@UGent.be.
Subjects: Electromagnetics, Technology and Engineering, Computer Networks and Communications, Computer science, Frequency band, MIMO, RiMAX, Channel sounding, lcsh:TK7800-8360, Ultra-wideband, 02 engineering and technology, Channel modeling, lcsh:Telecommunication, [SPI]Engineering Sciences [physics], Narrowband, Multipath estimation, INDOOR ENVIRONMENTS, SYSTEMS, lcsh:TK5101-6720, 0202 electrical engineering, electronic engineering, information engineering, Computer Science::Networking and Internet Architecture, Dense multipath components, Wideband, ComputingMilieux_MISCELLANEOUS, Computer Science::Information Theory, Specular multipath components, Physics::Computational Physics, DIFFUSE-SCATTERING, Research, lcsh:Electronics, 020208 electrical & electronic engineering, COMPONENTS, Indoor environment, 020206 networking & telecommunications, Multipath propagation, modeling, Channel, Computer Science Applications, Signal Processing, DISTANCE, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Algorithm, Communication channel, PARAMETER-ESTIMATION
Abstract: International audience; This work presents an extension of the high-resolution RiMAX multipath estimation algorithm, enabling the analysis of frequency-dependent propagation parameters for ultra-wideband (UWB) channel modeling. Since RiMAX is a narrowband algorithm, it does not account for the frequency-dependency of the radio channel or the environment. As such, the impact of certain materials in which these systems operate can no longer be considered constant with respect to frequency, preventing an accurate estimation of multipath parameters for UWB communication. In order to track both the specular and dense multipath components (SMC and DMC) over frequency, an extension to the RiMAX algorithm was developed that can process UWB measurement data. The advantage of our approach is that geometrical propagation parameters do not appear or disappear from one sub-band onto the next. The UWB-RiMAX algorithm makes it possible to re-evaluate common radio channel parameters for DMC in the wideband scenario, and to extend the well-known deterministic propagation model comprising of SMC alone, towards a more hybrid model containing the stochastic contributions from the DMC’s distributed diffuse scattering as well.Our algorithm was tested with synthetic radio channel models in an indoor environment, which show that our algorithm can match up to 99% of the SMC parameters according to the multipath component distance (MCD) metric and that the DMC reverberation time known from the theory of room electromagnetics can be estimated on average with an error margin of less than 2 ns throughout the UWB frequency band. We also present some preliminary results in an indoor environment, which indicate a strong presence of DMC and thus diffuse scattering. The DMC power represents up to 50% of the total measured power for the lower UWB frequencies and reduces to around 30% for the higher UWB frequencies.
Published: 2018

39. Precision, Reliability, and Effect Size of Slope Variance in Latent Growth Curve Models: Implications for Statistical Power Analysis

Author: Andreas M. Brandmaier, Timo von Oertzen, Paolo Ghisletta, Ulman Lindenberger, and Christopher Hertzog
Subjects: Effective error, Design, longitudinal study design, effect size, lcsh:BF1-990, Oldest-old, Effect size, Growth curve (statistics), structural equation modeling, 050105 experimental psychology, Statistical power, Structural equation models, Structural equation modeling, Equivalent models, effective error, 0504 sociology, ddc:150, Statistics, Methods, Psychology, 0501 psychology and cognitive sciences, linear latent growth curve model, Reliability (statistics), General Psychology, Longitudinal study design, Individual-differences, statistical power, Parameter-estimation, reliability, Estimation theory, Covariance structure-analysis, Longitudinal data, 05 social sciences, 050401 social sciences methods, Likelihood ratio test, Variance (accounting), Covariance, Reliability, lcsh:Psychology, Linear latent growth curve model, Sample size determination, Likelihood-ratio test, Sample-size
Abstract: Published: 17 April 2018 Latent Growth Curve Models (LGCM) have become a standard technique to model change over time. Prediction and explanation of inter-individual differences in change are major goals in lifespan research. The major determinants of statistical power to detect individual differences in change are the magnitude of true inter-individual differences in linear change (LGCM slope variance), design precision, alpha level, and sample size. Here, we show that design precision can be expressed as the inverse of effective error. Effective error is determined by instrument reliability and the temporal arrangement of measurement occasions. However, it also depends on another central LGCM component, the variance of the latent intercept and its covariance with the latent slope. We derive a new reliability index for LGCM slope variance-effective curve reliability (ECR)-by scaling slope variance against effective error. ECR is interpretable as a standardized effect size index. We demonstrate how effective error, ECR, and statistical power for a likelihood ratio test of zero slope variance formally relate to each other and how they function as indices of statistical power. We also provide a computational approach to derive ECR for arbitrary intercept-slope covariance. With practical use cases, we argue for the complementary utility of the proposed indices of a study's sensitivity to detect slope variance when making a priori longitudinal design decisions or communicating study designs. Max Planck Society
Published: 2018

40. Potential of Space-Borne PolInSAR for Forest Canopy Height Estimation Over India-A Case Study Using Fully Polarimetric L-, C-, and X-Band SAR Data

Author: KHATI, U, SINGH, G, and KUMAR, S
Subjects: polarimetric SAR interferometry (PolInSAR), tropical forest, multifrequency, TEMPORAL DECORRELATION, Above-ground biomass (AGB), RADAR, BIOMASS, forest height, INTERFEROMETRY, MODEL, BOREAL FOREST, POL-INSAR, INVERSION, TANDEM-X, PARAMETER-ESTIMATION
Abstract: This paper aims to demonstrate the potential of space-borne datasets for forest height estimation over Indian tropical forests. Fully polarimetric space-borne SAR interferometry (PolInSAR) data is acquired at the L-, C-, and X-band frequencies. The X-band data are acquired in two modes-single pass and repeat pass. The datasets are compensated for decorrelation due to SNR and varying spatial baselines. The remaining major decorrelation is the volumetric decorrelation, which is modeled using random volume over ground model to estimate the PolInSAR height for all the three frequencies. The modified Three-stage inversion algorithm is utilized for forest stand height estimation. The effect of forest biomass on height inversion accuracy is assessed. Furthermore, a first-order estimate of the absolute temporal decorrelation is demonstrated for the repeat-pass space-borne PolInSAR datasets. Extensive field validation campaigns are carried out in the tropical forest ranges. The forest height is inverted using data at all the three frequencies and validated with field measured values. The zero-temporal baseline bistatic TerraSAR-X/TanDEM-X and the L-band ALOS-2/PALSAR-2 result in a good height inversion with r(2) and RMSE of 0.77 and 1.86 m (X-band) and 0.75 and 1.94 m (L-band), respectively. Furthermore, a comparison of estimated height of ALOS-2/PALSAR-2, TerraSAR-X, and RadarSAT-2 has been done with respect to the estimated height of TerraSAR-X/TanDEM-X. It is observed that RMSEof height inversion with respect to TerraSAR-X/TanDEM-X height is found to be 5.4 m, 7.6 m, and 12.8mfor ALOS-2/PALSAR2, RadarSAT-2, and TerraSAR-X, respectively.
Published: 2018

41. Remotely sensed ET for streamflow modelling in catchments with contrasting flow characteristics: an attempt to improve efficiency

Author: KUNNATH-POOVAKKA, A, RYU, D, RENZULLO, LJ, and GEORGE, B
Subjects: SOIL-MOISTURE DATA, Remotely sensed (RS) data, Regionalization, Evapotranspiration (ET), LEAF-AREA INDEX, DREAM, UNGAUGED BASINS, RAINFALL-RUNOFF MODELS, SENSING DATA, DATA ASSIMILATION, Calibration, DISTRIBUTED HYDROLOGICAL MODEL, SATELLITE DATA, SOUTHEAST AUSTRALIA, PARAMETER-ESTIMATION
Abstract: An efficient calibration with remotely sensed (RS) data is important for accurate predictions at ungauged catchments. This study investigates the advantages of streamflow-sensitive regionalization on calibration with RS evapotranspiration (ET). Regionalization experiments are performed at 28 catchments in Australia. The catchments are classified into three groups based on annual rainfall and runoff coefficients. Streamflow, RS ET, and a multi-objective RS ET-streamflow calibration are performed using the DiffeRential Evolution Adaptive Metropolis algorithm in each catchment. Simplified Australian Water Resource Assessment-Landscape model is calibrated for a selection of five parameters. Posterior probability distributions of parameters from three calibrations performed at donor catchments in each group are inspected to find the parameter for regionalization in the individual group. In group 1 of wetter catchments, regionalization of parameter FsoilEmax (soil evaporation scaling factor) helps to simplify the calibration without any deterioration in ET, soil moisture (SM) and streamflow predictions. Regionalization of parameter Beta (coefficient describing rate of hydraulic conductivity increase with water content) in group 2 assists to improve the streamflow predictions with no decrement in ET and SM predictions. However, regionalization is not able to provide satisfactory results in group 3. Group 3 includes low-yielding catchments, with average annual rainfall below 1000 mm/year and runoff coefficient less than 0.1, where traditional streamflow calibration also fails to produce accurate results. This study concludes that streamflow-sensitive regionalization is effective for improving the efficacy of RS ET calibration in wetter catchments.
Published: 2018
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42. Kepler Object of Interest Network: I. First results combining ground- and space-based observations of Kepler systems with transit timing variations

Author: Jesús Hernández, Stefan Dreizler, Lev Tal-Or, V. Krushinsky, Šarūnas Mikolaitis, E. Pakštienė, Simon Albrecht, Hans J. Deeg, Sergio Hoyer, Vaishali Parkash, Aviv Ofir, S. Khalafinejad, Eric Agol, Alexios Liakos, Matthias Mallonn, A. C. Becker, C. von Essen, Ignasi Ribas, S. Rusov, E. Herrero, Xuan Wang, L. Sun, C. Delgado-Correal, S. Lalitha, P. Boumis, Katja Poppenhaeger, A. A. Popov, E. N. Sokov, M. L. Moyano D'Angelo, Gražina Tautvaišienė, Iraida A. Sokova, Rimvydas Janulis, Sven Wedemeyer, Shenghong Gu, J. Freudenthal, D. C. Fabrycky, Brett M. Morris, Institut d'Astrophysique de Paris (IAP), Institut national des sciences de l'Univers (INSU - CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Laboratoire d'Astrophysique de Marseille (LAM), and Aix Marseille Université (AMU)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)
Subjects: 010504 meteorology & atmospheric sciences, planets and satellites: dynamical evolution and stability, Astrophysics, 01 natural sciences, PHOTOMETRIC PRECISION, photometric [techniques], techniques: photometric, Spitzer Space Telescope, Planet, Primary (astronomy), photometers [instrumentation], DATA HANDLING, Transit (astronomy), 010303 astronomy & astrophysics, Physics, Earth and Planetary Astrophysics (astro-ph.EP), DATA ANALYSIS TECHNIQUES, [SDU.ASTR]Sciences of the Universe [physics]/Astrophysics [astro-ph], TIMING CIRCUITS, STAR, SERIES, Exoplanet, TIME, instrumentation: photometers, CONFIRMATION, CURVE FITTING, Data analysis, NASA, SPACE-BASED OBSERVATIONS, EXTRASOLAR PLANETS, PARAMETER-ESTIMATION, dynamical evolution and stability [planets and satellites], FOS: Physical sciences, MASS, Kepler, 0103 physical sciences, data analysis [methods], Kepler object of interest, PHOTOMETRY, 0105 earth and related environmental sciences, PLANETARY SYSTEM, LIGHT CURVES, CCD PHOTOMETRY, NORTHERN HEMISPHERES, Astronomy and Astrophysics, DYNAMICAL EVOLUTION AND STABILITIES [PLANETS AND SATELLITES], methods: data analysis, Space and Planetary Science, [SDU]Sciences of the Universe [physics], INFORMATION ANALYSIS, SATELLITES, STARS, Astrophysics - Earth and Planetary Astrophysics
Abstract: During its four years of photometric observations, the Kepler space telescope detected thousands of exoplanets and exoplanet candidates. One of Kepler's greatest heritages has been the confirmation and characterization of hundreds of multi-planet systems via Transit Timing Variations (TTVs). However, there are many interesting candidate systems displaying TTVs on such long time scales that the existing Kepler observations are of insufficient length to confirm and characterize them by means of this technique. To continue with Kepler's unique work we have organized the "Kepler Object of Interest Network" (KOINet). The goals of KOINet are, among others, to complete the TTV curves of systems where Kepler did not cover the interaction timescales well. KOINet has been operational since March, 2014. Here we show some promising first results obtained from analyzing seven primary transits of KOI-0410.01, KOI-0525.01, KOI-0760.01, and KOI-0902.01 in addition to Kepler data, acquired during the first and second observing seasons of KOINet. While carefully choosing the targets we set demanding constraints about timing precision (at least 1 minute) and photometric precision (as good as 1 part per thousand) that were achieved by means of our observing strategies and data analysis techniques. For KOI-0410.01, new transit data revealed a turn-over of its TTVs. We carried out an in-depth study of the system, that is identified in the NASA's Data Validation Report as false positive. Among others, we investigated a gravitationally-bound hierarchical triple star system, and a planet-star system. While the simultaneous transit fitting of ground and space-based data allowed for a planet solution, we could not fully reject the three-star scenario. New data, already scheduled in the upcoming 2018 observing season, will set tighter constraints on the nature of the system., 22 figures
Published: 2018

43. Cramér-Rao Bounds for SNR Estimation of Oversampled Linearly Modulated Signals

Author: Javier Villares, Wilfried Gappmair, Roberto Lopez-Valcarce, Carlos Mosquera, Jaume Riba, Universitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions, and Universitat Politècnica de Catalunya. SPCOM - Grup de Recerca de Processament del Senyal i Comunicacions
Subjects: Signal processing, Parameter-estimation, Signal to noise ratio, Nonconstant modulus constellations, Matched filter, Oversampling, Systems, SIMO channels, Estimator, Cramer-Rao bounds, Filter (signal processing), Tractament del senyal, Enginyeria de la telecomunicació::Processament del senyal [Àrees temàtiques de la UPC], Signal-to-noise ratio (imaging), Aliasing, Control theory, Signal Processing, Adaptation, Electrical and Electronic Engineering, Symbol rate, Cramér–Rao bound, Algorithm, BPSK, Mathematics
Abstract: © 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Most signal-to-noise ratio (SNR) estimators use the receiver matched filter output sampled at the symbol rate, an approach which does not preserve all information in the analog waveform due to aliasing. Thus, it is relevant to ask whether avoiding aliasing could improve SNR estimation. To this end, we compute the corresponding data-aided (DA) and non-data-aided (NDA) Cramer-Rao bounds (CRBs). We adopt a novel dual filter framework, which is shown to be information-preserving under suitable conditions and considerably simplifies the analysis. It is shown that the CRB can be substantially reduced by exploiting any available excess bandwidth, depending on the modulation scheme, the SNR range, and the estimator type (DA or NDA).
Published: 2015

44. Climate and hydrological variability: the catchment filtering role

Author: Ignacio Andrés-Doménech, Alberto Montanari, Juan B. Marco, Rafael García-Bartual, I. Andrés-Doménech, R. García-Bartual, A. Montanari, J. B. Marco, Andres-Domenech, I., Garcia-Bartual, R., Montanari, A., and Marco, J. B.
Subjects: Mediterranean climate, Return period, INGENIERIA HIDRAULICA, DURATION, Drainage basin, Climate change, STOCHASTIC RAINFALL, lcsh:Technology, GENERALIZED PARETO DISTRIBUTION, lcsh:TD1-1066, HYDROLOGY, Generalized Pareto distribution, DISTRIBUTIONS, lcsh:Environmental technology. Sanitary engineering, lcsh:Environmental sciences, lcsh:GE1-350, geography, geography.geographical_feature_category, lcsh:T, lcsh:Geography. Anthropology. Recreation, FLOOD FREQUENCY, PROBABILITY, THRESHOLDS, Infiltration (hydrology), FRESH-WATER RESOURCES, Hydrology, climate, lcsh:G, Rainfall-runoff, Climatology, Environmental science, Probability distribution, Surface runoff, PARAMETER-ESTIMATION
Abstract: Measuring the impact of climate change on flood frequency is a complex and controversial task. Identifying hydrological changes is difficult given the factors, other than climate variability, which lead to significant variations in runoff series. The catchment filtering role is often overlooked and thus may hinder the correct identification of climate variability signatures on hydrological processes. Does climate variability necessarily imply hydrological variability? This research aims to analytically derive the flood frequency distribution based on realistic hypotheses about the rainfall process and the rainfall runoff transformation. The annual maximum peak flow probability distribution is analytically derived to quantify the filtering effect of the rainfall runoff process on climate change. A sensitivity analysis is performed according to typical semi-arid Mediterranean climatic and hydrological conditions, assuming a simple but common scheme for the rainfall runoff transformation in small-size ungauged catchments, i.e. the CN-SCS model. Variability in annual maximum peak flows and its statistical significance are analysed when changes in the climatic input are introduced. Results show that depending on changes in the annual number of rainfall events, the catchment filtering role is particularly significant, especially when the event rainfall volume distribution is not strongly skewed. Results largely depend on the return period: for large return periods, peak flow variability is significantly affected by the climatic input, while for lower return periods, infiltration processes smooth out the impact of climate change
Published: 2015

45. Tracing the molecular basis of transcriptional dynamics in noisy data by using an experiment-based mathematical model

Author: Joke Blom, Carsten Carlberg, Frank J. Bruggeman, Katja N. Rybakova, Aleksandra Tomaszewska, Simon van Mourik, Hans V. Westerhoff, Molecular Cell Physiology, Systems Bioinformatics, Mathematics, AIMMS, Synthetic Systems Biology (SILS, FNWI), and Evolutionary Intelligence
Subjects: Transcription, Genetic, receptor, RNA Splicing, RNA Stability, Farm Technology, elongation, Biology, parameter-estimation, Perilipin-2, Cell Line, Tumor, Gene expression, Genetics, adrp expression, Humans, Epigenetics, RNA, Messenger, Gene, Transcription factor, Regulation of gene expression, Messenger RNA, promoter, Models, Genetic, Gene regulation, Chromatin and Epigenetics, RNA, Membrane Proteins, gene-expression, Cell biology, networks, RNA splicing, differentiation-related protein, cells, Agrarische Bedrijfstechnologie, rna degradation
Abstract: Changes in transcription factor levels, epigenetic status, splicing kinetics and mRNA degradation can each contribute to changes in the mRNA dynamics of a gene. We present a novel method to identify which of these processes is changed in cells in response to external signals or as a result of a diseased state. The method employs a mathematical model, for which the kinetics of gene regulation, splicing, elongation and mRNA degradation were estimated from experimental data of transcriptional dynamics. The time-dependent dynamics of several species of adipose differentiation-related protein (ADRP) mRNA were measured in response to ligand activation of the transcription factor peroxisome proliferator-activated receptor delta (PPARdelta). We validated the method by monitoring the mRNA dynamics upon gene activation in the presence of a splicing inhibitor. Our mathematical model correctly identifies splicing as the inhibitor target, despite the noise in the data.
Published: 2015

46. The role of rating curve uncertainty in real-time flood forecasting

Author: Ocio, D, Le Vine, N, Westerberg, I, Pappenberger, F, and Buytaert, W
Subjects: PARTICLE FILTER, Science & Technology, Environmental Engineering, SCHEME, Environmental Sciences & Ecology, flow measurement error, FREQUENCY, 0905 Civil Engineering, 0907 Environmental Engineering, DATA ASSIMILATION, Physical Sciences, Limnology, ENSEMBLE KALMAN FILTER, DISTRIBUTED HYDROLOGICAL MODEL, Water Resources, MANAGEMENT, Marine & Freshwater Biology, real-time flood forecasting, UNGAUGED CATCHMENTS, Life Sciences & Biomedicine, rating curve uncertainty, 1402 Applied Economics, Environmental Sciences, RAINFALL-RUNOFF MODEL, PARAMETER-ESTIMATION
Abstract: Data assimilation has been widely tested for flood forecasting, although its use in operational systems is mainly limited to a simple statistical error correction. This can be due to the complexity involved in making more advanced formal assumptions about the nature of the model and measurement errors. Recent advances in the definition of rating curve uncertainty allow estimating a flow measurement error that includes both aleatory and epistemic uncertainties more explicitly and rigorously than in the current practice. The aim of this study is to understand the effect such a more rigorous definition of the flow measurement error has on real-time data assimilation and forecasting. This study, therefore, develops a comprehensive probabilistic framework that considers the uncertainty in model forcing data, model structure, and flow observations. Three common data assimilation techniques are evaluated: (1) Autoregressive error correction, (2) Ensemble Kalman Filter, and (3) Regularized Particle Filter, and applied to two locations in the flood-prone Oria catchment in the Basque Country, northern Spain. The results show that, although there is a better match between the uncertain forecasted and uncertain true flows, there is a low sensitivity for the threshold exceedances used to issue flood warnings. This suggests that a standard flow measurement error model, with a spread set to a fixed flow fraction, represents a reasonable trade-off between complexity and realism. Standard models are therefore recommended for operational flood forecasting for sites with well-defined stage-discharge curves that are based on a large range of flow observations.
Published: 2017

47. Effect of emotions in a lexical decision task a diffusion model analysis

Author: Jean-Luc Kop, Renaud Brochard, Daniel Zagar, Vivien Dioux, Hélène Maire, Laboratoire de psychologie de l'interaction et des relations intersubjectives (INTERPSY), Université de Lorraine (UL), Laboratoire lorrain de psychologie et neurosciences de la dynamique des comportements (2LPN), Centre des Sciences du Goût et de l'Alimentation [Dijon] (CSGA), Institut National de la Recherche Agronomique (INRA)-Université de Bourgogne (UB)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Centre National de la Recherche Scientifique (CNRS), AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement, Laboratoire de Socio-Psychologie et Management du Sport (SPMS), Université de Bourgogne (UB), Laboratoire Chrono-environnement - UFC (UMR 6249) (LCE), Université Bourgogne Franche-Comté [COMUE] (UBFC)-Centre National de la Recherche Scientifique (CNRS)-Université de Franche-Comté (UFC), Centre National de la Recherche Scientifique (CNRS)-Université de Franche-Comté (UFC)-Université Bourgogne Franche-Comté [COMUE] (UBFC), Centre National de la Recherche Scientifique (CNRS)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Institut National de la Recherche Agronomique (INRA)-Université de Bourgogne (UB), Laboratoire de psychologie de l'interaction et des relations intersubjectives ( INTERPSY ), Université de Lorraine ( UL ), Centre des Sciences du Goût et de l'Alimentation [Dijon] ( CSGA ), Institut National de la Recherche Agronomique ( INRA ) -Université de Bourgogne ( UB ) -AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Centre National de la Recherche Scientifique ( CNRS ), Laboratoire Chrono-environnement ( LCE ), Université Bourgogne Franche-Comté ( UBFC ) -Centre National de la Recherche Scientifique ( CNRS ) -Université de Franche-Comté ( UFC ), Laboratoire de Socio-Psychologie et Management du Sport ( SPMS ), and Université de Bourgogne ( UB )
Subjects: [ SDV.AEN ] Life Sciences [q-bio]/Food and Nutrition, media_common.quotation_subject, mood, word recognition, Embarrassment, emotion, psychology, parameter-estimation, behavioral disciplines and activities, 050105 experimental psychology, Task (project management), information, stimuli, lexical decision task, 03 medical and health sciences, 0302 clinical medicine, Lexical decision task, Semantic memory, 0501 psychology and cognitive sciences, General Psychology, media_common, response, semantic memory, 05 social sciences, core affect, diffusion model, sequence effect, 16. Peace & justice, Sadness, Mood, Word recognition, depression, [SCCO.PSYC]Cognitive science/Psychology, Happiness, Psychology, [SDV.AEN]Life Sciences [q-bio]/Food and Nutrition, 030217 neurology & neurosurgery, Cognitive psychology
Abstract: Abstract. This study measured the effect of emotional states on lexical decision task performance and investigated which underlying components (physiological, attentional orienting, executive, lexical, and/or strategic) are affected. We did this by assessing participants’ performance on a lexical decision task, which they completed before and after an emotional state induction task. The sequence effect, usually produced when participants repeat a task, was significantly smaller in participants who had received one of the three emotion inductions (happiness, sadness, embarrassment) than in control group participants (neutral induction). Using the diffusion model ( Ratcliff, 1978 ) to resolve the data into meaningful parameters that correspond to specific psychological components, we found that emotion induction only modulated the parameter reflecting the physiological and/or attentional orienting components, whereas the executive, lexical, and strategic components were not altered. These results suggest that emotional states have an impact on the low-level mechanisms underlying mental chronometric tasks.
Published: 2017

48. Forecast and analysis of the cosmological redshift drift

Author: Vincenzo Salzano, Iker Leanizbarrutia, and Ruth Lazkoz
Subjects: Cosmology and Nongalactic Astrophysics (astro-ph.CO), Physics and Astronomy (miscellaneous), supernovae, FOS: Physical sciences, lcsh:Astrophysics, Scale (descriptive set theory), Astrophysics::Cosmology and Extragalactic Astrophysics, General Relativity and Quantum Cosmology (gr-qc), parameter-estimation, 01 natural sciences, General Relativity and Quantum Cosmology, scale, symbols.namesake, models, lcsh:QB460-466, 0103 physical sciences, lcsh:Nuclear and particle physics. Atomic energy. Radioactivity, Statistical physics, dark energy, 010303 astronomy & astrophysics, Engineering (miscellaneous), Physics, COSMIC cancer database, 010308 nuclear & particles physics, Estimation theory, Markov chain Monte Carlo, sandage-loeb test, Redshift, Data set, constant, symbols, Dark energy, lcsh:QC770-798, Baryon acoustic oscillations, baryon acoustic-oscillations, Regular Article - Theoretical Physics, hubble-space-telescope, constraints, Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract: The cosmological redshift drift could lead to the next step in high-precision cosmic geometric observations, becoming a direct and irrefutable test for cosmic acceleration. In order to test the viability and possible properties of this effect, also called Sandage-Loeb (SL) test, we generate a model independent mock data set so as to compare its constraining power with that of the future mock data sets of Type Ia Supernovae (SNe) and Baryon Acoustic Oscillations (BAO). The performance of those data sets is analyzed by testing several cosmological models with the Markov chain Monte Carlo (MCMC) method, both independently and combining all data sets. Final results show that, in general, SL data sets allow for remarkable constraints on the matter density parameter today $\Omega_m$ on every tested model, showing also a great complementarity with SNe and BAO data regarding dark energy (DE) parameters., Comment: 17 pages, 8 figures, accepted and to be published in EPJC
Published: 2017
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49. Distributed Evaluation of Local Sensitivity Analysis (DELSA), with application to hydrological models

Subjects: indexes, WIMEK, net ecosystem exchange, climate-change, measuring uncertainty importance, land model, rate coefficients, Hydrology and Quantitative Water Management, coupled reaction systems, parameter-estimation, groundwater-flow system, information-content, Hydrologie en Kwantitatief Waterbeheer
Abstract: 1] This paper presents a hybrid local-global sensitivity analysis method termed the Distributed Evaluation of Local Sensitivity Analysis (DELSA), which is used here to identify important and unimportant parameters and evaluate how model parameter importance changes as parameter values change. DELSA uses derivative-based “local” methods to obtain the distribution of parameter sensitivity across the parameter space, which promotes consideration of sensitivity analysis results in the context of simulated dynamics. This work presents DELSA, discusses how it relates to existing methods, and uses two hydrologic test cases to compare its performance with the popular global, variance-based Sobol' method. The first test case is a simple nonlinear reservoir model with two parameters. The second test case involves five alternative “bucket-style” hydrologic models with up to 14 parameters applied to a medium-sized catchment (200 km2) in the Belgian Ardennes. Results show that in both examples, Sobol' and DELSA identify similar important and unimportant parameters, with DELSA enabling more detailed insight at much lower computational cost. For example, in the real-world problem the time delay in runoff is the most important parameter in all models, but DELSA shows that for about 20% of parameter sets it is not important at all and alternative mechanisms and parameters dominate. Moreover, the time delay was identified as important in regions producing poor model fits, whereas other parameters were identified as more important in regions of the parameter space producing better model fits. The ability to understand how parameter importance varies through parameter space is critical to inform decisions about, for example, additional data collection and model development. The ability to perform such analyses with modest computational requirements provides exciting opportunities to evaluate complicated models as well as many alternative models.
Published: 2014

50. A physiologically based kinetic model for bacterial sulfide oxidation

Author: Marco de Graaff, Nadine C. Boelee, Pim L. F. van den Bosch, Johannes B.M. Klok, Albert J.H. Janssen, and Karel J. Keesman
Subjects: Bio Process Engineering, Natronobacterium, Hydrogen sulfide, pathways, hydrogen sulfide, oxidatie, parameter-estimation, chemistry.chemical_compound, Bioreactors, Waste Management, hydrogen-sulfide, Oxidizing agent, Organic chemistry, Hydrogen Sulfide, sulfur-oxidizing bacteria, Waste Management and Disposal, Water Science and Technology, chemistry.chemical_classification, mechanisms, afvalwaterbehandeling, biology, afvalwaterbehandelingsinstallaties, Chemistry, Ecological Modeling, Cytochrome c, Quinones, Cytochromes c, alkalibacillus haloalkaliphilus, Nitrogen Cycle, biotechnologie, Pollution, Systems and Control Group, regel- en systeemtechniek, dissolved sodium sulfide, Milieutechnologie, Oxidation-Reduction, microbial physiology, biotechnology, Biotechnology, Environmental Engineering, Sulfide, oxidation, Archaeal Proteins, Quality of nursing and allied health care [NCEBP 6], Kinetics, zwavelwaterstof, waste water treatment plants, Models, Biological, microbiële fysiologie, Bacterial Proteins, soda lakes, ontzwaveling, Bioreactor, Civil and Structural Engineering, Bacterial oxidation, WIMEK, thiosulfate, bioreactors, desulfurization, Leerstoelgroep Meet-, regel- en systeemtechniek, waste water treatment, Chemical engineering, Yield (chemistry), biology.protein, Environmental Technology, biologically produced sulfur, Leerstoelgroep Meet
Abstract: Item does not contain fulltext In the biotechnological process for hydrogen sulfide removal from gas streams, a variety of oxidation products can be formed. Under natron-alkaline conditions, sulfide is oxidized by haloalkaliphilic sulfide oxidizing bacteria via flavocytochrome c oxidoreductase. From previous studies, it was concluded that the oxidation-reduction state of cytochrome c is a direct measure for the bacterial end-product formation. Given this physiological feature, incorporation of the oxidation state of cytochrome c in a mathematical model for the bacterial oxidation kinetics will yield a physiologically based model structure. This paper presents a physiologically based model, describing the dynamic formation of the various end-products in the biodesulfurization process. It consists of three elements: 1) Michaelis-Menten kinetics combined with 2) a cytochrome c driven mechanism describing 3) the rate determining enzymes of the respiratory system of haloalkaliphilic sulfide oxidizing bacteria. The proposed model is successfully validated against independent data obtained from biological respiration tests and bench scale gas-lift reactor experiments. The results demonstrate that the model is a powerful tool to describe product formation for haloalkaliphilic biomass under dynamic conditions. The model predicts a maximum S(0) formation of about 98 mol%. A future challenge is the optimization of this bioprocess by improving the dissolved oxygen control strategy and reactor design.
Published: 2013

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