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1. Enabling End-to-End Secure Federated Learning in Biomedical Research on Heterogeneous Computing Environments with APPFLx

Author: Hoang, Trung-Hieu, Fuhrman, Jordan, Madduri, Ravi, Li, Miao, Chaturvedi, Pranshu, Li, Zilinghan, Kim, Kibaek, Ryu, Minseok, Chard, Ryan, Huerta, E. A., and Giger, Maryellen
Subjects: Computer Science - Distributed, Parallel, and Cluster Computing
Abstract: Facilitating large-scale, cross-institutional collaboration in biomedical machine learning projects requires a trustworthy and resilient federated learning (FL) environment to ensure that sensitive information such as protected health information is kept confidential. In this work, we introduce APPFLx, a low-code FL framework that enables the easy setup, configuration, and running of FL experiments across organizational and administrative boundaries while providing secure end-to-end communication, privacy-preserving functionality, and identity management. APPFLx is completely agnostic to the underlying computational infrastructure of participating clients. We demonstrate the capability of APPFLx as an easy-to-use framework for accelerating biomedical studies across institutions and healthcare systems while maintaining the protection of private medical data in two case studies: (1) predicting participant age from electrocardiogram (ECG) waveforms, and (2) detecting COVID-19 disease from chest radiographs. These experiments were performed securely across heterogeneous compute resources, including a mixture of on-premise high-performance computing and cloud computing, and highlight the role of federated learning in improving model generalizability and performance when aggregating data from multiple healthcare systems. Finally, we demonstrate that APPFLx serves as a convenient and easy-to-use framework for accelerating biomedical studies across institutions and healthcare system while maintaining the protection of private medical data.
Published: 2023

2. Waveform Modelling for the Laser Interferometer Space Antenna

Author: LISA Consortium Waveform Working Group, Afshordi, Niayesh, Akçay, Sarp, Seoane, Pau Amaro, Antonelli, Andrea, Aurrekoetxea, Josu C., Barack, Leor, Barausse, Enrico, Benkel, Robert, Bernard, Laura, Bernuzzi, Sebastiano, Berti, Emanuele, Bonetti, Matteo, Bonga, Béatrice, Bozzola, Gabriele, Brito, Richard, Buonanno, Alessandra, Cárdenas-Avendaño, Alejandro, Casals, Marc, Chernoff, David F., Chua, Alvin J. K., Clough, Katy, Colleoni, Marta, Dhesi, Mekhi, Druart, Adrien, Durkan, Leanne, Faye, Guillaume, Ferguson, Deborah, Field, Scott E., Gabella, William E., García-Bellido, Juan, Gracia-Linares, Miguel, Gerosa, Davide, Green, Stephen R., Haney, Maria, Hannam, Mark, Heffernan, Anna, Hinderer, Tanja, Helfer, Thomas, Hughes, Scott A., Husa, Sascha, Isoyama, Soichiro, Katz, Michael L., Kavanagh, Chris, Khanna, Gaurav, Kidder, Larry E., Korol, Valeriya, Küchler, Lorenzo, Laguna, Pablo, Larrouturou, François, Tiec, Alexandre Le, Leather, Benjamin, Lim, Eugene A., Lim, Hyun, Littenberg, Tyson B., Long, Oliver, Lousto, Carlos O., Lovelace, Geoffrey, Lukes-Gerakopoulos, Georgios, Lynch, Philip, Macedo, Rodrigo P., Markakis, Charalampos, Maggio, Elisa, Mandel, Ilya, Maselli, Andrea, Mathews, Josh, Mourier, Pierre, Neilsen, David, Nagar, Alessandro, Nichols, David A., Novák, Jan, Okounkova, Maria, O'Shaughnessy, Richard, Oshita, Naritaka, O'Toole, Conor, Pan, Zhen, Pani, Paolo, Pappas, George, Paschalidis, Vasileios, Pfeiffer, Harald P., Pompili, Lorenzo, Pound, Adam, Pratten, Geraint, Rüter, Hannes R., Ruiz, Milton, Sam, Zeyd, Sberna, Laura, Shapiro, Stuart L., Shoemaker, Deirdre M., Sopuerta, Carlos F., Spiers, Andrew, Sundar, Hari, Tamanini, Nicola, Thompson, Jonathan E., Toubiana, Alexandre, Tsokaros, Antonios, Upton, Samuel D., van de Meent, Maarten, Vernieri, Daniele, Wachter, Jeremy M., Warburton, Niels, Wardell, Barry, Witek, Helvi, Witzany, Vojtěch, Yang, Huan, Zilhão, Miguel, Albertini, Angelica, Arun, K. G., Bezares, Miguel, Bonilla, Alexander, Chapman-Bird, Christian, Cownden, Bradley, Cunningham, Kevin, Devitt, Chris, Dolan, Sam, Duque, Francisco, Dyson, Conor, Fryer, Chris L., Gair, Jonathan R., Giacomazzo, Bruno, Gupta, Priti, Han, Wen-Biao, Haas, Roland, Hirschmann, Eric W., Huerta, E. A., Jetzer, Philippe, Kelly, Bernard, Khalil, Mohammed, Lewis, Jack, Lloyd-Ronning, Nicole, Marsat, Sylvain, Nardini, Germano, Neef, Jakob, Ottewill, Adrian, Pantelidou, Christiana, Piovano, Gabriel Andres, Redondo-Yuste, Jaime, Sagunski, Laura, Stein, Leo C., Skoupý, Viktor, Sperhake, Ulrich, Speri, Lorenzo, Spieksma, Thomas F. M., Stevens, Chris, Trestini, David, and Vañó-Viñuales, Alex
Subjects: General Relativity and Quantum Cosmology, Astrophysics - High Energy Astrophysical Phenomena
Abstract: LISA, the Laser Interferometer Space Antenna, will usher in a new era in gravitational-wave astronomy. As the first anticipated space-based gravitational-wave detector, it will expand our view to the millihertz gravitational-wave sky, where a spectacular variety of interesting new sources abound: from millions of ultra-compact binaries in our Galaxy, to mergers of massive black holes at cosmological distances; from the beginnings of inspirals that will venture into the ground-based detectors' view to the death spiral of compact objects into massive black holes, and many sources in between. Central to realising LISA's discovery potential are waveform models, the theoretical and phenomenological predictions of the pattern of gravitational waves that these sources emit. This white paper is presented on behalf of the Waveform Working Group for the LISA Consortium. It provides a review of the current state of waveform models for LISA sources, and describes the significant challenges that must yet be overcome., Comment: 239 pages, 11 figures, white paper from the LISA Consortium Waveform Working Group, invited for submission to Living Reviews in Relativity, updated with comments from community
Published: 2023

3. AI ensemble for signal detection of higher order gravitational wave modes of quasi-circular, spinning, non-precessing binary black hole mergers

Author: Tian, Minyang, Huerta, E. A., and Zheng, Huihuo
Subjects: Astrophysics - Instrumentation and Methods for Astrophysics, Computer Science - Artificial Intelligence, General Relativity and Quantum Cosmology, 68T01, 68T35, 83C35, 83C57
Abstract: We introduce spatiotemporal-graph models that concurrently process data from the twin advanced LIGO detectors and the advanced Virgo detector. We trained these AI classifiers with 2.4 million IMRPhenomXPHM waveforms that describe quasi-circular, spinning, non-precessing binary black hole mergers with component masses $m_{\{1,2\}}\in[3M_\odot, 50 M_\odot]$, and individual spins $s^z_{\{1,2\}}\in[-0.9, 0.9]$; and which include the $(\ell, |m|) = \{(2, 2), (2, 1), (3, 3), (3, 2), (4, 4)\}$ modes, and mode mixing effects in the $\ell = 3, |m| = 2$ harmonics. We trained these AI classifiers within 22 hours using distributed training over 96 NVIDIA V100 GPUs in the Summit supercomputer. We then used transfer learning to create AI predictors that estimate the total mass of potential binary black holes identified by all AI classifiers in the ensemble. We used this ensemble, 3 classifiers for signal detection and 2 total mass predictors, to process a year-long test set in which we injected 300,000 signals. This year-long test set was processed within 5.19 minutes using 1024 NVIDIA A100 GPUs in the Polaris supercomputer (for AI inference) and 128 CPU nodes in the ThetaKNL supercomputer (for post-processing of noise triggers), housed at the Argonne Leadership Computing Facility. These studies indicate that our AI ensemble provides state-of-the-art signal detection accuracy, and reports 2 misclassifications for every year of searched data. This is the first AI ensemble designed to search for and find higher order gravitational wave mode signals., Comment: 4 pages, 2 figures, 1 table; v2: 5 pages, 2 figures, 1 table, accepted to NeurIPS 2023 workshop on Machine Learning and the Physical Sciences
Published: 2023

4. FedCompass: Efficient Cross-Silo Federated Learning on Heterogeneous Client Devices using a Computing Power Aware Scheduler

Author: Li, Zilinghan, Chaturvedi, Pranshu, He, Shilan, Chen, Han, Singh, Gagandeep, Kindratenko, Volodymyr, Huerta, E. A., Kim, Kibaek, and Madduri, Ravi
Subjects: Computer Science - Machine Learning, Computer Science - Distributed, Parallel, and Cluster Computing
Abstract: Cross-silo federated learning offers a promising solution to collaboratively train robust and generalized AI models without compromising the privacy of local datasets, e.g., healthcare, financial, as well as scientific projects that lack a centralized data facility. Nonetheless, because of the disparity of computing resources among different clients (i.e., device heterogeneity), synchronous federated learning algorithms suffer from degraded efficiency when waiting for straggler clients. Similarly, asynchronous federated learning algorithms experience degradation in the convergence rate and final model accuracy on non-identically and independently distributed (non-IID) heterogeneous datasets due to stale local models and client drift. To address these limitations in cross-silo federated learning with heterogeneous clients and data, we propose FedCompass, an innovative semi-asynchronous federated learning algorithm with a computing power-aware scheduler on the server side, which adaptively assigns varying amounts of training tasks to different clients using the knowledge of the computing power of individual clients. FedCompass ensures that multiple locally trained models from clients are received almost simultaneously as a group for aggregation, effectively reducing the staleness of local models. At the same time, the overall training process remains asynchronous, eliminating prolonged waiting periods from straggler clients. Using diverse non-IID heterogeneous distributed datasets, we demonstrate that FedCompass achieves faster convergence and higher accuracy than other asynchronous algorithms while remaining more efficient than synchronous algorithms when performing federated learning on heterogeneous clients. The source code for FedCompass is available at https://github.com/APPFL/FedCompass., Comment: Accepted as poster at The Twelfth International Conference on Learning Representations (ICLR 2024)
Published: 2023

5. APPFLx: Providing Privacy-Preserving Cross-Silo Federated Learning as a Service

Author: Li, Zilinghan, He, Shilan, Chaturvedi, Pranshu, Hoang, Trung-Hieu, Ryu, Minseok, Huerta, E. A., Kindratenko, Volodymyr, Fuhrman, Jordan, Giger, Maryellen, Chard, Ryan, Kim, Kibaek, and Madduri, Ravi
Subjects: Computer Science - Machine Learning
Abstract: Cross-silo privacy-preserving federated learning (PPFL) is a powerful tool to collaboratively train robust and generalized machine learning (ML) models without sharing sensitive (e.g., healthcare of financial) local data. To ease and accelerate the adoption of PPFL, we introduce APPFLx, a ready-to-use platform that provides privacy-preserving cross-silo federated learning as a service. APPFLx employs Globus authentication to allow users to easily and securely invite trustworthy collaborators for PPFL, implements several synchronous and asynchronous FL algorithms, streamlines the FL experiment launch process, and enables tracking and visualizing the life cycle of FL experiments, allowing domain experts and ML practitioners to easily orchestrate and evaluate cross-silo FL under one platform. APPFLx is available online at https://appflx.link
Published: 2023

6. APACE: AlphaFold2 and advanced computing as a service for accelerated discovery in biophysics

Author: Park, Hyun, Patel, Parth, Haas, Roland, and Huerta, E. A.
Subjects: Quantitative Biology - Biomolecules, Computer Science - Artificial Intelligence, Computer Science - Distributed, Parallel, and Cluster Computing, I.2
Abstract: The prediction of protein 3D structure from amino acid sequence is a computational grand challenge in biophysics, and plays a key role in robust protein structure prediction algorithms, from drug discovery to genome interpretation. The advent of AI models, such as AlphaFold, is revolutionizing applications that depend on robust protein structure prediction algorithms. To maximize the impact, and ease the usability, of these novel AI tools we introduce APACE, AlphaFold2 and advanced computing as a service, a novel computational framework that effectively handles this AI model and its TB-size database to conduct accelerated protein structure prediction analyses in modern supercomputing environments. We deployed APACE in the Delta and Polaris supercomputers, and quantified its performance for accurate protein structure predictions using four exemplar proteins: 6AWO, 6OAN, 7MEZ, and 6D6U. Using up to 300 ensembles, distributed across 200 NVIDIA A100 GPUs, we found that APACE is up to two orders of magnitude faster than off-the-self AlphaFold2 implementations, reducing time-to-solution from weeks to minutes. This computational approach may be readily linked with robotics laboratories to automate and accelerate scientific discovery., Comment: 7 pages, 4 figures, 2 tables
Published: 2023
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7. Star Cluster Classification using Deep Transfer Learning with PHANGS-HST

Author: Hannon, Stephen, Whitmore, Bradley C., Lee, Janice C., Thilker, David A., Deger, Sinan, Huerta, E. A., Wei, Wei, Mobasher, Bahram, Klessen, Ralf, Boquien, Mederic, Dale, Daniel A., Chevance, Melanie, Grasha, Kathryn, Sanchez-Blazquez, Patricia, Williams, Thomas, Scheuermann, Fabian, Groves, Brent, Kim, Hwihyun, Kruijssen, J. M. Diederick, and Team, the PHANGS-HST
Subjects: Astrophysics - Astrophysics of Galaxies, Astrophysics - Solar and Stellar Astrophysics
Abstract: Currently available star cluster catalogues from HST imaging of nearby galaxies heavily rely on visual inspection and classification of candidate clusters. The time-consuming nature of this process has limited the production of reliable catalogues and thus also post-observation analysis. To address this problem, deep transfer learning has recently been used to create neural network models which accurately classify star cluster morphologies at production scale for nearby spiral galaxies (D < 20 Mpc). Here, we use HST UV-optical imaging of over 20,000 sources in 23 galaxies from the Physics at High Angular Resolution in Nearby GalaxieS (PHANGS) survey to train and evaluate two new sets of models: i) distance-dependent models, based on cluster candidates binned by galaxy distance (9-12 Mpc, 14-18 Mpc, 18-24 Mpc), and ii) distance-independent models, based on the combined sample of candidates from all galaxies. We find that the overall accuracy of both sets of models is comparable to previous automated star cluster classification studies (~60-80 per cent) and show improvement by a factor of two in classifying asymmetric and multi-peaked clusters from PHANGS-HST. Somewhat surprisingly, while we observe a weak negative correlation between model accuracy and galactic distance, we find that training separate models for the three distance bins does not significantly improve classification accuracy. We also evaluate model accuracy as a function of cluster properties such as brightness, colour, and SED-fit age. Based on the success of these experiments, our models will provide classifications for the full set of PHANGS-HST candidate clusters (N ~ 200,000) for public release., Comment: 16 pages, 10 figures
Published: 2023
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8. Physics-inspired spatiotemporal-graph AI ensemble for the detection of higher order wave mode signals of spinning binary black hole mergers

Author: Tian, Minyang, Huerta, E. A., Zheng, Huihuo, and Kumar, Prayush
Subjects: Astrophysics - Instrumentation and Methods for Astrophysics, Computer Science - Artificial Intelligence, General Relativity and Quantum Cosmology, 68T01, 68T35, 83C35, 83C57
Abstract: We present a new class of AI models for the detection of quasi-circular, spinning, non-precessing binary black hole mergers whose waveforms include the higher order gravitational wave modes $(l, |m|)=\{(2, 2), (2, 1), (3, 3), (3, 2), (4, 4)\}$, and mode mixing effects in the $l = 3, |m| = 2$ harmonics. These AI models combine hybrid dilated convolution neural networks to accurately model both short- and long-range temporal sequential information of gravitational waves; and graph neural networks to capture spatial correlations among gravitational wave observatories to consistently describe and identify the presence of a signal in a three detector network encompassing the Advanced LIGO and Virgo detectors. We first trained these spatiotemporal-graph AI models using synthetic noise, using 1.2 million modeled waveforms to densely sample this signal manifold, within 1.7 hours using 256 A100 GPUs in the Polaris supercomputer at the ALCF. Our distributed training approach had optimal performance, and strong scaling up to 512 A100 GPUs. With these AI ensembles we processed data from a three detector network, and found that an ensemble of 4 AI models achieves state-of-the-art performance for signal detection, and reports two misclassifications for every decade of searched data. We distributed AI inference over 128 GPUs in the Polaris supercomputer and 128 nodes in the Theta supercomputer, and completed the processing of a decade of gravitational wave data from a three detector network within 3.5 hours. Finally, we fine-tuned these AI ensembles to process the entire month of February 2020, which is part of the O3b LIGO/Virgo observation run, and found 6 gravitational waves, concurrently identified in Advanced LIGO and Advanced Virgo data, and zero false positives. This analysis was completed in one hour using one A100 GPU., Comment: 14 pages, 6 figures, and 3 tables
Published: 2023
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9. A generative artificial intelligence framework based on a molecular diffusion model for the design of metal-organic frameworks for carbon capture

Author: Park, Hyun, Yan, Xiaoli, Zhu, Ruijie, Huerta, E. A., Chaudhuri, Santanu, Cooper, Donny, Foster, Ian, and Tajkhorshid, Emad
Subjects: Condensed Matter - Materials Science, Computer Science - Artificial Intelligence, I.2
Abstract: Metal-organic frameworks (MOFs) exhibit great promise for CO2 capture. However, finding the best performing materials poses computational and experimental grand challenges in view of the vast chemical space of potential building blocks. Here, we introduce GHP-MOFassemble, a generative artificial intelligence (AI), high performance framework for the rational and accelerated design of MOFs with high CO2 adsorption capacity and synthesizable linkers. GHP-MOFassemble generates novel linkers, assembled with one of three pre-selected metal nodes (Cu paddlewheel, Zn paddlewheel, Zn tetramer) into MOFs in a primitive cubic topology. GHP-MOFassemble screens and validates AI-generated MOFs for uniqueness, synthesizability, structural validity, uses molecular dynamics simulations to study their stability and chemical consistency, and crystal graph neural networks and Grand Canonical Monte Carlo simulations to quantify their CO2 adsorption capacities. We present the top six AI-generated MOFs with CO2 capacities greater than 2 $m mol/g$, i.e., higher than 96.9% of structures in the hypothetical MOF dataset., Comment: 25 pages, 17 figures, 6 tables, accepted to Nature Communications Chemistry. This work was awarded the HPCwire 2023 Editors' Choice Awards for Best Use of High Performance Data Analytics \& Artificial Intelligence see https://www.hpcwire.com/2023-readers-editors-choice-data-analytics-ai/
Published: 2023
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10. FAIR for AI: An interdisciplinary and international community building perspective.

Author: Huerta, E, Blaiszik, Ben, Brinson, L, Bouchard, Kristofer, Diaz, Daniel, Doglioni, Caterina, Emani, Murali, Foster, Ian, Fox, Geoffrey, Harris, Philip, Heinrich, Lukas, Jha, Shantenu, Katz, Daniel, Kindratenko, Volodymyr, Kirkpatrick, Christine, Lassila-Perini, Kati, Madduri, Ravi, Neubauer, Mark, Psomopoulos, Fotis, Roy, Avik, Rübel, Oliver, Zhao, Zhizhen, Zhu, Ruike, and Duarte, Javier
Abstract: A foundational set of findable, accessible, interoperable, and reusable (FAIR) principles were proposed in 2016 as prerequisites for proper data management and stewardship, with the goal of enabling the reusability of scholarly data. The principles were also meant to apply to other digital assets, at a high level, and over time, the FAIR guiding principles have been re-interpreted or extended to include the software, tools, algorithms, and workflows that produce data. FAIR principles are now being adapted in the context of AI models and datasets. Here, we present the perspectives, vision, and experiences of researchers from different countries, disciplines, and backgrounds who are leading the definition and adoption of FAIR principles in their communities of practice, and discuss outcomes that may result from pursuing and incentivizing FAIR AI research. The material for this report builds on the FAIR for AI Workshop held at Argonne National Laboratory on June 7, 2022.
Published: 2023

11. Magnetohydrodynamics with Physics Informed Neural Operators

Author: Rosofsky, Shawn G. and Huerta, E. A.
Subjects: Physics - Computational Physics, Astrophysics - High Energy Astrophysical Phenomena, Computer Science - Machine Learning, I.2.0, J.2
Abstract: The modeling of multi-scale and multi-physics complex systems typically involves the use of scientific software that can optimally leverage extreme scale computing. Despite major developments in recent years, these simulations continue to be computationally intensive and time consuming. Here we explore the use of AI to accelerate the modeling of complex systems at a fraction of the computational cost of classical methods, and present the first application of physics informed neural operators to model 2D incompressible magnetohydrodynamics simulations. Our AI models incorporate tensor Fourier neural operators as their backbone, which we implemented with the TensorLY package. Our results indicate that physics informed neural operators can accurately capture the physics of magnetohydrodynamics simulations that describe laminar flows with Reynolds numbers $Re\leq250$. We also explore the applicability of our AI surrogates for turbulent flows, and discuss a variety of methodologies that may be incorporated in future work to create AI models that provide a computationally efficient and high fidelity description of magnetohydrodynamics simulations for a broad range of Reynolds numbers. The scientific software developed in this project is released with this manuscript., Comment: 32 pages, 24 figures, 1 table. First application of physics informed neural operators to solve magnetohydrodynamics equations, v2: Accepted to Machine Learning: Science and Technology
Published: 2023
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12. End-to-end AI framework for interpretable prediction of molecular and crystal properties

Author: Park, Hyun, Zhu, Ruijie, Huerta, E. A., Chaudhuri, Santanu, Tajkhorshid, Emad, and Cooper, Donny
Subjects: Condensed Matter - Materials Science, Computer Science - Artificial Intelligence, Computer Science - Machine Learning, I.2
Abstract: We introduce an end-to-end computational framework that allows for hyperparameter optimization using the DeepHyper library, accelerated model training, and interpretable AI inference. The framework is based on state-of-the-art AI models including CGCNN, PhysNet, SchNet, MPNN, MPNN-transformer, and TorchMD-NET. We employ these AI models along with the benchmark QM9, hMOF, and MD17 datasets to showcase how the models can predict user-specified material properties within modern computing environments. We demonstrate transferable applications in the modeling of small molecules, inorganic crystals and nanoporous metal organic frameworks with a unified, standalone framework. We have deployed and tested this framework in the ThetaGPU supercomputer at the Argonne Leadership Computing Facility, and in the Delta supercomputer at the National Center for Supercomputing Applications to provide researchers with modern tools to conduct accelerated AI-driven discovery in leadership-class computing environments. We release these digital assets as open source scientific software in GitLab, and ready-to-use Jupyter notebooks in Google Colab., Comment: 20 pages, 10 images, 6 tables; v2: accepted to Machine Learning: Science and Technology
Published: 2022
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13. FAIR AI Models in High Energy Physics

Author: Duarte, Javier, Li, Haoyang, Roy, Avik, Zhu, Ruike, Huerta, E. A., Diaz, Daniel, Harris, Philip, Kansal, Raghav, Katz, Daniel S., Kavoori, Ishaan H., Kindratenko, Volodymyr V., Mokhtar, Farouk, Neubauer, Mark S., Park, Sang Eon, Quinnan, Melissa, Rusack, Roger, and Zhao, Zhizhen
Subjects: High Energy Physics - Experiment, Computer Science - Machine Learning, Physics - Computational Physics
Abstract: The findable, accessible, interoperable, and reusable (FAIR) data principles provide a framework for examining, evaluating, and improving how data is shared to facilitate scientific discovery. Generalizing these principles to research software and other digital products is an active area of research. Machine learning (ML) models -- algorithms that have been trained on data without being explicitly programmed -- and more generally, artificial intelligence (AI) models, are an important target for this because of the ever-increasing pace with which AI is transforming scientific domains, such as experimental high energy physics (HEP). In this paper, we propose a practical definition of FAIR principles for AI models in HEP and describe a template for the application of these principles. We demonstrate the template's use with an example AI model applied to HEP, in which a graph neural network is used to identify Higgs bosons decaying to two bottom quarks. We report on the robustness of this FAIR AI model, its portability across hardware architectures and software frameworks, and its interpretability., Comment: 34 pages, 9 figures, 10 tables
Published: 2022
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14. Numerical relativity higher order gravitational waveforms of eccentric, spinning, non-precessing binary black hole mergers

Author: Joshi, Abhishek V., Rosofsky, Shawn G., Haas, Roland, and Huerta, E. A.
Subjects: General Relativity and Quantum Cosmology
Abstract: We use the open source, community-driven, numerical relativity software, the Einstein Toolkit to study the physics of eccentric, spinning, nonprecessing binary black hole mergers with mass-ratios $q=\{2, 4, 6\}$, individual dimensionless spin parameters $\chi_{1z}=\pm0.6$, $\chi_{2z}=\pm0.3$, that include higher order gravitational wave modes $\ell\leq4$, except for memory modes. Assuming stellar mass binary black hole mergers that may be detectable by the advanced LIGO detectors, we find that including modes up to $\ell=4$ increases the signal-to-noise of compact binaries between $3.5\%$ to $35\%$, compared to signals that only include the $\ell=|m|=2$ mode. We use two waveform models, TEOBResumS and SEOBNRE, which incorporate spin and eccentricity corrections in the waveform dynamics, to quantify the orbital eccentricity of our numerical relativity catalog in a gauge-invariant manner through fitting factor calculations. Our findings indicate that the inclusion of higher order wave modes has a measurable effect in the recovery of moderately and highly eccentric black hole mergers, and thus it is essential to develop waveform models and signal processing tools that accurately describe the physics of these astrophysical sources., Comment: 13 pages, 5 figures, updated manuscript with added waveform comparisons to TEOBResumS. accepted in Phys. Rev. D
Published: 2022
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15. FAIR for AI: An interdisciplinary and international community building perspective

Author: Huerta, E. A., Blaiszik, Ben, Brinson, L. Catherine, Bouchard, Kristofer E., Diaz, Daniel, Doglioni, Caterina, Duarte, Javier M., Emani, Murali, Foster, Ian, Fox, Geoffrey, Harris, Philip, Heinrich, Lukas, Jha, Shantenu, Katz, Daniel S., Kindratenko, Volodymyr, Kirkpatrick, Christine R., Lassila-Perini, Kati, Madduri, Ravi K., Neubauer, Mark S., Psomopoulos, Fotis E., Roy, Avik, Rübel, Oliver, Zhao, Zhizhen, and Zhu, Ruike
Subjects: Computer Science - Computers and Society, Computer Science - Human-Computer Interaction, Computer Science - Machine Learning, High Energy Physics - Experiment, I.2.0, E.0
Abstract: A foundational set of findable, accessible, interoperable, and reusable (FAIR) principles were proposed in 2016 as prerequisites for proper data management and stewardship, with the goal of enabling the reusability of scholarly data. The principles were also meant to apply to other digital assets, at a high level, and over time, the FAIR guiding principles have been re-interpreted or extended to include the software, tools, algorithms, and workflows that produce data. FAIR principles are now being adapted in the context of AI models and datasets. Here, we present the perspectives, vision, and experiences of researchers from different countries, disciplines, and backgrounds who are leading the definition and adoption of FAIR principles in their communities of practice, and discuss outcomes that may result from pursuing and incentivizing FAIR AI research. The material for this report builds on the FAIR for AI Workshop held at Argonne National Laboratory on June 7, 2022., Comment: 10 pages, comments welcome!; v2: 12 pages, accepted to Scientific Data
Published: 2022
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16. MLGWSC-1: The first Machine Learning Gravitational-Wave Search Mock Data Challenge

Author: Schäfer, Marlin B., Zelenka, Ondřej, Nitz, Alexander H., Wang, He, Wu, Shichao, Guo, Zong-Kuan, Cao, Zhoujian, Ren, Zhixiang, Nousi, Paraskevi, Stergioulas, Nikolaos, Iosif, Panagiotis, Koloniari, Alexandra E., Tefas, Anastasios, Passalis, Nikolaos, Salemi, Francesco, Vedovato, Gabriele, Klimenko, Sergey, Mishra, Tanmaya, Brügmann, Bernd, Cuoco, Elena, Huerta, E. A., Messenger, Chris, and Ohme, Frank
Subjects: Astrophysics - Instrumentation and Methods for Astrophysics, Astrophysics - High Energy Astrophysical Phenomena, Computer Science - Machine Learning, General Relativity and Quantum Cosmology
Abstract: We present the results of the first Machine Learning Gravitational-Wave Search Mock Data Challenge (MLGWSC-1). For this challenge, participating groups had to identify gravitational-wave signals from binary black hole mergers of increasing complexity and duration embedded in progressively more realistic noise. The final of the 4 provided datasets contained real noise from the O3a observing run and signals up to a duration of 20 seconds with the inclusion of precession effects and higher order modes. We present the average sensitivity distance and runtime for the 6 entered algorithms derived from 1 month of test data unknown to the participants prior to submission. Of these, 4 are machine learning algorithms. We find that the best machine learning based algorithms are able to achieve up to 95% of the sensitive distance of matched-filtering based production analyses for simulated Gaussian noise at a false-alarm rate (FAR) of one per month. In contrast, for real noise, the leading machine learning search achieved 70%. For higher FARs the differences in sensitive distance shrink to the point where select machine learning submissions outperform traditional search algorithms at FARs $\geq 200$ per month on some datasets. Our results show that current machine learning search algorithms may already be sensitive enough in limited parameter regions to be useful for some production settings. To improve the state-of-the-art, machine learning algorithms need to reduce the false-alarm rates at which they are capable of detecting signals and extend their validity to regions of parameter space where modeled searches are computationally expensive to run. Based on our findings we compile a list of research areas that we believe are the most important to elevate machine learning searches to an invaluable tool in gravitational-wave signal detection., Comment: 25 pages, 6 figures, 4 tables, additional material available at https://github.com/gwastro/ml-mock-data-challenge-1
Published: 2022
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17. FAIR principles for AI models with a practical application for accelerated high energy diffraction microscopy

Author: Ravi, Nikil, Chaturvedi, Pranshu, Huerta, E. A., Liu, Zhengchun, Chard, Ryan, Scourtas, Aristana, Schmidt, K. J., Chard, Kyle, Blaiszik, Ben, and Foster, Ian
Subjects: Computer Science - Artificial Intelligence, Condensed Matter - Materials Science, Computer Science - Machine Learning, 68T01, 68T05, I.2, J.2
Abstract: A concise and measurable set of FAIR (Findable, Accessible, Interoperable and Reusable) principles for scientific data is transforming the state-of-practice for data management and stewardship, supporting and enabling discovery and innovation. Learning from this initiative, and acknowledging the impact of artificial intelligence (AI) in the practice of science and engineering, we introduce a set of practical, concise, and measurable FAIR principles for AI models. We showcase how to create and share FAIR data and AI models within a unified computational framework combining the following elements: the Advanced Photon Source at Argonne National Laboratory, the Materials Data Facility, the Data and Learning Hub for Science, and funcX, and the Argonne Leadership Computing Facility (ALCF), in particular the ThetaGPU supercomputer and the SambaNova DataScale system at the ALCF AI Testbed. We describe how this domain-agnostic computational framework may be harnessed to enable autonomous AI-driven discovery., Comment: 11 pages, 3 figures; Accepted to Scientific Data; for press release see https://www.anl.gov/article/argonne-scientists-promote-fair-standards-for-managing-artificial-intelligence-models and https://www.ncsa.illinois.edu/ncsa-student-researchers-lead-authors-on-award-winning-paper; Received 2022 HPCwire Readers' Choice Award on Best Use of High Performance Data Analytics & Artificial Intelligence
Published: 2022
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18. Applications of physics informed neural operators

Author: Rosofsky, Shawn G., Majed, Hani Al, and Huerta, E. A.
Subjects: Physics - Computational Physics, Astrophysics - Instrumentation and Methods for Astrophysics, Computer Science - Artificial Intelligence, Computer Science - Machine Learning, General Relativity and Quantum Cosmology
Abstract: We present an end-to-end framework to learn partial differential equations that brings together initial data production, selection of boundary conditions, and the use of physics-informed neural operators to solve partial differential equations that are ubiquitous in the study and modeling of physics phenomena. We first demonstrate that our methods reproduce the accuracy and performance of other neural operators published elsewhere in the literature to learn the 1D wave equation and the 1D Burgers equation. Thereafter, we apply our physics-informed neural operators to learn new types of equations, including the 2D Burgers equation in the scalar, inviscid and vector types. Finally, we show that our approach is also applicable to learn the physics of the 2D linear and nonlinear shallow water equations, which involve three coupled partial differential equations. We release our artificial intelligence surrogates and scientific software to produce initial data and boundary conditions to study a broad range of physically motivated scenarios. We provide the source code, an interactive website to visualize the predictions of our physics informed neural operators, and a tutorial for their use at the Data and Learning Hub for Science., Comment: 15 pages, 12 figures
Published: 2022
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19. Interpreting a Machine Learning Model for Detecting Gravitational Waves

Author: Safarzadeh, Mohammadtaher, Khan, Asad, Huerta, E. A., and Wattenberg, Martin
Subjects: General Relativity and Quantum Cosmology, Astrophysics - High Energy Astrophysical Phenomena, Computer Science - Artificial Intelligence
Abstract: We describe a case study of translational research, applying interpretability techniques developed for computer vision to machine learning models used to search for and find gravitational waves. The models we study are trained to detect black hole merger events in non-Gaussian and non-stationary advanced Laser Interferometer Gravitational-wave Observatory (LIGO) data. We produced visualizations of the response of machine learning models when they process advanced LIGO data that contains real gravitational wave signals, noise anomalies, and pure advanced LIGO noise. Our findings shed light on the responses of individual neurons in these machine learning models. Further analysis suggests that different parts of the network appear to specialize in local versus global features, and that this difference appears to be rooted in the branched architecture of the network as well as noise characteristics of the LIGO detectors. We believe efforts to whiten these "black box" models can suggest future avenues for research and help inform the design of interpretable machine learning models for gravitational wave astrophysics., Comment: 19 pages, to be submitted, comments are welcome. Movies based on this work can be accessed via: https://www.youtube.com/watch?v=SXFGMOtJwn0 https://www.youtube.com/watch?v=itVCj9gpmAs
Published: 2022

20. Inference-optimized AI and high performance computing for gravitational wave detection at scale

Author: Chaturvedi, Pranshu, Khan, Asad, Tian, Minyang, Huerta, E. A., and Zheng, Huihuo
Subjects: General Relativity and Quantum Cosmology, Astrophysics - Instrumentation and Methods for Astrophysics, Computer Science - Artificial Intelligence, Computer Science - Distributed, Parallel, and Cluster Computing, Computer Science - Machine Learning, 68T10, 85-08, 83C35, 83C57, I.2
Abstract: We introduce an ensemble of artificial intelligence models for gravitational wave detection that we trained in the Summit supercomputer using 32 nodes, equivalent to 192 NVIDIA V100 GPUs, within 2 hours. Once fully trained, we optimized these models for accelerated inference using NVIDIA TensorRT. We deployed our inference-optimized AI ensemble in the ThetaGPU supercomputer at Argonne Leadership Computer Facility to conduct distributed inference. Using the entire ThetaGPU supercomputer, consisting of 20 nodes each of which has 8 NVIDIA A100 Tensor Core GPUs and 2 AMD Rome CPUs, our NVIDIA TensorRT-optimized AI ensemble processed an entire month of advanced LIGO data (including Hanford and Livingston data streams) within 50 seconds. Our inference-optimized AI ensemble retains the same sensitivity of traditional AI models, namely, it identifies all known binary black hole mergers previously identified in this advanced LIGO dataset and reports no misclassifications, while also providing a 3X inference speedup compared to traditional artificial intelligence models. We used time slides to quantify the performance of our AI ensemble to process up to 5 years worth of advanced LIGO data. In this synthetically enhanced dataset, our AI ensemble reports an average of one misclassification for every month of searched advanced LIGO data. We also present the receiver operating characteristic curve of our AI ensemble using this 5 year long advanced LIGO dataset. This approach provides the required tools to conduct accelerated, AI-driven gravitational wave detection at scale., Comment: 19 pages, 8 figures; v2. Accepted to Frontiers in Artificial Intelligence, Special Issue: Efficient AI in Particle Physics and Astrophysics
Published: 2022
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21. Crop diversification practices in Europe: an economic cross-case study comparison

Author: Zabala, J. A., Martínez-García, V., Martínez-Paz, J. M., López-Becerra, E. I., Nasso, M., Díaz-Pereira, E., Sánchez-Navarro, V., Álvaro-Fuentes, J., González-Rosado, M., Farina, R., Di Bene, C., Huerta, E., Jurrius, A., Frey-Treseler, K., Lóczy, D., Fosci, L., Blasi, E., Lehtonen, H., and Alcon, F.
Published: 2023
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22. AI and extreme scale computing to learn and infer the physics of higher order gravitational wave modes of quasi-circular, spinning, non-precessing binary black hole mergers

Author: Khan, Asad, Huerta, E. A., and Kumar, Prayush
Subjects: Astrophysics - Instrumentation and Methods for Astrophysics, Computer Science - Artificial Intelligence, General Relativity and Quantum Cosmology, 68T10, 85-08, 83C35, 83C57, I.2
Abstract: We use artificial intelligence (AI) to learn and infer the physics of higher order gravitational wave modes of quasi-circular, spinning, non precessing binary black hole mergers. We trained AI models using 14 million waveforms, produced with the surrogate model NRHybSur3dq8, that include modes up to $\ell \leq 4$ and $(5,5)$, except for $(4,0)$ and $(4,1)$, that describe binaries with mass-ratios $q\leq8$, individual spins $s^z_{\{1,2\}}\in[-0.8, 0.8]$, and inclination angle $\theta\in[0,\pi]$.Our probabilistic AI surrogates can accurately constrain the mass-ratio, individual spins, effective spin, and inclination angle of numerical relativity waveforms that describe such signal manifold. We compared the predictions of our AI models with Gaussian process regression, random forest, k-nearest neighbors, and linear regression, and with traditional Bayesian inference methods through the PyCBC Inference toolkit, finding that AI outperforms all these approaches in terms of accuracy, and are between three to four orders of magnitude faster than traditional Bayesian inference methods. Our AI surrogates were trained within 3.4 hours using distributed training on 1,536 NVIDIA V100 GPUs in the Summit supercomputer., Comment: 22 pages, 12 figures
Published: 2021
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23. Interpretable AI forecasting for numerical relativity waveforms of quasi-circular, spinning, non-precessing binary black hole mergers

Author: Khan, Asad, Huerta, E. A., and Zheng, Huihuo
Subjects: General Relativity and Quantum Cosmology, Astrophysics - Instrumentation and Methods for Astrophysics, Computer Science - Artificial Intelligence, 68T10, 85-08, 83C35, 83C57, I.2
Abstract: We present a deep-learning artificial intelligence model that is capable of learning and forecasting the late-inspiral, merger and ringdown of numerical relativity waveforms that describe quasi-circular, spinning, non-precessing binary black hole mergers. We used the NRHybSur3dq8 surrogate model to produce train, validation and test sets of $\ell=|m|=2$ waveforms that cover the parameter space of binary black hole mergers with mass-ratios $q\leq8$ and individual spins $|s^z_{\{1,2\}}| \leq 0.8$. These waveforms cover the time range $t\in[-5000\textrm{M}, 130\textrm{M}]$, where $t=0M$ marks the merger event, defined as the maximum value of the waveform amplitude. We harnessed the ThetaGPU supercomputer at the Argonne Leadership Computing Facility to train our AI model using a training set of 1.5 million waveforms. We used 16 NVIDIA DGX A100 nodes, each consisting of 8 NVIDIA A100 Tensor Core GPUs and 2 AMD Rome CPUs, to fully train our model within 3.5 hours. Our findings show that artificial intelligence can accurately forecast the dynamical evolution of numerical relativity waveforms in the time range $t\in[-100\textrm{M}, 130\textrm{M}]$. Sampling a test set of 190,000 waveforms, we find that the average overlap between target and predicted waveforms is $\gtrsim99\%$ over the entire parameter space under consideration. We also combined scientific visualization and accelerated computing to identify what components of our model take in knowledge from the early and late-time waveform evolution to accurately forecast the latter part of numerical relativity waveforms. This work aims to accelerate the creation of scalable, computationally efficient and interpretable artificial intelligence models for gravitational wave astrophysics., Comment: 15 pages, 7 figures, 2 appendices, 1 interactive visualization at https://khanx169.github.io/gw_forecasting/interactive_results.html
Published: 2021
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24. Acute phase characteristics and long-term complications of pulmonary embolism in COVID-19 compared to non-COVID-19 cohort: a large single-centre study

Author: Franco-Moreno, A., Brown-Lavalle, D., Campos-Arenas, M., Rodríguez-Ramírez, N., Muñoz-Roldán, C., Rubio-Aguilera, A. I., Muñoz-Rivas, N., de Girón, J. Bascuñana-Morejón, Fernández-Vidal, E., Palma-Huerta, E., Estévez-Alonso, S., Rodríguez-Gómez, B., Manzano-Valera, S., Pedrero-Tomé, R., Casado-Suela, M., Bibiano-Guillén, C., Mir-Montero, M., Torres-Macho, J., and Bustamante-Fermosel, A.
Published: 2023
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25. A FAIR and AI-ready Higgs boson decay dataset

Author: Chen, Yifan, Huerta, E. A., Duarte, Javier, Harris, Philip, Katz, Daniel S., Neubauer, Mark S., Diaz, Daniel, Mokhtar, Farouk, Kansal, Raghav, Park, Sang Eon, Kindratenko, Volodymyr V., Zhao, Zhizhen, and Rusack, Roger
Subjects: High Energy Physics - Experiment, Computer Science - Artificial Intelligence, Computer Science - Databases, High Energy Physics - Phenomenology, I.2, J.2
Abstract: To enable the reusability of massive scientific datasets by humans and machines, researchers aim to adhere to the principles of findability, accessibility, interoperability, and reusability (FAIR) for data and artificial intelligence (AI) models. This article provides a domain-agnostic, step-by-step assessment guide to evaluate whether or not a given dataset meets these principles. We demonstrate how to use this guide to evaluate the FAIRness of an open simulated dataset produced by the CMS Collaboration at the CERN Large Hadron Collider. This dataset consists of Higgs boson decays and quark and gluon background, and is available through the CERN Open Data Portal. We use additional available tools to assess the FAIRness of this dataset, and incorporate feedback from members of the FAIR community to validate our results. This article is accompanied by a Jupyter notebook to visualize and explore this dataset. This study marks the first in a planned series of articles that will guide scientists in the creation of FAIR AI models and datasets in high energy particle physics., Comment: 13 pages, 3 figures. v2: Accepted to Nature Scientific Data. Learn about the FAIR4HEP project at https://fair4hep.github.io. See our invited Behind the Paper Blog in Springer Nature Research Data Community at https://go.nature.com/3oMVYxo
Published: 2021
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26. Star Cluster Classification in the PHANGS-HST Survey: Comparison between Human and Machine Learning Approaches

Author: Whitmore, Bradley C., Lee, Janice C., Chandar, Rupali, Thilker, David A., Hannon, Stephen, Wei, Wei, Huerta, E. A., Bigiel, Frank, Boquien, Médéric, Chevance, Mélanie, Dale, Daniel A., Deger, Sinan, Grasha, Kathryn, Klessen, Ralf S., Kruijssen, J. M. Diederik, Larson, Kirsten L., Mok, Angus, Rosolowsky, Erik, Schinnerer, Eva, Schruba, Andreas, Ubeda, Leonardo, Van Dyk, Schuyler D., Watkins, Elizabeth, and Williams, Thomas
Subjects: Astrophysics - Astrophysics of Galaxies
Abstract: When completed, the PHANGS-HST project will provide a census of roughly 50,000 compact star clusters and associations, as well as human morphological classifications for roughly 20,000 of those objects. These large numbers motivated the development of a more objective and repeatable method to help perform source classifications. In this paper we consider the results for five PHANGS-HST galaxies (NGC 628, NGC 1433, NGC 1566, NGC 3351, NGC 3627) using classifications from two convolutional neural network architectures (RESNET and VGG) trained using deep transfer learning techniques. The results are compared to classifications performed by humans. The primary result is that the neural network classifications are comparable in quality to the human classifications with typical agreement around 70 to 80$\%$ for Class 1 clusters (symmetric, centrally concentrated) and 40 to 70$\%$ for Class 2 clusters (asymmetric, centrally concentrated). If Class 1 and 2 are considered together the agreement is 82 $\pm$ 3$\%$. Dependencies on magnitudes, crowding, and background surface brightness are examined. A detailed description of the criteria and methodology used for the human classifications is included along with an examination of systematic differences between PHANGS-HST and LEGUS. The distribution of data points in a colour-colour diagram is used as a 'figure of merit' to further test the relative performances of the different methods. The effects on science results (e.g., determinations of mass and age functions) of using different cluster classification methods are examined and found to be minimal., Comment: 28 pages, 25 figures. Accepted for publication in Monthly Notices of the Royal Astronomical Society. Version with full resolution figures found at https://sites.google.com/view/phangs/publications
Published: 2021
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27. All-sky Search for Continuous Gravitational Waves from Isolated Neutron Stars in the Early O3 LIGO Data

Author: The LIGO Scientific Collaboration, the Virgo Collaboration, the KAGRA Collaboration, Abbott, R., Abbott, T. D., Abraham, S., Acernese, F., Ackley, K., Adams, A., Adams, C., Adhikari, R. X., Adya, V. B., Affeldt, C., Agarwal, D., Agathos, M., Agatsuma, K., Aggarwal, N., Aguiar, O. D., Aiello, L., Ain, A., Ajith, P., Akutsu, T., Aleman, K. M., Allen, G., Allocca, A., Altin, P. A., Amato, A., Anand, S., Ananyeva, A., Anderson, S. B., Anderson, W. G., Ando, M., Angelova, S. V., Ansoldi, S., Antelis, J. M., Antier, S., Appert, S., Arai, Koya, Arai, Koji, Arai, Y., Araki, S., Araya, A., Araya, M. C., Areeda, J. S., Arène, M., Aritomi, N., Arnaud, N., Aronson, S. M., Asada, H., Asali, Y., Ashton, G., Aso, Y., Aston, S. M., Astone, P., Aubin, F., Aufmuth, P., AultONeal, K., Austin, C., Babak, S., Badaracco, F., Bader, M. K. M., Bae, S., Bae, Y., Baer, A. M., Bagnasco, S., Bai, Y., Baiotti, L., Baird, J., Bajpai, R., Ball, M., Ballardin, G., Ballmer, S. W., Bals, M., Balsamo, A., Baltus, G., Banagiri, S., Bankar, D., Bankar, R. S., Barayoga, J. C., Barbieri, C., Barish, B. C., Barker, D., Barneo, P., Barone, F., Barr, B., Barsotti, L., Barsuglia, M., Barta, D., Bartlett, J., Barton, M. A., Bartos, I., Bassiri, R., Basti, A., Bawaj, M., Bayley, J. C., Baylor, A. C., Bazzan, M., Bécsy, B., Bedakihale, V. M., Bejger, M., Belahcene, I., Benedetto, V., Beniwal, D., Benjamin, M. G., Bennett, T. F., Bentley, J. D., BenYaala, M., Bergamin, F., Berger, B. K., Bernuzzi, S., Bersanetti, D., Bertolini, A., Betzwieser, J., Bhandare, R., Bhandari, A. V., Bhattacharjee, D., Bhaumik, S., Bidler, J., Bilenko, I. A., Billingsley, G., Birney, R., Birnholtz, O., Biscans, S., Bischi, M., Biscoveanu, S., Bisht, A., Biswas, B., Bitossi, M., Bizouard, M. -A., Blackburn, J. K., Blackman, J., Blair, C. D., Blair, D. G., Blair, R. M., Bobba, F., Bode, N., Boer, M., Bogaert, G., Boldrini, M., Bondu, F., Bonilla, E., Bonnand, R., Booker, P., Boom, B. A., Bork, R., Boschi, V., Bose, N., Bose, S., Bossilkov, V., Boudart, V., Bouffanais, Y., Bozzi, A., Bradaschia, C., Brady, P. R., Bramley, A., Branch, A., Branchesi, M., Brau, J. E., Breschi, M., Briant, T., Briggs, J. H., Brillet, A., Brinkmann, M., Brockill, P., Brooks, A. F., Brooks, J., Brown, D. D., Brunett, S., Bruno, G., Bruntz, R., Bryant, J., Buikema, A., Bulik, T., Bulten, H. J., Buonanno, A., Buscicchio, R., Buskulic, D., Byer, R. L., Cadonati, L., Caesar, M., Cagnoli, G., Cahillane, C., Cain III, H. W., Bustillo, J. Calderón, Callaghan, J. D., Callister, T. A., Calloni, E., Camp, J. B., Canepa, M., Cannavacciuolo, M., Cannon, K. C., Cao, H., Cao, J., Cao, Z., Capocasa, E., Capote, E., Carapella, G., Carbognani, F., Carlin, J. B., Carney, M. F., Carpinelli, M., Carullo, G., Carver, T. L., Diaz, J. Casanueva, Casentini, C., Castaldi, G., Caudill, S., Cavaglià, M., Cavalier, F., Cavalieri, R., Cella, G., Cerdá-Durán, P., Cesarini, E., Chaibi, W., Chakravarti, K., Champion, B., Chan, C. -H., Chan, C., Chan, C. L., Chan, M., Chandra, K., Chanial, P., Chao, S., Charlton, P., Chase, E. A., Chassande-Mottin, E., Chatterjee, D., Chaturvedi, M., Chen, A., Chen, C., Chen, H. Y., Chen, J., Chen, K., Chen, X., Chen, Y. -B., Chen, Y. -R., Chen, Z., Cheng, H., Cheong, C. K., Cheung, H. Y., Chia, H. Y., Chiadini, F., Chiang, C-Y., Chierici, R., Chincarini, A., Chiofalo, M. L., Chiummo, A., Cho, G., Cho, H. S., Choate, S., Choudhary, R. K., Choudhary, S., Christensen, N., Chu, H., Chu, Q., Chu, Y-K., Chua, S., Chung, K. W., Ciani, G., Ciecielag, P., Cieślar, M., Cifaldi, M., Ciobanu, A. A., Ciolfi, R., Cipriano, F., Cirone, A., Clara, F., Clark, E. N., Clark, J. A., Clarke, L., Clearwater, P., Clesse, S., Cleva, F., Coccia, E., Cohadon, P. -F., Cohen, D. E., Cohen, L., Colleoni, M., Collette, C. G., Colpi, M., Compton, C. M., Constancio Jr., M., Conti, L., Cooper, S. J., Corban, P., Corbitt, T. R., Cordero-Carrión, I., Corezzi, S., Corley, K. R., Cornish, N., Corre, D., Corsi, A., Cortese, S., Costa, C. A., Cotesta, R., Coughlin, M. W., Coughlin, S. B., Coulon, J. -P., Countryman, S. T., Cousins, B., Couvares, P., Covas, P. B., Coward, D. M., Cowart, M. J., Coyne, D. C., Coyne, R., Creighton, J. D. E., Creighton, T. D., Criswell, A. W., Croquette, M., Crowder, S. G., Cudell, J. R., Cullen, T. J., Cumming, A., Cummings, R., Cuoco, E., Curyło, M., Canton, T. Dal, Dálya, G., Dana, A., DaneshgaranBajastani, L. M., D'Angelo, B., Danilishin, S. L., D'Antonio, S., Danzmann, K., Darsow-Fromm, C., Dasgupta, A., Datrier, L. E. H., Dattilo, V., Dave, I., Davier, M., Davies, G. S., Davis, D., Daw, E. J., Dean, R., DeBra, D., Deenadayalan, M., Degallaix, J., De Laurentis, M., Deléglise, S., Del Favero, V., De Lillo, F., De Lillo, N., Del Pozzo, W., DeMarchi, L. M., De Matteis, F., D'Emilio, V., Demos, N., Dent, T., Depasse, A., De Pietri, R., De Rosa, R., De Rossi, C., DeSalvo, R., De Simone, R., Dhurandhar, S., Díaz, M. C., Diaz-Ortiz Jr., M., Didio, N. A., Dietrich, T., Di Fiore, L., Di Fronzo, C., Di Giorgio, C., Di Giovanni, F., Di Girolamo, T., Di Lieto, A., Ding, B., Di Pace, S., Di Palma, I., Di Renzo, F., Divakarla, A. K., Dmitriev, A., Doctor, Z., D'Onofrio, L., Donovan, F., Dooley, K. L., Doravari, S., Dorrington, I., Drago, M., Driggers, J. C., Drori, Y., Du, Z., Ducoin, J. -G., Dupej, P., Durante, O., D'Urso, D., Duverne, P. -A., Dwyer, S. E., Easter, P. J., Ebersold, M., Eddolls, G., Edelman, B., Edo, T. B., Edy, O., Effler, A., Eguchi, S., Eichholz, J., Eikenberry, S. S., Eisenmann, M., Eisenstein, R. A., Ejlli, A., Enomoto, Y., Errico, L., Essick, R. C., Estellés, H., Estevez, D., Etienne, Z., Etzel, T., Evans, M., Evans, T. M., Ewing, B. E., Fafone, V., Fair, H., Fairhurst, S., Fan, X., Farah, A. M., Farinon, S., Farr, B., Farr, W. M., Farrow, N. W., Fauchon-Jones, E. J., Favata, M., Fays, M., Fazio, M., Feicht, J., Fejer, M. M., Feng, F., Fenyvesi, E., Ferguson, D. L., Fernandez-Galiana, A., Ferrante, I., Ferreira, T. A., Fidecaro, F., Figura, P., Fiori, I., Fishbach, M., Fisher, R. P., Fittipaldi, R., Fiumara, V., Flaminio, R., Floden, E., Flynn, E., Fong, H., Font, J. A., Fornal, B., Forsyth, P. W. F., Franke, A., Frasca, S., Frasconi, F., Frederick, C., Frei, Z., Freise, A., Frey, R., Fritschel, P., Frolov, V. V., Fronzé, G. G., Fujii, Y., Fujikawa, Y., Fukunaga, M., Fukushima, M., Fulda, P., Fyffe, M., Gabbard, H. A., Gadre, B. U., Gaebel, S. M., Gair, J. R., Gais, J., Galaudage, S., Gamba, R., Ganapathy, D., Ganguly, A., Gao, D., Gaonkar, S. G., Garaventa, B., García-Núñez, C., García-Quirós, C., Garufi, F., Gateley, B., Gaudio, S., Gayathri, V., Ge, G., Gemme, G., Gennai, A., George, J., Gergely, L., Gewecke, P., Ghonge, S., Ghosh, Abhirup., Ghosh, Archisman, Ghosh, Shaon, Ghosh, Shrobana, Ghosh, Sourath, Giacomazzo, B., Giacoppo, L., Giaime, J. A., Giardina, K. D., Gibson, D. R., Gier, C., Giesler, M., Giri, P., Gissi, F., Glanzer, J., Gleckl, A. E., Godwin, P., Goetz, E., Goetz, R., Gohlke, N., Goncharov, B., González, G., Gopakumar, A., 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. C., Green, R., Gretarsson, A. M., Gretarsson, E. M., Griffith, D., Griffiths, W., Griggs, H. L., Grignani, G., Grimaldi, A., Grimes, E., Grimm, S. J., Grote, H., Grunewald, S., Gruning, P., Guerrero, J. G., Guidi, G. M., Guimaraes, A. R., Guixé, G., Gulati, H. K., Guo, H. -K., Guo, Y., Gupta, Anchal, Gupta, Anuradha, Gupta, P., Gustafson, E. K., Gustafson, R., Guzman, F., Ha, S., Haegel, L., Hagiwara, A., Haino, S., Halim, O., Hall, E. D., Hamilton, E. Z., Hammond, G., Han, W. -B., Haney, M., Hanks, J., Hanna, C., Hannam, M. D., Hannuksela, O. A., Hansen, H., Hansen, T. J., Hanson, J., Harder, T., Hardwick, T., Haris, K., Harms, J., Harry, G. M., Harry, I. W., Hartwig, D., Hasegawa, K., Haskell, B., Hasskew, R. K., Haster, C. -J., Hattori, K., Haughian, K., Hayakawa, H., Hayama, K., Hayes, F. J., Healy, J., Heidmann, A., Heintze, M. C., Heinze, J., Heinzel, J., Heitmann, H., Hellman, F., Hello, P., Helmling-Cornell, A. F., Hemming, G., Hendry, M., Heng, I. S., Hennes, E., Hennig, J., Hennig, M. H., Vivanco, F. Hernandez, Heurs, M., Hild, S., Hill, P., Himemoto, Y., Hines, A. S., Hiranuma, Y., Hirata, N., Hirose, E., Hochheim, S., Hofman, D., Hohmann, J. N., Holgado, A. M., Holland, N. A., Hollows, I. J., Holmes, Z. J., Holt, K., Holz, D. 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Subjects: General Relativity and Quantum Cosmology, Astrophysics - High Energy Astrophysical Phenomena
Abstract: We report on an all-sky search for continuous gravitational waves in the frequency band 20-2000\,Hz and with a frequency time derivative in the range of $[-1.0, +0.1]\times10^{-8}$\,Hz/s. Such a signal could be produced by a nearby, spinning and slightly non-axisymmetric isolated neutron star in our galaxy. This search uses the LIGO data from the first six months of Advanced LIGO's and Advanced Virgo's third observational run, O3. No periodic gravitational wave signals are observed, and 95\%\ confidence-level (CL) frequentist upper limits are placed on their strengths. The lowest upper limits on worst-case (linearly polarized) strain amplitude $h_0$ are $~1.7\times10^{-25}$ near 200\,Hz. For a circularly polarized source (most favorable orientation), the lowest upper limits are $\sim6.3\times10^{-26}$. These strict frequentist upper limits refer to all sky locations and the entire range of frequency derivative values. For a population-averaged ensemble of sky locations and stellar orientations, the lowest 95\%\ CL upper limits on the strain amplitude are $\sim1.\times10^{-25}$. These upper limits improve upon our previously published all-sky results, with the greatest improvement (factor of $\sim$2) seen at higher frequencies, in part because quantum squeezing has dramatically improved the detector noise level relative to the second observational run, O2. These limits are the most constraining to date over most of the parameter space searched., Comment: 28 pages, 7 figures
Published: 2021
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28. Observation of gravitational waves from two neutron star-black hole coalescences

Author: The LIGO Scientific Collaboration, the Virgo Collaboration, the KAGRA Collaboration, Abbott, R., Abbott, T. D., Abraham, S., Acernese, F., Ackley, K., Adams, A., Adams, C., Adhikari, R. X., Adya, V. B., Affeldt, C., Agarwal, D., Agathos, M., Agatsuma, K., Aggarwal, N., Aguiar, O. D., Aiello, L., Ain, A., Ajith, P., Akutsu, T., Aleman, K. M., Allen, G., Allocca, A., Altin, P. A., Amato, A., Anand, S., Ananyeva, A., Anderson, S. B., Anderson, W. G., Ando, M., Angelova, S. V., Ansoldi, S., Antelis, J. M., Antier, S., Appert, S., Arai, Koya, Arai, Koji, Arai, Y., Araki, S., Araya, A., Araya, M. C., Areeda, J. S., Arène, M., Aritomi, N., Arnaud, N., Aronson, S. M., Arun, K. G., Asada, H., Asali, Y., Ashton, G., Aso, Y., Aston, S. M., Astone, P., Aubin, F., Aufmuth, P., AultONeal, K., Austin, C., Babak, S., Badaracco, F., Bader, M. K. M., Bae, S., Bae, Y., Baer, A. M., Bagnasco, S., Bai, Y., Baiotti, L., Baird, J., Bajpai, R., Ball, M., Ballardin, G., Ballmer, S. W., Bals, M., Balsamo, A., Baltus, G., Banagiri, S., Bankar, D., Bankar, R. S., Barayoga, J. C., Barbieri, C., Barish, B. C., Barker, D., Barneo, P., Barone, F., Barr, B., Barsotti, L., Barsuglia, M., Barta, D., Bartlett, J., Barton, M. A., Bartos, I., Bassiri, R., Basti, A., Bawaj, M., Bayley, J. C., Baylor, A. C., Bazzan, M., Bécsy, B., Bedakihale, V. M., Bejger, M., Belahcene, I., Benedetto, V., Beniwal, D., Benjamin, M. G., Benkel, R., Bennett, T. F., Bentley, J. D., BenYaala, M., Bergamin, F., Berger, B. K., Bernuzzi, S., Berry, C. P. L., Bersanetti, D., Bertolini, A., Betzwieser, J., Bhandare, R., Bhandari, A. V., Bhattacharjee, D., Bhaumik, S., Bidler, J., Bilenko, I. A., Billingsley, G., Birney, R., Birnholtz, O., Biscans, S., Bischi, M., Biscoveanu, S., Bisht, A., Biswas, B., Bitossi, M., Bizouard, M. -A., Blackburn, J. K., Blackman, J., Blair, C. D., Blair, D. G., Blair, R. M., Bobba, F., Bode, N., Boer, M., Bogaert, G., Boldrini, M., Bondu, F., Bonilla, E., Bonnand, R., Booker, P., Boom, B. A., Bork, R., Boschi, V., Bose, N., Bose, S., Bossilkov, V., Boudart, V., Bouffanais, Y., Bozzi, A., Bradaschia, C., Brady, P. R., Bramley, A., Branch, A., Branchesi, M., Brau, J. E., Breschi, M., Briant, T., Briggs, J. H., Brillet, A., Brinkmann, M., Brockill, P., Brooks, A. F., Brooks, J., Brown, D. D., Brunett, S., Bruno, G., Bruntz, R., Bryant, J., Buikema, A., Bulik, T., Bulten, H. J., Buonanno, A., Buscicchio, R., Buskulic, D., Byer, R. L., Cadonati, L., Caesar, M., Cagnoli, G., Cahillane, C., Cain III, H. W., Bustillo, J. Calderón, Callaghan, J. D., Callister, T. A., Calloni, E., Camp, J. B., Canepa, M., Cannavacciuolo, M., Cannon, K. C., Cao, H., Cao, J., Cao, Z., Capocasa, E., Capote, E., Carapella, G., Carbognani, F., Carlin, J. B., Carney, M. F., Carpinelli, M., Carullo, G., Carver, T. L., Diaz, J. Casanueva, Casentini, C., Castaldi, G., Caudill, S., Cavaglià, M., Cavalier, F., Cavalieri, R., Cella, G., Cerdá-Durán, P., Cesarini, E., Chaibi, W., Chakravarti, K., Champion, B., Chan, C. -H., Chan, C., Chan, C. L., Chan, M., Chandra, K., Chanial, P., Chao, S., Charlton, P., Chase, E. A., Chassande-Mottin, E., Chatterjee, D., Chaturvedi, M., Chatziioannou, K., Chen, A., Chen, C., Chen, H. Y., Chen, J., Chen, K., Chen, X., Chen, Y. -B., Chen, Y. -R., Chen, Z., Cheng, H., Cheong, C. K., Cheung, H. Y., Chia, H. Y., Chiadini, F., Chiang, C-Y., Chierici, R., Chincarini, A., Chiofalo, M. L., Chiummo, A., Cho, G., Cho, H. S., Choate, S., Choudhary, R. K., Choudhary, S., Christensen, N., Chu, H., Chu, Q., Chu, Y-K., Chua, S., Chung, K. W., Ciani, G., Ciecielag, P., Cieślar, M., Cifaldi, M., Ciobanu, A. A., Ciolfi, R., Cipriano, F., Cirone, A., Clara, F., Clark, E. N., Clark, J. A., Clarke, L., Clearwater, P., Clesse, S., Cleva, F., Coccia, E., Cohadon, P. -F., Cohen, D. E., Cohen, L., Colleoni, M., Collette, C. G., Colpi, M., Compton, C. M., Constancio Jr., M., Conti, L., Cooper, S. J., Corban, P., Corbitt, T. R., Cordero-Carrión, I., Corezzi, S., Corley, K. R., Cornish, N., Corre, D., Corsi, A., Cortese, S., Costa, C. A., Cotesta, R., Coughlin, M. W., Coughlin, S. B., Coulon, J. -P., Countryman, S. T., Cousins, B., Couvares, P., Covas, P. B., Coward, D. M., Cowart, M. J., Coyne, D. C., Coyne, R., Creighton, J. D. E., Creighton, T. D., Criswell, A. W., Croquette, M., Crowder, S. G., Cudell, J. R., Cullen, T. J., Cumming, A., Cummings, R., Cuoco, E., Curyło, M., Canton, T. Dal, Dálya, G., Dana, A., DaneshgaranBajastani, L. M., D'Angelo, B., Danilishin, S. L., D'Antonio, S., Danzmann, K., Darsow-Fromm, C., Dasgupta, A., Datrier, L. E. H., Dattilo, V., Dave, I., Davier, M., Davies, G. S., Davis, D., Daw, E. J., Dean, R., DeBra, D., Deenadayalan, M., Degallaix, J., De Laurentis, M., Deléglise, S., Del Favero, V., De Lillo, F., De Lillo, N., Del Pozzo, W., DeMarchi, L. M., De Matteis, F., D'Emilio, V., Demos, N., Dent, T., Depasse, A., De Pietri, R., De Rosa, R., De Rossi, C., DeSalvo, R., De Simone, R., Dhurandhar, S., Díaz, M. C., Diaz-Ortiz Jr., M., Didio, N. A., Dietrich, T., Di Fiore, L., Di Fronzo, C., Di Giorgio, C., Di Giovanni, F., Di Girolamo, T., Di Lieto, A., Ding, B., Di Pace, S., Di Palma, I., Di Renzo, F., Divakarla, A. K., Dmitriev, A., Doctor, Z., D'Onofrio, L., Donovan, F., Dooley, K. L., Doravari, S., Dorrington, I., Drago, M., Driggers, J. C., Drori, Y., Du, Z., Ducoin, J. -G., Dupej, P., Durante, O., D'Urso, D., Duverne, P. -A., Dwyer, S. E., Easter, P. J., Ebersold, M., Eddolls, G., Edelman, B., Edo, T. B., Edy, O., Effler, A., Eguchi, S., Eichholz, J., Eikenberry, S. S., Eisenmann, M., Eisenstein, R. A., Ejlli, A., Enomoto, Y., Errico, L., Essick, R. C., Estellés, H., Estevez, D., Etienne, Z., Etzel, T., Evans, M., Evans, T. M., Ewing, B. E., Fafone, V., Fair, H., Fairhurst, S., Fan, X., Farah, A. M., Farinon, S., Farr, B., Farr, W. M., Farrow, N. W., Fauchon-Jones, E. J., Favata, M., Fays, M., Fazio, M., Feicht, J., Fejer, M. M., Feng, F., Fenyvesi, E., Ferguson, D. L., Fernandez-Galiana, A., Ferrante, I., Ferreira, T. A., Fidecaro, F., Figura, P., Fiori, I., Fishbach, M., Fisher, R. P., Fittipaldi, R., Fiumara, V., Flaminio, R., Floden, E., Flynn, E., Fong, H., Font, J. A., Fornal, B., Forsyth, P. W. F., Franke, A., Frasca, S., Frasconi, F., Frederick, C., Frei, Z., Freise, A., Frey, R., Fritschel, P., Frolov, V. V., Fronzé, G. G., Fujii, Y., Fujikawa, Y., Fukunaga, M., Fukushima, M., Fulda, P., Fyffe, M., Gabbard, H. A., Gadre, B. U., Gaebel, S. M., Gair, J. R., Gais, J., Galaudage, S., Gamba, R., Ganapathy, D., Ganguly, A., Gao, D., Gaonkar, S. G., Garaventa, B., García-Núñez, C., García-Quirós, C., Garufi, F., Gateley, B., Gaudio, S., Gayathri, V., Ge, G., Gemme, G., Gennai, A., George, J., Gergely, L., Gewecke, P., Ghonge, S., Ghosh, Abhirup., Ghosh, Archisman, Ghosh, Shaon, Ghosh, Shrobana, Ghosh, Sourath, Giacomazzo, B., Giacoppo, L., Giaime, J. A., Giardina, K. D., Gibson, D. R., Gier, C., Giesler, M., Giri, P., Gissi, F., Glanzer, J., Gleckl, A. E., Godwin, P., Goetz, E., Goetz, R., Gohlke, N., Goncharov, B., González, G., Gopakumar, A., 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. C., Green, R., Gretarsson, A. M., Gretarsson, E. M., Griffith, D., Griffiths, W., Griggs, H. L., Grignani, G., Grimaldi, A., Grimes, E., Grimm, S. J., Grote, H., Grunewald, S., Gruning, P., Guerrero, J. G., Guidi, G. M., Guimaraes, A. R., Guixé, G., Gulati, H. K., Guo, H. -K., Guo, Y., Gupta, Anchal, Gupta, Anuradha, Gupta, P., Gustafson, E. K., Gustafson, R., Guzman, F., Ha, S., Haegel, L., Hagiwara, A., Haino, S., Halim, O., Hall, E. D., Hamilton, E. Z., Hammond, G., Han, W. -B., Haney, M., Hanks, J., Hanna, C., Hannam, M. D., Hannuksela, O. A., Hansen, H., Hansen, T. J., Hanson, J., Harder, T., Hardwick, T., Haris, K., Harms, J., Harry, G. M., Harry, I. W., Hartwig, D., Hasegawa, K., Haskell, B., Hasskew, R. K., Haster, C. -J., Hattori, K., Haughian, K., Hayakawa, H., Hayama, K., Hayes, F. J., Healy, J., Heidmann, A., Heintze, M. C., Heinze, J., Heinzel, J., Heitmann, H., Hellman, F., Hello, P., Helmling-Cornell, A. F., Hemming, G., Hendry, M., Heng, I. S., Hennes, E., Hennig, J., Hennig, M. H., Vivanco, F. Hernandez, Heurs, M., Hild, S., Hill, P., Himemoto, Y., Hinderer, T., Hines, A. S., Hiranuma, Y., Hirata, N., Hirose, E., Ho, W. C. G., Hochheim, S., Hofman, D., Hohmann, J. N., Holgado, A. M., Holland, N. A., Hollows, I. J., Holmes, Z. J., Holt, K., Holz, D. E., Hong, Z., Hopkins, P., Hough, J., Howell, E. J., Hoy, C. G., Hoyland, D., Hreibi, A., Hsieh, B-H., Hsu, Y., Huang, G-Z., Huang, H-Y., Huang, P., Huang, Y-C., Huang, Y. -J., Huang, Y. -W., Hübner, M. T., Huddart, A. D., Huerta, E. A., Hughey, B., Hui, D. C. Y., Hui, V., Husa, S., Huttner, S. H., Huxford, R., Huynh-Dinh, T., Ide, S., Idzkowski, B., Iess, A., Ikenoue, B., Imam, S., Inayoshi, K., Inchauspe, H., Ingram, C., Inoue, Y., Intini, G., Ioka, K., Isi, M., Isleif, K., Ito, K., Itoh, Y., Iyer, B. R., Izumi, K., JaberianHamedan, V., Jacqmin, T., Jadhav, S. J., Jadhav, S. P., James, A. L., Jan, A. Z., Jani, K., Janssens, K., Janthalur, N. N., Jaranowski, P., Jariwala, D., Jaume, R., Jenkins, A. C., Jeon, C., Jeunon, M., Jia, W., Jiang, J., Jin, H. -B., Johns, G. R., Jones, A. W., Jones, D. I., Jones, J. D., Jones, P., Jones, R., Jonker, R. J. G., Ju, L., Jung, K., Jung, P., Junker, J., Kaihotsu, K., Kajita, T., Kakizaki, M., Kalaghatgi, C. V., Kalogera, V., Kamai, B., Kamiizumi, M., Kanda, N., Kandhasamy, S., Kang, G., Kanner, J. B., Kao, Y., Kapadia, S. J., Kapasi, D. P., Karat, S., Karathanasis, C., Karki, S., Kashyap, R., Kasprzack, M., Kastaun, W., Katsanevas, S., Katsavounidis, E., Katzman, W., Kaur, T., Kawabe, K., Kawaguchi, K., Kawai, N., Kawasaki, T., Kéfélian, F., Keitel, D., Key, J. S., Khadka, S., Khalili, F. Y., Khan, I., Khan, S., Khazanov, E. A., Khetan, N., Khursheed, M., Kijbunchoo, N., Kim, C., Kim, J. C., Kim, J., Kim, K., Kim, W. S., Kim, Y. -M., Kimball, C., Kimura, N., King, P. J., Kinley-Hanlon, M., Kirchhoff, R., Kissel, J. S., Kita, N., Kitazawa, H., Kleybolte, L., Klimenko, S., Knee, A. M., Knowles, T. D., Knyazev, E., Koch, P., Koekoek, G., Kojima, Y., Kokeyama, K., Koley, S., Kolitsidou, P., Kolstein, M., Komori, K., Kondrashov, V., Kong, A. K. H., Kontos, A., Koper, N., Korobko, M., Kotake, K., Kovalam, M., Kozak, D. B., Kozakai, C., Kozu, R., Kringel, V., Krishnendu, N. V., Królak, A., Kuehn, G., Kuei, F., Kumar, A., Kumar, P., Kumar, Rahul, Kumar, Rakesh, Kume, J., Kuns, K., Kuo, C., Kuo, H-S., Kuromiya, Y., Kuroyanagi, S., Kusayanagi, K., Kwak, K., Kwang, S., Laghi, D., Lalande, E., Lam, T. L., Lamberts, A., Landry, M., Landry, P., Lane, B. B., Lang, R. N., Lange, J., Lantz, B., La Rosa, I., Lartaux-Vollard, A., Lasky, P. D., Laxen, M., Lazzarini, A., Lazzaro, C., Leaci, P., Leavey, S., Lecoeuche, Y. K., Lee, H. K., Lee, H. M., Lee, H. W., Lee, J., Lee, K., Lee, R., Lehmann, J., Lemaître, A., Leon, E., Leonardi, M., Leroy, N., Letendre, N., Levin, Y., Leviton, J. N., Li, A. K. Y., Li, B., Li, J., Li, K. L., Li, T. G. F., Li, X., Lin, C-Y., Lin, F-K., Lin, F-L., Lin, H. L., Lin, L. C. -C., Linde, F., Linker, S. D., Linley, J. N., Littenberg, T. B., Liu, G. C., Liu, J., Liu, K., Liu, X., Llorens-Monteagudo, M., Lo, R. K. L., Lockwood, A., Lollie, M. L., London, L. T., Longo, A., Lopez, D., Lorenzini, M., Loriette, V., Lormand, M., Losurdo, G., Lough, J. D., Lousto, C. O., Lovelace, G., Lück, H., Lumaca, D., Lundgren, A. P., Luo, L. -W., Macas, R., MacInnis, M., Macleod, D. M., MacMillan, I. A. O., Macquet, A., Hernandez, I. Magaña, Magaña-Sandoval, F., Magazzù, C., Magee, R. M., Maggiore, R., Majorana, E., Makarem, C., Maksimovic, I., Maliakal, S., Malik, A., Man, N., Mandic, V., Mangano, V., Mango, J. L., Mansell, G. L., Manske, M., Mantovani, M., Mapelli, M., Marchesoni, F., Marchio, M., Marion, F., Mark, Z., Márka, S., Márka, Z., Markakis, C., Markosyan, A. S., Markowitz, A., Maros, E., Marquina, A., Marsat, S., Martelli, F., Martin, I. W., Martin, R. M., Martinez, M., Martinez, V., Martinovic, K., Martynov, D. V., Marx, E. J., Masalehdan, H., Mason, K., Massera, E., Masserot, A., Massinger, T. J., Masso-Reid, M., Mastrogiovanni, S., Matas, A., Mateu-Lucena, M., Matichard, F., Matiushechkina, M., Mavalvala, N., McCann, J. J., McCarthy, R., McClelland, D. E., McClincy, P., McCormick, S., McCuller, L., McGhee, G. I., McGuire, S. C., McIsaac, C., McIver, J., McManus, D. J., McRae, T., McWilliams, S. T., Meacher, D., Mehmet, M., Mehta, A. K., Melatos, A., Melchor, D. A., Mendell, G., Menendez-Vazquez, A., Menoni, C. S., Mercer, R. A., Mereni, L., Merfeld, K., Merilh, E. L., Merritt, J. D., Merzougui, M., Meshkov, S., Messenger, C., Messick, C., Meyers, P. M., Meylahn, F., Mhaske, A., Miani, A., Miao, H., Michaloliakos, I., Michel, C., Michimura, Y., Middleton, H., Milano, L., Miller, A. L., Millhouse, M., Mills, J. C., Milotti, E., Milovich-Goff, M. C., Minazzoli, O., Minenkov, Y., Mio, N., Mir, Ll. M., Mishkin, A., Mishra, C., Mishra, T., Mistry, T., Mitra, S., Mitrofanov, V. P., Mitselmakher, G., Mittleman, R., Miyakawa, O., Miyamoto, A., Miyazaki, Y., Miyo, K., Miyoki, S., Mo, Geoffrey, Mogushi, K., Mohapatra, S. R. P., Mohite, S. R., Molina, I., Molina-Ruiz, M., Mondin, M., Montani, M., Moore, C. J., Moraru, D., Morawski, F., More, A., Moreno, C., Moreno, G., Mori, Y., Morisaki, S., Moriwaki, Y., Mours, B., Mow-Lowry, C. M., Mozzon, S., Muciaccia, F., Mukherjee, Arunava, Mukherjee, D., Mukherjee, Soma, Mukherjee, Subroto, Mukund, N., Mullavey, A., Munch, J., Muñiz, E. A., Murray, P. G., Musenich, R., Nadji, S. L., Nagano, K., Nagano, S., Nagar, A., Nakamura, K., Nakano, H., Nakano, M., Nakashima, R., Nakayama, Y., Nardecchia, I., Narikawa, T., Naticchioni, L., Nayak, B., Nayak, R. K., Negishi, R., Neil, B. F., Neilson, J., Nelemans, G., Nelson, T. J. N., Nery, M., Neunzert, A., Ng, K. Y., Ng, S. W. S., Nguyen, C., Nguyen, P., Nguyen, T., Quynh, L. Nguyen, Ni, W. -T., Nichols, S. A., Nishizawa, A., Nissanke, S., Nocera, F., Noh, M., Norman, M., North, C., Nozaki, S., Nuttall, L. K., Oberling, J., O'Brien, B. D., Obuchi, Y., O'Dell, J., Ogaki, W., Oganesyan, G., Oh, J. J., Oh, K., Oh, S. H., Ohashi, M., Ohishi, N., Ohkawa, M., Ohme, F., Ohta, H., Okada, M. A., Okutani, Y., Okutomi, K., Olivetto, C., Oohara, K., Ooi, C., Oram, R., O'Reilly, B., Ormiston, R. G., Ormsby, N. D., Ortega, L. F., O'Shaughnessy, R., O'Shea, E., Oshino, S., Ossokine, S., Osthelder, C., Otabe, S., Ottaway, D. J., Overmier, H., Pace, A. E., Pagano, G., Page, M. A., Pagliaroli, G., Pai, A., Pai, S. A., Palamos, J. R., Palashov, O., Palomba, C., Pan, K., Panda, P. K., Pang, H., Pang, P. T. H., Pankow, C., Pannarale, F., Pant, B. C., Paoletti, F., Paoli, A., Paolone, A., Parisi, A., Park, J., Parker, W., Pascucci, D., Pasqualetti, A., Passaquieti, R., Passuello, D., Patel, M., Patricelli, B., Payne, E., Pechsiri, T. C., Pedraza, M., Pegoraro, M., Pele, A., Arellano, F. E. Peña, Penn, S., Perego, A., Pereira, A., Pereira, T., Perez, C. J., Périgois, C., Perreca, A., Perriès, S., Petermann, J., Petterson, D., Pfeiffer, H. P., Pham, K. A., Phukon, K. S., Piccinni, O. J., Pichot, M., Piendibene, M., Piergiovanni, F., Pierini, L., Pierro, V., Pillant, G., Pilo, F., Pinard, L., Pinto, I. M., Piotrzkowski, B. J., Piotrzkowski, K., Pirello, M., Pitkin, M., Placidi, E., Plastino, W., Pluchar, C., Poggiani, R., Polini, E., Pong, D. Y. T., Ponrathnam, S., Popolizio, P., Porter, E. K., Powell, J., Pracchia, M., Pradier, T., Prajapati, A. K., Prasai, K., Prasanna, R., Pratten, G., Prestegard, T., Principe, M., Prodi, G. 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., Rail, S. X., Raja, S., Rajan, C., Ramirez, K. E., Ramirez, T. D., Ramos-Buades, A., Rana, J., Rapagnani, P., Rapol, U. D., Ratto, B., Ray, A., Raymond, V., Raza, N., Razzano, M., Read, J., Rees, L. A., Regimbau, T., Rei, L., Reid, S., Reitze, D. H., Relton, P., Rettegno, P., Ricci, F., Richardson, C. J., Richardson, J. W., Richardson, L., Ricker, P. M., Riemenschneider, G., Riles, K., Rizzo, M., Robertson, N. A., Robie, R., 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., Rose, C. A., Rosińska, D., Rosofsky, S. G., Ross, M. P., Rowan, S., Rowlinson, S. J., Roy, Santosh, Roy, Soumen, Rozza, D., Ruggi, P., Ryan, K., Sachdev, S., Sadecki, T., Sadiq, J., Sago, N., Saito, S., Saito, Y., Sakai, K., Sakai, Y., Sakellariadou, M., Sakuno, Y., Salafia, O. S., Salconi, L., Saleem, M., Salemi, F., Samajdar, A., Sanchez, E. J., Sanchez, J. H., Sanchez, L. E., Sanchis-Gual, N., Sanders, J. R., Sanuy, A., Saravanan, T. R., Sarin, N., Sassolas, B., Satari, H., Sathyaprakash, B. S., Sato, S., Sato, T., Sauter, O., Savage, R. L., Savant, V., Sawada, T., Sawant, D., Sawant, H. 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Subjects: Astrophysics - High Energy Astrophysical Phenomena
Abstract: We report the observation of gravitational waves from two compact binary coalescences in LIGO's and Virgo's third observing run with properties consistent with neutron star-black hole (NSBH) binaries. The two events are named GW200105_162426 and GW200115_042309, abbreviated as GW200105 and GW200115; the first was observed by LIGO Livingston and Virgo, and the second by all three LIGO-Virgo detectors. The source of GW200105 has component masses $8.9^{+1.2}_{-1.5}\,M_\odot$ and $1.9^{+0.3}_{-0.2}\,M_\odot$, whereas the source of GW200115 has component masses $5.7^{+1.8}_{-2.1}\,M_\odot$ and $1.5^{+0.7}_{-0.3}\,M_\odot$ (all measurements quoted at the 90% credible level). The probability that the secondary's mass is below the maximal mass of a neutron star is 89%-96% and 87%-98%, respectively, for GW200105 and GW200115, with the ranges arising from different astrophysical assumptions. The source luminosity distances are $280^{+110}_{-110}$ Mpc and $300^{+150}_{-100}$ Mpc, respectively. The magnitude of the primary spin of GW200105 is less than 0.23 at the 90% credible level, and its orientation is unconstrained. For GW200115, the primary spin has a negative spin projection onto the orbital angular momentum at 88% probability. We are unable to constrain spin or tidal deformation of the secondary component for either event. We infer a NSBH merger rate density of $45^{+75}_{-33}\,\mathrm{Gpc}^{-3} \mathrm{yr}^{-1}$ when assuming GW200105 and GW200115 are representative of the NSBH population, or $130^{+112}_{-69}\,\mathrm{Gpc}^{-3} \mathrm{yr}^{-1}$ under the assumption of a broader distribution of component masses.
Published: 2021
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29. Searches for continuous gravitational waves from young supernova remnants in the early third observing run of Advanced LIGO and Virgo

Author: The LIGO Scientific Collaboration, the Virgo Collaboration, the KAGRA Collaboration, Abbott, R., Abbott, T. D., Abraham, S., Acernese, F., Ackley, K., Adams, A., Adams, C., Adhikari, R. X., Adya, V. B., Affeldt, C., Agarwal, D., Agathos, M., Agatsuma, K., Aggarwal, N., Aguiar, O. D., Aiello, L., Ain, A., Ajith, P., Akutsu, T., Aleman, K. M., Allen, G., Allocca, A., Altin, P. A., Amato, A., Anand, S., Ananyeva, A., Anderson, S. B., Anderson, W. G., Ando, M., Angelova, S. V., Ansoldi, S., Antelis, J. M., Antier, S., Appert, S., Arai, Koya, Arai, Koji, Arai, Y., Araki, S., Araya, A., Araya, M. C., Areeda, J. S., Arène, M., Aritomi, N., Arnaud, N., Aronson, S. M., Arun, K. G., Asada, H., Asali, Y., Ashton, G., Aso, Y., Aston, S. M., Astone, P., Aubin, F., Aufmuth, P., AultONeal, K., Austin, C., Babak, S., Badaracco, F., Bader, M. K. M., Bae, S., Bae, Y., Baer, A. M., Bagnasco, S., Bai, Y., Baiotti, L., Baird, J., Bajpai, R., Ball, M., Ballardin, G., Ballmer, S. 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Subjects: Astrophysics - High Energy Astrophysical Phenomena
Abstract: We present results of three wide-band directed searches for continuous gravitational waves from 15 young supernova remnants in the first half of the third Advanced LIGO and Virgo observing run. We use three search pipelines with distinct signal models and methods of identifying noise artifacts. Without ephemerides of these sources, the searches are conducted over a frequency band spanning from 10~Hz to 2~kHz. We find no evidence of continuous gravitational radiation from these sources. We set upper limits on the intrinsic signal strain at 95\% confidence level in sample sub-bands, estimate the sensitivity in the full band, and derive the corresponding constraints on the fiducial neutron star ellipticity and $r$-mode amplitude. The best 95\% confidence constraints placed on the signal strain are $7.7\times 10^{-26}$ and $7.8\times 10^{-26}$ near 200~Hz for the supernova remnants G39.2--0.3 and G65.7+1.2, respectively. The most stringent constraints on the ellipticity and $r$-mode amplitude reach $\lesssim 10^{-7}$ and $ \lesssim 10^{-5}$, respectively, at frequencies above $\sim 400$~Hz for the closest supernova remnant G266.2--1.2/Vela Jr., Comment: https://dcc.ligo.org/P2000479
Published: 2021
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30. Search for lensing signatures in the gravitational-wave observations from the first half of LIGO-Virgo's third observing run

Author: The LIGO Scientific Collaboration, the Virgo Collaboration, Abbott, R., Abbott, T. D., Abraham, S., Acernese, F., Ackley, K., Adams, A., Adams, C., Adhikari, R. X., Adya, V. B., Affeldt, C., Agarwal, D., Agathos, M., Agatsuma, K., Aggarwal, N., Aguiar, O. D., Aiello, L., Ain, A., Ajith, P., Aleman, K. M., Allen, G., Allocca, A., Altin, P. A., Amato, A., Anand, S., Ananyeva, A., Anderson, S. B., Anderson, W. G., Angelova, S. V., Ansoldi, S., Antelis, J. M., Antier, S., Appert, S., Arai, K., Araya, M. C., Areeda, J. S., Arène, M., Arnaud, N., Aronson, S. M., Arun, K. G., Asali, Y., Ashton, G., Aston, S. M., Astone, P., Aubin, F., Aufmuth, P., AultONeal, K., Austin, C., Babak, S., Badaracco, F., Bader, M. K. M., Bae, S., Baer, A. M., Bagnasco, S., Bai, Y., Baird, J., Ball, M., Ballardin, G., Ballmer, S. W., Bals, M., Balsamo, A., Baltus, G., Banagiri, S., Bankar, D., Bankar, R. S., Barayoga, J. C., Barbieri, C., Barish, B. C., Barker, D., Barneo, P., Barone, F., Barr, B., Barsotti, L., Barsuglia, M., Barta, D., Bartlett, J., Barton, M. A., Bartos, I., Bassiri, R., Basti, A., Bawaj, M., Bayley, J. C., Baylor, A. C., Bazzan, M., Bécsy, B., Bedakihale, V. M., Bejger, M., Belahcene, I., Benedetto, V., Beniwal, D., Benjamin, M. G., Bennett, T. F., Bentley, J. D., BenYaala, M., Bergamin, F., Berger, B. K., Bernuzzi, S., Berry, C. P. L., Bersanetti, D., Bertolini, A., Betzwieser, J., Bhandare, R., Bhandari, A. V., Bhattacharjee, D., Bhaumik, S., Bidler, J., Bilenko, I. A., Billingsley, G., Birney, R., Birnholtz, O., Biscans, S., Bischi, M., Biscoveanu, S., Bisht, A., Biswas, B., Bitossi, M., Bizouard, M. -A., Blackburn, J. K., Blackman, J., Blair, C. D., Blair, D. G., Blair, R. M., Bobba, F., Bode, N., Boer, M., Bogaert, G., Boldrini, M., Bondu, F., Bonilla, E., Bonnand, R., Booker, P., Boom, B. 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Subjects: General Relativity and Quantum Cosmology, Astrophysics - High Energy Astrophysical Phenomena
Abstract: We search for signatures of gravitational lensing in the gravitational-wave signals from compact binary coalescences detected by Advanced LIGO and Advanced Virgo during O3a, the first half of their third observing run. We study: 1) the expected rate of lensing at current detector sensitivity and the implications of a non-observation of strong lensing or a stochastic gravitational-wave background on the merger-rate density at high redshift; 2) how the interpretation of individual high-mass events would change if they were found to be lensed; 3) the possibility of multiple images due to strong lensing by galaxies or galaxy clusters; and 4) possible wave-optics effects due to point-mass microlenses. Several pairs of signals in the multiple-image analysis show similar parameters and, in this sense, are nominally consistent with the strong lensing hypothesis. However, taking into account population priors, selection effects, and the prior odds against lensing, these events do not provide sufficient evidence for lensing. Overall, we find no compelling evidence for lensing in the observed gravitational-wave signals from any of these analyses., Comment: 31 pages and 6 figures. Accepted by the Astrophysical Journal
Published: 2021
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31. Advances in Machine and Deep Learning for Modeling and Real-time Detection of Multi-Messenger Sources

Author: Huerta, E. A. and Zhao, Zhizhen
Subjects: Astrophysics - Instrumentation and Methods for Astrophysics, Astrophysics - High Energy Astrophysical Phenomena, Computer Science - Artificial Intelligence, Computer Science - Machine Learning, General Relativity and Quantum Cosmology, 83-02, 83-04, 83-08, 85-08, 85-10, 68T07, 68T20, I.2, I.3, I.5, J.2
Abstract: We live in momentous times. The science community is empowered with an arsenal of cosmic messengers to study the Universe in unprecedented detail. Gravitational waves, electromagnetic waves, neutrinos and cosmic rays cover a wide range of wavelengths and time scales. Combining and processing these datasets that vary in volume, speed and dimensionality requires new modes of instrument coordination, funding and international collaboration with a specialized human and technological infrastructure. In tandem with the advent of large-scale scientific facilities, the last decade has experienced an unprecedented transformation in computing and signal processing algorithms. The combination of graphics processing units, deep learning, and the availability of open source, high-quality datasets, have powered the rise of artificial intelligence. This digital revolution now powers a multi-billion dollar industry, with far-reaching implications in technology and society. In this chapter we describe pioneering efforts to adapt artificial intelligence algorithms to address computational grand challenges in Multi-Messenger Astrophysics. We review the rapid evolution of these disruptive algorithms, from the first class of algorithms introduced in early 2017, to the sophisticated algorithms that now incorporate domain expertise in their architectural design and optimization schemes. We discuss the importance of scientific visualization and extreme-scale computing in reducing time-to-insight and obtaining new knowledge from the interplay between models and data., Comment: 30 pages, 11 figures. Invited chapter for "Handbook of Gravitational Wave Astronomy"; v2: updated to reflect published version
Published: 2021
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32. Constraints from LIGO O3 data on gravitational-wave emission due to r-modes in the glitching pulsar PSR J0537-6910

Author: The LIGO Scientific Collaboration, the Virgo Collaboration, the KAGRA Collaboration, Abbott, R., Abbott, T. D., Abraham, S., Acernese, F., Ackley, K., Adams, A., Adams, C., Adhikari, R. X., Adya, V. B., Affeldt, C., Agarwal, D., Agathos, M., Agatsuma, K., Aggarwal, N., Aguiar, O. D., Aiello, L., Ain, A., Ajith, P., Akutsu, T., Aleman, K. M., Allen, G., Allocca, A., Altin, P. A., Amato, A., Anand, S., Ananyeva, A., Anderson, S. B., Anderson, W. G., Ando, M., Angelova, S. V., Ansoldi, S., Antelis, J. M., Antier, S., Appert, S., Arai, Koya, Arai, Koji, Arai, Y., Araki, S., Araya, A., Araya, M. C., Areeda, J. S., Arène, M., Aritomi, N., Arnaud, N., Aronson, S. M., Arun, K. G., Asada, H., Asali, Y., Ashton, G., Aso, Y., Aston, S. M., Astone, P., Aubin, F., Aufmuth, P., AultONeal, K., Austin, C., Babak, S., Badaracco, F., Bader, M. K. M., Bae, S., Bae, Y., Baer, A. M., Bagnasco, S., Bai, Y., Baiotti, L., Baird, J., Bajpai, R., Ball, M., Ballardin, G., Ballmer, S. W., Bals, M., Balsamo, A., Baltus, G., Banagiri, S., Bankar, D., Bankar, R. S., Barayoga, J. C., Barbieri, C., Barish, B. C., Barker, D., Barneo, P., Barone, F., Barr, B., Barsotti, L., Barsuglia, M., Barta, D., Bartlett, J., Barton, M. A., Bartos, I., Bassiri, R., Basti, A., Bawaj, M., Bayley, J. C., Baylor, A. C., Bazzan, M., Bécsy, B., Bedakihale, V. M., Bejger, M., Belahcene, I., Benedetto, V., Beniwal, D., Benjamin, M. G., Bennett, T. F., Bentley, J. D., BenYaala, M., Bergamin, F., Berger, B. K., Bernuzzi, S., Bersanetti, D., Bertolini, A., Betzwieser, J., Bhandare, R., Bhandari, A. V., Bhattacharjee, D., Bhaumik, S., Bidler, J., Bilenko, I. A., Billingsley, G., Birney, R., Birnholtz, O., Biscans, S., Bischi, M., Biscoveanu, S., Bisht, A., Biswas, B., Bitossi, M., Bizouard, M. -A., Blackburn, J. K., Blackman, J., Blair, C. D., Blair, D. G., Blair, R. M., Bobba, F., Bode, N., Boer, M., Bogaert, G., Boldrini, M., Bondu, F., Bonilla, E., Bonnand, R., Booker, P., Boom, B. A., Bork, R., Boschi, V., Bose, N., Bose, S., Bossilkov, V., Boudart, V., Bouffanais, Y., Bozzi, A., Bradaschia, C., Brady, P. R., Bramley, A., Branch, A., Branchesi, M., Brau, J. E., Breschi, M., Briant, T., Briggs, J. H., Brillet, A., Brinkmann, M., Brockill, P., Brooks, A. F., Brooks, J., Brown, D. D., Brunett, S., Bruno, G., Bruntz, R., Bryant, J., Buikema, A., Bulik, T., Bulten, H. J., Buonanno, A., Buscicchio, R., Buskulic, D., Byer, R. L., Cadonati, L., Caesar, M., Cagnoli, G., Cahillane, C., Cain III, H. W., Bustillo, J. Calderón, Callaghan, J. D., Callister, T. A., Calloni, E., Camp, J. B., Canepa, M., Cannavacciuolo, M., Cannon, K. C., Cao, H., Cao, J., Cao, Z., Capocasa, E., Capote, E., Carapella, G., Carbognani, F., Carlin, J. B., Carney, M. F., Carpinelli, M., Carullo, G., Carver, T. L., Diaz, J. 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C., Estellés, H., Estevez, D., Etienne, Z., Etzel, T., Evans, M., Evans, T. M., Ewing, B. E., Fafone, V., Fair, H., Fairhurst, S., Fan, X., Farah, A. M., Farinon, S., Farr, B., Farr, W. M., Farrow, N. W., Fauchon-Jones, E. J., Favata, M., Fays, M., Fazio, M., Feicht, J., Fejer, M. M., Feng, F., Fenyvesi, E., Ferguson, D. L., Fernandez-Galiana, A., Ferrante, I., Ferreira, T. A., Fidecaro, F., Figura, P., Fiori, I., Fishbach, M., Fisher, R. P., Fittipaldi, R., Fiumara, V., Flaminio, R., Floden, E., Flynn, E., Fong, H., Font, J. A., Fornal, B., Forsyth, P. W. F., Franke, A., Frasca, S., Frasconi, F., Frederick, C., Frei, Z., Freise, A., Frey, R., Fritschel, P., Frolov, V. V., Fronzé, G. G., Fujii, Y., Fujikawa, Y., Fukunaga, M., Fukushima, M., Fulda, P., Fyffe, M., Gabbard, H. A., Gadre, B. U., Gaebel, S. M., Gair, J. R., Gais, J., Galaudage, S., Gamba, R., Ganapathy, D., Ganguly, A., Gao, D., Gaonkar, S. G., Garaventa, B., García-Núñez, C., García-Quirós, C., Garufi, F., Gateley, B., Gaudio, S., Gayathri, V., Ge, G., Gemme, G., Gennai, A., George, J., Gergely, L., Gewecke, P., Ghonge, S., Ghosh, Abhirup., Ghosh, Archisman, Ghosh, Shaon, Ghosh, Shrobana, Ghosh, Sourath, Giacomazzo, B., Giacoppo, L., Giaime, J. A., Giardina, K. D., Gibson, D. R., Gier, C., Giesler, M., Giri, P., Gissi, F., Glanzer, J., Gleckl, A. E., Godwin, P., Goetz, E., Goetz, R., Gohlke, N., Goncharov, B., González, G., Gopakumar, A., 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. C., Green, R., Gretarsson, A. M., Gretarsson, E. M., Griffith, D., Griffiths, W., Griggs, H. L., Grignani, G., Grimaldi, A., Grimes, E., Grimm, S. J., Grote, H., Grunewald, S., Gruning, P., Guerrero, J. G., Guidi, G. M., Guimaraes, A. R., Guixé, G., Gulati, H. 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Subjects: Astrophysics - High Energy Astrophysical Phenomena, General Relativity and Quantum Cosmology
Abstract: We present a search for continuous gravitational-wave emission due to r-modes in the pulsar PSR J0537-6910 using data from the LIGO-Virgo Collaboration observing run O3. PSR J0537-6910 is a young energetic X-ray pulsar and is the most frequent glitcher known. The inter-glitch braking index of the pulsar suggests that gravitational-wave emission due to r-mode oscillations may play an important role in the spin evolution of this pulsar. Theoretical models confirm this possibility and predict emission at a level that can be probed by ground-based detectors. In order to explore this scenario, we search for r-mode emission in the epochs between glitches by using a contemporaneous timing ephemeris obtained from NICER data. We do not detect any signals in the theoretically expected band of 86-97 Hz, and report upper limits on the amplitude of the gravitational waves. Our results improve on previous amplitude upper limits from r-modes in J0537-6910 by a factor of up to 3 and place stringent constraints on theoretical models for r-mode driven spin-down in PSR J0537-6910, especially for higher frequencies at which our results reach below the spin-down limit defined by energy conservation., Comment: 28 pages, 19 figures, accepted in ApJ
Published: 2021
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33. Search for anisotropic gravitational-wave backgrounds using data from Advanced LIGO and Advanced Virgo's first three observing runs

Author: The LIGO Scientific Collaboration, the Virgo Collaboration, the KAGRA Collaboration, Abbott, R., Abbott, T. D., Abraham, S., Acernese, F., Ackley, K., Adams, A., Adams, C., Adhikari, R. X., Adya, V. B., Affeldt, C., Agarwal, D., Agathos, M., Agatsuma, K., Aggarwal, N., Aguiar, O. D., Aiello, L., Ain, A., Ajith, P., Akutsu, T., Aleman, K. M., Allen, G., Allocca, A., Altin, P. A., Amato, A., Anand, S., Ananyeva, A., Anderson, S. B., Anderson, W. G., Ando, M., Angelova, S. V., Ansoldi, S., Antelis, J. M., Antier, S., Appert, S., Arai, Koya, Arai, Koji, Arai, Y., Araki, S., Araya, A., Araya, M. C., Areeda, J. S., Arène, M., Aritomi, N., Arnaud, N., Aronson, S. M., Asada, H., Asali, Y., Ashton, G., Aso, Y., Aston, S. M., Astone, P., Aubin, F., Aufmuth, P., AultONeal, K., Austin, C., Babak, S., Badaracco, F., Bader, M. K. M., Bae, S., Bae, Y., Baer, A. M., Bagnasco, S., Bai, Y., Baiotti, L., Baird, J., Bajpai, R., Ball, M., Ballardin, G., Ballmer, S. 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Subjects: General Relativity and Quantum Cosmology
Abstract: We report results from searches for anisotropic stochastic gravitational-wave backgrounds using data from the first three observing runs of the Advanced LIGO and Advanced Virgo detectors. For the first time, we include Virgo data in our analysis and run our search with a new efficient pipeline called {\tt PyStoch} on data folded over one sidereal day. We use gravitational-wave radiometry (broadband and narrow band) to produce sky maps of stochastic gravitational-wave backgrounds and to search for gravitational waves from point sources. A spherical harmonic decomposition method is employed to look for gravitational-wave emission from spatially-extended sources. Neither technique found evidence of gravitational-wave signals. Hence we derive 95\% confidence-level upper limit sky maps on the gravitational-wave energy flux from broadband point sources, ranging from $F_{\alpha, \Theta} < {\rm (0.013 - 7.6)} \times 10^{-8} {\rm erg \, cm^{-2} \, s^{-1} \, Hz^{-1}},$ and on the (normalized) gravitational-wave energy density spectrum from extended sources, ranging from $\Omega_{\alpha, \Theta} < {\rm (0.57 - 9.3)} \times 10^{-9} \, {\rm sr^{-1}}$, depending on direction ($\Theta$) and spectral index ($\alpha$). These limits improve upon previous limits by factors of $2.9 - 3.5$. We also set 95\% confidence level upper limits on the frequency-dependent strain amplitudes of quasimonochromatic gravitational waves coming from three interesting targets, Scorpius X-1, SN 1987A and the Galactic Center, with best upper limits range from $h_0 < {\rm (1.7-2.1)} \times 10^{-25},$ a factor of $\geq 2.0$ improvement compared to previous stochastic radiometer searches., Comment: 23 Pages, 9 Figures
Published: 2021
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34. Upper Limits on the Isotropic Gravitational-Wave Background from Advanced LIGO's and Advanced Virgo's Third Observing Run

Author: The LIGO Scientific Collaboration, the Virgo Collaboration, the KAGRA Collaboration, Abbott, R., Abbott, T. D., Abraham, S., Acernese, F., Ackley, K., Adams, A., Adams, C., Adhikari, R. X., Adya, V. B., Affeldt, C., Agarwal, D., Agathos, M., Agatsuma, K., Aggarwal, N., Aguiar, O. D., Aiello, L., Ain, A., Akutsu, T., Aleman, K. M., Allen, G., Allocca, A., Altin, P. A., Amato, A., Anand, S., Ananyeva, A., Anderson, S. B., Anderson, W. G., Ando, M., Angelova, S. V., Ansoldi, S., Antelis, J. M., Antier, S., Appert, S., Arai, Koya, Arai, Koji, Arai, Y., Araki, S., Araya, A., Araya, M. C., Areeda, J. S., Arène, M., Aritomi, N., Arnaud, N., Aronson, S. M., Asada, H., Asali, Y., Ashton, G., Aso, Y., Aston, S. M., Astone, P., Aubin, F., Aufmuth, P., AultONeal, K., Austin, C., Babak, S., Badaracco, F., Bader, M. K. M., Bae, S., Bae, Y., Baer, A. M., Bagnasco, S., Bai, Y., Baiotti, L., Baird, J., Bajpai, R., Ball, M., Ballardin, G., Ballmer, S. W., Bals, M., Balsamo, A., Baltus, G., Banagiri, S., Bankar, D., Bankar, R. S., Barayoga, J. C., Barbieri, C., Barish, B. C., Barker, D., Barneo, P., Barnum, S., Barone, F., Barr, B., Barsotti, L., Barsuglia, M., Barta, D., Bartlett, J., Barton, M. A., Bartos, I., Bassiri, R., Basti, A., Bawaj, M., Bayley, J. C., Baylor, A. C., Bazzan, M., Bécsy, B., Bedakihale, V. M., Bejger, M., Belahcene, I., Benedetto, V., Beniwal, D., Benjamin, M. G., Bennett, T. F., Bentley, J. D., BenYaala, M., Bergamin, F., Berger, B. K., Bernuzzi, S., Bersanetti, D., Bertolini, A., Betzwieser, J., Bhandare, R., Bhandari, A. V., Bhattacharjee, D., Bhaumik, S., Bidler, J., Bilenko, I. A., Billingsley, G., Birney, R., Birnholtz, O., Biscans, S., Bischi, M., Biscoveanu, S., Bisht, A., Biswas, B., Bitossi, M., Bizouard, M. -A., Blackburn, J. K., Blackman, J., Blair, C. D., Blair, D. G., Blair, R. M., Bobba, F., Bode, N., Boer, M., Bogaert, G., Boldrini, M., Bondu, F., Bonilla, E., Bonnand, R., Booker, P., Boom, B. 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L., Willke, B., Wilson, D. J., Winkler, W., Wipf, C. C., Wlodarczyk, T., Woan, G., Woehler, J., Wofford, J. K., Wong, I. C. F., Wu, C., Wu, D. S., Wu, H., Wu, S., Wysocki, D. M., Xiao, L., Xu, W-R., Yamada, T., Yamamoto, H., Yamamoto, Kazuhiro, Yamamoto, Kohei, Yamamoto, T., Yamashita, K., Yamazaki, R., Yang, F. W., Yang, L., Yang, Yang, Yang, Yi, Yang, Z., Yap, M. J., Yeeles, D. W., Yelikar, A. B., Ying, M., Yokogawa, K., Yokoyama, J., Yokozawa, T., Yoon, A., Yoshioka, T., Yu, Hang, Yu, Haocun, Yuzurihara, H., Zadrożny, A., Zanolin, M., Zeidler, S., Zelenova, T., Zendri, J. -P., Zevin, M., Zhan, M., Zhang, H., Zhang, J., Zhang, L., Zhang, R., Zhang, T., Zhao, C., Zhao, G., Zhao, Yue, Zhao, Yuhang, Zhou, Z., Zhu, X. J., Zhu, Z. -H., Zucker, M. E., and Zweizig, J.
Subjects: General Relativity and Quantum Cosmology, Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract: We report results of a search for an isotropic gravitational-wave background (GWB) using data from Advanced LIGO's and Advanced Virgo's third observing run (O3) combined with upper limits from the earlier O1 and O2 runs. Unlike in previous observing runs in the advanced detector era, we include Virgo in the search for the GWB. The results are consistent with uncorrelated noise, and therefore we place upper limits on the strength of the GWB. We find that the dimensionless energy density $\Omega_{\rm GW}\leq 5.8\times 10^{-9}$ at the 95% credible level for a flat (frequency-independent) GWB, using a prior which is uniform in the log of the strength of the GWB, with 99% of the sensitivity coming from the band 20-76.6 Hz; $\leq 3.4 \times 10^{-9}$ at 25 Hz for a power-law GWB with a spectral index of 2/3 (consistent with expectations for compact binary coalescences), in the band 20-90.6 Hz; and $\leq 3.9 \times 10^{-10}$ at 25 Hz for a spectral index of 3, in the band 20-291.6 Hz. These upper limits improve over our previous results by a factor of 6.0 for a flat GWB. We also search for a GWB arising from scalar and vector modes, which are predicted by alternative theories of gravity; we place upper limits on the strength of GWBs with these polarizations. We demonstrate that there is no evidence of correlated noise of magnetic origin by performing a Bayesian analysis that allows for the presence of both a GWB and an effective magnetic background arising from geophysical Schumann resonances. We compare our upper limits to a fiducial model for the GWB from the merger of compact binaries. Finally, we combine our results with observations of individual mergers andshow that, at design sensitivity, this joint approach may yield stronger constraints on the merger rate of binary black holes at $z \lesssim 2$ than can be achieved with individually resolved mergers alone. [abridged], Comment: 25 pages, 7 figures, Abstract abridged for arxiv submission
Published: 2021
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35. Constraints on cosmic strings using data from the third Advanced LIGO-Virgo observing run

Author: The LIGO Scientific Collaboration, the Virgo Collaboration, the KAGRA Collaboration, Abbott, R., Abbott, T. D., Abraham, S., Acernese, F., Ackley, K., Adams, A., Adams, C., Adhikari, R. X., Adya, V. B., Affeldt, C., Agarwal, D., Agathos, M., Agatsuma, K., Aggarwal, N., Aguiar, O. D., Aiello, L., Ain, A., Ajith, P., Akutsu, T., Aleman, K. M., Allen, G., Allocca, A., Altin, P. A., Amato, A., Anand, S., Ananyeva, A., Anderson, S. B., Anderson, W. G., Ando, M., Angelova, S. V., Ansoldi, S., Antelis, J. M., Antier, S., Appert, S., Arai, Koya, Arai, Koji, Arai, Y., Araki, S., Araya, A., Araya, M. C., Areeda, J. S., Arène, M., Aritomi, N., Arnaud, N., Aronson, S. M., Asada, H., Asali, Y., Ashton, G., Aso, Y., Aston, S. M., Astone, P., Aubin, F., Auclair, P., Aufmuth, P., AultONeal, K., Austin, C., Babak, S., Badaracco, F., Bader, M. K. M., Bae, S., Bae, Y., Baer, A. M., Bagnasco, S., Bai, Y., Baiotti, L., Baird, J., Bajpai, R., Ball, M., Ballardin, G., Ballmer, S. W., Bals, M., Balsamo, A., Baltus, G., Banagiri, S., Bankar, D., Bankar, R. S., Barayoga, J. C., Barbieri, C., Barish, B. C., Barker, D., Barneo, P., Barnum, S., Barone, F., Barr, B., Barsotti, L., Barsuglia, M., Barta, D., Bartlett, J., Barton, M. A., Bartos, I., Bassiri, R., Basti, A., Bawaj, M., Bayley, J. C., Baylor, A. C., Bazzan, M., Bécsy, B., Bedakihale, V. M., Bejger, M., Belahcene, I., Benedetto, V., Beniwal, D., Benjamin, M. G., Bennett, T. F., Bentley, J. D., BenYaala, M., Bergamin, F., Berger, B. K., Bernuzzi, S., Bersanetti, D., Bertolini, A., Betzwieser, J., Bhandare, R., Bhandari, A. V., Bhattacharjee, D., Bhaumik, S., Bidler, J., Bilenko, I. A., Billingsley, G., Birney, R., Birnholtz, O., Biscans, S., Bischi, M., Biscoveanu, S., Bisht, A., Biswas, B., Bitossi, M., Bizouard, M. -A., Blackburn, J. K., Blackman, J., Blair, C. D., Blair, D. G., Blair, R. M., Bobba, F., Bode, N., Boer, M., Bogaert, G., Boldrini, M., Bondu, F., Bonilla, E., Bonnand, R., Booker, P., Boom, B. 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Subjects: General Relativity and Quantum Cosmology, Astrophysics - Cosmology and Nongalactic Astrophysics, High Energy Physics - Theory
Abstract: We search for gravitational-wave signals produced by cosmic strings in the Advanced LIGO and Virgo full O3 data set. Search results are presented for gravitational waves produced by cosmic string loop features such as cusps, kinks and, for the first time, kink-kink collisions.cA template-based search for short-duration transient signals does not yield a detection. We also use the stochastic gravitational-wave background energy density upper limits derived from the O3 data to constrain the cosmic string tension, $G\mu$, as a function of the number of kinks, or the number of cusps, for two cosmic string loop distribution models.cAdditionally, we develop and test a third model which interpolates between these two models. Our results improve upon the previous LIGO-Virgo constraints on $G\mu$ by one to two orders of magnitude depending on the model which is tested. In particular, for one loop distribution model, we set the most competitive constraints to date, $G\mu\lesssim 4\times 10^{-15}$., Comment: 20 pages, 10 figures
Published: 2021
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36. Diving below the spin-down limit: Constraints on gravitational waves from the energetic young pulsar PSR J0537-6910

Author: The LIGO Scientific Collaboration, the Virgo Collaboration, the KAGRA Collaboration, Abbott, R., Abbott, T. D., Abraham, S., Acernese, F., Ackley, K., Adams, A., Adams, C., Adhikari, R. X., Adya, V. B., Affeldt, C., Agarwal, D., Agathos, M., Agatsuma, K., Aggarwal, N., Aguiar, O. D., Aiello, L., Ain, A., Ajith, P., Akutsu, T., Aleman, K. M., Allen, G., Allocca, A., Altin, P. A., Amato, A., Anand, S., Ananyeva, A., Anderson, S. B., Anderson, W. G., Ando, M., Angelova, S. V., Ansoldi, S., Antelis, J. M., Antier, S., Appert, S., Arai, Koya, Arai, Koji, Arai, Y., Araki, S., Araya, A., Araya, M. C., Areeda, J. S., Arène, M., Aritomi, N., Arnaud, N., Aronson, S. M., Arun, K. G., Asada, H., Asali, Y., Ashton, G., Aso, Y., Aston, S. M., Astone, P., Aubin, F., Aufmuth, P., AultONeal, K., Austin, C., Babak, S., Badaracco, F., Bader, M. K. M., Bae, S., Bae, Y., Baer, A. M., Bagnasco, S., Bai, Y., Baiotti, L., Baird, J., Bajpai, R., Ball, M., Ballardin, G., Ballmer, S. 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Subjects: Astrophysics - High Energy Astrophysical Phenomena, General Relativity and Quantum Cosmology
Abstract: We present a search for continuous gravitational-wave signals from the young, energetic X-ray pulsar PSR J0537-6910 using data from the second and third observing runs of LIGO and Virgo. The search is enabled by a contemporaneous timing ephemeris obtained using NICER data. The NICER ephemeris has also been extended through 2020 October and includes three new glitches. PSR J0537-6910 has the largest spin-down luminosity of any pulsar and is highly active with regards to glitches. Analyses of its long-term and inter-glitch braking indices provided intriguing evidence that its spin-down energy budget may include gravitational-wave emission from a time-varying mass quadrupole moment. Its 62 Hz rotation frequency also puts its possible gravitational-wave emission in the most sensitive band of LIGO/Virgo detectors. Motivated by these considerations, we search for gravitational-wave emission at both once and twice the rotation frequency. We find no signal, however, and report our upper limits. Assuming a rigidly rotating triaxial star, our constraints reach below the gravitational-wave spin-down limit for this star for the first time by more than a factor of two and limit gravitational waves from the $l=m=2$ mode to account for less than 14% of the spin-down energy budget. The fiducial equatorial ellipticity is limited to less than about 3e-5, which is the third best constraint for any young pulsar., Comment: 21 pages, 5 figures, published in ApJL
Published: 2020
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37. All-sky search in early O3 LIGO data for continuous gravitational-wave signals from unknown neutron stars in binary systems

Author: The LIGO Scientific Collaboration, the Virgo Collaboration, Abbott, R., Abbott, T. D., Abraham, S., Acernese, F., Ackley, K., Adams, A., Adams, C., Adhikari, R. X., Adya, V. B., Affeldt, C., Agarwal, D., Agathos, M., Agatsuma, K., Aggarwal, N., Aguiar, O. D., Aiello, L., Ain, A., Ajith, P., Aleman, K. M., Allen, G., Allocca, A., Altin, P. A., Amato, A., Anand, S., Ananyeva, A., Anderson, S. B., Anderson, W. G., Angelova, S. V., Ansoldi, S., Antelis, J. M., Antier, S., Appert, S., Arai, K., Araya, M. C., Areeda, J. S., Arène, M., Arnaud, N., Aronson, S. M., Arun, K. G., Asali, Y., Ashton, G., Aston, S. M., Astone, P., Aubin, F., Aufmuth, P., AultONeal, K., Austin, C., Babak, S., Badaracco, F., Bader, M. K. M., Bae, S., Baer, A. M., Bagnasco, S., Bai, Y., Baird, J., Ball, M., Ballardin, G., Ballmer, S. W., Bals, M., Balsamo, A., Baltus, G., Banagiri, S., Bankar, D., Bankar, R. S., Barayoga, J. C., Barbieri, C., Barish, B. C., Barker, D., Barneo, P., Barnum, S., Barone, F., Barr, B., Barsotti, L., Barsuglia, M., Barta, D., Bartlett, J., Barton, M. A., Bartos, I., Bassiri, R., Basti, A., Bawaj, M., Bayley, J. C., Baylor, A. C., Bazzan, M., Bécsy, B., Bedakihale, V. M., Bejger, M., Belahcene, I., Benedetto, V., Beniwal, D., Benjamin, M. G., Bennett, T. F., Bentley, J. D., BenYaala, M., Bergamin, F., Berger, B. K., Bernuzzi, S., Bersanetti, D., Bertolini, A., Betzwieser, J., Bhandare, R., Bhandari, A. V., Bhattacharjee, D., Bhaumik, S., Bidler, J., Bilenko, I. A., Billingsley, G., Birney, R., Birnholtz, O., Biscans, S., Bischi, M., Biscoveanu, S., Bisht, A., Biswas, B., Bitossi, M., Bizouard, M. -A., Blackburn, J. K., Blackman, J., Blair, C. D., Blair, D. G., Blair, R. M., Bobba, F., Bode, N., Boer, M., Bogaert, G., Boldrini, M., Bondu, F., Bonilla, E., Bonnand, R., Booker, P., Boom, B. A., Bork, R., Boschi, V., Bose, N., Bose, S., Bossilkov, V., Boudart, V., Bouffanais, Y., Bozzi, A., Bradaschia, C., Brady, P. R., Bramley, A., Branch, A., Branchesi, M., Brau, J. E., Breschi, M., Briant, T., Briggs, J. H., Brillet, A., Brinkmann, M., Brockill, P., Brooks, A. F., Brooks, J., Brown, D. D., Brunett, S., Bruno, G., Bruntz, R., Bryant, J., Buikema, A., Bulik, T., Bulten, H. J., Buonanno, A., Buscicchio, R., Buskulic, D., Byer, R. L., Cadonati, L., Caesar, M., Cagnoli, G., Cahillane, C., Cain III, H. W., Bustillo, J. Calderón, Callaghan, J. D., Callister, T. A., Calloni, E., Camp, J. B., Canepa, M., Cannavacciuolo, M., Cannon, K. C., Cao, H., Cao, J., Capote, E., Carapella, G., Carbognani, F., Carlin, J. B., Carney, M. F., Carpinelli, M., Carullo, G., Carver, T. L., Diaz, J. Casanueva, Casentini, C., Castaldi, G., Caudill, S., Cavaglià, M., Cavalier, F., Cavalieri, R., Cella, G., Cerdá-Durán, P., Cesarini, E., Chaibi, W., Chakravarti, K., Champion, B., Chan, C. -H., Chan, C., Chan, C. L., Chandra, K., Chanial, P., Chao, S., Charlton, P., Chase, E. A., Chassande-Mottin, E., Chatterjee, D., Chaturvedi, M., Chatziioannou, K., Chen, A., Chen, H. Y., Chen, J., Chen, X., Chen, Y., Chen, Z., Cheng, H., Cheong, C. K., Cheung, H. Y., Chia, H. Y., Chiadini, F., Chierici, R., Chincarini, A., Chiofalo, M. L., Chiummo, A., Cho, G., Cho, H. S., Choate, S., Choudhary, R. K., Choudhary, S., Christensen, N., Chu, Q., Chua, S., Chung, K. W., Ciani, G., Ciecielag, P., Cieślar, M., Cifaldi, M., Ciobanu, A. A., Ciolfi, R., Cipriano, F., Cirone, A., Clara, F., Clark, E. N., Clark, J. A., Clarke, L., Clearwater, P., Clesse, S., Cleva, F., Coccia, E., Cohadon, P. -F., Cohen, D. E., Cohen, L., Colleoni, M., Collette, C. G., Colpi, M., Compton, C. M., Constancio Jr., M., Conti, L., Cooper, S. J., Corban, P., Corbitt, T. R., Cordero-Carrión, I., Corezzi, S., Corley, K. R., Cornish, N., Corre, D., Corsi, A., Cortese, S., Costa, C. A., Cotesta, R., Coughlin, M. W., Coughlin, S. B., Coulon, J. -P., Countryman, S. T., Cousins, B., Couvares, P., Covas, P. B., Coward, D. M., Cowart, M. J., Coyne, D. C., Coyne, R., Creighton, J. D. E., Creighton, T. D., Criswell, A. W., Croquette, M., Crowder, S. G., Cudell, J. R., Cullen, T. J., Cumming, A., Cummings, R., Cuoco, E., Curyło, M., Canton, T. Dal, Dálya, G., Dana, A., DaneshgaranBajastani, L. M., D'Angelo, B., Danilishin, S. L., D'Antonio, S., Danzmann, K., Darsow-Fromm, C., Dasgupta, A., Datrier, L. E. H., Dattilo, V., Dave, I., Davier, M., Davies, G. S., Davis, D., Daw, E. J., Dean, R., DeBra, D., Deenadayalan, M., Degallaix, J., De Laurentis, M., Deléglise, S., Del Favero, V., De Lillo, F., De Lillo, N., Del Pozzo, W., DeMarchi, L. M., De Matteis, F., D'Emilio, V., Demos, N., Dent, T., Depasse, A., De Pietri, R., De Rosa, R., De Rossi, C., DeSalvo, R., De Simone, R., Dhurandhar, S., Díaz, M. C., Diaz-Ortiz Jr., M., Didio, N. A., Dietrich, T., Di Fiore, L., Di Fronzo, C., Di Giorgio, C., Di Giovanni, F., Di Girolamo, T., Di Lieto, A., Ding, B., Di Pace, S., Di Palma, I., Di Renzo, F., Divakarla, A. K., Dmitriev, A., Doctor, Z., D'Onofrio, L., Donovan, F., Dooley, K. L., Doravari, S., Dorrington, I., Drago, M., Driggers, J. C., Drori, Y., Du, Z., Ducoin, J. -G., Dupej, P., Durante, O., D'Urso, D., Duverne, P. -A., Dwyer, S. E., Easter, P. J., Ebersold, M., Eddolls, G., Edelman, B., Edo, T. B., Edy, O., Effler, A., Eichholz, J., Eikenberry, S. S., Eisenmann, M., Eisenstein, R. A., Ejlli, A., Errico, L., Essick, R. C., Estellés, H., Estevez, D., Etienne, Z., Etzel, T., Evans, M., Evans, T. M., Ewing, B. E., Fafone, V., Fair, H., Fairhurst, S., Fan, X., Farah, A. M., Farinon, S., Farr, B., Farr, W. M., Farrow, N. W., Fauchon-Jones, E. J., Favata, M., Fays, M., Fazio, M., Feicht, J., Fejer, M. M., Feng, F., Fenyvesi, E., Ferguson, D. L., Fernandez-Galiana, A., Ferrante, I., Ferreira, T. A., Fidecaro, F., Figura, P., Fiori, I., Fishbach, M., Fisher, R. P., Fishner, J. M., Fittipaldi, R., Fiumara, V., Flaminio, R., Floden, E., Flynn, E., Fong, H., Font, J. A., Fornal, B., Forsyth, P. W. F., Franke, A., Frasca, S., Frasconi, F., Frederick, C., Frei, Z., Freise, A., Frey, R., Fritschel, P., Frolov, V. V., Fronzé, G. G., Fulda, P., Fyffe, M., Gabbard, H. A., Gadre, B. U., Gaebel, S. M., Gair, J. R., Gais, J., Galaudage, S., Gamba, R., Ganapathy, D., Ganguly, A., Gaonkar, S. G., Garaventa, B., García-Núñez, C., García-Quirós, C., Garufi, F., Gateley, B., Gaudio, S., Gayathri, V., Gemme, G., Gennai, A., George, J., Gergely, L., Gewecke, P., Ghonge, S., Ghosh, Abhirup., Ghosh, Archisman, Ghosh, Shaon, Ghosh, Shrobana, Ghosh, Sourath, Giacomazzo, B., Giacoppo, L., Giaime, J. A., Giardina, K. D., Gibson, D. R., Gier, C., Giesler, M., Giri, P., Gissi, F., Glanzer, J., Gleckl, A. E., Godwin, P., Goetz, E., Goetz, R., Gohlke, N., Goncharov, B., González, G., Gopakumar, A., 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. C., Green, R., Gretarsson, A. M., Gretarsson, E. M., Griffith, D., Griffiths, W., Griggs, H. L., Grignani, G., Grimaldi, A., Grimes, E., Grimm, S. J., Grote, H., Grunewald, S., Gruning, P., Guerrero, J. G., Guidi, G. M., Guimaraes, A. R., Guixé, G., Gulati, H. K., Guo, H. -K., Guo, Y., Gupta, Anchal, Gupta, Anuradha, Gupta, P., Gustafson, E. K., Gustafson, R., Guzman, F., Haegel, L., Halim, O., Hall, E. D., Hamilton, E. Z., Hammond, G., Haney, M., Hanks, J., Hanna, C., Hannam, M. D., Hannuksela, O. A., Hansen, H., Hansen, T. J., Hanson, J., Harder, T., Hardwick, T., Haris, K., Harms, J., Harry, G. M., Harry, I. W., Hartwig, D., Haskell, B., Hasskew, R. K., Haster, C. -J., Haughian, K., Hayes, F. J., Healy, J., Heidmann, A., Heintze, M. C., Heinze, J., Heinzel, J., Heitmann, H., Hellman, F., Hello, P., Helmling-Cornell, A. F., Hemming, G., Hendry, M., Heng, I. S., Hennes, E., Hennig, J., Hennig, M. H., Vivanco, F. Hernandez, Heurs, M., Hild, S., Hill, P., Hines, A. S., Hochheim, S., Hofman, D., Hohmann, J. N., Holgado, A. M., Holland, N. A., Hollows, I. J., Holmes, Z. J., Holt, K., Holz, D. E., Hopkins, P., Hough, J., Howell, E. J., Hoy, C. G., Hoyland, D., Hreibi, A., Hsu, Y., Huang, Y., Hübner, M. T., Huddart, A. D., Huerta, E. A., Hughey, B., Hui, V., Husa, S., Huttner, S. H., Huxford, R., Huynh-Dinh, T., Idzkowski, B., Iess, A., Inchauspe, H., Ingram, C., Intini, G., Isi, M., Isleif, K., Iyer, B. R., JaberianHamedan, V., Jacqmin, T., Jadhav, S. J., Jadhav, S. P., James, A. L., Jan, A. Z., Jani, K., Janssens, K., Janthalur, N. N., Jaranowski, P., Jariwala, D., Jaume, R., Jenkins, A. C., Jeunon, M., Jia, W., Jiang, J., Johns, G. R., Jones, A. W., Jones, D. I., Jones, J. D., Jones, P., Jones, R., Jonker, R. J. G., Ju, L., Junker, J., Kalaghatgi, C. V., Kalogera, V., Kamai, B., Kandhasamy, S., Kang, G., Kanner, J. B., Kao, Y., Kapadia, S. J., Kapasi, D. P., Karathanasis, C., Karki, S., Kashyap, R., Kasprzack, M., Kastaun, W., Katsanevas, S., Katsavounidis, E., Katzman, W., Kaur, T., Kawabe, K., Kéfélian, F., Keitel, D., Key, J. S., Khadka, S., Khalili, F. Y., Khan, I., Khan, S., Khazanov, E. A., Khetan, N., Khursheed, M., Kijbunchoo, N., Kim, C., Kim, J. C., Kim, K., Kim, W. S., Kim, Y. -M., Kimball, C., King, P. J., Kinley-Hanlon, M., Kirchhoff, R., Kissel, J. S., Kleybolte, L., Klimenko, S., Knee, A. M., Knowles, T. D., Knyazev, E., Koch, P., Koekoek, G., Koley, S., Kolitsidou, P., Kolstein, M., Komori, K., Kondrashov, V., Kontos, A., Koper, N., Korobko, M., Kovalam, M., Kozak, D. B., Kringel, V., Krishnendu, N. V., Królak, A., Kuehn, G., Kuei, F., Kumar, A., Kumar, P., Kumar, Rahul, Kumar, Rakesh, Kuns, K., Kwang, S., Laghi, D., Lalande, E., Lam, T. L., Lamberts, A., Landry, M., Lane, B. B., Lang, R. N., Lange, J., Lantz, B., La Rosa, I., Lartaux-Vollard, A., Lasky, P. D., Laxen, M., Lazzarini, A., Lazzaro, C., Leaci, P., Leavey, S., Lecoeuche, Y. K., Lee, H. M., Lee, H. W., Lee, J., Lee, K., Lehmann, J., Lemaître, A., Leon, E., Leroy, N., Letendre, N., Levin, Y., Leviton, J. N., Li, A. K. Y., Li, B., Li, J., Li, T. G. F., Li, X., Linde, F., Linker, S. D., Linley, J. N., Littenberg, T. B., Liu, J., Liu, K., Liu, X., Llorens-Monteagudo, M., Lo, R. K. L., Lockwood, A., Lollie, M. L., London, L. T., Longo, A., Lopez, D., Lorenzini, M., Loriette, V., Lormand, M., Losurdo, G., Lough, J. D., Lousto, C. O., Lovelace, G., Lück, H., Lumaca, D., Lundgren, A. P., Macas, R., MacInnis, M., Macleod, D. M., MacMillan, I. A. O., Macquet, A., Hernandez, I. Magaña, Magaña-Sandoval, F., Magazzù, C., Magee, R. M., Maggiore, R., Majorana, E., Maksimovic, I., Maliakal, S., Malik, A., Man, N., Mandic, V., Mangano, V., Mango, J. L., Mansell, G. L., Manske, M., Mantovani, M., Mapelli, M., Marchesoni, F., Marion, F., Mark, Z., Márka, S., Márka, Z., Markakis, C., Markosyan, A. S., Markowitz, A., Maros, E., Marquina, A., Marsat, S., Martelli, F., Martin, I. W., Martin, R. M., Martinez, M., Martinez, V., Martinovic, K., Martynov, D. V., Marx, E. J., Masalehdan, H., Mason, K., Massera, E., Masserot, A., Massinger, T. J., Masso-Reid, M., Mastrogiovanni, S., Matas, A., Mateu-Lucena, M., Matichard, F., Matiushechkina, M., Mavalvala, N., McCann, J. J., McCarthy, R., McClelland, D. E., McClincy, P., McCormick, S., McCuller, L., McGhee, G. I., McGuire, S. C., McIsaac, C., McIver, J., McManus, D. J., McRae, T., McWilliams, S. T., Meacher, D., Mehmet, M., Mehta, A. K., Melatos, A., Melchor, D. A., Mendell, G., Menendez-Vazquez, A., Menoni, C. S., Mercer, R. A., Mereni, L., Merfeld, K., Merilh, E. L., Merritt, J. D., Merzougui, M., Meshkov, S., Messenger, C., Messick, C., Meyers, P. M., Meylahn, F., Mhaske, A., Miani, A., Miao, H., Michaloliakos, I., Michel, C., Middleton, H., Milano, L., Miller, A. L., Millhouse, M., Mills, J. C., Milotti, E., Milovich-Goff, M. C., Minazzoli, O., Minenkov, Y., Mir, Ll. M., Mishkin, A., Mishra, C., Mishra, T., Mistry, T., Mitra, S., Mitrofanov, V. P., Mitselmakher, G., Mittleman, R., Mo, Geoffrey, Mogushi, K., Mohapatra, S. R. P., Mohite, S. R., Molina, I., Molina-Ruiz, M., Mondin, M., Montani, M., Moore, C. J., Moraru, D., Morawski, F., More, A., Moreno, C., Moreno, G., Morisaki, S., Mours, B., Mow-Lowry, C. M., Mozzon, S., Muciaccia, F., Mukherjee, Arunava, Mukherjee, D., Mukherjee, Soma, Mukherjee, Subroto, Mukund, N., Mullavey, A., Munch, J., Muñiz, E. A., Murray, P. G., Musenich, R., Nadji, S. L., Nagar, A., Nardecchia, I., Naticchioni, L., Nayak, B., Nayak, R. K., Neil, B. F., Neilson, J., Nelemans, G., Nelson, T. J. N., Nery, M., Neunzert, A., Ng, K. Y., Ng, S. W. S., Nguyen, C., Nguyen, P., Nguyen, T., Nichols, S. A., Nissanke, S., Nocera, F., Noh, M., Norman, M., North, C., Nuttall, L. K., Oberling, J., O'Brien, B. D., O'Dell, J., Oganesyan, G., Oh, J. J., Oh, S. H., Ohme, F., Ohta, H., Okada, M. A., Olivetto, C., Oram, R., O'Reilly, B., Ormiston, R. G., Ormsby, N. D., Ortega, L. F., O'Shaughnessy, R., O'Shea, E., Ossokine, S., Osthelder, C., Ottaway, D. J., Overmier, H., Pace, A. E., Pagano, G., Page, M. A., Pagliaroli, G., Pai, A., Pai, S. A., Palamos, J. R., Palashov, O., Palomba, C., Panda, P. K., Pang, P. T. H., Pankow, C., Pannarale, F., Pant, B. C., Paoletti, F., Paoli, A., Paolone, A., Parker, W., Pascucci, D., Pasqualetti, A., Passaquieti, R., Passuello, D., Patel, M., Patricelli, B., Payne, E., Pechsiri, T. C., Pedraza, M., Pegoraro, M., Pele, A., Penn, S., Perego, A., Pereira, A., Pereira, T., Perez, C. J., Périgois, C., Perreca, A., Perriès, S., Petermann, J., Petterson, D., Pfeiffer, H. P., Pham, K. A., Phukon, K. S., Piccinni, O. J., Pichot, M., Piendibene, M., Piergiovanni, F., Pierini, L., Pierro, V., Pillant, G., Pilo, F., Pinard, L., Pinto, I. M., Piotrzkowski, B. J., Piotrzkowski, K., Pirello, M., Pitkin, M., Placidi, E., Plastino, W., Pluchar, C., Poggiani, R., Polini, E., Pong, D. Y. T., Ponrathnam, S., Popolizio, P., Porter, E. K., Powell, J., Pracchia, M., Pradier, T., Prajapati, A. K., Prasai, K., Prasanna, R., Pratten, G., Prestegard, T., Principe, M., Prodi, G. 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., Rail, S. X., Raja, S., Rajan, C., Ramirez, K. E., Ramirez, T. D., Ramos-Buades, A., Rana, J., Rapagnani, P., Rapol, U. D., Ratto, B., Raymond, V., Raza, N., Razzano, M., Read, J., Rees, L. A., Regimbau, T., Rei, L., Reid, S., Reitze, D. H., Relton, P., Rettegno, P., Ricci, F., Richardson, C. J., Richardson, J. W., Richardson, L., Ricker, P. M., Riemenschneider, G., Riles, K., Rizzo, M., Robertson, N. A., Robie, R., 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., Rose, C. A., Rosińska, D., Rosofsky, S. G., Ross, M. P., Rowan, S., Rowlinson, S. J., Roy, Santosh, Roy, Soumen, Rozza, D., Ruggi, P., Ryan, K., Sachdev, S., Sadecki, T., Sadiq, J., Sakellariadou, M., Salafia, O. S., Salconi, L., Saleem, M., Salemi, F., Samajdar, A., Sanchez, E. J., Sanchez, J. H., Sanchez, L. E., Sanchis-Gual, N., Sanders, J. R., Sanuy, A., Saravanan, T. R., Sarin, N., Sassolas, B., Satari, H., Sauter, O., Savage, R. L., Savant, V., Sawant, D., Sawant, H. L., Sayah, S., Schaetzl, D., Scheel, M., Scheuer, J., Schindler-Tyka, A., Schmidt, P., Schnabel, R., Schneewind, M., Schofield, R. M. S., Schönbeck, A., Schulte, B. W., Schutz, B. F., Schwartz, E., Scott, J., Scott, S. M., Seglar-Arroyo, M., Seidel, E., Sellers, D., Sergeev, A., Sengupta, A. S., Sennett, N., Sentenac, D., Seo, E. G., Sequino, V., Setyawati, Y., Shaffer, T., Shahriar, M. S., Shams, B., Sharifi, S., Sharma, A., Sharma, P., Shawhan, P., Shcheblanov, N. S., 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., 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., Spagnuolo, V., Spencer, A. P., Spera, M., Srivastava, A. K., Srivastava, V., Staats, K., Stachie, C., Steer, D. A., Steinlechner, J., Steinlechner, S., Stops, D. J., Stover, M., Strain, K. A., Strang, L. C., 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., Szewczyk, P., Tacca, M., Tait, S. C., Talbot, C., Tanasijczuk, A. J., Tanner, D. B., Tao, D., Tapia, A., Martin, E. N. Tapia San, Tasson, J. D., Tenorio, R., Terkowski, L., Test, M., Thirugnanasambandam, M. P., 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., Torres-Forné, A., Torrie, C. I., Melo, I. Tosta e, Töyrä, D., Trapananti, A., Travasso, F., Traylor, G., Tringali, M. C., Tripathee, A., Troiano, L., Trovato, A., Trudeau, R. J., Tsai, D. S., Tsai, D., 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., Brand, J. F. J. van den, Broeck, C. Van Den, Vander-Hyde, D. C., van der Schaaf, L., van Heijningen, J. V., Vardaro, M., Vargas, A. F., Varma, V., 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., Villa-Ortega, V., Vinet, J. -Y., Vitale, S., Vo, T., Vocca, H., von Reis, E. R. G., von Wrangel, J., Vorvick, C., Vyatchanin, S. P., Wade, L. E., Wade, M., Wagner, K. J., Walet, R. C., Walker, M., Wallace, G. S., Wallace, L., Walsh, S., Wang, J. Z., Wang, W. H., Ward, R. L., Warner, J., Was, M., Washington, N. Y., Watchi, J., Weaver, B., Wei, L., Weinert, M., Weinstein, A. J., Weiss, R., Weller, C. M., Wellmann, F., Wen, L., Weßels, P., Westhouse, J. W., Wette, K., Whelan, J. T., White, D. D., Whiting, B. F., Whittle, C., Wilken, D., Williams, D., Williams, M. J., Williamson, A. R., Willis, J. L., Willke, B., Wilson, D. J., Winkler, W., Wipf, C. C., Wlodarczyk, T., Woan, G., Woehler, J., Wofford, J. K., Wong, I. C. F., Wu, D. S., Wysocki, D. M., Xiao, L., Yamamoto, H., Yang, F. W., Yang, L., Yang, Y., Yang, Z., Yap, M. J., Yeeles, D. W., Yelikar, A. B., Ying, M., Yoon, A., Yu, Hang, Yu, Haocun, Zadrożny, A., Zanolin, M., Zelenova, T., Zendri, J. -P., Zevin, M., Zhang, J., Zhang, L., Zhang, R., Zhang, T., Zhao, C., Zhao, G., Zhao, Y., Zhou, Z., Zhu, X. J., Zucker, M. E., and Zweizig, J.
Subjects: General Relativity and Quantum Cosmology, Astrophysics - High Energy Astrophysical Phenomena
Abstract: Rapidly spinning neutron stars are promising sources of persistent, continuous gravitational waves. Detecting such a signal would allow probing of the physical properties of matter under extreme conditions. A significant fraction of the known pulsar population belongs to binary systems. Searching for unknown neutron stars in binary systems requires specialized algorithms to address unknown orbital frequency modulations. We present a search for continuous gravitational waves emitted by neutron stars in binary systems in early data from the third observing run of the Advanced LIGO and Advanced Virgo detectors using the semicoherent, GPU-accelerated, BinarySkyHough pipeline. The search analyzes the most sensitive frequency band of the LIGO detectors, 50 - 300 Hz. Binary orbital parameters are split into four regions, comprising orbital periods of 3 - 45 days and projected semimajor axes of 2 - 40 light-seconds. No detections are reported. We estimate the sensitivity of the search using simulated continuous wave signals, achieving the most sensitive results to date across the analyzed parameter space., Comment: 23 pages, 12 figures, 7 tables
Published: 2020
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38. Accelerated, Scalable and Reproducible AI-driven Gravitational Wave Detection

Author: Huerta, E. A., Khan, Asad, Huang, Xiaobo, Tian, Minyang, Levental, Maksim, Chard, Ryan, Wei, Wei, Heflin, Maeve, Katz, Daniel S., Kindratenko, Volodymyr, Mu, Dawei, Blaiszik, Ben, and Foster, Ian
Subjects: General Relativity and Quantum Cosmology, Astrophysics - Instrumentation and Methods for Astrophysics, Computer Science - Artificial Intelligence, Computer Science - Distributed, Parallel, and Cluster Computing, 68T01, 68T35, 83C35, 83C57
Abstract: The development of reusable artificial intelligence (AI) models for wider use and rigorous validation by the community promises to unlock new opportunities in multi-messenger astrophysics. Here we develop a workflow that connects the Data and Learning Hub for Science, a repository for publishing AI models, with the Hardware Accelerated Learning (HAL) cluster, using funcX as a universal distributed computing service. Using this workflow, an ensemble of four openly available AI models can be run on HAL to process an entire month's worth (August 2017) of advanced Laser Interferometer Gravitational-Wave Observatory data in just seven minutes, identifying all four all four binary black hole mergers previously identified in this dataset and reporting no misclassifications. This approach combines advances in AI, distributed computing, and scientific data infrastructure to open new pathways to conduct reproducible, accelerated, data-driven discovery., Comment: 17 pages, 5 figures; v2: 12 pages, 6 figures. Accepted to Nature Astronomy. See also the Behind the Paper blog in Nature Astronomy "https://astronomycommunity.nature.com/posts/from-disruption-to-sustained-innovation-artificial-intelligence-for-gravitational-wave-astrophysics"
Published: 2020
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39. Deep Learning with Quantized Neural Networks for Gravitational Wave Forecasting of Eccentric Compact Binary Coalescence

Author: Wei, Wei, Huerta, E. A., Yun, Mengshen, Loutrel, Nicholas, Shaikh, Md Arif, Kumar, Prayush, Haas, Roland, and Kindratenko, Volodymyr
Subjects: General Relativity and Quantum Cosmology, I.2, I.5
Abstract: We present the first application of deep learning forecasting for binary neutron stars, neutron star - black hole systems, and binary black hole mergers that span an eccentricity range e <= 0.9. We train neural networks that describe these astrophysical populations, and then test their performance by injecting simulated eccentric signals in advanced LIGO noise available at the \texttt{Gravitational Wave Open Science Center} to: 1) quantify how fast neural networks identify these signals before the binary components merge; 2) quantify how accurately neural networks estimate the time to merger once gravitational waves are identified; and 3) estimate the time-dependent sky localization of these events from early detection to merger. Our findings show that deep learning can identify eccentric signals from a few seconds (for binary black holes) up to tens of seconds (for binary neutron stars) prior to merger. A quantized version of our neural networks achieves 4x reduction in model size, and up to 2.5x inference speed up. These novel algorithms may be used to facilitate time-sensitive multi-messenger astrophysics observations of compact binaries in dense stellar environments., Comment: 14 pages, 7 figures
Published: 2020
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40. Initial Data and Eccentricity Reduction Toolkit for Binary Black Hole Numerical Relativity Waveforms

Author: Habib, Sarah, Ramos-Buades, Antoni, Huerta, E. A., Husa, Sascha, Haas, Roland, and Etienne, Zachariah
Subjects: General Relativity and Quantum Cosmology
Abstract: The production of numerical relativity waveforms that describe quasicircular binary black hole mergers requires high-quality initial data, and an algorithm to iteratively reduce residual eccentricity. To date, these tools remain closed source, or in commercial software that prevents their use in high performance computing platforms. To address these limitations, and to ensure that the broader numerical relativity community has access to these tools, herein we provide all the required elements to produce high-quality numerical relativity simulations in supercomputer platforms, namely: open source parameter files to numerical simulate spinning black hole binaries with asymmetric mass-ratios; open source $\texttt{Python}$ tools to produce high-quality initial data for numerical relativity simulations of spinning black hole binaries on quasi-circular orbits; open source $\texttt{Python}$ tools for eccentricity reduction, both as stand-alone software and deployed in the $\texttt{Einstein Toolkit}$'s software infrastructure. This open source toolkit fills in a critical void in the literature at a time when numerical relativity has an ever increasing role in the study and interpretation of gravitational wave sources. As part of our community building efforts, and to streamline and accelerate the use of these resources, we provide tutorials that describe, step by step, how to obtain and use these open source numerical relativity tools., Comment: 11 pages, 1 figure, 1 table. Open source software available at: https://github.com/NCSAGravity/eccred
Published: 2020
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41. Deep Learning Ensemble for Real-time Gravitational Wave Detection of Spinning Binary Black Hole Mergers

Author: Wei, Wei, Khan, Asad, Huerta, E. A., Huang, Xiaobo, and Tian, Minyang
Subjects: General Relativity and Quantum Cosmology, I.2, I.5
Abstract: We introduce the use of deep learning ensembles for real-time, gravitational wave detection of spinning binary black hole mergers. This analysis consists of training independent neural networks that simultaneously process strain data from multiple detectors. The output of these networks is then combined and processed to identify significant noise triggers. We have applied this methodology in O2 and O3 data finding that deep learning ensembles clearly identify binary black hole mergers in open source data available at the Gravitational-Wave Open Science Center. We have also benchmarked the performance of this new methodology by processing 200 hours of open source, advanced LIGO noise from August 2017. Our findings indicate that our approach identifies real gravitational wave sources in advanced LIGO data with a false positive rate of 1 misclassification for every 2.7 days of searched data. A follow up of these misclassifications identified them as glitches. Our deep learning ensemble represents the first class of neural network classifiers that are trained with millions of modeled waveforms that describe quasi-circular, spinning, non-precessing, binary black hole mergers. Once fully trained, our deep learning ensemble processes advanced LIGO strain data faster than real-time using 4 NVIDIA V100 GPUs., Comment: 10 pages, 7 figures
Published: 2020
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42. Tests of General Relativity with Binary Black Holes from the second LIGO-Virgo Gravitational-Wave Transient Catalog

Author: The LIGO Scientific Collaboration, the Virgo Collaboration, Abbott, R., Abbott, T. D., Abraham, S., Acernese, F., Ackley, K., Adams, A., Adams, C., Adhikari, R. X., Adya, V. B., Affeldt, C., Agathos, M., Agatsuma, K., Aggarwal, N., Aguiar, O. D., Aiello, L., Ain, A., Ajith, P., Allen, G., Allocca, A., Altin, P. A., Amato, A., Anand, S., Ananyeva, A., Anderson, S. B., Anderson, W. G., Angelova, S. V., Ansoldi, S., Antelis, J. M., Antier, S., Appert, S., Arai, K., Araya, M. C., Areeda, J. S., Arène, M., Arnaud, N., Aronson, S. M., Arun, K. G., Asali, Y., Ascenzi, S., Ashton, G., Aston, S. M., Astone, P., Aubin, F., Aufmuth, P., AultONeal, K., Austin, C., Avendano, V., Babak, S., Badaracco, F., Bader, M. K. M., Bae, S., Baer, A. M., Bagnasco, S., Baird, J., Ball, M., Ballardin, G., Ballmer, S. W., Bals, A., Balsamo, A., Baltus, G., Banagiri, S., Bankar, D., Bankar, R. S., Barayoga, J. C., Barbieri, C., Barish, B. C., 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. C., Bazzan, M., Becher, B. R., Bécsy, B., Bedakihale, V. M., Bejger, M., Belahcene, I., Beniwal, D., Benjamin, M. G., Benkel, R., Bennett, T. F., Bentley, J. D., Bergamin, F., Berger, B. K., Bergmann, G., Bernuzzi, S., Berry, C. P. L., Bersanetti, D., Bertolini, A., Betzwieser, J., Bhandare, R., Bhandari, A. V., Bhattacharjee, D., Bidler, J., Bilenko, I. A., Billingsley, G., Birney, R., Birnholtz, O., Biscans, S., Bischi, M., Biscoveanu, S., Bisht, A., Bitossi, M., Bizouard, M. -A., Blackburn, J. K., Blackman, J., Blair, C. D., Blair, D. G., Blair, R. M., Blanch, O., Bobba, F., Bode, N., Boer, M., Boetzel, Y., Bogaert, G., Boldrini, M., Bondu, F., Bonilla, E., Bonnand, R., Booker, P., Boom, B. A., Borhanian, S., Bork, R., Boschi, V., Bose, N., Bose, S., Bossilkov, V., Boudart, V., Bouffanais, Y., Bozzi, A., Bradaschia, C., Brady, P. R., Bramley, A., Branchesi, M., Brau, J. E., Breschi, M., Briant, T., Briggs, J. H., Brighenti, F., Brillet, A., Brinkmann, M., Brockill, P., Brooks, A. F., Brooks, J., Brown, D. D., Brunett, S., Bruno, G., Bruntz, R., Buikema, A., Bulik, T., Bulten, H. J., Buonanno, A., Buskulic, D., Byer, R. L., Cabero, M., Cadonati, L., Caesar, M., Cagnoli, G., Cahillane, C., Bustillo, J. Calderón, Callaghan, J. D., Callister, T. A., Calloni, E., Camp, J. B., Canepa, M., Cannon, K. C., Cao, H., Cao, J., Carapella, G., Carbognani, F., Carney, M. F., Carpinelli, M., Carullo, G., Carver, T. L., Diaz, J. Casanueva, Casentini, C., Caudill, S., Cavaglià, M., Cavalier, F., Cavalieri, R., Cella, G., Cerdá-Durán, P., Cesarini, E., Chaibi, W., Chakravarti, K., Chan, C. -L., Chan, C., Chandra, K., Chanial, P., Chao, S., Charlton, P., Chase, E. A., Chassande-Mottin, E., Chatterjee, D., Chaturvedi, M., Chatziioannou, K., Chen, A., Chen, H. Y., Chen, X., Chen, Y., Cheng, H. -P., Cheong, C. K., Chia, H. Y., Chiadini, F., Chierici, R., Chincarini, A., Chiummo, A., Cho, G., Cho, H. S., Cho, M., Choate, S., Christensen, N., Chu, Q., Chua, S., Chung, K. W., Chung, S., Ciani, G., Ciecielag, P., Cieślar, M., Cifaldi, M., Ciobanu, A. A., Ciolfi, R., Cipriano, F., Cirone, A., Clara, F., Clark, E. N., Clark, J. A., Clarke, L., Clearwater, P., Clesse, S., Cleva, F., Coccia, E., Cohadon, P. -F., Cohen, D. E., Colleoni, M., Collette, C. G., Collins, C., Colpi, M., Constancio Jr., M., Conti, L., Cooper, S. J., Corban, P., Corbitt, T. R., Cordero-Carrión, I., Corezzi, S., Corley, K. R., Cornish, N., Corre, D., Corsi, A., Cortese, S., Costa, C. A., Cotesta, R., Coughlin, M. W., Coughlin, S. B., Coulon, J. -P., Countryman, S. T., Couvares, P., Covas, P. B., Coward, D. M., Cowart, M. J., Coyne, D. C., Coyne, R., Creighton, J. D. E., Creighton, T. D., Croquette, M., Crowder, S. G., Cudell, J. R., Cullen, T. J., Cumming, A., Cummings, R., Cunningham, L., Cuoco, E., Cury{l}o, M., Canton, T. Dal, Dálya, G., Dana, A., DaneshgaranBajastani, L. M., D'Angelo, B., Danilishin, S. L., D'Antonio, S., Danzmann, K., Darsow-Fromm, C., Dasgupta, A., Datrier, L. E. H., Dattilo, V., Dave, I., Davier, M., Davies, G. S., Davis, D., Daw, E. J., Dean, R., DeBra, D., Deenadayalan, M., Degallaix, J., De Laurentis, M., Deléglise, S., Del Favero, V., De Lillo, F., De Lillo, N., Del Pozzo, W., DeMarchi, L. M., 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., Dhani, A., Dhurandhar, S., Díaz, M. C., Diaz-Ortiz Jr., M., Didio, N. A., 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. K., Dmitriev, A., Doctor, Z., D'Onofrio, L., Donovan, F., Dooley, K. L., Doravari, S., Dorrington, I., Downes, T. P., Drago, M., Driggers, J. C., Du, Z., Ducoin, J. -G., Dudi, R., Dupej, P., Durante, O., D'Urso, D., Duverne, P. -A., Dwyer, S. E., Easter, P. J., Eddolls, G., Edelman, B., Edo, T. B., Edy, O., Effler, A., Eichholz, J., Eikenberry, S. S., Eisenmann, M., Eisenstein, R. A., Ejlli, A., Errico, L., Essick, R. C., Estellés, H., Estevez, D., Etienne, Z. B., Etzel, T., Evans, M., Evans, T. M., Ewing, B. E., Fafone, V., Fair, H., Fairhurst, S., Fan, X., Farah, A. M., Farinon, S., Farr, B., Farr, W. M., Fauchon-Jones, E. J., Favata, M., Fays, M., Fazio, M., Feicht, J., Fejer, M. M., Feng, F., Fenyvesi, E., Ferguson, D. L., Fernandez-Galiana, A., Ferrante, I., Ferreira, T. A., Fidecaro, F., Figura, P., Fiori, I., Fiorucci, D., Fishbach, M., Fisher, R. P., Fishner, J. M., Fittipaldi, R., Fitz-Axen, M., Fiumara, V., Flaminio, R., Floden, E., Flynn, E., Fong, H., Font, J. A., Forsyth, P. W. F., Fournier, J. -D., Frasca, S., Frasconi, F., Frei, Z., Freise, A., Frey, R., Frey, V., Fritschel, P., Frolov, V. V., Fronzé, G. G., Fulda, P., Fyffe, M., Gabbard, H. A., Gadre, B. U., Gaebel, S. M., Gair, J. R., Gais, J., Galaudage, S., Gamba, R., Ganapathy, D., Ganguly, A., Gaonkar, S. G., Garaventa, B., García-Quirós, C., Garufi, F., Gateley, B., Gaudio, S., Gayathri, V., Gemme, G., Gennai, A., George, D., George, J., George, R. N., Gergely, L., Ghonge, S., Ghosh, Abhirup, Ghosh, Archisman, Ghosh, S., Giacomazzo, B., Giacoppo, L., Giaime, J. A., Giardina, K. D., Gibson, D. R., Gier, C., Gill, K., Giri, P., Glanzer, J., Gleckl, A. E., Godwin, P., Goetz, E., Goetz, R., Gohlke, N., Goncharov, B., González, G., Gopakumar, A., Gossan, S. E., 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. C., Green, R., Gretarsson, E. M., Griggs, H. L., Grignani, G., Grimaldi, A., Grimes, E., Grimm, S. J., Grote, H., Grunewald, S., Gruning, P., Guerrero, J. G., Guidi, G. M., Guimaraes, A. R., Guixé, G., Gulati, H. K., Guo, Y., Gupta, Anchal, Gupta, Anuradha, Gupta, P., Gustafson, E. K., Gustafson, R., Guzman, F., Haegel, L., Halim, O., Hall, E. D., Hamilton, E. Z., Hammond, G., Haney, M., Hanke, M. M., Hanks, J., Hanna, C., Hannam, M. D., Hannuksela, O. A., Hannuksela, O., Hansen, H., Hansen, T. J., Hanson, J., Harder, T., Hardwick, T., Haris, K., Harms, J., Harry, G. M., Harry, I. W., Hartwig, D., Hasskew, R. K., Haster, C. -J., Haughian, K., Hayes, F. J., Healy, J., Heidmann, A., Heintze, M. C., Heinze, J., Heinzel, J., Heitmann, H., Hellman, F., Hello, P., Helmling-Cornell, A. F., Hemming, G., Hendry, M., Heng, I. S., Hennes, E., Hennig, J., Hennig, M. H., Vivanco, F. Hernandez, Heurs, M., Hild, S., Hill, P., Hines, A. S., Hochheim, S., Hofgard, E., Hofman, D., Hohmann, J. N., Holgado, A. M., Holland, N. A., Hollows, I. J., Holmes, Z. J., Holt, K., Holz, D. E., Hopkins, P., Horst, C., Hough, J., Howell, E. J., Hoy, C. G., Hoyland, D., Huang, Y., Hübner, M. T., Huddart, A. D., Huerta, E. A., Hughey, B., Hui, V., Husa, S., Huttner, S. H., Hutzler, B. M., Huxford, R., Huynh-Dinh, T., Idzkowski, B., Iess, A., Imperato, S., Inchauspe, H., Ingram, C., Intini, G., Isi, M., Iyer, B. R., JaberianHamedan, V., Jacqmin, T., Jadhav, S. J., Jadhav, S. P., James, A. L., Jani, K., Janssens, K., Janthalur, N. N., Jaranowski, P., Jariwala, D., Jaume, R., Jenkins, A. C., Jeunon, M., Jiang, J., Johns, G. R., Johnson-McDaniel, N. K., Jones, A. W., Jones, D. I., Jones, J. D., Jones, P., 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., Kapasi, D. P., Karathanasis, C., Karki, S., Kashyap, R., Kasprzack, M., Kastaun, W., Katsanevas, S., Katsavounidis, E., Katzman, W., Kawabe, K., Kéfélian, F., Keitel, D., Key, J. S., Khadka, S., Khalili, F. Y., Khan, I., Khan, S., Khazanov, E. A., Khetan, N., Khursheed, M., Kijbunchoo, N., Kim, C., Kim, G. J., Kim, J. C., Kim, K., Kim, W. S., Kim, Y. -M., Kimball, C., King, P. J., Kinley-Hanlon, M., Kirchhoff, R., Kissel, J. S., Kleybolte, L., Klimenko, S., Knowles, T. D., Knyazev, E., Koch, P., Koehlenbeck, S. M., Koekoek, G., Koley, S., Kolstein, M., Komori, K., Kondrashov, V., Kontos, A., Koper, N., Korobko, M., Korth, W. Z., Kovalam, M., Kozak, D. B., Krämer, C., Kringel, V., Krishnendu, N. V., Królak, A., Kuehn, G., Kumar, A., Kumar, P., Kumar, Rahul, Kumar, Rakesh, Kuns, K., Kwang, S., Lackey, B. D., Laghi, D., Lalande, E., Lam, T. L., Lamberts, A., Landry, M., Lane, B. B., Lang, R. N., Lange, J., Lantz, B., Lanza, R. K., La Rosa, I., Lartaux-Vollard, A., Lasky, P. D., Laxen, M., Lazzarini, A., Lazzaro, C., Leaci, P., Leavey, S., Lecoeuche, Y. K., Lee, H. M., Lee, H. 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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., Zhou, M., Zhou, Z., Zhu, X. J., Zimmerman, A. B., Zucker, M. E., and Zweizig, J.
Subjects: General Relativity and Quantum Cosmology, Astrophysics - High Energy Astrophysical Phenomena
Abstract: Gravitational waves enable tests of general relativity in the highly dynamical and strong-field regime. Using events detected by LIGO-Virgo up to 1 October 2019, we evaluate the consistency of the data with predictions from the theory. We first establish that residuals from the best-fit waveform are consistent with detector noise, and that the low- and high-frequency parts of the signals are in agreement. We then consider parametrized modifications to the waveform by varying post-Newtonian and phenomenological coefficients, improving past constraints by factors of ${\sim}2$; we also find consistency with Kerr black holes when we specifically target signatures of the spin-induced quadrupole moment. Looking for gravitational-wave dispersion, we tighten constraints on Lorentz-violating coefficients by a factor of ${\sim}2.6$ and bound the mass of the graviton to $m_g \leq 1.76 \times 10^{-23} \mathrm{eV}/c^2$ with 90% credibility. We also analyze the properties of the merger remnants by measuring ringdown frequencies and damping times, constraining fractional deviations away from the Kerr frequency to $\delta \hat{f}_{220} = 0.03^{+0.38}_{-0.35}$ for the fundamental quadrupolar mode, and $\delta \hat{f}_{221} = 0.04^{+0.27}_{-0.32}$ for the first overtone; additionally, we find no evidence for postmerger echoes. Finally, we determine that our data are consistent with tensorial polarizations through a template-independent method. When possible, we assess the validity of general relativity based on collections of events analyzed jointly. We find no evidence for new physics beyond general relativity, for black hole mimickers, or for any unaccounted systematics., Comment: 24 pages + appendices, 19 figures; journal version
Published: 2020
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43. Population Properties of Compact Objects from the Second LIGO-Virgo Gravitational-Wave Transient Catalog

Author: The LIGO Scientific Collaboration, the Virgo Collaboration, Abbott, R., Abbott, T. D., Abraham, S., Acernese, F., Ackley, K., Adams, A., Adams, C., Adhikari, R. X., Adya, V. B., Affeldt, C., Agathos, M., Agatsuma, K., Aggarwal, N., Aguiar, O. D., Aiello, L., Ain, A., Ajith, P., Allen, G., Allocca, A., Altin, P. A., Amato, A., Anand, S., Ananyeva, A., Anderson, S. B., Anderson, W. G., Angelova, S. V., Ansoldi, S., Antelis, J. M., Antier, S., Appert, S., Arai, K., Araya, M. C., Areeda, J. S., Arène, M., Arnaud, N., Aronson, S. M., Arun, K. G., Asali, Y., Ascenzi, S., Ashton, G., Aston, S. M., Astone, P., Aubin, F., Aufmuth, P., AultONeal, K., Austin, C., Avendano, V., Babak, S., Badaracco, F., Bader, M. K. M., Bae, S., Baer, A. M., Bagnasco, S., Baird, J., Ball, M., Ballardin, G., Ballmer, S. W., Bals, A., Balsamo, A., Baltus, G., Banagiri, S., Bankar, D., Bankar, R. S., Barayoga, J. C., Barbieri, C., Barish, B. C., 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. C., Bazzan, M., Becher, B. R., Bécsy, B., Bedakihale, V. M., Bejger, M., Belahcene, I., Beniwal, D., Benjamin, M. G., Bennett, T. F., Bentley, J. D., Bergamin, F., Berger, B. K., Bergmann, G., Bernuzzi, S., Berry, C. P. L., Bersanetti, D., Bertolini, A., Betzwieser, J., Bhandare, R., Bhandari, A. V., Bhattacharjee, D., Bidler, J., Bilenko, I. A., Billingsley, G., Birney, R., Birnholtz, O., Biscans, S., Bischi, M., Biscoveanu, S., Bisht, A., Bitossi, M., Bizouard, M. -A., Blackburn, J. K., Blackman, J., Blair, C. D., Blair, D. G., Blair, R. M., Blanch, O., Bobba, F., Bode, N., Boer, M., Boetzel, Y., Bogaert, G., Boldrini, M., Bondu, F., Bonilla, E., Bonnand, R., Booker, P., Boom, B. A., Bork, R., Boschi, V., Bose, S., Bossilkov, V., Boudart, V., Bouffanais, Y., Bozzi, A., Bradaschia, C., Brady, P. R., Bramley, A., Branchesi, M., Brau, J. E., Breschi, M., Briant, T., Briggs, J. H., Brighenti, F., Brillet, A., Brinkmann, M., Brockill, P., Brooks, A. F., Brooks, J., Brown, D. D., Brunett, S., Bruno, G., Bruntz, R., Buikema, A., Bulik, T., Bulten, H. J., Buonanno, A., Buscicchio, R., Buskulic, D., Byer, R. L., Cabero, M., Cadonati, L., Caesar, M., Cagnoli, G., Cahillane, C., Bustillo, J. Calderón, Callaghan, J. D., Callister, T. A., Calloni, E., Camp, J. B., Canepa, M., Cannon, K. C., Cao, H., Cao, J., Carapella, G., Carbognani, F., Carney, M. F., Carpinelli, M., Carullo, G., Carver, T. L., Diaz, J. Casanueva, Casentini, C., Caudill, S., Cavaglià, M., Cavalier, F., Cavalieri, R., Cella, G., Cerdá-Durán, P., Cesarini, E., Chaibi, W., Chakravarti, K., Chan, C. -L., Chan, C., Chandra, K., Chanial, P., Chao, S., Charlton, P., Chase, E. A., Chassande-Mottin, E., Chatterjee, D., Chattopadhyay, D., Chaturvedi, M., Chatziioannou, K., Chen, A., Chen, H. 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R., Zadrożny, A., Zanolin, M., Zelenova, T., Zendri, J. -P., Zevin, M., Zhang, J., Zhang, L., Zhang, R., Zhang, T., Zhao, C., Zhao, G., Zhou, M., Zhou, Z., Zhu, X. J., Zimmerman, A. B., Zucker, M. E., and Zweizig, J.
Subjects: Astrophysics - High Energy Astrophysical Phenomena, General Relativity and Quantum Cosmology
Abstract: We report on the population of the 47 compact binary mergers detected with a false-alarm rate 1/yr in the second LIGO--Virgo Gravitational-Wave Transient Catalog, GWTC-2. We observe several characteristics of the merging binary black hole (BBH) population not discernible until now. First, we find that the primary mass spectrum contains structure beyond a power-law with a sharp high-mass cut-off; it is more consistent with a broken power law with a break at $39.7^{+20.3}_{-9.1}\,M_\odot$, or a power law with a Gaussian feature peaking at $33.1^{+4.0}_{-5.6}\,M_\odot$ (90\% credible interval). While the primary mass distribution must extend to $\sim65\,M_\odot$ or beyond, only $2.9^{+3.5}_{1.7}\%$ of systems have primary masses greater than $45\,M_\odot$. Second, we find that a fraction of BBH systems have component spins misaligned with the orbital angular momentum, giving rise to precession of the orbital plane. Moreover, 12% to 44% of BBH systems have spins tilted by more than $90^\circ$, giving rise to a negative effective inspiral spin parameter $\chi_\mathrm{eff}$. Under the assumption that such systems can only be formed by dynamical interactions, we infer that between 25% and 93% of BBH with non-vanishing $|\chi_\mathrm{eff}| > 0.01$ are dynamically assembled. Third, we estimate merger rates, finding $\mathcal{R}_\text{BBH} = 23.9^{+14.3}_{8.6}$ Gpc$^{-3}$ yr$^{-1}$ for BBH and $\mathcal{R}_\text{BNS}= 320^{+490}_{-240}$ Gpc$^{-3}$ yr$^{-1}$ for binary neutron stars. We find that the BBH rate likely increases with redshift ($85\%$ credibility), but not faster than the star-formation rate ($86\%$ credibility). Additionally, we examine recent exceptional events in the context of our population models, finding that the asymmetric masses of GW190412 and the high component masses of GW190521 are consistent with our models, but the low secondary mass of GW190814 makes it an outlier., Comment: 53 pages, including 24 pages main text, 18 pages appendix, 30 figures
Published: 2020
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44. GWTC-2: Compact Binary Coalescences Observed by LIGO and Virgo During the First Half of the Third Observing Run

Author: Abbott, R., Abbott, T. D., Abraham, S., Acernese, F., Ackley, K., Adams, A., Adams, C., Adhikari, R. X., Adya, V. B., Affeldt, C., Agathos, M., Agatsuma, K., Aggarwal, N., Aguiar, O. D., Aiello, L., Ain, A., Ajith, P., Akcay, S., Allen, G., Allocca, A., Altin, P. A., Amato, A., Anand, S., Ananyeva, A., Anderson, S. B., Anderson, W. G., Angelova, S. V., Ansoldi, S., Antelis, J. M., Antier, S., Appert, S., Arai, K., Araya, M. C., Areeda, J. S., Arène, M., Arnaud, N., Aronson, S. M., Arun, K. G., Asali, Y., Ascenzi, S., Ashton, G., Aston, S. M., Astone, P., Aubin, F., Aufmuth, P., AultONeal, K., Austin, C., Avendano, V., Babak, S., Badaracco, F., Bader, M. K. M., Bae, S., Baer, A. M., Bagnasco, S., Baird, J., Ball, M., Ballardin, G., Ballmer, S. W., Bals, A., Balsamo, A., Baltus, G., Banagiri, S., Bankar, D., Bankar, R. S., Barayoga, J. C., Barbieri, C., Barish, B. C., 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. C., Bazzan, M., Becher, B. R., Bécsy, B., Bedakihale, V. M., Bejger, M., Belahcene, I., Beniwal, D., Benjamin, M. G., Bennett, T. F., Bentley, J. D., Bergamin, F., Berger, B. K., Bergmann, G., Bernuzzi, S., Berry, C. P. L., Bersanetti, D., Bertolini, A., Betzwieser, J., Bhandare, R., Bhandari, A. V., Bhattacharjee, D., Bidler, J., Bilenko, I. A., Billingsley, G., Birney, R., Birnholtz, O., Biscans, S., Bischi, M., Biscoveanu, S., Bisht, A., Bitossi, M., Bizouard, M. -A., Blackburn, J. K., Blackman, J., Blair, C. D., Blair, D. G., Blair, R. M., Blanch, O., Bobba, F., Bode, N., Boer, M., Boetzel, Y., Bogaert, G., Boldrini, M., Bondu, F., Bonnand, R., Bonilla, E., Booker, P., Boom, B. A., Bork, R., Boschi, V., Bose, S., Bossilkov, V., Boudart, V., Bouffanais, Y., Bozzi, A., Bradaschia, C., Brady, P. 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Subjects: General Relativity and Quantum Cosmology, Astrophysics - High Energy Astrophysical Phenomena
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: 2020
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45. Search for Gravitational Waves Associated with Gamma-Ray Bursts Detected by Fermi and Swift During the LIGO-Virgo Run O3a

Author: The LIGO Scientific Collaboration, the Virgo Collaboration, Abbott, R., Abbott, T. D., Abraham, S., Acernese, F., Ackley, K., Adams, C., Adhikari, R. X., Adya, V. B., Affeldt, C., Agathos, M., Agatsuma, K., Aggarwal, N., Aguiar, O. D., Aich, A., Aiello, L., Ain, A., Ajith, P., Allen, G., Allocca, A., Altin, P. A., Amato, A., Anand, S., Ananyeva, A., Anderson, S. B., Anderson, W. G., Angelova, S. V., Ansoldi, S., Antier, S., Appert, S., Arai, K., Araya, M. C., Areeda, J. S., Arène, M., Arnaud, N., Aronson, S. M., Asali, Y., Ascenzi, S., Ashton, G., Assiduo, M., Aston, S. M., Astone, P., Aubin, F., Aufmuth, P., AultONeal, K., Austin, C., Avendano, V., Babak, S., Bacon, P., Badaracco, F., Bader, M. K. M., Bae, S., Baer, A. M., Baird, J., Baldaccini, F., Ballardin, G., Ballmer, S. W., Bals, A., Balsamo, A., Baltus, G., Banagiri, S., Bankar, D., Bankar, R. S., Barayoga, J. C., Barbieri, C., Barish, B. C., Barker, D., Barkett, K., Barneo, P., Barone, F., Barr, B., Barsotti, L., Barsuglia, M., Barta, D., Bartlett, J., Bartos, I., Bassiri, R., Basti, A., Bawaj, M., Bayley, J. C., Bazzan, M., Bécsy, B., Bejger, M., Belahcene, I., Bell, A. S., Beniwal, D., Benjamin, M. G., Bentley, J. D., Bergamin, F., Berger, B. K., Bergmann, G., Bernuzzi, S., Berry, C. P. L., Bersanetti, D., Bertolini, A., Betzwieser, J., Bhandare, R., Bhandari, A. V., Bianchi, A., Bidler, J., Biggs, E., Bilenko, I. A., Billingsley, G., Birney, R., Birnholtz, O., Biscans, S., Bischi, M., Biscoveanu, S., Bisht, A., Bissenbayeva, G., Bitossi, M., Bizouard, M. A., Blackburn, J. K., Blackman, J., Blair, C. D., Blair, D. G., Blair, R. M., Bobba, F., Bode, N., Boer, M., Boetzel, Y., Bogaert, G., Bondu, F., Bonilla, E., Bonnand, R., Booker, P., Boom, B. A., Bork, R., Boschi, V., Bose, S., Bossilkov, V., Bosveld, J., Bouffanais, Y., Bozzi, A., Bradaschia, C., Brady, P. R., Bramley, A., Branchesi, M., Brau, J. E., Breschi, M., Briant, T., Briggs, J. H., Brighenti, F., Brillet, A., Brinkmann, M., Brockill, P., Brooks, A. F., Brooks, J., Brown, D. D., Brunett, S., Bruno, G., Bruntz, R., Buikema, A., Bulik, T., Bulten, H. J., Buonanno, A., Buskulic, D., Byer, R. L., Cabero, M., Cadonati, L., Cagnoli, G., Cahillane, C., Bustillo, J. Calderón, Callaghan, J. D., Callister, T. A., Calloni, E., Camp, J. B., Canepa, M., Santoro, G. Caneva, Cannon, K. C., Cao, H., Cao, J., Carapella, G., Carbognani, F., Caride, S., Carney, M. F., Carullo, G., Carver, T. L., Diaz, J. Casanueva, Casentini, C., Castañeda, J., Caudill, S., Cavaglià, M., Cavalier, F., Cavalieri, R., Cella, G., Cerdá-Durán, P., Cesarini, E., Chaibi, O., Chakravarti, K., Chan, C., Chan, M., Chao, S., Charlton, P., Chase, E. A., Chassande-Mottin, E., Chatterjee, D., Chaturvedi, M., Chen, H. Y., Chen, X., Chen, Y., Cheng, H. -P., Cheong, C. K., Chia, H. Y., Chiadini, F., Chierici, R., Chincarini, A., Chiummo, A., Cho, G., Cho, H. S., Cho, M., Christensen, N., Chu, Q., Chua, S., Chung, K. W., Chung, S., Ciani, G., Ciecielag, P., Cie{ś}lar, M., Ciobanu, A. A., Ciolfi, R., Cipriano, F., Cirone, A., Clara, F., Clark, J. A., Clearwater, P., Clesse, S., Cleva, F., Coccia, E., Cohadon, P. -F., Cohen, D., Colleoni, M., Collette, C. G., Collins, C., Colpi, M., Constancio Jr., M., Conti, L., Cooper, S. J., Corban, P., Corbitt, T. R., Cordero-Carrión, I., Corezzi, S., Corley, K. R., Cornish, N., Corre, D., Corsi, A., Cortese, S., Costa, C. A., Cotesta, R., Coughlin, M. W., Coughlin, S. B., Coulon, J. -P., Countryman, S. T., Couvares, P., Covas, P. B., Coward, D. M., Cowart, M. J., Coyne, D. C., Coyne, R., Creighton, J. D. E., Creighton, T. D., Cripe, J., Croquette, M., Crowder, S. G., Cudell, J. -R., Cullen, T. J., Cumming, A., Cummings, R., Cunningham, L., Cuoco, E., Curylo, M., Canton, T. Dal, Dálya, G., Dana, A., Daneshgaran-Bajastani, L. M., D'Angelo, B., Danilishin, S. L., D'Antonio, S., Danzmann, K., Darsow-Fromm, C., Dasgupta, A., Datrier, L. E. H., Dattilo, V., Dave, I., Davier, M., Davies, G. S., Davis, D., Daw, E. J., DeBra, D., Deenadayalan, M., Degallaix, J., De Laurentis, M., Deléglise, S., Delfavero, M., De Lillo, N., Del Pozzo, W., DeMarchi, L. M., D'Emilio, V., Demos, N., Dent, T., De Pietri, R., De Rosa, R., De Rossi, C., DeSalvo, R., de Varona, O., Dhurandhar, S., Díaz, M. C., Diaz-Ortiz Jr., M., Dietrich, T., Di Fiore, L., Di Fronzo, 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. K., Dmitriev, A., Doctor, Z., Donovan, F., Dooley, K. L., Doravari, S., Dorrington, I., Downes, T. P., Drago, M., Driggers, J. C., Du, Z., Ducoin, J. -G., Dupej, P., Durante, O., D'Urso, D., Dwyer, S. E., Easter, P. J., Eddolls, G., Edelman, B., Edo, T. B., Edy, O., Effler, A., Ehrens, P., Eichholz, J., Eikenberry, S. S., Eisenmann, M., Eisenstein, R. A., Ejlli, A., Errico, L., Essick, R. C., Estelles, H., Estevez, D., Etienne, Z. B., Etzel, T., Evans, M., Evans, T. M., Ewing, B. E., Fafone, V., Fairhurst, S., Fan, X., Farinon, S., Farr, B., Farr, W. M., Fauchon-Jones, E. J., Favata, M., Fays, M., Fazio, M., Feicht, J., Fejer, M. M., Feng, F., Fenyvesi, E., Ferguson, D. L., Fernandez-Galiana, A., Ferrante, I., Ferreira, E. C., Ferreira, T. A., Fidecaro, F., Fiori, I., Fiorucci, D., Fishbach, M., Fisher, R. P., Fittipaldi, R., Fitz-Axen, M., Fiumara, V., Flaminio, R., Floden, E., Flynn, E., Fong, H., Font, J. A., Forsyth, P. W. F., Fournier, J. -D., Frasca, S., Frasconi, F., Frei, Z., Freise, A., Frey, R., Frey, V., Fritschel, P., Frolov, V. V., Fronzè, G., Fulda, P., Fyffe, M., Gabbard, H. A., Gadre, B. U., Gaebel, S. M., Gair, J. R., Galaudage, S., Ganapathy, D., Gaonkar, S. G., García-Quirós, C., Garufi, F., Gateley, B., Gaudio, S., Gayathri, V., Gemme, G., Genin, E., Gennai, A., George, D., George, J., Gergely, L., Ghonge, S., Ghosh, Abhirup, Ghosh, Archisman, Ghosh, S., Giacomazzo, B., Giaime, J. A., Giardina, K. D., Gibson, D. R., Gier, C., Gill, K., Glanzer, J., Gniesmer, J., Godwin, P., Goetz, E., Goetz, R., Gohlke, N., Goncharov, B., González, G., Gopakumar, A., Gossan, S. E., Gosselin, M., Gouaty, R., Grace, B., Grado, A., Granata, M., Grant, A., Gras, S., Grassia, P., Gray, C., Gray, R., Greco, G., Green, A. C., Green, R., Gretarsson, E. M., Griggs, H. L., Grignani, G., Grimaldi, A., Grimm, S. J., Grote, H., Grunewald, S., Gruning, P., Guidi, G. M., Guimaraes, A. R., Guixé, G., Gulati, H. K., Guo, Y., Gupta, A., Gupta, Anchal, Gupta, P., Gustafson, E. K., Gustafson, R., Haegel, L., Halim, O., Hall, E. D., Hamilton, E. Z., Hammond, G., Haney, M., Hanke, M. M., Hanks, J., Hanna, C., Hannam, M. D., Hannuksela, O. A., Hansen, T. J., Hanson, J., Harder, T., Hardwick, T., Haris, K., Harms, J., Harry, G. M., Harry, I. W., Hasskew, R. K., Haster, C. -J., Haughian, K., Hayes, F. J., Healy, J., Heidmann, A., Heintze, M. C., Heinze, J., Heitmann, H., Hellman, F., Hello, P., Hemming, G., Hendry, M., Heng, I. S., Hennes, E., Hennig, J., Heurs, M., Hild, S., Hinderer, T., Hoback, S. Y., Hochheim, S., Hofgard, E., Hofman, D., Holgado, A. M., Holland, N. A., Holt, K., Holz, D. E., Hopkins, P., Horst, C., Hough, J., Howell, E. J., Hoy, C. G., Huang, Y., Hübner, M. T., Huerta, E. A., Huet, D., Hughey, B., Hui, V., Husa, S., Huttner, S. H., Huxford, R., Huynh-Dinh, T., Idzkowski, B., Iess, A., Inchauspe, H., Ingram, C., Intini, G., Isac, J. -M., Isi, M., Iyer, B. R., Jacqmin, T., Jadhav, S. J., Jadhav, S. P., James, A. L., Jani, K., Janthalur, N. N., Jaranowski, P., Jariwala, D., Jaume, R., Jenkins, A. C., Jiang, J., Johns, G. R., Jones, A. W., Jones, D. I., Jones, J. D., Jones, P., 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., Kashyap, R., Kasprzack, M., Kastaun, W., Katsanevas, S., Katsavounidis, E., Katzman, W., Kaufer, S., Kawabe, K., Kéfélian, F., Keitel, D., Keivani, A., Kennedy, R., Key, J. S., Khadka, S., Khalili, F. Y., Khan, I., Khan, S., Khan, Z. A., Khazanov, E. A., Khetan, N., Khursheed, M., Kijbunchoo, N., Kim, Chunglee, Kim, G. J., Kim, J. C., Kim, K., Kim, W., Kim, W. S., Kim, Y. -M., Kimball, C., King, P. J., Kinley-Hanlon, M., Kirchhoff, R., Kissel, J. S., Kleybolte, L., Klimenko, S., Knowles, T. D., Knyazev, E., Koch, P., Koehlenbeck, S. M., Koekoek, G., Koley, S., Kondrashov, V., Kontos, A., Koper, N., Korobko, M., Korth, W. Z., Kovalam, M., Kozak, D. B., Kringel, V., Krishnendu, N. V., Królak, A., Krupinski, N., Kuehn, G., Kumar, A., Kumar, P., Kumar, Rahul, Kumar, Rakesh, Kumar, S., Kuo, L., Kutynia, A., Lackey, B. D., Laghi, D., Lalande, E., Lam, T. L., Lamberts, A., Landry, M., Lane, B. B., Lang, R. N., Lange, J., Lantz, B., Lanza, R. K., La Rosa, I., Lartaux-Vollard, A., Lasky, P. D., Laxen, M., Lazzarini, A., Lazzaro, C., Leaci, P., Leavey, S., Lecoeuche, Y. K., Lee, C. H., Lee, H. M., Lee, H. W., Lee, J., Lee, K., Lehmann, J., Leroy, N., Letendre, N., Levin, Y., Li, A. K. Y., Li, J., li, K., Li, T. G. F., Li, X., Linde, F., Linker, S. D., Linley, J. N., Littenberg, T. B., Liu, J., Liu, X., Llorens-Monteagudo, M., Lo, R. K. L., Lockwood, 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., Ma, Y., Macas, R., Macfoy, S., MacInnis, M., Macleod, D. M., MacMillan, I. A. O., Macquet, A., Hernandez, I. Magaña, Magaña-Sandoval, F., Magee, R. M., Majorana, E., Maksimovic, I., Malik, A., Man, N., Mandic, V., Mangano, V., Mansell, G. L., Manske, M., Mantovani, M., Mapelli, M., Marchesoni, F., Marion, F., Márka, S., Márka, Z., Markakis, C., Markosyan, A. S., Markowitz, A., Maros, E., Marquina, A., Marsat, S., Martelli, F., Martin, I. W., Martin, R. M., Martinez, V., Martynov, D. V., Masalehdan, H., Mason, K., Massera, E., Masserot, A., Massinger, T. J., Masso-Reid, M., Mastrogiovanni, S., Matas, A., Matichard, F., Mavalvala, N., Maynard, E., McCann, J. J., McCarthy, R., McClelland, D. E., McCormick, S., McCuller, L., McGuire, S. C., McIsaac, C., McIver, J., McManus, D. J., McRae, T., McWilliams, S. T., Meacher, D., Meadors, G. D., Mehmet, M., Mehta, A. K., Villa, E. Mejuto, Melatos, A., Mendell, G., Mercer, R. A., Mereni, L., Merfeld, K., Merilh, E. L., Merritt, J. D., Merzougui, M., Meshkov, S., Messenger, C., Messick, C., Metzdorff, R., Meyers, P. M., Meylahn, F., Mhaske, A., Miani, A., Miao, H., Michaloliakos, I., Michel, C., Middleton, H., Milano, L., Miller, A. L., Millhouse, M., Mills, J. C., Milotti, E., Milovich-Goff, M. C., Minazzoli, O., Minenkov, Y., Mishkin, A., Mishra, C., Mistry, T., Mitra, S., Mitrofanov, V. P., Mitselmakher, G., Mittleman, R., Mo, G., Mogushi, K., Mohapatra, S. R. P., Mohite, S. R., Molina-Ruiz, M., Mondin, M., Montani, M., Moore, C. J., Moraru, D., Morawski, F., Moreno, G., Morisaki, S., Mours, B., Mow-Lowry, C. M., Mozzon, S., Muciaccia, F., Mukherjee, Arunava, Mukherjee, D., Mukherjee, S., Mukherjee, Subroto, Mukund, N., Mullavey, A., Munch, J., Muñiz, E. A., Murray, P. G., Nagar, A., Nardecchia, I., Naticchioni, L., Nayak, R. K., Neil, B. F., Neilson, J., Nelemans, G., Nelson, T. J. N., Nery, M., Neunzert, A., Ng, K. Y., Ng, S., Nguyen, C., Nguyen, P., Nichols, D., Nichols, S. A., Nissanke, S., Nocera, F., Noh, M., North, C., Nothard, D., Nuttall, L. K., Oberling, J., O'Brien, B. D., Oganesyan, G., Ogin, G. H., Oh, J. J., Oh, S. H., Ohme, F., Ohta, H., Okada, M. A., Oliver, M., Olivetto, C., Oppermann, P., Oram, Richard J., O'Reilly, B., Ormiston, R. G., Ormsby, N., Ortega, L. F., O'Shaughnessy, R., Ossokine, S., Osthelder, C., Ottaway, D. J., Overmier, H., Owen, B. J., Pace, A. E., Pagano, G., Page, M. A., Pagliaroli, G., Pai, A., Pai, S. A., Palamos, J. R., Palashov, O., Palomba, C., Pan, H., Panda, P. K., Pang, P. T. H., Pankow, C., Pannarale, F., Pant, B. C., Paoletti, F., Paoli, A., Parida, A., Parker, W., Pascucci, D., Pasqualetti, A., Passaquieti, R., Passuello, D., Patel, M., Patricelli, B., Payne, E., Pearlstone, B. L., Pechsiri, T. C., Pedersen, A. J., Pedraza, M., Pele, A., Penn, S., Perego, A., Perez, C. J., Périgois, C., Perreca, A., Perriès, S., Petermann, J., Pfeiffer, H. P., Phelps, M., Phukon, K. S., Piccinni, O. J., Pichot, M., Piendibene, M., Piergiovanni, F., Pierro, V., Pillant, G., Pinard, L., Pinto, I. M., Piotrzkowski, K., Pirello, M., Pitkin, M., Plastino, W., Poggiani, R., Pong, D. Y. T., Ponrathnam, S., Popolizio, P., Porter, E. K., Powell, J., Prajapati, A. K., Prasai, K., Prasanna, R., Pratten, G., Prestegard, T., Principe, M., Prodi, G. A., Prokhorov, L., Punturo, M., Puppo, P., Pürrer, M., Qi, H., Quetschke, V., Quinonez, P. J., Raab, F. J., Raaijmakers, G., Radkins, H., Radulesco, N., Raffai, P., Rafferty, H., Raja, S., Rajan, C., Rajbhandari, B., Rakhmanov, M., Ramirez, K. E., Ramos-Buades, A., Rana, Javed, Rao, K., Rapagnani, P., 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., Ricker, P. M., Riemenschneider, G., Riles, K., Rizzo, M., Robertson, N. A., Robinet, F., Rocchi, A., Rodriguez-Soto, R. D., Rolland, L., Rollins, J. G., Roma, V. J., Romanelli, M., Romano, R., Romel, C. L., Romero-Shaw, I. M., Romie, J. H., Rose, C. A., Rose, D., Rose, K., Rosińska, D., Rosofsky, S. G., Ross, M. P., Rowan, S., Rowlinson, S. J., Roy, P. K., Roy, Santosh, Roy, Soumen, Ruggi, P., Rutins, G., Ryan, K., Sachdev, S., Sadecki, T., Sakellariadou, M., Salafia, O. S., Salconi, L., Saleem, M., Samajdar, A., Sanchez, E. J., Sanchez, L. E., Sanchis-Gual, N., Sanders, J. R., Santiago, K. A., Santos, E., 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., 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., Seidel, E., Sellers, D., Sengupta, A. S., Sennett, N., Sentenac, D., Sequino, V., Sergeev, A., Setyawati, Y., Shaddock, D. A., 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., Siellez, K., 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., Son, E. J., Soni, S., Sorazu, B., Sordini, V., Sorrentino, F., Souradeep, T., Sowell, E., Spencer, A. P., Spera, M., Srivastava, A. K., Srivastava, V., Staats, K., Stachie, C., Standke, M., Steer, D. A., Steinke, M., Steinlechner, J., Steinlechner, S., Steinmeyer, D., Stocks, D., Stops, D. J., Stover, M., Strain, K. A., Stratta, G., Strunk, A., Sturani, R., Stuver, A. L., Sudhagar, S., Sudhir, V., Summerscales, T. Z., 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., Tápai, M., Tapia, A., Martin, E. N. Tapia San, Tasson, J. D., Taylor, R., Tenorio, R., Terkowski, L., Thirugnanasambandam, M. P., Thomas, M., Thomas, P., Thompson, J. E., Thondapu, S. R., Thorne, K. A., Thrane, E., Tinsman, C. L., Saravanan, T. 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E., Wade, M., Walet, R., Walker, M., Wallace, G. S., Wallace, L., Walsh, S., Wang, J. Z., Wang, S., Wang, W. H., Ward, R. L., Warden, Z. A., Warner, J., Was, M., Watchi, J., Weaver, B., Wei, L. -W., Weinert, M., Weinstein, A. J., Weiss, R., Wellmann, F., Wen, L., Weßels, P., Westhouse, J. W., Wette, K., Whelan, J. T., Whiting, B. F., Whittle, C., Wilken, D. M., Williams, D., Williamson, A. R., Willis, J. L., Willke, B., Winkler, W., Wipf, C. C., Wittel, H., Woan, G., Woehler, J., Wofford, J. K., Wong, I. C. F., Wright, J. L., Wu, D. S., Wysocki, D. M., Xiao, L., Yamamoto, H., Yang, L., Yang, Y., Yang, Z., Yap, M. J., Yazback, M., Yeeles, D. W., Yu, Hang, Yu, Haocun, Yuen, S. H. R., Zadrożny, A. K., Zadrożny, A., Zanolin, M., Zelenova, T., Zendri, J. -P., Zevin, M., Zhang, J., Zhang, L., Zhang, T., Zhao, C., Zhao, G., Zheng, Y., Zhou, M., Zhou, Z., Zhu, X. J., Zucker, M. E., and Zweizig, J.
Subjects: Astrophysics - High Energy Astrophysical Phenomena, General Relativity and Quantum Cosmology
Abstract: We search for gravitational-wave transients associated with gamma-ray bursts detected by the Fermi and Swift satellites during the first part of the third observing run of Advanced LIGO and Advanced Virgo (1 April 2019 15:00 UTC - 1 October 2019 15:00 UTC). 105 gamma-ray bursts were analyzed using a search for generic gravitational-wave transients; 32 gamma-ray bursts were analyzed with a search that specifically targets neutron star binary mergers as short gamma-ray burst progenitors. We describe a method to calculate the probability that triggers from the binary merger targeted search are astrophysical and apply that method to the most significant gamma-ray bursts in that search. We find no significant evidence for gravitational-wave signals associated with the gamma-ray bursts that we followed up, nor for a population of unidentified subthreshold signals. We consider several source types and signal morphologies, and report for these lower bounds on the distance to each gamma-ray burst., Comment: 17 pages, 5 figures, 2 tables
Published: 2020
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46. Deep learning for gravitational wave forecasting of neutron star mergers

Author: Wei, Wei and Huerta, E. A.
Subjects: General Relativity and Quantum Cosmology, I.2, I.5
Abstract: We introduce deep learning time-series forecasting for gravitational wave detection of binary neutron star mergers. This method enables the identification of these signals in real advanced LIGO data up to 30 seconds before merger. When applied to GW170817, our deep learning forecasting method identifies the presence of this gravitational wave signal 10 seconds before merger. This novel approach requires a single GPU for inference, and may be used as part of an early warning system for time-sensitive multi-messenger searches., Comment: 10 pages, 4 figures
Published: 2020
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47. Properties and astrophysical implications of the 150 Msun binary black hole merger GW190521

Author: The LIGO Scientific Collaboration, the Virgo Collaboration, Abbott, R., Abbott, T. D., Abraham, S., Acernese, F., Ackley, K., Adams, C., Adhikari, R. X., Adya, V. B., Affeldt, C., Agathos, M., Agatsuma, K., Aggarwal, N., Aguiar, O. D., Aich, A., Aiello, L., Ain, A., Ajith, P., Akcay, S., Allen, G., Allocca, A., Altin, P. A., Amato, A., Anand, S., Ananyeva, A., Anderson, S. B., Anderson, W. G., Angelova, S. V., Ansoldi, S., Antier, S., Appert, S., Arai, K., Araya, M. C., Areeda, J. S., Arène, M., Arnaud, N., Aronson, S. M., Arun, K. G., Asali, Y., Ascenzi, S., Ashton, G., Aston, S. M., Astone, P., Aubin, F., Aufmuth, P., AultONeal, K., Austin, C., Avendano, V., Babak, S., Bacon, P., Badaracco, F., Bader, M. K. M., Bae, S., Baer, A. M., Baird, J., Baldaccini, F., Ballardin, G., Ballmer, S. W., Bals, A., Balsamo, A., Baltus, G., Banagiri, S., Bankar, D., Bankar, R. S., Barayoga, J. C., Barbieri, C., Barish, B. C., Barker, D., Barkett, K., Barneo, P., Barone, F., Barr, B., Barsotti, L., Barsuglia, M., Barta, D., Bartlett, J., Bartos, I., Bassiri, R., Basti, A., Bawaj, M., Bayley, J. C., Bazzan, M., Bécsy, B., Bejger, M., Belahcene, I., Bell, A. S., Beniwal, D., Benjamin, M. G., Bentley, J. D., Bergamin, F., Berger, B. K., Bergmann, G., Bernuzzi, S., Berry, C. P. L., Bersanetti, D., Bertolini, A., Betzwieser, J., Bhandare, R., Bhandari, A. V., Bidler, J., Biggs, E., Bilenko, I. A., Billingsley, G., Birney, R., Birnholtz, O., Biscans, S., Bischi, M., Biscoveanu, S., Bisht, A., Bissenbayeva, G., Bitossi, M., Bizouard, M. A., Blackburn, J. K., Blackman, J., Blair, C. D., Blair, D. G., Blair, R. M., Bobba, F., Bode, N., Boer, M., Boetzel, Y., Bogaert, G., Bondu, F., Bonilla, E., Bonnand, R., Booker, P., Boom, B. A., Bork, R., Boschi, V., Bose, S., Bossilkov, V., Bosveld, J., Bouffanais, Y., Bozzi, A., Bradaschia, C., Brady, P. R., Bramley, A., Branchesi, M., Brau, J. E., Breschi, M., Briant, T., Briggs, J. H., Brighenti, F., Brillet, A., Brinkmann, M., Brockill, P., Brooks, A. F., Brooks, J., Brown, D. D., Brunett, S., Bruno, G., Bruntz, R., Buikema, A., Bulik, T., Bulten, H. J., Buonanno, A., Buskulic, R. Buscicchio D., Byer, R. L., Cabero, M., Cadonati, L., Cagnoli, G., Cahillane, C., Bustillo, J. Calderón, Callaghan, J. D., Callister, T. A., Calloni, E., Camp, J. B., Canepa, M., Cannon, K. C., Cao, H., Cao, J., Carapella, G., Carbognani, F., Caride, S., Carney, M. F., Carullo, G., Diaz, J. Casanueva, Casentini, C., Castañeda, J., Caudill, S., Cavaglià, M., Cavalier, F., Cavalieri, R., Cella, G., Cerdá-Durán, P., Cesarini, E., Chaibi, O., Chakravarti, K., Chan, C., Chan, M., Chandra, K., Chao, S., Charlton, P., Chase, E. A., Chassande-Mottin, E., Chatterjee, D., Chaturvedi, M., Chatziioannou, K., Chen, H. Y., Chen, X., Chen, Y., Cheng, H. -P., Cheong, C. K., Chia, H. Y., Chiadini, F., Chierici, R., Chincarini, A., Chiummo, A., Cho, G., Cho, H. S., Cho, M., Christensen, N., Chu, Q., Chua, S., Chung, K. W., Chung, S., Ciani, G., Ciecielag, P., Cie{ś}lar, M., Ciobanu, A. A., Ciolfi, R., Cipriano, F., Cirone, A., Clara, F., Clark, J. A., Clearwater, P., Clesse, S., Cleva, F., Coccia, E., Cohadon, P. -F., Cohen, D., Colleoni, M., Collette, C. G., Collins, C., Colpi, M., Constancio Jr., M., Conti, L., Cooper, S. J., Corban, P., Corbitt, T. R., Cordero-Carrión, I., Corezzi, S., Corley, K. R., Cornish, N., Corre, D., Corsi, A., Cortese, S., Costa, C. A., Cotesta, R., Coughlin, M. W., Coughlin, S. B., Coulon, J. -P., Countryman, S. T., Couvares, P., Covas, P. B., Coward, D. M., Cowart, M. J., Coyne, D. C., Coyne, R., Creighton, J. D. E., Creighton, T. D., Cripe, J., Croquette, M., Crowder, S. G., Cudell, J. -R., Cullen, T. J., Cumming, A., Cummings, R., Cunningham, L., Cuoco, E., Curylo, M., Canton, T. Dal, Dálya, G., Dana, A., Daneshgaran-Bajastani, L. M., D'Angelo, B., Danilishin, S. L., D'Antonio, S., Danzmann, K., Darsow-Fromm, C., Dasgupta, A., Datrier, L. E. H., Dattilo, V., Dave, I., Davier, M., Davies, G. S., Davis, D., Daw, E. J., DeBra, D., Deenadayalan, M., Degallaix, J., De Laurentis, M., Deléglise, S., Delfavero, M., De Lillo, N., Del Pozzo, W., DeMarchi, L. M., D'Emilio, V., Demos, N., Dent, T., De Pietri, R., De Rosa, R., De Rossi, C., DeSalvo, R., de Varona, O., Dhurandhar, S., Díaz, M. C., Diaz-Ortiz Jr., M., Dietrich, T., Di Fiore, L., Di Fronzo, 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. K., Dmitriev, A., Doctor, Z., Donovan, F., Dooley, K. L., Doravari, S., Dorrington, I., Downes, T. P., Drago, M., Driggers, J. C., Du, Z., Ducoin, J. -G., Dupej, P., Durante, O., D'Urso, D., Dwyer, S. E., Easter, P. J., Eddolls, G., Edelman, B., Edo, T. B., Edy, O., Effler, A., Ehrens, P., Eichholz, J., Eikenberry, S. S., Eisenmann, M., Eisenstein, R. A., Ejlli, A., Errico, L., Essick, R. C., Estelles, H., Estevez, D., Etienne, Z. B., Etzel, T., Evans, M., Evans, T. M., Ewing, B. E., Fafone, V., Fairhurst, S., Fan, X., Farinon, S., Farr, B., Farr, W. M., Fauchon-Jones, E. J., Favata, M., Fays, M., Fazio, M., Feicht, J., Fejer, M. M., Feng, F., Fenyvesi, E., Ferguson, D. L., Fernandez-Galiana, A., Ferrante, I., Ferreira, E. C., Ferreira, T. A., Fidecaro, F., Fiori, I., Fiorucci, D., Fishbach, M., Fisher, R. P., Fittipaldi, R., Fitz-Axen, M., Fiumara, V., Flaminio, R., Floden, E., Flynn, E., Fong, H., Font, J. A., Forsyth, P. W. F., Fournier, J. -D., Frasca, S., Frasconi, F., Frei, Z., Freise, A., Frey, R., Frey, V., Fritschel, P., Frolov, V. V., Fronzè, G., Fulda, P., Fyffe, M., Gabbard, H. A., Gadre, B. U., Gaebel, S. M., Gair, J. R., Galaudage, S., Ganapathy, D., Ganguly, A., Gaonkar, S. G., García-Quirós, C., Garufi, F., Gateley, B., Gaudio, S., Gayathri, V., Gemme, G., Genin, E., Gennai, A., George, D., George, J., Gergely, L., Ghonge, S., Ghosh, Abhirup, Ghosh, Archisman, Ghosh, S., Giacomazzo, B., Giaime, J. A., Giardina, K. D., Gibson, D. R., Gier, C., Gill, K., Glanzer, J., Gniesmer, J., Godwin, P., Goetz, E., Goetz, R., Gohlke, N., Goncharov, B., González, G., Gopakumar, A., Gossan, S. E., Gosselin, M., Gouaty, R., Grace, B., Grado, A., Granata, M., Grant, A., Gras, S., Grassia, P., Gray, C., Gray, R., Greco, G., Green, A. C., Green, R., Gretarsson, E. M., Griggs, H. L., Grignani, G., Grimaldi, A., Grimm, S. J., Grote, H., Grunewald, S., Gruning, P., Guidi, G. M., Guimaraes, A. R., Guixé, G., Gulati, H. K., Guo, Y., Gupta, A., Gupta, Anchal, Gupta, P., Gustafson, E. K., Gustafson, R., Haegel, L., Halim, O., Hall, E. D., Hamilton, E. Z., Hammond, G., Haney, M., Hanke, M. M., Hanks, J., Hanna, C., Hannam, M. D., Hannuksela, O. A., Hansen, T. J., Hanson, J., Harder, T., Hardwick, T., Haris, K., Harms, J., Harry, G. M., Harry, I. W., Hasskew, R. K., Haster, C. -J., Haughian, K., Hayes, F. J., Healy, J., Heidmann, A., Heintze, M. C., Heinze, J., Heitmann, H., Hellman, F., Hello, P., Hemming, G., Hendry, M., Heng, I. S., Hennes, E., Hennig, J., Heurs, M., Hild, S., Hinderer, T., Hoback, S. Y., Hochheim, S., Hofgard, E., Hofman, D., Holgado, A. M., Holland, N. A., Holt, K., Holz, D. E., Hopkins, P., Horst, C., Hough, J., Howell, E. J., Hoy, C. G., Huang, Y., Hübner, M. T., Huerta, E. A., Huet, D., Hughey, B., Hui, V., Husa, S., Huttner, S. H., Huxford, R., Huynh-Dinh, T., Idzkowski, B., Iess, A., Inchauspe, H., Ingram, C., Intini, G., Isac, J. -M., Isi, M., Iyer, B. R., Jacqmin, T., Jadhav, S. J., Jadhav, S. P., James, A. L., Jani, K., Janthalur, N. N., Jaranowski, P., Jariwala, D., Jaume, R., Jenkins, A. C., Jiang, J., Johns, G. R., Johnson-McDaniel, N. K., Jones, A. W., Jones, D. I., Jones, J. D., Jones, P., 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., Kashyap, R., Kasprzack, M., Kastaun, W., Katsanevas, S., Katsavounidis, E., Katzman, W., Kaufer, S., Kawabe, K., Kéfélian, F., Keitel, D., Keivani, A., Kennedy, R., Key, J. S., Khadka, S., Khalili, F. Y., Khan, I., Khan, S., Khan, Z. A., Khazanov, E. A., Khetan, N., Khursheed, M., Kijbunchoo, N., Kim, Chunglee, Kim, G. J., Kim, J. C., Kim, K., Kim, W., Kim, W. S., Kim, Y. -M., Kimball, C., King, P. J., Kinley-Hanlon, M., Kirchhoff, R., Kissel, J. S., Kleybolte, L., Klimenko, S., Knowles, T. D., Knyazev, E., Koch, P., Koehlenbeck, S. M., Koekoek, G., Koley, S., Kondrashov, V., Kontos, A., Koper, N., Korobko, M., Korth, W. Z., Kovalam, M., Kozak, D. B., Kringel, V., Krishnendu, N. V., Królak, A., Krupinski, N., Kuehn, G., Kumar, A., Kumar, P., Kumar, Rahul, Kumar, Rakesh, Kumar, S., Kuo, L., Kutynia, A., Lackey, B. D., Laghi, D., Lalande, E., Lam, T. L., Lamberts, A., Landry, M., Lane, B. B., Lang, R. N., Lange, J., Lantz, B., Lanza, R. K., La Rosa, I., Lartaux-Vollard, A., Lasky, P. D., Laxen, M., Lazzarini, A., Lazzaro, C., Leaci, P., Leavey, S., Lecoeuche, Y. K., Lee, C. H., Lee, H. M., Lee, H. W., Lee, J., Lee, K., Lehmann, J., Leroy, N., Letendre, N., Levin, Y., Li, A. K. Y., Li, J., li, K., Li, T. G. F., Li, X., Linde, F., Linker, S. D., Linley, J. N., Littenberg, T. B., Liu, J., Liu, X., Llorens-Monteagudo, M., Lo, R. K. L., Lockwood, 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., Ma, Y., Macas, R., Macfoy, S., MacInnis, M., Macleod, D. M., MacMillan, I. A. O., Macquet, A., Hernandez, I. Magaña, Magaña-Sandoval, F., Magee, R. M., Majorana, E., Maksimovic, I., Malik, A., Man, N., Mandic, V., Mangano, V., Mansell, G. L., Manske, M., Mantovani, M., Mapelli, M., Marchesoni, F., Marion, F., Márka, S., Márka, Z., Markakis, C., Markosyan, A. S., Markowitz, A., Maros, E., Marquina, A., Marsat, S., Martelli, F., Martin, I. W., Martin, R. M., Martinez, V., Martynov, D. V., Masalehdan, H., Mason, K., Massera, E., Masserot, A., Massinger, T. J., Masso-Reid, M., Mastrogiovanni, S., Matas, A., Matichard, F., Mavalvala, N., Maynard, E., McCann, J. J., McCarthy, R., McClelland, D. E., McCormick, S., McCuller, L., McGuire, S. C., McIsaac, C., McIver, J., McManus, D. J., McRae, T., McWilliams, S. T., Meacher, D., Meadors, G. D., Mehmet, M., Mehta, A. K., Villa, E. Mejuto, Melatos, A., Mendell, G., Mercer, R. A., Mereni, L., Merfeld, K., Merilh, E. L., Merritt, J. D., Merzougui, M., Meshkov, S., Messenger, C., Messick, C., Metzdorff, R., Meyers, P. 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Subjects: Astrophysics - High Energy Astrophysical Phenomena, General Relativity and Quantum Cosmology
Abstract: The gravitational-wave signal GW190521 is consistent with a binary black hole merger source at redshift 0.8 with unusually high component masses, $85^{+21}_{-14}\,M_{\odot}$ and $66^{+17}_{-18}\,M_{\odot}$, compared to previously reported events, and shows mild evidence for spin-induced orbital precession. The primary falls in the mass gap predicted by (pulsational) pair-instability supernova theory, in the approximate range $65 - 120\,M_{\odot}$. The probability that at least one of the black holes in GW190521 is in that range is 99.0%. The final mass of the merger $(142^{+28}_{-16}\,M_{\odot})$ classifies it as an intermediate-mass black hole. Under the assumption of a quasi-circular binary black hole coalescence, we detail the physical properties of GW190521's source binary and its post-merger remnant, including component masses and spin vectors. Three different waveform models, as well as direct comparison to numerical solutions of general relativity, yield consistent estimates of these properties. Tests of strong-field general relativity targeting the merger-ringdown stages of coalescence indicate consistency of the observed signal with theoretical predictions. We estimate the merger rate of similar systems to be $0.13^{+0.30}_{-0.11}\,{\rm Gpc}^{-3}\,\rm{yr}^{-1}$. We discuss the astrophysical implications of GW190521 for stellar collapse, and for the possible formation of black holes in the pair-instability mass gap through various channels: via (multiple) stellar coalescence, or via hierarchical merger of lower-mass black holes in star clusters or in active galactic nuclei. We find it to be unlikely that GW190521 is a strongly lensed signal of a lower-mass black hole binary merger. We also discuss more exotic possible sources for GW190521, including a highly eccentric black hole binary, or a primordial black hole binary., Comment: 39 pages, 13 figures; data available at https://dcc.ligo.org/P2000158-v4/public
Published: 2020
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48. GW190521: A Binary Black Hole Merger with a Total Mass of $150 ~ M_{\odot}$

Author: The LIGO Scientific Collaboration, the Virgo Collaboration, Abbott, R., Abbott, T. D., Abraham, S., Acernese, F., Ackley, K., Adams, C., Adhikari, R. X., Adya, V. B., Affeldt, C., Agathos, M., Agatsuma, K., Aggarwal, N., Aguiar, O. D., Aich, A., Aiello, L., Ain, A., Ajith, P., Akcay, S., Allen, G., Allocca, A., Altin, P. A., Amato, A., Anand, S., Ananyeva, A., Anderson, S. B., Anderson, W. G., Angelova, S. V., Ansoldi, S., Antier, S., Appert, S., Arai, K., Araya, M. C., Areeda, J. S., Arène, M., Arnaud, N., Aronson, S. M., Arun, K. G., Asali, Y., Ascenzi, S., Ashton, G., Aston, S. M., Astone, P., Aubin, F., Aufmuth, P., AultONeal, K., Austin, C., Avendano, V., Babak, S., Bacon, P., Badaracco, F., Bader, M. K. M., Bae, S., Baer, A. M., Baird, J., Baldaccini, F., Ballardin, G., Ballmer, S. W., Bals, A., Balsamo, A., Baltus, G., Banagiri, S., Bankar, D., Bankar, R. S., Barayoga, J. C., Barbieri, C., Barish, B. C., Barker, D., Barkett, K., Barneo, P., Barone, F., Barr, B., Barsotti, L., Barsuglia, M., Barta, D., Bartlett, J., Bartos, I., Bassiri, R., Basti, A., Bawaj, M., Bayley, J. C., Bazzan, M., Bécsy, B., Bejger, M., Belahcene, I., Bell, A. S., Beniwal, D., Benjamin, M. G., Bentley, J. D., Bergamin, F., Berger, B. K., Bergmann, G., Bernuzzi, S., Berry, C. P. L., Bersanetti, D., Bertolini, A., Betzwieser, J., Bhandare, R., Bhandari, A. V., Bidler, J., Biggs, E., Bilenko, I. A., Billingsley, G., Birney, R., Birnholtz, O., Biscans, S., Bischi, M., Biscoveanu, S., Bisht, A., Bissenbayeva, G., Bitossi, M., Bizouard, M. A., Blackburn, J. K., Blackman, J., Blair, C. D., Blair, D. G., Blair, R. M., Bobba, F., Bode, N., Boer, M., Boetzel, Y., Bogaert, G., Bondu, F., Bonilla, E., Bonnand, R., Booker, P., Boom, B. A., Bork, R., Boschi, V., Bose, S., Bossilkov, V., Bosveld, J., Bouffanais, Y., Bozzi, A., Bradaschia, C., Brady, P. R., Bramley, A., Branchesi, M., Brau, J. 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K., Zadrożny, A., Zanolin, M., Zelenova, T., Zendri, J. -P., Zevin, M., Zhang, J., Zhang, L., Zhang, T., Zhao, C., Zhao, G., Zhou, M., Zhou, Z., Zhu, X. J., Zimmerman, A. B., Zucker, M. E., and Zweizig, J.
Subjects: General Relativity and Quantum Cosmology, Astrophysics - High Energy Astrophysical Phenomena
Abstract: On May 21, 2019 at 03:02:29 UTC Advanced LIGO and Advanced Virgo observed a short duration gravitational-wave signal, GW190521, with a three-detector network signal-to-noise ratio of 14.7, and an estimated false-alarm rate of 1 in 4900 yr using a search sensitive to generic transients. If GW190521 is from a quasicircular binary inspiral, then the detected signal is consistent with the merger of two black holes with masses of $85^{+21}_{-14} M_{\odot}$ and $66^{+17}_{-18} M_{\odot}$ (90 % credible intervals). We infer that the primary black hole mass lies within the gap produced by (pulsational) pair-instability supernova processes, and has only a 0.32 % probability of being below $65 M_{\odot}$. We calculate the mass of the remnant to be $142^{+28}_{-16} M_{\odot}$, which can be considered an intermediate mass black hole (IMBH). The luminosity distance of the source is $5.3^{+2.4}_{-2.6}$ Gpc, corresponding to a redshift of $0.82^{+0.28}_{-0.34}$. The inferred rate of mergers similar to GW190521 is $0.13^{+0.30}_{-0.11}\,\mathrm{Gpc}^{-3}\,\mathrm{yr}^{-1}$., Comment: Supplementary Material at https://dcc.ligo.org/LIGO-P2000020/Public
Published: 2020
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49. Observation of eccentric binary black hole mergers with second and third generation gravitational wave detector networks

Author: Chen, Zhuo, Huerta, E. A., Adamo, Joseph, Haas, Roland, O'Shea, Eamonn, Kumar, Prayush, and Moore, Chris
Subjects: General Relativity and Quantum Cosmology, Astrophysics - High Energy Astrophysical Phenomena, J.2
Abstract: [Abridged] We introduce an improved version of the Eccentric, Non-spinning, Inspiral-Gaussian-process Merger Approximant (ENIGMA) waveform model. We find that this ready-to-use model can: (i) produce physically consistent signals when sampling over 1M samples chosen over the $m_{\{1,\,2\}}\in[5M_{\odot},\,50M_{\odot}]$ parameter space, and the entire range of binary inclination angles; (ii) produce waveforms within 0.04 seconds from an initial gravitational wave frequency $f_{\textrm{GW}} =15\,\textrm{Hz}$ and at a sample rate of 8192 Hz; and (iii) reproduce the physics of quasi-circular mergers. We utilize ENIGMA to compute the expected signal-to-noise ratio (SNR) distributions of eccentric binary black hole mergers assuming the existence of second and third generation gravitational wave detector networks that include the twin LIGO detectors, Virgo, KAGRA, LIGO-India, a LIGO-type detector in Australia, Cosmic Explorer, and the Einstein Telescope. In the context of advanced LIGO-type detectors, we find that the SNR of eccentric mergers is always larger than quasi-circular mergers for systems with $e_0\leq0.4$ at $f_{\textrm{GW}} =10\,\textrm{Hz}$, even if the timespan of eccentric signals is just a third of quasi-circular systems with identical total mass and mass-ratio. For Cosmic Explorer-type detector networks, we find that eccentric mergers have similar SNRs than quasi-circular systems for $e_0\leq0.3$ at $f_{\textrm{GW}} =10\,\textrm{Hz}$. Systems with $e_0\sim0.5$ at $f_{\textrm{GW}} =10\,\textrm{Hz}$ have SNRs that range between 50%-90% of the SNR produced by quasi-circular mergers, even if these eccentric signals are just between a third to a tenth the length of quasi-circular systems. For Einstein Telescope-type detectors, we find that eccentric mergers have similar SNRs than quasi-circular systems for $e_0\leq0.4$ at $f_{\textrm{GW}} =5\,\textrm{Hz}$., Comment: 25 pages, 19 figures, 1 appendix. Accepted to Phys. Rev. D
Published: 2020
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50. Gravitational-wave constraints on the equatorial ellipticity of millisecond pulsars

Author: The LIGO Scientific Collaboration, the Virgo Collaboration, Abbott, R., Abbott, T. D., Abraham, S., Acernese, F., Ackley, K., Adams, A., Adams, C., Adhikari, R. X., Adya, V. B., Affeldt, C., Agathos, M., Agatsuma, K., Aggarwal, N., Aguiar, O. D., Aiello, L., Ain, A., Ajith, P., Allen, G., Allocca, A., Altin, P. A., Amato, A., Anand, S., Ananyeva, A., Anderson, S. B., Anderson, W. G., Angelova, S. V., Ansoldi, S., Antelis, J. M., Antier, S., Appert, S., Arai, K., Araya, M. C., Areeda, J. S., Arène, M., Arnaud, N., Aronson, S. M., Arun, K. G., Asali, Y., Ascenzi, S., Ashton, G., Aston, S. M., Astone, P., Aubin, F., Aufmuth, P., AultONeal, K., Austin, C., Avendano, V., Babak, S., Badaracco, F., Bader, M. K. M., Bae, S., Baer, A. M., Bagnasco, S., Baird, J., Ball, M., Ballardin, G., Ballmer, S. W., Bals, A., Balsamo, A., Baltus, G., Banagiri, S., Bankar, D., Bankar, R. S., Barayoga, J. C., Barbieri, C., Barish, B. C., 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. C., Bazzan, M., Becher, B. R., Bécsy, B., Bedakihale, V. M., Bejger, M., Belahcene, I., Beniwal, D., Benjamin, M. G., Bennett, T. F., Bentley, J. D., Bergamin, F., Berger, B. K., Bergmann, G., Bernuzzi, S., Bersanetti, D., Bertolini, A., Betzwieser, J., Bhandare, R., Bhandari, A. V., Bhattacharjee, D., Bidler, J., Bilenko, I. A., Billingsley, G., Birney, R., Birnholtz, O., Biscans, S., Bischi, M., Biscoveanu, S., Bisht, A., Bitossi, M., Bizouard, M. -A., Blackburn, J. K., Blackman, J., Blair, C. D., Blair, D. G., Blair, R. M., Blanch, O., Bobba, F., Bode, N., Boer, M., Boetzel, Y., Bogaert, G., Boldrini, M., Bondu, F., Bonnand, R., Booker, P., Boom, B. A., Bork, R., Boschi, V., Bose, S., Bossilkov, V., Boudart, V., Bouffanais, Y., Bozzi, A., Bradaschia, C., Brady, P. R., Bramley, A., Branchesi, M., Brau, J. E., Breschi, M., Briant, T., Briggs, J. H., Brighenti, F., Brillet, A., Brinkmann, M., Brockill, P., Brooks, A. F., Brooks, J., Brown, D. D., Brunett, S., Bruno, G., Bruntz, R., Buikema, A., Bulik, T., Bulten, H. J., Buonanno, A., Buskulic, D., Byer, R. L., Cabero, M., Cadonati, L., Caesar, M., Cagnoli, G., Cahillane, C., Bustillo, J. Calderón, Callaghan, J. D., Callister, T. A., Calloni, E., Camp, J. B., Canepa, M., Cannon, K. C., Cao, H., Cao, J., Carapella, G., Carbognani, F., Carney, M. F., Carpinelli, M., Carullo, G., Carver, T. L., Diaz, J. Casanueva, Casentini, C., Caudill, S., Cavaglià, M., Cavalier, F., Cavalieri, R., Cella, G., Cerdá-Durán, P., Cesarini, E., Chaibi, W., Chakravarti, K., Chan, C. -L., Chan, C., Chandra, K., Chanial, P., Chao, S., Charlton, P., Chase, E. A., Chassande-Mottin, E., Chatterjee, D., Chaturvedi, M., Chen, A., Chen, H. Y., Chen, X., Chen, Y., Cheng, H. -P., Cheong, C. K., Chia, H. Y., Chiadini, F., Chierici, R., Chincarini, A., Chiummo, A., Cho, G., Cho, H. S., Cho, M., Choate, S., Christensen, N., Chu, Q., Chua, S., Chung, K. W., Chung, S., Ciani, G., Ciecielag, P., Cieślar, M., Cifaldi, M., Ciobanu, A. A., Ciolfi, R., Cipriano, F., Cirone, A., Clara, F., Clark, E. N., Clark, J. A., Clarke, L., Clearwater, P., Clesse, S., Cleva, F., Coccia, E., Cohadon, P. -F., Cohen, D. E., Colleoni, M., Collette, C. G., Collins, C., Colpi, M., Constancio Jr., M., Conti, L., Cooper, S. J., Corban, P., Corbitt, T. R., Cordero-Carrión, I., Corezzi, S., Corley, K. R., Cornish, N., Corre, D., Corsi, A., Cortese, S., Costa, C. A., Cotesta, R., Coughlin, M. W., Coughlin, S. B., Coulon, J. -P., Countryman, S. T., Couvares, P., Covas, P. B., Coward, D. M., Cowart, M. J., Coyne, D. C., Coyne, R., Creighton, J. D. E., Creighton, T. D., Croquette, M., Crowder, S. G., Cudell, J. R., Cullen, T. J., Cumming, A., Cummings, R., Cunningham, L., Cuoco, E., Curylo, M., Canton, T. Dal, Dálya, G., Dana, A., DaneshgaranBajastani, L. M., D'Angelo, B., Danilishin, S. L., D'Antonio, S., Danzmann, K., Darsow-Fromm, C., Dasgupta, A., Datrier, L. E. H., Dattilo, V., Dave, I., Davier, M., Davies, G. S., Davis, D., Daw, E. J., Dean, R., DeBra, D., Deenadayalan, M., Degallaix, J., De Laurentis, M., Deléglise, S., Del Favero, V., De Lillo, F., De Lillo, N., Del Pozzo, W., DeMarchi, L. M., 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., Díaz, M. C., Diaz-Ortiz Jr., M., Didio, N. A., 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. K., Dmitriev, A., Doctor, Z., D'Onofrio, L., Donovan, F., Dooley, K. L., Doravari, S., Dorrington, I., Downes, T. P., Drago, M., Driggers, J. C., Du, Z., Ducoin, J. -G., Dupej, P., Durante, O., D'Urso, D., Duverne, P. -A., Dwyer, S. E., Easter, P. J., Eddolls, G., Edelman, B., Edo, T. B., Edy, O., Effler, A., Eichholz, J., Eikenberry, S. S., Eisenmann, M., Eisenstein, R. A., Ejlli, A., Errico, L., Essick, R. C., Estellés, H., Estevez, D., Etienne, Z. B., Etzel, T., Evans, M., Evans, T. M., Ewing, B. E., Fafone, V., Fair, H., Fairhurst, S., Fan, X., Farah, A. M., Farinon, S., Farr, B., Farr, W. M., Fauchon-Jones, E. J., Favata, M., Fays, M., Fazio, M., Feicht, J., Fejer, M. M., Feng, F., Fenyvesi, E., Ferguson, D. L., Fernandez-Galiana, A., Ferrante, I., Ferreira, T. A., Fidecaro, F., Figura, P., Fiori, I., Fiorucci, D., Fishbach, M., Fisher, R. P., Fishner, J. M., Fittipaldi, R., Fitz-Axen, M., Fiumara, V., Flaminio, R., Floden, E., Flynn, E., Fong, H., Font, J. A., Forsyth, P. W. F., Fournier, J. -D., Frasca, S., Frasconi, F., Frei, Z., Freise, A., Frey, R., Frey, V., Fritschel, P., Frolov, V. V., Fronzé, G. G., Fulda, P., Fyffe, M., Gabbard, H. A., Gadre, B. U., Gaebel, S. M., Gair, J. R., Gais, J., Galaudage, S., Gamba, R., Ganapathy, D., Ganguly, A., Gaonkar, S. G., Garaventa, B., García-Quirós, C., Garufi, F., Gateley, B., Gaudio, S., Gayathri, V., Gemme, G., Gennai, A., George, D., George, J., Gergely, L., Ghonge, S., Ghosh, Abhirup, Ghosh, Archisman, Ghosh, S., Giacomazzo, B., Giacoppo, L., Giaime, J. A., Giardina, K. D., Gibson, D. R., Gier, C., Gill, K., Giri, P., Glanzer, J., Gleckl, A. E., Godwin, P., Goetz, E., Goetz, R., Gohlke, N., Goncharov, B., González, G., Gopakumar, A., Gossan, S. E., 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. C., Green, R., Gretarsson, E. M., Griggs, H. L., Grignani, G., Grimaldi, A., Grimes, E., Grimm, S. J., Grote, H., Grunewald, S., Gruning, P., Guerrero, J. G., Guidi, G. M., Guimaraes, A. R., Guixé, G., Gulati, H. K., Guo, Y., Gupta, Anchal, Gupta, Anuradha, Gupta, P., Gustafson, E. K., Gustafson, R., Guzman, F., Haegel, L., Halim, O., Hall, E. D., Hamilton, E. Z., Hammond, G., Haney, M., Hanke, M. M., Hanks, J., Hanna, C., Hannam, M. D., Hannuksela, O. A., Hannuksela, O., Hansen, H., Hansen, T. J., Hanson, J., Harder, T., Hardwick, T., Haris, K., Harms, J., Harry, G. M., Harry, I. W., Hartwig, D., Hasskew, R. K., Haster, C. -J., Haughian, K., Hayes, F. J., Healy, J., Heidmann, A., Heintze, M. C., Heinze, J., Heinzel, J., Heitmann, H., Hellman, F., Hello, P., Helmling-Cornell, A. F., Hemming, G., Hendry, M., Heng, I. S., Hennes, E., Hennig, J., Hennig, M. H., Vivanco, F. Hernandez, Heurs, M., Hild, S., Hill, P., Hines, A. S., Hochheim, S., Hofgard, E., Hofman, D., Hohmann, J. N., Holgado, A. M., Holland, N. A., Hollows, I. J., Holmes, Z. J., Holt, K., Holz, D. E., Hopkins, P., Horst, C., Hough, J., Howell, E. J., Hoy, C. G., Hoyland, D., Huang, Y., Hübner, M. T., Huddart, A. D., Huerta, E. A., Hughey, B., Hui, V., Husa, S., Huttner, S. H., Hutzler, B. M., Huxford, R., Huynh-Dinh, T., Idzkowski, B., Iess, A., Imperato, S., Inchauspe, H., Ingram, C., Intini, G., Isi, M., Iyer, B. R., JaberianHamedan, V., Jacqmin, T., Jadhav, S. J., Jadhav, S. P., James, A. L., Jani, K., Janssens, K., Janthalur, N. N., Jaranowski, P., Jariwala, D., Jaume, R., Jenkins, A. C., Jeunon, M., Jiang, J., Johns, G. R., Jones, A. W., Jones, D. I., Jones, J. D., Jones, P., 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., Kapasi, D. P., Karathanasis, C., Karki, S., Kashyap, R., Kasprzack, M., Kastaun, W., Katsanevas, S., Katsavounidis, E., Katzman, W., Kawabe, K., Kéfélian, F., Keitel, D., Key, J. S., Khadka, S., Khalili, F. Y., Khan, I., Khan, S., Khazanov, E. A., Khetan, N., Khursheed, M., Kijbunchoo, N., Kim, C., Kim, G. J., Kim, J. C., Kim, K., Kim, W. S., Kim, Y. -M., Kimball, C., King, P. J., Kinley-Hanlon, M., Kirchhoff, R., Kissel, J. S., Kleybolte, L., Klimenko, S., Knowles, T. D., Knyazev, E., Koch, P., Koehlenbeck, S. M., Koekoek, G., Koley, S., Kolstein, M., Komori, K., Kondrashov, V., Kontos, A., Koper, N., Korobko, M., Korth, W. Z., Kovalam, M., Kozak, D. B., Krämer, C., Kringel, V., Krishnendu, N. V., Królak, A., Kuehn, G., Kumar, A., Kumar, P., Kumar, Rahul, Kumar, Rakesh, Kuns, K., Kwang, S., Lackey, B. D., Laghi, D., Lalande, E., Lam, T. L., Lamberts, A., Landry, M., Lane, B. B., Lang, R. N., Lange, J., Lantz, B., Lanza, R. K., La Rosa, I., Lartaux-Vollard, A., Lasky, P. D., Laxen, M., Lazzarini, A., Lazzaro, C., Leaci, P., Leavey, S., Lecoeuche, Y. K., Lee, H. M., Lee, H. W., Lee, J., Lee, K., Lehmann, J., Leon, E., Leroy, N., Letendre, N., Levin, Y., Li, A., Li, J., Li, K. J. L., Li, T. G. F., Li, X., Linde, F., Linker, S. D., Linley, J. N., Littenberg, T. B., Liu, J., Liu, X., Llorens-Monteagudo, M., Lo, R. K. L., Lockwood, 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., Ma, Y., Macas, R., MacInnis, M., Macleod, D. M., MacMillan, I. A. O., Macquet, A., Hernandez, I. Magaña, Magaña-Sandoval, F., Magazzù, C., Magee, R. M., Majorana, E., Maksimovic, I., Maliakal, S., Malik, A., Man, N., Mandic, V., Mangano, V., Mansell, G. L., Manske, M., Mantovani, M., Mapelli, M., Marchesoni, F., Marion, F., Márka, S., Márka, Z., Markakis, C., Markosyan, A. S., Markowitz, A., Maros, E., Marquina, A., Marsat, S., Martelli, F., Martin, I. W., Martin, R. M., Martinez, M., Martinez, V., Martynov, D. V., Masalehdan, H., Mason, K., Massera, E., Masserot, A., Massinger, T. J., Masso-Reid, M., Mastrogiovanni, S., Matas, A., Mateu-Lucena, M., Matichard, F., Matiushechkina, M., Mavalvala, N., Maynard, E., McCann, J. J., McCarthy, R., McClelland, D. E., McCormick, S., McCuller, L., McGuire, S. C., McIsaac, C., McIver, J., McManus, D. J., McRae, T., McWilliams, S. T., Meacher, D., Meadors, G. D., Mehmet, M., Mehta, A. K., Melatos, A., Melchor, D. A., Mendell, G., Menendez-Vazquez, A., Mercer, R. A., Mereni, L., Merfeld, K., Merilh, E. L., Merritt, J. D., Merzougui, M., Meshkov, S., Messenger, C., Messick, C., Metzdorff, R., Meyers, P. M., Meylahn, F., Mhaske, A., Miani, A., Miao, H., Michaloliakos, I., Michel, C., Middleton, H., Milano, L., Miller, A. L., Millhouse, M., Mills, J. C., Milotti, E., Milovich-Goff, M. C., Minazzoli, O., Minenkov, Y., Mir, Ll. M., Mishkin, A., Mishra, C., Mistry, T., Mitra, S., Mitrofanov, V. P., Mitselmakher, G., Mittleman, R., Mo, G., Mogushi, K., Mohapatra, S. R. P., Mohite, S. R., Molina, I., Molina-Ruiz, M., Mondin, M., Montani, M., Moore, C. J., Moraru, D., Morawski, F., Moreno, G., Morisaki, S., Mours, B., Mow-Lowry, C. M., Mozzon, S., Muciaccia, F., Mukherjee, Arunava, Mukherjee, D., Mukherjee, Soma, Mukherjee, Subroto, Mukund, N., Mullavey, A., Munch, J., Muñiz, E. A., Murray, P. G., Nadji, S. L., Nagar, A., Nardecchia, I., Naticchioni, L., Nayak, R. K., Neil, B. F., Neilson, J., Nelemans, G., Nelson, T. J. N., Nery, M., Neunzert, A., Ng, K. Y., Ng, S., Nguyen, C., Nguyen, P., Nguyen, T., Nichols, S. A., Nissanke, S., Nocera, F., Noh, M., North, C., Nothard, D., Nuttall, L. K., Oberling, J., O'Brien, B. D., O'Dell, J., Oganesyan, G., Ogin, G. H., Oh, J. J., Oh, S. H., Ohme, F., Ohta, H., Okada, M. A., Olivetto, C., Oppermann, P., Oram, R. J., O'Reilly, B., Ormiston, R. G., Ortega, L. F., O'Shaughnessy, R., Ossokine, S., Osthelder, C., Ottaway, D. J., Overmier, H., Owen, B. J., Pace, A. E., Pagano, G., Page, M. A., Pagliaroli, G., Pai, A., Pai, S. A., Palamos, J. R., Palashov, O., Palomba, C., Pan, H., Panda, P. K., Pang, T. H., Pankow, C., Pannarale, F., Pant, B. C., Paoletti, F., Paoli, A., Paolone, A., Parker, W., Pascucci, D., Pasqualetti, A., Passaquieti, R., Passuello, D., Patel, M., Patricelli, B., Payne, E., Pechsiri, T. C., Pedraza, M., Pegoraro, M., Pele, A., Penn, S., Perego, A., Perez, C. J., Périgois, C., Perreca, A., Perriès, S., Petermann, J., Petterson, D., Pfeiffer, H. P., Pham, K. A., Phukon, K. S., Piccinni, O. J., Pichot, M., Piendibene, M., Piergiovanni, F., Pierini, L., Pierro, V., Pillant, G., Pilo, F., Pinard, L., Pinto, I. M., Piotrzkowski, K., Pirello, M., Pitkin, M., Placidi, E., Plastino, W., Pluchar, C., Poggiani, R., Polini, E., Pong, D. Y. T., Ponrathnam, S., Popolizio, P., Porter, E. K., Poverman, A., Powell, J., Pracchia, M., Prajapati, A. K., Prasai, K., Prasanna, R., Pratten, G., Prestegard, T., Principe, M., Prodi, G. A., Prokhorov, L., Prosposito, P., 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., 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. 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Subjects: Astrophysics - High Energy Astrophysical Phenomena, General Relativity and Quantum Cosmology
Abstract: We present a search for continuous gravitational waves from five radio pulsars, comprising three recycled pulsars (PSR J0437-4715, PSR J0711-6830, and PSR J0737-3039A) and two young pulsars: the Crab pulsar (J0534+2200) and the Vela pulsar (J0835-4510). We use data from the third observing run of Advanced LIGO and Virgo combined with data from their first and second observing runs. For the first time we are able to match (for PSR J0437-4715) or surpass (for PSR J0711-6830) the indirect limits on gravitational-wave emission from recycled pulsars inferred from their observed spin-downs, and constrain their equatorial ellipticities to be less than $10^{-8}$. For each of the five pulsars, we perform targeted searches that assume a tight coupling between the gravitational-wave and electromagnetic signal phase evolution. We also present constraints on PSR J0711-6830, the Crab pulsar and the Vela pulsar from a search that relaxes this assumption, allowing the gravitational-wave signal to vary from the electromagnetic expectation within a narrow band of frequencies and frequency derivatives., Comment: 23 pages, 6 figures, 3 tables
Published: 2020
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