Author: "Pegoraro, A" / Publication Type: Reports - Searchworks@Jio Institute Digital Library Search Results

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228 results on '"Pegoraro, A"'

1. ProReco: A Process Discovery Recommender System

Author: Huang, Tsung-Hao, Junied, Tarek, Pegoraro, Marco, and van der Aalst, Wil M. P.
Subjects: Computer Science - Machine Learning, Computer Science - Information Retrieval
Abstract: Process discovery aims to automatically derive process models from historical execution data (event logs). While various process discovery algorithms have been proposed in the last 25 years, there is no consensus on a dominating discovery algorithm. Selecting the most suitable discovery algorithm remains a challenge due to competing quality measures and diverse user requirements. Manually selecting the most suitable process discovery algorithm from a range of options for a given event log is a time-consuming and error-prone task. This paper introduces ProReco, a Process discovery Recommender system designed to recommend the most appropriate algorithm based on user preferences and event log characteristics. ProReco incorporates state-of-the-art discovery algorithms, extends the feature pools from previous work, and utilizes eXplainable AI (XAI) techniques to provide explanations for its recommendations., Comment: 8 pages, 5 figures, 9 references
Published: 2025
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2. Control-flow anomaly detection by process mining-based feature extraction and dimensionality reduction

Author: Vitale, Francesco, Pegoraro, Marco, van der Aalst, Wil M. P., and Mazzocca, Nicola
Subjects: Computer Science - Machine Learning
Abstract: The business processes of organizations may deviate from normal control flow due to disruptive anomalies, including unknown, skipped, and wrongly-ordered activities. To identify these control-flow anomalies, process mining can check control-flow correctness against a reference process model through conformance checking, an explainable set of algorithms that allows linking any deviations with model elements. However, the effectiveness of conformance checking-based techniques is negatively affected by noisy event data and low-quality process models. To address these shortcomings and support the development of competitive and explainable conformance checking-based techniques for control-flow anomaly detection, we propose a novel process mining-based feature extraction approach with alignment-based conformance checking. This variant aligns the deviating control flow with a reference process model; the resulting alignment can be inspected to extract additional statistics such as the number of times a given activity caused mismatches. We integrate this approach into a flexible and explainable framework for developing techniques for control-flow anomaly detection. The framework combines process mining-based feature extraction and dimensionality reduction to handle high-dimensional feature sets, achieve detection effectiveness, and support explainability. The results show that the framework techniques implementing our approach outperform the baseline conformance checking-based techniques while maintaining the explainable nature of conformance checking. We also provide an explanation of why existing conformance checking-based techniques may be ineffective., Comment: 16 pages, 9 figures, 7 tables, 56 references
Published: 2025
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3. MovISAC: Coherent Imaging of Moving Targets with Distributed Asynchronous ISAC Devices

Author: Pegoraro, Jacopo, Tagliaferri, Dario, and Widmer, Joerg
Subjects: Electrical Engineering and Systems Science - Signal Processing
Abstract: Distributed integrated sensing and communication (ISAC) devices can overcome the traditional resolution limitations imposed by the signal bandwidth, cooperating to produce high-resolution images of the environment. However, existing phase-coherent imaging approaches are not suited to imaging multiple moving targets, since the Doppler effect causes a phase rotation that degrades the image focus and biases the targets' locations. In this paper, we propose MovISAC, the first coherent imaging method for moving targets using distributed asynchronous ISAC devices. Our approach obtains a set of high-resolution images of the environment, in which each image represents only targets moving with a selected velocity. To achieve this, MovISAC performs over-the-air (OTA) synchronization to compensate for timing, frequency, and phase offsets among distributed ISAC devices. Then, to solve the prohibitive complexity of an exhaustive search over the targets' velocities, we introduce a new association algorithm to match the Doppler shifts observed by each ISAC device pair to the corresponding spatial peaks obtained by standard imaging methods. This gives MovISAC the unique capability of pre-compensating the Doppler shift for each target before forming the images, thus recasting it from an undesired impairment to image formation to an additional means for resolving the targets. We perform extensive numerical simulations showing that our approach achieves extremely high resolution, superior robustness to clutter thanks to the additional target separation in the Doppler domain, and obtains cm- and cm/s-level target localization and velocity estimation errors. MovISAC significantly outperforms existing imaging methods that do not compensate for the Doppler shift, demonstrating up to 18 times lower localization error and enabling velocity estimation., Comment: 13 pages, 13 figures, 1 table
Published: 2025

4. How to Entangle or Disentangle Gaussian States with Hong-Ou-Mandel Interference

Author: Schlue, Fabian, Folge, Patrick, Nomura, Takefumi, Held, Philip, Pegoraro, Federico, Stefszky, Michael, Brecht, Benjamin, Barnett, Stephen M., and Silberhorn, Christine
Subjects: Quantum Physics
Abstract: The interference of two indistinguishable photons from a photon pair on a balanced beam splitter, where each photon enters in one of the input ports, yields a maximally entangled N00N-state shared between the outputs of the beam splitter through Hong-Ou-Mandel interference. At the same time, interference between the two modes of a two-mode squeezed state - the generalized output of a parametric photon-pair source - yields two independent, hence separable, single-mode squeezed states. Notably, both experiments use the exact same experimental setup. We resolve this seeming contradiction by measuring photon-number resolved quantum interference of the two modes of a two-mode squeezed state from an engineered parametric down-conversion source and show that the notion of inseparability is a result of post-selection on the presence of photons. Indeed, the complete joint photon statistics including the vacuum component are separable, as expected from textbook calculations.
Published: 2025

5. SafeSplit: A Novel Defense Against Client-Side Backdoor Attacks in Split Learning (Full Version)

Author: Rieger, Phillip, Pegoraro, Alessandro, Kumari, Kavita, Abera, Tigist, Knauer, Jonathan, and Sadeghi, Ahmad-Reza
Subjects: Computer Science - Cryptography and Security, Computer Science - Distributed, Parallel, and Cluster Computing, Computer Science - Machine Learning
Abstract: Split Learning (SL) is a distributed deep learning approach enabling multiple clients and a server to collaboratively train and infer on a shared deep neural network (DNN) without requiring clients to share their private local data. The DNN is partitioned in SL, with most layers residing on the server and a few initial layers and inputs on the client side. This configuration allows resource-constrained clients to participate in training and inference. However, the distributed architecture exposes SL to backdoor attacks, where malicious clients can manipulate local datasets to alter the DNN's behavior. Existing defenses from other distributed frameworks like Federated Learning are not applicable, and there is a lack of effective backdoor defenses specifically designed for SL. We present SafeSplit, the first defense against client-side backdoor attacks in Split Learning (SL). SafeSplit enables the server to detect and filter out malicious client behavior by employing circular backward analysis after a client's training is completed, iteratively reverting to a trained checkpoint where the model under examination is found to be benign. It uses a two-fold analysis to identify client-induced changes and detect poisoned models. First, a static analysis in the frequency domain measures the differences in the layer's parameters at the server. Second, a dynamic analysis introduces a novel rotational distance metric that assesses the orientation shifts of the server's layer parameters during training. Our comprehensive evaluation across various data distributions, client counts, and attack scenarios demonstrates the high efficacy of this dual analysis in mitigating backdoor attacks while preserving model utility., Comment: To appear at NDSS 2025; 18 pages, 6 Tables, and 11 figures
Published: 2025
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6. Quality Assurance and Quality Control of the $26~\text{m}^2$ SiPM production for the DarkSide-20k dark matter experiment

Author: Acerbi, F., Adhikari, P., Agnes, P., Ahmad, I., Albergo, S., Albuquerque, I. F., Alexander, T., Alton, A. K., Amaudruz, P., Aprile, M. Angiolilli. E., Corona, M. Atzori, Auty, D. J., Ave, M., Avetisov, I. C., Azzolini, O., Back, H. O., Balmforth, Z., Olmedo, A. Barrado, Barrillon, P., Batignani, G., Bhowmick, P., Bloem, M., Blua, S., Bocci, V., Bonivento, W., Bottino, B., Boulay, M. G., Buchowicz, A., Bussino, S., Busto, J., Cadeddu, M., Cadoni, M., Calabrese, R., Camillo, V., Caminata, A., Canci, N., Capra, A., Caravati, M., Cardenas-Montes, M., Cargioli, N., Carlini, M., Castello, P., Cavalcante, P., Cebrian, S., Ruiz, J. Cela, Chashin, S., Chepurnov, A., Cifarelli, L., Cintas, D., Cleveland, B., Coadou, Y., Cocco, V., Colaiuda, D., Vilda, E. Conde, Consiglio, L., Costa, B. S., Czubak, M., D'Auria, S., Rolo, M. D. Da Rocha, Darbo, G., Davini, S., de Asmundis, R., De Cecco, S., Dellacasa, G., Derbin, A. V., Di Capua, F., Di Noto, L., Di Stefano, P., Dias, L. K., Dionisi, C., Dolganov, G., Dordei, F., Dronik, V., Elersich, A., Ellingwood, E., Erjavec, T., Fearon, N., Diaz, M. Fernandez, Ficorella, A., Fiorillo, G., Franchini, P., Franco, D., Gatti, H. Frandini, Frolov, E., Gabriele, F., Gahan, D., Galbiati, C., Galiski, G., Gallina, G., Gallus, G., Garbini, M., Abia, P. Garcia, Gawdzik, A., Gendotti, A., Giovanetti, G. K., Casanueva, V. Goicoechea, Gola, A., Grandi, L., Grauso, G., di Cortona, G. Grilli, Grobov, A., Gromov, M., Gulino, M., Guo, C., Hackett, B. R., Hallin, A., Hamer, A., Haranczyk, M., Hessel, T., Horikawa, S., Hu, J., Hubaut, F., Hucker, J., Hugues, T., Hungerford, E. V., Ianni, A., Ippoliti, G., Ippolito, V., Jamil, A., Jillings, C., Keloth, R., Kemmerich, N., Kemp, A., Kester, Carlos E., Kimura, M., Kondo, K., Korga, G., Kotsiopoulou, L., Koulosousas, S., Kubankin, A., Kunze, P., Kuss, M., Kuźniak, M., Kuzwa, M., La Commara, M., Lai, M., LeGuirriec, E., Leason, E., Leoni, A., Lidey, L., Lissia, M., Luzzi, L., Lychagina, O., Macfadyen, O., Machulin, I. N., Manecki, S., Manthos, I., Marasciulli, A., Margutti, G., Mari, S. M., Mariani, C., Maricic, J., Martinez, M., Martoff, C. J., Matteucci, G., Mavrokoridis, K., Mazza, E., McDonald, A. B., Merzi, S., Messina, A., Milincic, R., Minutoli, S., Mitra, A., Monroe, J., Moretti, E., Morrocchi, M., Mroz, T., Muratova, V. N., Murphy, M., Murra, M., Muscas, C., Musico, P., Nania, R., Nessi, M., Nieradka, G., Nikolopoulos, K., Nikoloudaki, E., Nowak, J., Olchanski, K., Oleinik, A., Oleynikov, V., Organtini, P., de Solrzano, A. Ortiz, Pallavicini, M., Pandola, L., Pantic, E., Paoloni, E., Papi, D., Pastuszak, G., Paternoster, G., Pegoraro, P. A., Pelczar, K., Perez, R., Pesudo, V., Piacentini, S., Pino, N., Plante, G., Pocar, A., Poehlmann, M., Pordes, S., Pralavorio, P., Preosti, E., Price, D., Puglia, S., Bazetto, M. Queiroga, Ragusa, F., Ramachers, Y., Ramirez, A., Ravinthiran, S., Razeti, M., Renshaw, A. L., Rescigno, M., Resconi, S., Retiere, F., Rignanese, L. P., Rivetti, A., Roberts, A., Roberts, C., Rogers, G., Romero, L., Rossi, M., Rubbia, A., Rudik, D., Sabia, M., Salomone, P., Samoylov, O., Sanfilippo, S., Santone, D., Santorelli, R., Santos, E. Moura, Savarese, C., Scapparone, E., Schuckman II, F. G., Scioli, G., Semenov, D. A., Sheshukov, A., Simeone, M., Skensved, P., Skorokhvatov, M. D., Smirnov, O., Smirnova, T., Smith, B., Sotnikov, A., Spadoni, F., Spangenberg, M., Stefanizzi, R., Steri, A., Stornelli, V., Stracka, S., Sulis, S., Sung, A., Sunny, C., Suvorov, Y., Szelc, A. M., Taborda, O., Tartaglia, R., Taylor, A., Taylor, J., Testera, G., Thieme, K., Thompson, A., Torres-Lara, S., Tricomi, A., Unzhakov, E. V., Van Uffelen, M., Viant, T., Viel, S., Vishneva, A., Vogelaar, R. B., Vossebeld, J., Vyas, B., Wada, M., Walczak, M., Wang, Y., Wang, H., Westerdale, S., Williams, L., Wojaczyski, R., Wojcik, M. M., Wojcik, M., Wright, T., Xie, Y., Yang, C., Yin, J., Zabihi, A., Zakhary, P., Zani, A., Zhang, Y., Zhu, T., Zichichi, A., Zuzel, G., and Zykova, M. P.
Subjects: Physics - Instrumentation and Detectors
Abstract: DarkSide-20k is a novel liquid argon dark matter detector currently under construction at the Laboratori Nazionali del Gran Sasso (LNGS) of the Istituto Nazionale di Fisica Nucleare (INFN) that will push the sensitivity for Weakly Interacting Massive Particle (WIMP) detection into the neutrino fog. The core of the apparatus is a dual-phase Time Projection Chamber (TPC), filled with \SI{50} {tonnes} of low radioactivity underground argon (UAr) acting as the WIMP target. NUV-HD-Cryo Silicon Photomultipliers (SiPM)s designed by Fondazione Bruno Kessler (FBK) (Povo, Trento, Italy) were selected as the photon sensors covering two $10.5~\text{m}^2$ Optical Planes, one at each end of the TPC, and a total of $5~\text{m}^2$ photosensitive surface for the liquid argon veto detectors. This paper describes the Quality Assurance and Quality Control (QA/QC) plan and procedures accompanying the production of FBK~NUV-HD-Cryo SiPM wafers manufactured by LFoundry s.r.l. (Avezzano, AQ, Italy). SiPM characteristics are measured at 77~K at the wafer level with a custom-designed probe station. As of May~2024, 603 of the 1400 production wafers (43\% of the total) for DarkSide-20k were tested, including wafers from all 57 production Lots. The wafer yield is $93.6\pm2.5$\%, which exceeds the 80\% specification defined in the original DarkSide-20k production plan.
Published: 2024

7. Using Deep Reinforcement Learning to Enhance Channel Sampling Patterns in Integrated Sensing and Communication

Author: Mason, Federico and Pegoraro, Jacopo
Subjects: Electrical Engineering and Systems Science - Signal Processing
Abstract: In Integrated Sensing And Communication (ISAC) systems, estimating the micro-Doppler (mD) spectrogram of a target requires combining channel estimates retrieved from communication with ad-hoc sensing packets, which cope with the sparsity of the communication traffic. Hence, the mD quality depends on the transmission strategy of the sensing packets, which is still a challenging problem with no known solutions. In this letter, we design a deep Reinforcement Learning (RL) framework that fragments such a problem into a sequence of simpler decisions and takes advantage of the mD temporal evolution for maximizing the reconstruction performance. Our method is the first that learns sampling patterns to directly optimize the mD quality, enabling the adaptation of ISAC systems to variable communication traffic. We validate the proposed approach on a dataset of real channel measurements, reaching up to 40% higher mD reconstruction accuracy and several times lower computational complexity than state-of-the-art methods., Comment: 6 pages, 3 figures, 1 table
Published: 2024

8. Demonstration of a Photonic Time-Multiplexed C-NOT Gate

Author: Pegoraro, Federico, Held, Philip, Lammers, Jonas, Brecht, Benjamin, and Silberhorn, Christine
Subjects: Quantum Physics, Physics - Optics
Abstract: The two-qubit controlled-not (C-NOT) gate is an essential component in the construction of a gate-based quantum computer. In fact, its operation, combined with single qubit rotations allows to realise any quantum circuit. Several strategies have been adopted in order to build quantum gates, among them the photonic one offers the dual advantage of excellent isolation from the external environment and ease of manipulation at the single qubit level. Here we adopt a scalable time-multiplexed approach in order to build a fully reconfigurable architecture capable of implementing a post-selected interferometric scheme that implements the C-NOT operation with a fidelity of $(93.8\pm1.4)\%$. We use our experimental platform to generate the four Bell states., Comment: 9 pages, 4 figures
Published: 2024

9. AsyMov: Integrated Sensing and Communications with Asynchronous Moving Devices

Author: Ventura, Gianmaria, Bhalli, Zaman, Rossi, Michele, and Pegoraro, Jacopo
Subjects: Electrical Engineering and Systems Science - Signal Processing
Abstract: Estimating the Doppler frequency shift caused by moving targets is one of the key objectives of Integrated Sensing And Communication (ISAC) systems. In the case of Wi-Fi sensing, a reliable estimation of the Doppler enables applications such as target classification, human activity recognition, and gait analysis. However, in practical scenarios, Doppler estimation is hindered by the movement of transmitter and receiver devices, and by the phase offsets caused by their clock asynchrony. Existing approaches have separately addressed these two aspects, either assuming clock-synchronous moving devices or asynchronous static ones. Jointly tackling device motion and clock asynchrony is extremely challenging, as the Doppler shift from device movement differs for each propagation path and the phase offsets are time-varying. In this paper, we present AsyMov, a method to estimate the bistatic Doppler frequency of a target and the device velocity in ISAC setups featuring mobile and asynchronous devices. Our method leverages the channel impulse response at the receiver, by originally exploiting the invariance of phase offsets across propagation paths and the bistatic geometry. Moreover, AsyMov handles irregular channel sampling in the time domain and can be seamlessly integrated with device velocity measurements obtained from onboard sensors (if available), to enhance its reliability. AsyMov is thoroughly characterized from a theoretical perspective, via numerical simulation, and experimentally, implementing it on an IEEE 802.11ay testbed. Numerical and experimental results show superior performance against traditional methods based on the discrete Fourier transform and are on par with scenarios featuring static ISAC devices., Comment: 13 pages, 13 figures, 3 tables
Published: 2024

10. Formulation of a one-dimensional electrostatic plasma model for testing the validity of kinetic theory

Author: Pegoraro, Francesco, Morrison, Philip J., Manzini, Davide, and Califano, Francesco
Subjects: Physics - Plasma Physics
Abstract: We present a one-dimensional (1-D) model composed of aligned, electrostatically interacting charged disks, conceived in order to address in a computable model the validity of the Bogoliubov assumption on the decay of particle correlations in the Born-Bogoliubov-Green-Kirkwood-Yvon hierarchy. This assumption is a basic premise of plasma kinetic theory. The disk model exhibits spatially 1-D features at short distances, but retains 3-D features at large distances. Here the collective dynamics of this model plasma is investigated by solving the corresponding Vlasov equation. In addition, the implementation of the model for the numerical validation of the Bogoliubov assumption is formulated.
Published: 2024

11. Ranking the Top-K Realizations of Stochastically Known Event Logs

Author: Lepsien, Arvid, Pegoraro, Marco, Fonger, Frederik, Langhammer, Dominic, Aleknonytė-Resch, Milda, and Koschmider, Agnes
Subjects: Computer Science - Databases, Computer Science - Machine Learning
Abstract: Various kinds of uncertainty can occur in event logs, e.g., due to flawed recording, data quality issues, or the use of probabilistic models for activity recognition. Stochastically known event logs make these uncertainties transparent by encoding multiple possible realizations for events. However, the number of realizations encoded by a stochastically known log grows exponentially with its size, making exhaustive exploration infeasible even for moderately sized event logs. Thus, considering only the top-K most probable realizations has been proposed in the literature. In this paper, we implement an efficient algorithm to calculate a top-K realization ranking of an event log under event independence within O(Kn), where n is the number of uncertain events in the log. This algorithm is used to investigate the benefit of top-K rankings over top-1 interpretations of stochastically known event logs. Specifically, we analyze the usefulness of top-K rankings against different properties of the input data. We show that the benefit of a top-K ranking depends on the length of the input event log and the distribution of the event probabilities. The results highlight the potential of top-K rankings to enhance uncertainty-aware process mining techniques.
Published: 2024

12. Benchmarking the design of the cryogenics system for the underground argon in DarkSide-20k

Author: Collaboration, DarkSide-20k, Acerbi, F., Adhikari, P., Agnes, P., Ahmad, I., Albergo, S., Albuquerque, I. F. M., Alexander, T., Alton, A. K., Amaudruz, P., Angiolilli, M., Aprile, E., Ardito, R., Corona, M. Atzori, Auty, D. J., Ave, M., Avetisov, I. C., Azzolini, O., Back, H. O., Balmforth, Z., Olmedo, A. Barrado, Barrillon, P., Batignani, G., Bhowmick, P., Blua, S., Bocci, V., Bonivento, W., Bottino, B., Boulay, M. G., Buchowicz, A., Bussino, S., Busto, J., Cadeddu, M., Cadoni, M., Calabrese, R., Camillo, V., Caminata, A., Canci, N., Capra, A., Caravati, M., Cárdenas-Montes, M., Cargioli, N., Carlini, M., Castellani, A., Castello, P., Cavalcante, P., Cebrian, S., Ruiz, J. Cela, Chashin, S., Chepurnov, A., Cifarelli, L., Cintas, D., Citterio, M., Cleveland, B., Coadou, Y., Cocco, V., Colaiuda, D., Vilda, E. Conde, Consiglio, L., Costa, B. S., Czubak, M., D'Aniello, M., D'Auria, S., Rolo, M. D. Da Rocha, Darbo, G., Davini, S., De Cecco, S., De Guido, G., Dellacasa, G., Derbin, A. V., Devoto, A., Di Capua, F., Di Ludovico, A., Di Noto, L., Di Stefano, P., Dias, L. K., Mairena, D. Díaz, Ding, X., Dionisi, C., Dolganov, G., Dordei, F., Dronik, V., Elersich, A., Ellingwood, E., Erjavec, T., Diaz, M. Fernandez, Ficorella, A., Fiorillo, G., Franchini, P., Franco, D., Gatti, H. Frandini, Frolov, E., Gabriele, F., Gahan, D., Galbiati, C., Galiński, G., Gallina, G., Gallus, G., Garbini, M., Abia, P. Garcia, Gawdzik, A., Gendotti, A., Ghisi, A., Giovanetti, G. K., Casanueva, V. Goicoechea, Gola, A., Grandi, L., Grauso, G., di Cortona, G. Grilli, Grobov, A., Gromov, M., Guerzoni, M., Gulino, M., Guo, C., Hackett, B. R., Hallin, A., Hamer, A., Haranczyk, M., Harrop, B., Hessel, T., Hill, S., Horikawa, S., Hu, J., Hubaut, F., Hucker, J., Hugues, T., Hungerford, E. V., Ianni, A., Ippolito, V., Jamil, A., Jillings, C., Jois, S., Kachru, P., Keloth, R., Kemmerich, N., Kemp, A., Kendziora, C. L., Kimura, M., Kish, A., Kondo, K., Korga, G., Kotsiopoulou, L., Koulosousas, S., Kubankin, A., Kunzé, P., Kuss, M., Kuźniak, M., Kuzwa, M., La Commara, M., Lai, M., Guirriec, E. Le, Leason, E., Leoni, A., Lidey, L., Lissia, M., Luzzi, L., Lychagina, O., Macfadyen, O., Machulin, I. N., Manecki, S., Manthos, I., Mapelli, L., Marasciulli, A., Mari, S. M., Mariani, C., Maricic, J., Martinez, M., Martoff, C. J., Matteucci, G., Mavrokoridis, K., McDonald, A. B., Mclaughlin, J., Merzi, S., Messina, A., Milincic, R., Minutoli, S., Mitra, A., Moharana, A., Moioli, S., Monroe, J., Moretti, E., Morrocchi, M., Mroz, T., Muratova, V. N., Murphy, M., Murra, M., Muscas, C., Musico, P., Nania, R., Nessi, M., Nieradka, G., Nikolopoulos, K., Nikoloudaki, E., Nowak, J., Olchanski, K., Oleinik, A., Oleynikov, V., Organtini, P., de Solórzano, A. Ortiz, Pallavicini, M., Pandola, L., Pantic, E., Paoloni, E., Papi, D., Pastuszak, G., Paternoster, G., Peck, A., Pegoraro, P. A., Pelczar, K., Pellegrini, L. A., Perez, R., Perotti, F., Pesudo, V., Piacentini, S. I., Pino, N., Plante, G., Pocar, A., Poehlmann, M., Pordes, S., Pralavorio, P., Price, D., Puglia, S., Bazetto, M. Queiroga, Ragusa, F., Ramachers, Y., Ramirez, A., Ravinthiran, S., Razeti, M., Renshaw, A. L., Rescigno, M., Retiere, F., Rignanese, L. P., Rivetti, A., Roberts, A., Roberts, C., Rogers, G., Romero, L., Rossi, M., Rubbia, A., Rudik, D., Sabia, M., Salomone, P., Samoylov, O., Sandford, E., Sanfilippo, S., Santone, D., Santorelli, R., Santos, E. M., Savarese, C., Scapparone, E., Schillaci, G., Schuckman II, F. G., Scioli, G., Semenov, D. A., Shalamova, V., Sheshukov, A., Simeone, M., Skensved, P., Skorokhvatov, M. D., Smirnov, O., Smirnova, T., Smith, B., Sotnikov, A., Spadoni, F., Spangenberg, M., Stefanizzi, R., Steri, A., Stornelli, V., Stracka, S., Sulis, S., Sung, A., Sunny, C., Suvorov, Y., Szelc, A. M., Taborda, O., Tartaglia, R., Taylor, A., Taylor, J., Tedesco, S., Testera, G., Thieme, K., Thompson, A., Thorpe, T. N., Tonazzo, A., Torres-Lara, S., Tricomi, A., Unzhakov, E. V., Vallivilayil, T. J., Van Uffelen, M., Velazquez-Fernandez, L., Viant, T., Viel, S., Vishneva, A., Vogelaar, R. B., Vossebeld, J., Vyas, B., Wada, M., Walczak, M. B., Wang, H., Wang, Y., Westerdale, S., Williams, L., Wojaczyński, R., Wojcik, M., Wojcik, M. M., Wright, T., Xiao, X., Xie, Y., Yang, C., Yin, J., Zabihi, A., Zakhary, P., Zani, A., Zhang, Y., Zhu, T., Zichichi, A., Zuzel, G., and Zykova, M. P.
Subjects: Physics - Instrumentation and Detectors, High Energy Physics - Experiment
Abstract: DarkSide-20k (DS-20k) is a dark matter detection experiment under construction at the Laboratori Nazionali del Gran Sasso (LNGS) in Italy. It utilises ~100 t of low radioactivity argon from an underground source (UAr) in its inner detector, with half serving as target in a dual-phase time projection chamber (TPC). The UAr cryogenics system must maintain stable thermodynamic conditions throughout the experiment's lifetime of over 10 years. Continuous removal of impurities and radon from the UAr is essential for maximising signal yield and mitigating background. We are developing an efficient and powerful cryogenics system with a gas purification loop with a target circulation rate of 1000 slpm. Central to its design is a condenser operated with liquid nitrogen which is paired with a gas heat exchanger cascade, delivering a combined cooling power of more than 8 kW. Here we present the design choices in view of the DS-20k requirements, in particular the condenser's working principle and the cooling control, and we show test results obtained with a dedicated benchmarking platform at CERN and LNGS. We find that the thermal efficiency of the recirculation loop, defined in terms of nitrogen consumption per argon flow rate, is 95 % and the pressure in the test cryostat can be maintained within $\pm$(0.1-0.2) mbar. We further detail a 5-day cool-down procedure of the test cryostat, maintaining a cooling rate typically within -2 K/h, as required for the DS-20k inner detector. Additionally, we assess the circuit's flow resistance, and the heat transfer capabilities of two heat exchanger geometries for argon phase change, used to provide gas for recirculation. We conclude by discussing how our findings influence the finalisation of the system design, including necessary modifications to meet requirements and ongoing testing activities., Comment: 44 pages, 25 figures, 1 table. Updated to match the published journal version
Published: 2024
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13. Long-living Equilibria in Kinetic Astrophysical Plasma Turbulence

Author: Imbrogno, Mario, Meringolo, Claudio, Servidio, Sergio, Cruz-Osorio, Alejandro, Cerutti, Benoît, and Pegoraro, Francesco
Subjects: Physics - Plasma Physics, Astrophysics - High Energy Astrophysical Phenomena
Abstract: Turbulence in classical fluids is characterized by persistent structures that emerge from the chaotic landscape. We investigate the analogous process in fully kinetic plasma turbulence by using high-resolution, direct numerical simulations in two spatial dimensions. We observe the formation of long-living vortices with a profile typical of macroscopic, magnetically dominated force-free states. Inspired by the Harris pinch model for inhomogeneous equilibria, we describe these metastable solutions with a self-consistent kinetic model in a cylindrical coordinate system centered on a representative vortex, starting from an explicit form of the particle velocity distribution function. Such new equilibria can be simplified to a Gold-Hoyle solution of the modified force-free state. Turbulence is mediated by the long-living structures, accompanied by transients in which such vortices merge and form self-similarly new metastable equilibria. This process can be relevant to the comprehension of various astrophysical phenomena, going from the formation of plasmoids in the vicinity of massive compact objects to the emergence of coherent structures in the heliosphere., Comment: 12 pages, 5 figures (Main Text), 3 figures (Appendices)
Published: 2024

14. HiSAC: High-Resolution Sensing with Multiband Communication Signals

Author: Pegoraro, Jacopo, Lacruz, Jesus O., Rossi, Michele, and Widmer, Joerg
Subjects: Electrical Engineering and Systems Science - Signal Processing
Abstract: Integrated Sensing And Communication (ISAC ) systems are expected to perform accurate radar sensing while having minimal impact on communication. Ideally, sensing should only reuse communication resources, especially for spectrum which is contended by many applications. However, this poses a great challenge in that communication systems often operate on narrow subbands with low sensing resolution. Combining contiguous subbands has shown significant resolution gain in active localization. However, multiband ISAC remains unexplored due to communication subbands being highly sparse (non-contiguous) and affected by phase offsets that prevent their aggregation (incoherent). To tackle these problems, we design HiSAC, the first multiband ISAC system that combines diverse subbands across a wide frequency range to achieve super-resolved passive ranging. To solve the non-contiguity and incoherence of subbands, HiSAC combines them progressively, exploiting an anchor propagation path between transmitter and receiver in an optimization problem to achieve phase coherence. HiSAC fully reuses pilot signals in communication systems, it applies to different frequencies and can combine diverse technologies, e.g., 5G-NR and WiGig. We implement HiSAC on an experimental platform in the millimeter-wave unlicensed band and test it on objects and humans. Our results show it enhances the sensing resolution by up to 20 times compared to single-band processing while occupying the same spectrum., Comment: 15 pages, 15 figures
Published: 2024
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15. DarkSide-20k sensitivity to light dark matter particles

Author: Collaboration, DarkSide-20k, Acerbi, F., Adhikari, P., Agnes, P., Ahmad, I., Albergo, S., Albuquerque, I. F. M., Alexander, T., Alton, A. K., Amaudruz, P., Angiolilli, M., Aprile, E., Ardito, R., Corona, M. Atzori, Auty, D. J., Ave, M., Avetisov, I. C., Azzolini, O., Back, H. O., Balmforth, Z., Olmedo, A. Barrado, Barrillon, P., Batignani, G., Bhowmick, P., Blua, S., Bocci, V., Bonivento, W., Bottino, B., Boulay, M. G., Buchowicz, A., Bussino, S., Busto, J., Cadeddu, M., Cadoni, M., Calabrese, R., Camillo, V., Caminata, A., Canci, N., Capra, A., Caravati, M., Cárdenas-Montes, M., Cargioli, N., Carlini, M., Castellani, A., Castello, P., Cavalcante, P., Cebrian, S., Ruiz, J. M. Cela, Chashin, S., Chepurnov, A., Cifarelli, L., Cintas, D., Citterio, M., Cleveland, B., Coadou, Y., Cocco, V., Colaiuda, D., Vilda, E. Conde, Consiglio, L., Costa, B. S., Czubak, M., D'Aniello, M., D'Auria, S., Rolo, M. D. Da Rocha, Darbo, G., Davini, S., De Cecco, S., De Guido, G., Dellacasa, G., Derbin, A. V., Devoto, A., Di Capua, F., Di Ludovico, A., Di Noto, L., Di Stefano, P., Dias, L. K., Mairena, D. Díaz, Ding, X., Dionisi, C., Dolganov, G., Dordei, F., Dronik, V., Elersich, A., Ellingwood, E., Erjavec, T., Diaz, M. Fernandez, Ficorella, A., Fiorillo, G., Franchini, P., Franco, D., Gatti, H. Frandini, Frolov, E., Gabriele, F., Gahan, D., Galbiati, C., Galiński, G., Gallina, G., Gallus, G., Garbini, M., Abia, P. Garcia, Gawdzik, A., Gendotti, A., Ghisi, A., Giovanetti, G. K., Casanueva, V. Goicoechea, Gola, A., Grandi, L., Grauso, G., di Cortona, G. Grilli, Grobov, A., Gromov, M., Guerzoni, M., Gulino, M., Guo, C., Hackett, B. R., Hallin, A., Hamer, A., Haranczyk, M., Harrop, B., Hessel, T., Hill, S., Horikawa, S., Hu, J., Hubaut, F., Hucker, J., Hugues, T., Hungerford, E. V., Ianni, A., Ippolito, V., Jamil, A., Jillings, C., Jois, S., Kachru, P., Keloth, R., Kemmerich, N., Kemp, A., Kendziora, C. L., Kimura, M., Kondo, K., Korga, G., Kotsiopoulou, L., Koulosousas, S., Kubankin, A., Kunzé, P., Kuss, M., Kuźniak, M., Kuzwa, M., La Commara, M., Lai, M., Guirriec, E. Le, Leason, E., Leoni, A., Lidey, L., Lissia, M., Luzzi, L., Lychagina, O., Macfadyen, O., Machulin, I. N., Manecki, S., Manthos, I., Mapelli, L., Marasciulli, A., Mari, S. M., Mariani, C., Maricic, J., Martinez, M., Martoff, C. J., Matteucci, G., Mavrokoridis, K., McDonald, A. B., Mclaughlin, J., Merzi, S., Messina, A., Milincic, R., Minutoli, S., Mitra, A., Moioli, S., Monroe, J., Moretti, E., Morrocchi, M., Mroz, T., Muratova, V. N., Murphy, M., Murra, M., Muscas, C., Musico, P., Nania, R., Nessi, M., Nieradka, G., Nikolopoulos, K., Nikoloudaki, E., Nowak, J., Olchanski, K., Oleinik, A., Oleynikov, V., Organtini, P., de Solórzano, A. Ortiz, Pallavicini, M., Pandola, L., Pantic, E., Paoloni, E., Papi, D., Pastuszak, G., Paternoster, G., Peck, A., Pegoraro, P. A., Pelczar, K., Pellegrini, L. A., Perez, R., Perotti, F., Pesudo, V., Piacentini, S., Pino, N., Plante, G., Pocar, A., Poehlmann, M., Pordes, S., Pralavorio, P., Price, D., Puglia, S., Bazetto, M. Queiroga, Ragusa, F., Ramachers, Y., Ramirez, A., Ravinthiran, S., Razeti, M., Renshaw, A. L., Rescigno, M., Retiere, F., Rignanese, L. P., Rivetti, A., Roberts, A., Roberts, C., Rogers, G., Romero, L., Rossi, M., Rubbia, A., Rudik, D., Sabia, M., Salomone, P., Samoylov, O., Sandford, E., Sanfilippo, S., Santone, D., Santorelli, R., Santos, E. M., Savarese, C., Scapparone, E., Schillaci, G., Schuckman II, F. G., Scioli, G., Semenov, D. A., Shalamova, V., Sheshukov, A., Simeone, M., Skensved, P., Skorokhvatov, M. D., Smirnov, O., Smirnova, T., Smith, B., Sotnikov, A., Spadoni, F., Spangenberg, M., Stefanizzi, R., Steri, A., Stornelli, V., Stracka, S., Sulis, S., Sung, A., Sunny, C., Suvorov, Y., Szelc, A. M., Taborda, O., Tartaglia, R., Taylor, A., Taylor, J., Tedesco, S., Testera, G., Thieme, K., Thompson, A., Tonazzo, A., Torres-Lara, S., Tricomi, A., Unzhakov, E. V., Vallivilayil, T. J., Van Uffelen, M., Velazquez-Fernandez, L., Viant, T., Viel, S., Vishneva, A., Vogelaar, R. B., Vossebeld, J., Vyas, B., Walczak, M. B., Wang, Y., Wang, H., Westerdale, S., Williams, L., Wojaczyński, R., Wojcik, M., Wojcik, M. M., Wright, T., Xie, Y., Yang, C., Yin, J., Zabihi, A., Zakhary, P., Zani, A., Zhang, Y., Zhu, T., Zichichi, A., Zuzel, G., and Zykova, M. P.
Subjects: High Energy Physics - Experiment, Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract: The dual-phase liquid argon time projection chamber is presently one of the leading technologies to search for dark matter particles with masses below 10 GeV/c$^2$. This was demonstrated by the DarkSide-50 experiment with approximately 50 kg of low-radioactivity liquid argon as target material. The next generation experiment DarkSide-20k, currently under construction, will use 1,000 times more argon and is expected to start operation in 2027. Based on the DarkSide-50 experience, here we assess the DarkSide-20k sensitivity to models predicting light dark matter particles, including Weakly Interacting Massive Particles (WIMPs) and sub-GeV/c$^2$ particles interacting with electrons in argon atoms. With one year of data, a sensitivity improvement to dark matter interaction cross-sections by at least one order of magnitude with respect to DarkSide-50 is expected for all these models. A sensitivity to WIMP--nucleon interaction cross-sections below $1\times10^{-42}$ cm$^2$ is achievable for WIMP masses above 800 MeV/c$^2$. With 10 years exposure, the neutrino fog can be reached for WIMP masses around 5 GeV/c$^2$., Comment: 13 pages, 4 figures, supplementary material (4 figures)
Published: 2024
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16. Latent Functional Maps: a spectral framework for representation alignment

Author: Fumero, Marco, Pegoraro, Marco, Maiorca, Valentino, Locatello, Francesco, and Rodolà, Emanuele
Subjects: Computer Science - Machine Learning
Abstract: Neural models learn data representations that lie on low-dimensional manifolds, yet modeling the relation between these representational spaces is an ongoing challenge. By integrating spectral geometry principles into neural modeling, we show that this problem can be better addressed in the functional domain, mitigating complexity, while enhancing interpretability and performances on downstream tasks. To this end, we introduce a multi-purpose framework to the representation learning community, which allows to: (i) compare different spaces in an interpretable way and measure their intrinsic similarity; (ii) find correspondences between them, both in unsupervised and weakly supervised settings, and (iii) to effectively transfer representations between distinct spaces.We validate our framework on various applications, ranging from stitching to retrieval tasks, and on multiple modalities, demonstrating that Latent Functional Maps can serve as a swiss-army knife for representation alignment.
Published: 2024

17. Hardware-based stack buffer overflow attack detection on RISC-V architectures

Author: Chenet, Cristiano Pegoraro, Zhang, Ziteng, Savino, Alessandro, and Di Carlo, Stefano
Subjects: Computer Science - Cryptography and Security
Abstract: This work evaluates how well hardware-based approaches detect stack buffer overflow (SBO) attacks in RISC-V systems. We conducted simulations on the PULP platform and examined micro-architecture events using semi-supervised anomaly detection techniques. The findings showed the challenge of detection performance. Thus, a potential solution combines software and hardware-based detectors concurrently, with hardware as the primary defense. The hardware-based approaches present compelling benefits that could enhance RISC-V-based architectures., Comment: 2 pages, 2 figures
Published: 2024

18. A new hybrid gadolinium nanoparticles-loaded polymeric material for neutron detection in rare event searches

Author: Collaboration, DarkSide-20k, Acerbi, F., Adhikari, P., Agnes, P., Ahmad, I., Albergo, S., Albuquerque, I. F., Alexander, T., Alton, A. K., Amaudruz, P., Angiolilli, M., Aprile, E., Ardito, R., Corona, M. Atzori, Auty, D. J., Ave, M., Avetisov, I. C., Azzolini, O., Back, H. O., Balmforth, Z., Olmedo, A. Barrado, Barrillon, P., Batignani, G., Bhowmick, P., Bocci, V., Bonivento, W., Bottino, B., Boulay, M. G., Buchowicz, A., Bussino, S., Busto, J., Cadeddu, M., Cadoni, M., Calabrese, R., Camillo, V., Caminata, A., Canci, N., Capra, A., Caravati, M., Cárdenas-Montes, M., Cargioli, N., Carlini, M., Castellani, A., Castello, P., Cavalcante, P., Cavallo, D., Cebrian, S., Ruiz, J. Cela, Chashin, S., Chepurnov, A., Cifarelli, L., Cintas, D., Citterio, M., Cleveland, B., Coadou, Y., Cocco, V., Colaiuda, D., Vilda, E. Conde, Consiglio, L., Costa, B. S., Czubak, M., D'Auria, S., Rolo, M. D. Da Rocha, Darbo, G., Davini, S., De Cecco, S., De Guido, G., Dellacasa, G., Derbin, A. V., Devoto, A., Di Capua, F., Di Ludovico, A., Di Noto, L., Di Stefano, P., Dias, L. K., Mairena, D. Díaz, Ding, X., Dionisi, C., Dolganov, G., Dordei, F., Dronik, V., Elersich, A., Ellingwood, E., Erjavec, T., Diaz, M. Fernandez, Ficorella, A., Fiorillo, G., Franchini, P., Franco, D., Gatti, H. Frandini, Frolov, E., Gabriele, F., Gahan, D., Galbiati, C., Galinski, G., Gallina, G., Garbini, M., Abia, P. Garcia, Gawdzik, A., Gendotti, A., Ghisi, A., Giovanetti, G. K., Casanueva, V. Goicoechea, Gola, A., Grandi, L., Grauso, G., di Cortona, G. Grilli, Grobov, A., Gromov, M., Guerzoni, M., Gulino, M., Guo, C., Hackett, B. R., Hallin, A., Hamer, A., Haranczyk, M., Harrop, B., Hessel, T., Hill, S., Horikawa, S., Hu, J., Hubaut, F., Hucker, J., Hugues, T., Hungerford, E. V., Ianni, A., Ippolito, V., Jamil, A., Jillings, C., Keloth, R., Kemmerich, N., Kemp, A., Kendziora, C. L., Kimura, M., Kondo, K., Korga, G., Kotsiopoulou, L., Koulosousas, S., Kubankin, A., Kuss, M., Kuzniak, M., Kuzwa, M., La Commara, M., Lai, M., Guirriec, E. Le, Leason, E., Leoni, A., Lidey, L., Lissia, M., Luzzi, L., Lychagina, O., Macfadyen, O., Machulin, I. N., Manecki, S., Manthos, I., Mapelli, L., Marasciulli, A., Mari, S. M., Mariani, C., Maricic, J., Marini, A., Martinez, M., Martoff, C. J., Matteucci, G., Mavrokoridis, K., McDonald, A. B., Mclaughlin, J., Merzi, S., Messina, A., Milincic, R., Minutoli, S., Mitra, A., Moharana, A., Moioli, S., Monroe, J., Moretti, E., Morrocchi, M., Mroz, T., Muratova, V. N., Murphy, M., Murra, M., Muscas, C., Musico, P., Nania, R., Nessi, M., Nieradka, G., Nikolopoulos, K., Nikoloudaki, E., Nowak, J., Olchanski, K., Oleinik, A., Oleynikov, V., Organtini, P., de Solórzano, A. Ortiz, Pallavicini, M., Pandola, L., Pantic, E., Paoloni, E., Papi, D., Pastuszak, G., Paternoster, G., Peddis, D., Pegoraro, P. A., Pelczar, K., Pellegrini, L. A., Perez, R., Perotti, F., Pesudo, V., Piacentini, S. I., Pino, N., Plante, G., Pocar, A., Poehlmann, M., Pordes, S., Pralavorio, P., Price, D., Puglia, S., Bazetto, M. Queiroga, Ragusa, F., Ramachers, Y., Ramirez, A., Ravinthiran, S., Razeti, M., Renshaw, A. L., Rescigno, M., Retiere, F., Rignanese, L. P., Rivetti, A., Roberts, A., Roberts, C., Rogers, G., Romero, L., Rossi, M., Rubbia, A., Rudik, D., Sabia, M., Sadashivajois, S., Salomone, P., Samoylov, O., Sandford, E., Sanfilippo, S., Santone, D., Santorelli, R., Santos, E. M., Savarese, C., Scapparone, E., Schillaci, G., Schuckman II, F. G., Scioli, G., Semenov, D. A., Sheshukov, A., Simeone, M., Skensved, P., Skorokhvatov, M. D., Slimani, S., Smirnov, O., Smirnova, T., Smith, B., Sotnikov, A., Spadoni, F., Spangenberg, M., Stefanizzi, R., Steri, A., Stornelli, V., Stracka, S., Sulis, S., Sung, A., Sunny, C., Suvorov, Y., Szelc, A. M., Taborda, O., Tartaglia, R., Taylor, A., Taylor, J., Tedesco, S., Testera, G., Thieme, K., Thompson, A., Tonazzo, A., Torres-Lara, S., Tosi, S., Tricomi, A., Unzhakov, E. V., Vallivilayil, T. J., Van Uffelen, M., Velazquez-Fernandez, L., Viant, T., Vicini, S., Viel, S., Vishneva, A., Vogelaar, R. B., Vossebeld, J., Vyas, B., Wada, M., Walczak, M. B., Wang, H., Wang, Y., Westerdale, S., Williams, L., Wojaczynski, R., Wojcik, M. M., Wojcik, M., Wright, T., Xie, Y., Yang, C., Yin, J., Zabihi, A., Zakhary, P., Zani, A., Zhang, Y., Zhu, T., Zichichi, A., Zuzel, G., and Zykova, M. P.
Subjects: Physics - Instrumentation and Detectors, High Energy Physics - Experiment
Abstract: Experiments aimed at direct searches for WIMP dark matter require highly effective reduction of backgrounds and control of any residual radioactive contamination. In particular, neutrons interacting with atomic nuclei represent an important class of backgrounds due to the expected similarity of a WIMP-nucleon interaction, so that such experiments often feature a dedicated neutron detector surrounding the active target volume. In the context of the development of DarkSide-20k detector at INFN Gran Sasso National Laboratory (LNGS), several R&D projects were conceived and developed for the creation of a new hybrid material rich in both hydrogen and gadolinium nuclei to be employed as an essential element of the neutron detector. Thanks to its very high cross-section for neutron capture, gadolinium is one of the most widely used elements in neutron detectors, while the hydrogen-rich material is instrumental in efficiently moderating the neutrons. In this paper results from one of the R&Ds are presented. In this effort the new hybrid material was obtained as a poly(methyl methacrylate) (PMMA) matrix, loaded with gadolinium oxide in the form of nanoparticles. We describe its realization, including all phases of design, purification, construction, characterization, and determination of mechanical properties of the new material.
Published: 2024
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19. Bistatic Doppler Frequency Estimation with Asynchronous Moving Devices for Integrated Sensing and Communications

Author: Ventura, Gianmaria, Bhalli, Zaman, Rossi, Michele, and Pegoraro, Jacopo
Subjects: Electrical Engineering and Systems Science - Signal Processing
Abstract: In this letter, we present for the first time a method to estimate the bistatic Doppler frequency of a target with clock asynchronous and mobile Integrated Sensing And Communication (ISAC) devices. Existing approaches have separately tackled the presence of phase offsets due to clock asynchrony or the additional Doppler shift due to device movement. However, in real ISAC scenarios, these two sources of phase nuisance are concurrently present, making the estimation of the target's Doppler frequency particularly challenging. Our method solves the problem using the sole wireless signal at the receiver, exploiting the invariance of phase offsets across multipath components and the bistatic geometry in an original way. The proposed method is validated via simulation, exploring the impact of different system parameters. Numerical results show that our approach is a viable way of estimating Doppler frequency in bistatic asynchronous ISAC scenarios with mobile devices.
Published: 2024

20. Process-Aware Analysis of Treatment Paths in Heart Failure Patients: A Case Study

Author: Beyel, Harry H., Verket, Marlo, Peeva, Viki, Rennert, Christian, Pegoraro, Marco, Schütt, Katharina, van der Aalst, Wil M. P., and Marx, Nikolaus
Subjects: Statistics - Applications, Computer Science - Artificial Intelligence
Abstract: Process mining in healthcare presents a range of challenges when working with different types of data within the healthcare domain. There is high diversity considering the variety of data collected from healthcare processes: operational processes given by claims data, a collection of events during surgery, data related to pre-operative and post-operative care, and high-level data collections based on regular ambulant visits with no apparent events. In this case study, a data set from the last category is analyzed. We apply process-mining techniques on sparse patient heart failure data and investigate whether an information gain towards several research questions is achievable. Here, available data are transformed into an event log format, and process discovery and conformance checking are applied. Additionally, patients are split into different cohorts based on comorbidities, such as diabetes and chronic kidney disease, and multiple statistics are compared between the cohorts. Conclusively, we apply decision mining to determine whether a patient will have a cardiovascular outcome and whether a patient will die., Comment: 10 pages, 3 figures, 9 tables, 31 references
Published: 2024
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21. DeepEclipse: How to Break White-Box DNN-Watermarking Schemes

Author: Pegoraro, Alessandro, Segna, Carlotta, Kumari, Kavita, and Sadeghi, Ahmad-Reza
Subjects: Computer Science - Cryptography and Security, Computer Science - Machine Learning
Abstract: Deep Learning (DL) models have become crucial in digital transformation, thus raising concerns about their intellectual property rights. Different watermarking techniques have been developed to protect Deep Neural Networks (DNNs) from IP infringement, creating a competitive field for DNN watermarking and removal methods. The predominant watermarking schemes use white-box techniques, which involve modifying weights by adding a unique signature to specific DNN layers. On the other hand, existing attacks on white-box watermarking usually require knowledge of the specific deployed watermarking scheme or access to the underlying data for further training and fine-tuning. We propose DeepEclipse, a novel and unified framework designed to remove white-box watermarks. We present obfuscation techniques that significantly differ from the existing white-box watermarking removal schemes. DeepEclipse can evade watermark detection without prior knowledge of the underlying watermarking scheme, additional data, or training and fine-tuning. Our evaluation reveals that DeepEclipse excels in breaking multiple white-box watermarking schemes, reducing watermark detection to random guessing while maintaining a similar model accuracy as the original one. Our framework showcases a promising solution to address the ongoing DNN watermark protection and removal challenges., Comment: To appear in the 33rd USENIX Security Symposium, August 2024, Philadelphia, PA, USA. 18 pages, 7 figures, 4 tables, 5 algorithms, 13 equations
Published: 2024

22. OCEL (Object-Centric Event Log) 2.0 Specification

Author: Berti, Alessandro, Koren, Istvan, Adams, Jan Niklas, Park, Gyunam, Knopp, Benedikt, Graves, Nina, Rafiei, Majid, Liß, Lukas, Unterberg, Leah Tacke Genannt, Zhang, Yisong, Schwanen, Christopher, Pegoraro, Marco, and van der Aalst, Wil M. P.
Subjects: Computer Science - Databases
Abstract: Object-Centric Event Logs (OCELs) form the basis for Object-Centric Process Mining (OCPM). OCEL 1.0 was first released in 2020 and triggered the development of a range of OCPM techniques. OCEL 2.0 forms the new, more expressive standard, allowing for more extensive process analyses while remaining in an easily exchangeable format. In contrast to the first OCEL standard, it can depict changes in objects, provide information on object relationships, and qualify these relationships to other objects or specific events. Compared to XES, it is more expressive, less complicated, and better readable. OCEL 2.0 offers three exchange formats: a relational database (SQLite), XML, and JSON format. This OCEL 2.0 specification document provides an introduction to the standard, its metamodel, and its exchange formats, aimed at practitioners and researchers alike.
Published: 2024

23. Sensing in Bi-Static ISAC Systems with Clock Asynchronism: A Signal Processing Perspective

Author: Wu, Kai, Pegoraro, Jacopo, Meneghello, Francesca, Zhang, J. Andrew, Lacruz, Jesus O., Widmer, Joerg, Restuccia, Francesco, Rossi, Michele, Huang, Xiaojing, Zhang, Daqing, Caire, Giuseppe, and Guo, Y. Jay
Subjects: Electrical Engineering and Systems Science - Signal Processing
Abstract: Integrated Sensing and Communication (ISAC) has been identified as a pillar usage scenario for the impending 6G era. Bi-static sensing, a major type of sensing in ISAC, is promising to expedite ISAC in the near future, as it requires minimal changes to the existing network infrastructure. However, a critical challenge for bi-static sensing is clock asynchronism due to the use of different clocks at far-separated transmitters and receivers. This causes the received signal to be affected by time-varying random phase offsets, severely degrading, or even failing, direct sensing. Hence, to effectively enable ISAC, considerable research has been directed toward addressing the clock asynchronism issue in bi-static sensing. This paper provides an overview of the issue and existing techniques developed in an ISAC background. Based on the review and comparison, we also draw insights into the future research directions and open problems, aiming to nurture the maturation of bi-static sensing in ISAC., Comment: 20 pages, 6 figures, 1 table
Published: 2024

24. Persistence diagrams for exploring the shape variability of abdominal aortic aneurysms

Author: Domanin, Dario Arnaldo, Pegoraro, Matteo, Trimarchi, Santi, Domanin, Maurizio, and Secchi, Piercesare
Subjects: Physics - Medical Physics, Quantitative Biology - Quantitative Methods
Abstract: Abdominal Aortic Aneurysm consists of a permanent dilation in the abodminal portion of the aorta and, along with its associated pathologies like calcifications and intraluminal thrombi, is one of the most important pathologies of the circulatory system. The shape of the aorta is among the primary drivers for these health issues, with particular reference to all the characteristics which affects the hemodynamics. Starting from the computed tomography angiography of a patient, we propose to summarize such information using tools derived from Topological Data Analysis, obtaining persistence diagrams which describe the irregularities of the lumen of the aorta. We showcase the effectiveness of such shape-related descriptors with a series of supervised and unsupervised case studies.
Published: 2024

25. Learned Spike Encoding of the Channel Response for Low-Power Environment Sensing

Author: Cicciarella, Eleonora, Mazzieri, Riccardo, Pegoraro, Jacopo, and Rossi, Michele
Subjects: Electrical Engineering and Systems Science - Signal Processing
Abstract: Radio Frequency (RF) sensing holds the potential for enabling pervasive monitoring applications. However, modern sensing algorithms imply complex operations, which clash with the energy-constrained nature of edge sensing devices. This calls for the development of new processing and learning techniques that can strike a suitable balance between performance and energy efficiency. Spiking Neural Networks (SNNs) have recently emerged as an energy-efficient alternative to conventional neural networks for edge computing applications. They process information in the form of sparse binary spike trains, thus potentially reducing energy consumption by several orders of magnitude. Their fruitful use for RF signal processing critically depends on the representation of RF signals in the form of spike signals. We underline that existing spike encoding algorithms to do so generally produce inaccurate signal representations and dense (i.e., inefficient) spike trains. In this work, we propose a lightweight neural architecture that learns a tailored spike encoding representations of RF channel responses by jointly reconstructing the input and its spectral content. By leveraging a tunable regularization term, our approach enables fine-grained control over the performance-energy trade-off of the system. Our numerical results show that the proposed method outperforms existing encoding algorithms in terms of reconstruction error and sparsity of the obtained spike encodings., Comment: 6 pages, 5figures, 2 tables
Published: 2024

26. FreqFed: A Frequency Analysis-Based Approach for Mitigating Poisoning Attacks in Federated Learning

Author: Fereidooni, Hossein, Pegoraro, Alessandro, Rieger, Phillip, Dmitrienko, Alexandra, and Sadeghi, Ahmad-Reza
Subjects: Computer Science - Cryptography and Security, Computer Science - Machine Learning
Abstract: Federated learning (FL) is a collaborative learning paradigm allowing multiple clients to jointly train a model without sharing their training data. However, FL is susceptible to poisoning attacks, in which the adversary injects manipulated model updates into the federated model aggregation process to corrupt or destroy predictions (untargeted poisoning) or implant hidden functionalities (targeted poisoning or backdoors). Existing defenses against poisoning attacks in FL have several limitations, such as relying on specific assumptions about attack types and strategies or data distributions or not sufficiently robust against advanced injection techniques and strategies and simultaneously maintaining the utility of the aggregated model. To address the deficiencies of existing defenses, we take a generic and completely different approach to detect poisoning (targeted and untargeted) attacks. We present FreqFed, a novel aggregation mechanism that transforms the model updates (i.e., weights) into the frequency domain, where we can identify the core frequency components that inherit sufficient information about weights. This allows us to effectively filter out malicious updates during local training on the clients, regardless of attack types, strategies, and clients' data distributions. We extensively evaluate the efficiency and effectiveness of FreqFed in different application domains, including image classification, word prediction, IoT intrusion detection, and speech recognition. We demonstrate that FreqFed can mitigate poisoning attacks effectively with a negligible impact on the utility of the aggregated model., Comment: To appear in the Network and Distributed System Security (NDSS) Symposium 2024. 16 pages, 8 figures, 12 tables, 1 algorithm, 3 equations
Published: 2023
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27. Knapsacks, Max-Flow and Circular Mapper Graphs

Author: Pegoraro, Matteo and Fajstrup, Lisbeth
Subjects: Mathematics - Algebraic Topology
Abstract: Motivated by the challenge of analyzing data sets with periodic boundary conditions to investigate transportation properties, we introduce a concept of circular max-flow for graphs mapped onto the circle. Unlike classical max-flow formulations, circular max-flow does not require the specification of a source and a target. Instead, it is defined by considering the flow through the slices of graph obtained via the fibers of its associated map onto the circle. Circular max-flow can be formulated as a minimum-cost circulation problem, allowing for an Integer Flow Theorem to be derived directly from classical results concerning negative cost cycles. While we hypothesize that all minimum-cost circulation problems with integer costs can be reformulated as circular flow problems, this broader equivalence is left for future exploration. In the core section of this work, we investigate the relationship between circular max-flow and the flow obtained by stacking infinite copies of the considered graph. We establish an equivalence between these two flows, which also provides an alternative proof of the Integer Flow Theorem. Additionally, we demonstrate connections between circular max-flow and a specific family of multidimensional knapsack problems. This connection shows that for this class of knapsack problems, solving the relaxed version is sufficient, avoiding the need for integer optimization. Finally, we test our definitions in some simulated scenarios related to the initial data analysis problem, extending a pipeline appeared previous works in materials science.
Published: 2023

28. DEMASQ: Unmasking the ChatGPT Wordsmith

Author: Kumari, Kavita, Pegoraro, Alessandro, Fereidooni, Hossein, and Sadeghi, Ahmad-Reza
Subjects: Computer Science - Cryptography and Security, Computer Science - Machine Learning
Abstract: The potential misuse of ChatGPT and other Large Language Models (LLMs) has raised concerns regarding the dissemination of false information, plagiarism, academic dishonesty, and fraudulent activities. Consequently, distinguishing between AI-generated and human-generated content has emerged as an intriguing research topic. However, current text detection methods lack precision and are often restricted to specific tasks or domains, making them inadequate for identifying content generated by ChatGPT. In this paper, we propose an effective ChatGPT detector named DEMASQ, which accurately identifies ChatGPT-generated content. Our method addresses two critical factors: (i) the distinct biases in text composition observed in human- and machine-generated content and (ii) the alterations made by humans to evade previous detection methods. DEMASQ is an energy-based detection model that incorporates novel aspects, such as (i) optimization inspired by the Doppler effect to capture the interdependence between input text embeddings and output labels, and (ii) the use of explainable AI techniques to generate diverse perturbations. To evaluate our detector, we create a benchmark dataset comprising a mixture of prompts from both ChatGPT and humans, encompassing domains such as medical, open Q&A, finance, wiki, and Reddit. Our evaluation demonstrates that DEMASQ achieves high accuracy in identifying content generated by ChatGPT., Comment: To appear in the Network and Distributed System Security (NDSS) Symposium 2024. 15 pages, 3 figures, 6 tables, 3 algorithms, 6 equations
Published: 2023
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29. Gaia Focused Product Release: Sources from Service Interface Function image analysis -- Half a million new sources in omega Centauri

Author: Gaia Collaboration, Weingrill, K., Mints, A., Castañeda, J., Kostrzewa-Rutkowska, Z., Davidson, M., De Angeli, F., Hernández, J., Torra, F., Ramos-Lerate, M., Babusiaux, C., Biermann, M., Crowley, C., Evans, D. W., Lindegren, L., Martín-Fleitas, J. M., Palaversa, L., Mieres, D. Ruz, Tisanić, K., Brown, A. G. A., Vallenari, A., Prusti, T., de Bruijne, J. H. J., Arenou, F., Barbier, A., Creevey, O. L., Ducourant, C., Eyer, L., Guerra, R., Hutton, A., Jordi, C., Klioner, S. A., Lammers, U., Luri, X., Mignard, F., Randich, S., Sartoretti, P., Smiljanic, R., Tanga, P., Walton, N. A., Bailer-Jones, C. A. L., Bastian, U., Cropper, M., Drimmel, R., Katz, D., Soubiran, C., van Leeuwen, F., Audard, M., Bakker, J., Blomme, R., Fabricius, C., Fouesneau, M., Frémat, Y., Galluccio, L., Guerrier, A., Masana, E., Messineo, R., Nicolas, C., Nienartowicz, K., Pailler, F., Panuzzo, P., Riclet, F., Roux, W., Seabroke, G. M., Sordo, R., Thévenin, F., Gracia-Abril, G., Portell, J., Teyssier, D., Altmann, M., Benson, K., Berthier, J., Burgess, P. W., Busonero, D., Busso, G., Cánovas, H., Carry, B., Cheek, N., Clementini, G., Damerdji, Y., de Teodoro, P., Delchambre, L., DellÓro, A., Garcia, E. Fraile, Garabato, D., García-Lario, P., Torres, N. Garralda, Gavras, P., Haigron, R., Hambly, N. C., Harrison, D. L., Hatzidimitriou, D., Hodgkin, S. T., Holl, B., Jamal, S., Jordan, S., Krone-Martins, A., Lanzafame, A. C., Löffler, W., Lorca, A., Marchal, O., Marrese, P. M., Moitinho, A., Muinonen, K., Campos, M. Nuñez, Oreshina-Slezak, I., Osborne, P., Pancino, E., Pauwels, T., Recio-Blanco, A., Riello, M., Rimoldini, L., Robin, A. C., Roegiers, T., Sarro, L. M., Schultheis, M., Siopis, C., Smith, M., Sozzetti, A., Utrilla, E., van Leeuwen, M., Abbas, U., Ábrahám, P., Aramburu, A. Abreu, Aerts, C., Altavilla, G., Álvarez, M. A., Alves, J., Anders, F., Anderson, R. I., Antoja, T., Baines, D., Baker, S. G., Balog, Z., Barache, C., Barbato, D., Barros, M., Barstow, M. A., Bartolomé, S., Bashi, D., Bauchet, N., Baudeau, N., Becciani, U., Bedin, L. R., Bellas-Velidis, I., Bellazzini, M., Beordo, W., Berihuete, A., Bernet, M., Bertolotto, C., Bertone, S., Bianchi, L., Binnenfeld, A., Blazere, A., Boch, T., Bombrun, A., Bouquillon, S., Bragaglia, A., Braine, J., Bramante, L., Breedt, E., Bressan, A., Brouillet, N., Brugaletta, E., Bucciarelli, B., Butkevich, A. G., Buzzi, R., Caffau, E., Cancelliere, R., Cannizzo, S., Cantat-Gaudin, T., Carballo, R., Carlucci, T., Carnerero, M. I., Carrasco, J. M., Carretero, J., Carton, S., Casamiquela, L., Castellani, M., Castro-Ginard, A., Cesare, V., Charlot, P., Chemin, L., Chiaramida, V., Chiavassa, A., Chornay, N., Collins, R., Contursi, G., Cooper, W. J., Cornez, T., Crosta, M., Dafonte, C., de Laverny, P., De Luise, F., De March, R., de Souza, R., de Torres, A., del Peloso, E. F., Delbo, M., Delgado, A., Dharmawardena, T. E., Diakite, S., Diener, C., Distefano, E., Dolding, C., Dsilva, K., Durán, J., Enke, H., Esquej, P., Fabre, C., Fabrizio, M., Faigler, S., Fatović, M., Fedorets, G., Fernández-Hernández, J., Fernique, P., Figueras, F., Fournier, Y., Fouron, C., Gai, M., Galinier, M., Garcia-Gutierrez, A., García-Torres, M., Garofalo, A., Gerlach, E., Geyer, R., Giacobbe, P., Gilmore, G., Girona, S., Giuffrida, G., Gomel, R., Gomez, A., González-Núñez, J., González-Santamaría, I., Gosset, E., Granvik, M., Barrera, V. Gregori, Gutiérrez-Sánchez, R., Haywood, M., Helmer, A., Helmi, A., Henares, K., Hidalgo, S. L., Hilger, T., Hobbs, D., Hottier, C., Huckle, H. E., Jabłońska, M., Jansen, F., Jiménez-Arranz, Ó., Campillo, J. Juaristi, Khanna, S., Kordopatis, G., Kóspál, Á, Kun, M., Lambert, S., Lanza, A. F., Campion, J. -F. Le, Lebreton, Y., Lebzelter, T., Leccia, S., Lecoeur-Taibi, I., Lecoutre, G., Liao, S., Liberato, L., Licata, E., Lindstrøm, H. E. P., Lister, T. A., Livanou, E., Lobel, A., Loup, C., Mahy, L., Mann, R. G., Manteiga, M., Marchant, J. M., Marconi, M., Pina, D. Marín, Marinoni, S., Marshall, D. J., Lozano, J. Martín, Marton, G., Mary, N., Masip, A., Massari, D., Mastrobuono-Battisti, A., Mazeh, T., McMillan, P. J., Meichsner, J., Messina, S., Michalik, D., Millar, N. R., Molina, D., Molinaro, R., Molnár, L., Monari, G., Monguió, M., Montegriffo, P., Montero, A., Mor, R., Mora, A., Morbidelli, R., Morel, T., Morris, D., Mowlavi, N., Munoz, D., Muraveva, T., Murphy, C. P., Musella, I., Nagy, Z., Nieto, S., Noval, L., Ogden, A., Ordenovic, C., Pagani, C., Pagano, I., Palicio, P. A., Pallas-Quintela, L., Panahi, A., Panem, C., Payne-Wardenaar, S., Pegoraro, L., Penttilä, A., Pesciullesi, P., Piersimoni, A. M., Pinamonti, M., Pineau, F. -X., Plachy, E., Plum, G., Poggio, E., Pourbaix, D., Prša, A., Pulone, L., Racero, E., Rainer, M., Raiteri, C. M., Ramos, P., Ratajczak, M., Fiorentin, P. Re, Regibo, S., Reylé, C., Ripepi, V., Riva, A., Rix, H. -W., Rixon, G., Robichon, N., Robin, C., Romero-Gómez, M., Rowell, N., Royer, F., Rybicki, K. A., Sadowski, G., Núñez, A. Sáez, Sellés, A. Sagristà, Sahlmann, J., Gimenez, V. Sanchez, Sanna, N., Santoveña, R., Sarasso, M., Riera, C. Sarrate, Sciacca, E., Segovia, J. C., Ségransan, D., Shahaf, S., Siebert, A., Siltala, L., Slezak, E., Smart, R. L., Snaith, O. N., Solano, E., Solitro, F., Souami, D., Souchay, J., Spina, L., Spitoni, E., Spoto, F., Squillante, L. A., Steele, I. A., Steidelmüller, H., Surdej, J., Szabados, L., Taris, F., Taylor, M. B., Teixeira, R., Tolomei, L., Elipe, G. Torralba, Trabucchi, M., Tsantaki, M., Ulla, A., Unger, N., Vanel, O., Vecchiato, A., Vicente, D., Voutsinas, S., Weiler, M., Wyrzykowski, Ł., Zhao, H., Zorec, J., Zwitter, T., Balaguer-Núñez, L., Leclerc, N., Morgenthaler, S., Robert, G., and Zucker, S.
Subjects: Astrophysics - Solar and Stellar Astrophysics, Astrophysics - Astrophysics of Galaxies
Abstract: Gaia's readout window strategy is challenged by very dense fields in the sky. Therefore, in addition to standard Gaia observations, full Sky Mapper (SM) images were recorded for nine selected regions in the sky. A new software pipeline exploits these Service Interface Function (SIF) images of crowded fields (CFs), making use of the availability of the full two-dimensional (2D) information. This new pipeline produced half a million additional Gaia sources in the region of the omega Centauri ($\omega$ Cen) cluster, which are published with this Focused Product Release. We discuss the dedicated SIF CF data reduction pipeline, validate its data products, and introduce their Gaia archive table. Our aim is to improve the completeness of the {\it Gaia} source inventory in a very dense region in the sky, $\omega$ Cen. An adapted version of {\it Gaia}'s Source Detection and Image Parameter Determination software located sources in the 2D SIF CF images. We validated the results by comparing them to the public {\it Gaia} DR3 catalogue and external Hubble Space Telescope data. With this Focused Product Release, 526\,587 new sources have been added to the {\it Gaia} catalogue in $\omega$ Cen. Apart from positions and brightnesses, the additional catalogue contains parallaxes and proper motions, but no meaningful colour information. While SIF CF source parameters generally have a lower precision than nominal {\it Gaia} sources, in the cluster centre they increase the depth of the combined catalogue by three magnitudes and improve the source density by a factor of ten. This first SIF CF data publication already adds great value to the {\it Gaia} catalogue. It demonstrates what to expect for the fourth {\it Gaia} catalogue, which will contain additional sources for all nine SIF CF regions.
Published: 2023
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30. Gaia Focused Product Release: A catalogue of sources around quasars to search for strongly lensed quasars

Author: Gaia Collaboration, Krone-Martins, A., Ducourant, C., Galluccio, L., Delchambre, L., Oreshina-Slezak, I., Teixeira, R., Braine, J., Campion, J. -F. Le, Mignard, F., Roux, W., Blazere, A., Pegoraro, L., Brown, A. G. A., Vallenari, A., Prusti, T., de Bruijne, J. H. J., Arenou, F., Babusiaux, C., Barbier, A., Biermann, M., Creevey, O. L., Evans, D. W., Eyer, L., Guerra, R., Hutton, A., Jordi, C., Klioner, S. A., Lammers, U., Lindegren, L., Luri, X., Randich, S., Sartoretti, P., Smiljanic, R., Tanga, P., Walton, N. A., Bailer-Jones, C. A. L., Bastian, U., Cropper, M., Drimmel, R., Katz, D., Soubiran, C., van Leeuwen, F., Audard, M., Bakker, J., Blomme, R., Castaneda, J., De Angeli, F., Fabricius, C., Fouesneau, M., Fremat, Y., Guerrier, A., Masana, E., Messineo, R., Nicolas, C., Nienartowicz, K., Pailler, F., Panuzzo, P., Riclet, F., Seabroke, G. M., Sordo, R., Thevenin, F., Gracia-Abril, G., Portell, J., Teyssier, D., Altmann, M., Benson, K., Berthier, J., Burgess, P. W., Busonero, D., Busso, G., Canovas, H., Carry, B., Cheek, N., Clementini, G., Damerdji, Y., Davidson, M., de Teodoro, P., Dell'Oro, A., Garcia, E. Fraile, Garabato, D., Garcia-Lario, P., Torres, N. Garralda, Gavras, P., Haigron, R., Hambly, N. C., Harrison, D. L., Hatzidimitriou, D., Hernandez, J., Hodgkin, S. T., Holl, B., Jamal, S., Jordan, S., Lanzafame, A. C., Loffler, W., Lorca, A., Marchal, O., Marrese, P. M., Moitinho, A., Muinonen, K., Campos, M. Nunez, Osborne, P., Pancino, E., Pauwels, T., Recio-Blanco, A., Riello, M., Rimoldini, L., Robin, A. C., Roegiers, T., Sarro, L. M., Schultheis, M., Siopis, C., Smith, M., Sozzetti, A., Utrilla, E., van Leeuwen, M., Weingrill, K., Abbas, U., Abraham, P., Aramburu, A. Abreu, Aerts, C., Altavilla, G., Alvarez, M. A., Alves, J., Anderson, R. I., Antoja, T., Baines, D., Baker, S. G., Balog, Z., Barache, C., Barbato, D., Barros, M., Barstow, M. A., Bartolome, S., Bashi, D., Bauchet, N., Baudeau, N., Becciani, U., Bedin, L. R., Bellas-Velidis, I., Bellazzini, M., Beordo, W., Berihuete, A., Bernet, M., Bertolotto, C., Bertone, S., Bianchi, L., Binnenfeld, A., Boch, T., Bombrun, A., Bouquillon, S., Bragaglia, A., Bramante, L., Breedt, E., Bressan, A., Brouillet, N., Brugaletta, E., Bucciarelli, B., Butkevich, A. G., Buzzi, R., Caffau, E., Cancelliere, R., Cannizzo, S., Carballo, R., Carlucci, T., Carnerero, M. I., Carrasco, J. M., Carretero, J., Carton, S., Casamiquela, L., Castellani, M., Castro-Ginard, A., Cesare, V., Charlot, P., Chemin, L., Chiaramida, V., Chiavassa, A., Chornay, N., Collins, R., Contursi, G., Cooper, W. J., Cornez, T., Crosta, M., Crowley, C., Dafonte, C., de Laverny, P., De Luise, F., De March, R., de Souza, R., de Torres, A., del Peloso, E. F., Delbo, M., Delgado, A., Dharmawardena, T. E., Diakite, S., Diener, C., Distefano, E., Dolding, C., Dsilva, K., Duran, J., Enke, H., Esquej, P., Fabre, C., Fabrizio, M., Faigler, S., Fatovic, M., Fedorets, G., Fernandez-Hernandez, J., Fernique, P., Figueras, F., Fournier, Y., Fouron, C., Gai, M., Galinier, M., Garcia-Gutierrez, A., Garcia-Torres, M., Garofalo, A., Gerlach, E., Geyer, R., Giacobbe, P., Gilmore, G., Girona, S., Giuffrida, G., Gomel, R., Gomez, A., Gonzalez-Nunez, J., Gonzalez-Santamaria, I., Gosset, E., Granvik, M., Barrera, V. Gregori, Gutierrez-Sanchez, R., Haywood, M., Helmer, A., Helmi, A., Henares, K., Hidalgo, S. L., Hilger, T., Hobbs, D., Hottier, C., Huckle, H. E., Jablonska, M., Jansen, F., Jimenez-Arranz, O., Campillo, J. Juaristi, Khanna, S., Kordopatis, G., Kospal, A., Kostrzewa-Rutkowska, Z., Kun, M., Lambert, S., Lanza, A. F., Lebreton, Y., Lebzelter, T., Leccia, S., Lecoeur-Taibi, I., Lecoutre, G., Liao, S., Liberato, L., Licata, E., Lindstrom, H. E. P., Lister, T. A., Livanou, E., Lobel, A., Loup, C., Mahy, L., Mann, R. G., Manteiga, M., Marchant, J. M., Marconi, M., Pina, D. Marin, Marinoni, S., Marshall, D. J., Lozano, J. Martin, Martin-Fleitas, J. M., Marton, G., Mary, N., Masip, A., Massari, D., Mastrobuono-Battisti, A., Mazeh, T., McMillan, P. J., Meichsner, J., Messina, S., Michalik, D., Millar, N. R., Mints, A., Molina, D., Molinaro, R., Molnar, L., Monari, G., Monguio, M., Montegriffo, P., Montero, A., Mor, R., Mora, A., Morbidelli, R., Morel, T., Morris, D., Mowlavi, N., Munoz, D., Muraveva, T., Murphy, C. P., Musella, I., Nagy, Z., Nieto, S., Noval, L., Ogden, A., Ordenovic, C., Pagani, C., Pagano, I., Palaversa, L., Palicio, P. A., Pallas-Quintela, L., Panahi, A., Panem, C., Payne-Wardenaar, S., Penttila, A., Pesciullesi, P., Piersimoni, A. M., Pinamonti, M., Pineau, F. -X., Plachy, E., Plum, G., Poggio, E., Pourbaix, D., Prsa, A., Pulone, L., Racero, E., Rainer, M., Raiteri, C. M., Ramos, P., Ramos-Lerate, M., Ratajczak, M., Fiorentin, P. Re, Regibo, S., Reyle, C., Ripepi, V., Riva, A., Rix, H. -W., Rixon, G., Robichon, N., Robin, C., Romero-Gomez, M., Rowell, N., Royer, F., Mieres, D. Ruz, Rybicki, K. A., Sadowski, G., Nunez, A. Saez, Selles, A. Sagrista, Sahlmann, J., Gimenez, V. Sanchez, Sanna, N., Santovena, R., Sarasso, M., Riera, C. Sarrate, Sciacca, E., Segovia, J. C., Segransan, D., Shahaf, S., Siebert, A., Siltala, L., Slezak, E., Smart, R. L., Snaith, O. N., Solano, E., Solitro, F., Souami, D., Souchay, J., Spina, L., Spitoni, E., Spoto, F., Squillante, L. A., Steele, I. A., Steidelmuller, H., Surdej, J., Szabados, L., Taris, F., Taylor, M. B., Tisanic, K., Tolomei, L., Torra, F., Elipe, G. Torralba, Trabucchi, M., Tsantaki, M., Ulla, A., Unger, N., Vanel, O., Vecchiato, A., Vicente, D., Voutsinas, S., Weiler, M., Wyrzykowski, L., Zhao, H., Zorec, J., Zwitter, T., Balaguer-Nunez, L., Leclerc, N., Morgenthaler, S., Robert, G., and Zucker, S.
Subjects: Astrophysics - Astrophysics of Galaxies, Astrophysics - Cosmology and Nongalactic Astrophysics, Astrophysics - Instrumentation and Methods for Astrophysics
Abstract: Context. Strongly lensed quasars are fundamental sources for cosmology. The Gaia space mission covers the entire sky with the unprecedented resolution of $0.18$" in the optical, making it an ideal instrument to search for gravitational lenses down to the limiting magnitude of 21. Nevertheless, the previous Gaia Data Releases are known to be incomplete for small angular separations such as those expected for most lenses. Aims. We present the Data Processing and Analysis Consortium GravLens pipeline, which was built to analyse all Gaia detections around quasars and to cluster them into sources, thus producing a catalogue of secondary sources around each quasar. We analysed the resulting catalogue to produce scores that indicate source configurations that are compatible with strongly lensed quasars. Methods. GravLens uses the DBSCAN unsupervised clustering algorithm to detect sources around quasars. The resulting catalogue of multiplets is then analysed with several methods to identify potential gravitational lenses. We developed and applied an outlier scoring method, a comparison between the average BP and RP spectra of the components, and we also used an extremely randomised tree algorithm. These methods produce scores to identify the most probable configurations and to establish a list of lens candidates. Results. We analysed the environment of 3 760 032 quasars. A total of 4 760 920 sources, including the quasars, were found within 6" of the quasar positions. This list is given in the Gaia archive. In 87\% of cases, the quasar remains a single source, and in 501 385 cases neighbouring sources were detected. We propose a list of 381 lensed candidates, of which we identified 49 as the most promising. Beyond these candidates, the associate tables in this Focused Product Release allow the entire community to explore the unique Gaia data for strong lensing studies further., Comment: 35 pages, 60 figures, accepted for publication by Astronomy and Astrophysics
Published: 2023
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31. Gaia Focused Product Release: Radial velocity time series of long-period variables

Author: Gaia Collaboration, Trabucchi, M., Mowlavi, N., Lebzelter, T., Lecoeur-Taibi, I., Audard, M., Eyer, L., García-Lario, P., Gavras, P., Holl, B., de Fombelle, G. Jevardat, Nienartowicz, K., Rimoldini, L., Sartoretti, P., Blomme, R., Frémat, Y., Marchal, O., Damerdji, Y., Brown, A. G. A., Guerrier, A., Panuzzo, P., Katz, D., Seabroke, G. M., Benson, K., Haigron, R., Smith, M., Lobel, A., Vallenari, A., Prusti, T., de Bruijne, J. H. J., Arenou, F., Babusiaux, C., Barbier, A., Biermann, M., Creevey, O. L., Ducourant, C., Evans, D. W., Guerra, R., Hutton, A., Jordi, C., Klioner, S. A., Lammers, U., Lindegren, L., Luri, X., Mignard, F., Randich, S., Smiljanic, R., Tanga, P., Walton, N. A., Bailer-Jones, C. A. L., Bastian, U., Cropper, M., Drimmel, R., Lattanzi, M. G., Soubiran, C., van Leeuwen, F., Bakker, J., Castañeda, J., De Angeli, F., Fabricius, C., Fouesneau, M., Galluccio, L., Masana, E., Messineo, R., Nicolas, C., Pailler, F., Riclet, F., Roux, W., Sordo, R., Thévenin, F., Gracia-Abril, G., Portell, J., Teyssier, D., Altmann, M., Berthier, J., Burgess, P. W., Busonero, D., Busso, G., Cánovas, H., Carry, B., Cheek, N., Clementini, G., Davidson, M., de Teodoro, P., Delchambre, L., Dell'Oro, A., Garcia, E. Fraile, Garabato, D., Torres, N. Garralda, Hambly, N. C., Harrison, D. L., Hatzidimitriou, D., Hernández, J., Hodgkin, S. T., Jamal, S., Jordan, S., Krone-Martins, A., Lanzafame, A. C., Löffler, W., Lorca, A., Marrese, P. M., Moitinho, A., Muinonen, K., Campos, M. Nuñez, Oreshina-Slezak, I., Osborne, P., Pancino, E., Pauwels, T., Recio-Blanco, A., Riello, M., Robin, A. C., Roegiers, T., Sarro, L. M., Schultheis, M., Siopis, C., Sozzetti, A., Utrilla, E., van Leeuwen, M., Weingrill, K., Abbas, U., Ábrahám, P., Aramburu, A. Abreu, Aerts, C., Altavilla, G., Álvarez, M. A., Alves, J., Anders, F., Anderson, R. I., Antoja, T., Baines, D., Baker, S. G., Balog, Z., Barache, C., Barbato, D., Barros, M., Barstow, M. A., Bartolomé, S., Bashi, D., Bauchet, N., Baudeau, N., Becciani, U., Bedin, L. R., Bellas-Velidis, I., Bellazzini, M., Beordo, W., Berihuete, A., Bernet, M., Bertolotto, C., Bertone, S., Bianchi, L., Binnenfeld, A., Blazere, A., Boch, T., Bombrun, A., Bouquillon, S., Bragaglia, A., Braine, J., Bramante, L., Breedt, E., Bressan, A., Brouillet, N., Brugaletta, E., Bucciarelli, B., Butkevich, A. G., Buzzi, R., Caffau, E., Cancelliere, R., Cannizzo, S., Carballo, R., Carlucci, T., Carnerero, M. I., Carrasco, J. M., Carretero, J., Carton, S., Casamiquela, L., Castellani, M., Castro-Ginard, A., Cesare, V., Charlot, P., Chemin, L., Chiaramida, V., Chiavassa, A., Chornay, N., Collins, R., Contursi, G., Cooper, W. J., Cornez, T., Crosta, M., Crowley, C., Dafonte, C., David, M., de Laverny, P., De Luise, F., De March, R., De Ridder, J., de Souza, R., de Torres, A., del Peloso, E. F., Delbo, M., Delgado, A., Dharmawardena, T. E., Diakite, S., Diener, C., Distefano, E., Dolding, C., Dsilva, K., Durán, J., Enke, H., Esquej, P., Fabre, C., Fabrizio, M., Faigler, S., Fatović, M., Fedorets, G., Fernández-Hernández, J., Fernique, P., Figueras, F., Fournier, Y., Fouron, C., Gai, M., Galinier, M., Garcia-Gutierrez, A., García-Torres, M., Garofalo, A., Gerlach, E., Geyer, R., Giacobbe, P., Gilmore, G., Girona, S., Giuffrida, G., Gomel, R., Gomez, A., González-Núñez, J., González-Santamaría, I., Gosset, E., Granvik, M., Barrera, V. Gregori, Gutiérrez-Sánchez, R., Haywood, M., Helmer, A., Helmi, A., Henares, K., Hidalgo, S. L., Hilger, T., Hobbs, D., Hottier, C., Huckle, H. E., Jabłońska, M., Jansen, F., Jiménez-Arranz, Ó., Campillo, J. Juaristi, Khanna, S., Kordopatis, G., Kóspál, Á, Kostrzewa-Rutkowska, Z., Kun, M., Lambert, S., Lanza, A. F., Campion, J. -F. Le, Lebreton, Y., Leccia, S., Lecoutre, G., Liao, S., Liberato, L., Licata, E., Lindstrøm, H. E. P., Lister, T. A., Livanou, E., Loup, C., Mahy, L., Mann, R. G., Manteiga, M., Marchant, J. M., Marconi, M., Pina, D. Marín, Marinoni, S., Marshall, D. J., Lozano, J. Martín, Martín-Fleitas, J. M., Marton, G., Mary, N., Masip, A., Massari, D., Mastrobuono-Battisti, A., Mazeh, T., McMillan, P. J., Meichsner, J., Messina, S., Michalik, D., Millar, N. R., Mints, A., Molina, D., Molinaro, R., Molnár, L., Monari, G., Monguió, M., Montegriffo, P., Montero, A., Mor, R., Mora, A., Morbidelli, R., Morel, T., Morris, D., Munoz, D., Muraveva, T., Murphy, C. P., Musella, I., Nagy, Z., Nieto, S., Noval, L., Ogden, A., Ordenovic, C., Pagani, C., Pagano, I., Palaversa, L., Palicio, P. A., Pallas-Quintela, L., Panahi, A., Panem, C., Payne-Wardenaar, S., Pegoraro, L., Penttilä, A., Pesciullesi, P., Piersimoni, A. M., Pinamonti, M., Pineau, F. -X., Plachy, E., Plum, G., Poggio, E., Pourbaix, D., Prša, A., Pulone, L., Racero, E., Rainer, M., Raiteri, C. M., Ramos, P., Ramos-Lerate, M., Ratajczak, M., Fiorentin, P. Re, Regibo, S., Reylé, C., Ripepi, V., Riva, A., Rix, H. -W., Rixon, G., Robichon, N., Robin, C., Romero-Gómez, M., Rowell, N., Royer, F., Mieres, D. Ruz, Rybicki, K. A., Sadowski, G., Núñez, A. Sáez, Sellés, A. Sagristà, Sahlmann, J., Gimenez, V. Sanchez, Sanna, N., Santoveña, R., Sarasso, M., Riera, C. Sarrate, Sciacca, E., Segovia, J. C., Ségransan, D., Shahaf, S., Siebert, A., Siltala, L., Slezak, E., Smart, R. L., Snaith, O. N., Solano, E., Solitro, F., Souami, D., Souchay, J., Spina, L., Spitoni, E., Spoto, F., Squillante, L. A., Steele, I. A., Steidelmüller, H., Surdej, J., Szabados, L., Taris, F., Taylor, M. B., Teixeira, R., Tisanić, K., Tolomei, L., Torra, F., Elipe, G. Torralba, Tsantaki, M., Ulla, A., Unger, N., Vanel, O., Vecchiato, A., Vicente, D., Voutsinas, S., Weiler, M., Wyrzykowski, Ł., Zhao, H., Zorec, J., Zwitter, T., Balaguer-Núñez, L., Leclerc, N., Morgenthaler, S., Robert, G., and Zucker, S.
Subjects: Astrophysics - Solar and Stellar Astrophysics
Abstract: The third Gaia Data Release (DR3) provided photometric time series of more than 2 million long-period variable (LPV) candidates. Anticipating the publication of full radial-velocity (RV) in DR4, this Focused Product Release (FPR) provides RV time series for a selection of LPVs with high-quality observations. We describe the production and content of the Gaia catalog of LPV RV time series, and the methods used to compute variability parameters published in the Gaia FPR. Starting from the DR3 LPVs catalog, we applied filters to construct a sample of sources with high-quality RV measurements. We modeled their RV and photometric time series to derive their periods and amplitudes, and further refined the sample by requiring compatibility between the RV period and at least one of the $G$, $G_{\rm BP}$, or $G_{\rm RP}$ photometric periods. The catalog includes RV time series and variability parameters for 9\,614 sources in the magnitude range $6\lesssim G/{\rm mag}\lesssim 14$, including a flagged top-quality subsample of 6\,093 stars whose RV periods are fully compatible with the values derived from the $G$, $G_{\rm BP}$, and $G_{\rm RP}$ photometric time series. The RV time series contain a mean of 24 measurements per source taken unevenly over a duration of about three years. We identify the great most sources (88%) as genuine LPVs, with about half of them showing a pulsation period and the other half displaying a long secondary period. The remaining 12% consists of candidate ellipsoidal binaries. Quality checks against RVs available in the literature show excellent agreement. We provide illustrative examples and cautionary remarks. The publication of RV time series for almost 10\,000 LPVs constitutes, by far, the largest such database available to date in the literature. The availability of simultaneous photometric measurements gives a unique added value to the Gaia catalog (abridged), Comment: 36 pages, 38 figures
Published: 2023

32. An Experimental Prototype for Multistatic Asynchronous ISAC

Author: Canil, Marco, Pegoraro, Jacopo, Lacruz, Jesus O., Mezzavilla, Marco, Rossi, Michele, Widmer, Joerg, and Rangan, Sundeep
Subjects: Electrical Engineering and Systems Science - Signal Processing
Abstract: We prototype and validate a multistatic mmWave ISAC system based on IEEE802.11ay. Compensation of the clock asynchrony between each TX and RX pair is performed using the sole LoS wireless signal propagation. As a result, our system provides concurrent target tracking and micro-Doppler estimation from multiple points of view, paving the way for practical multistatic data fusion. Our results on human movement sensing, complemented with precise, quantitative GT data, demonstrate the enhanced sensing capabilities of multistatic ISAC, due to the spatial diversity of the receiver nodes.
Published: 2023

33. A Joint Fermi-GBM and Swift-BAT Analysis of Gravitational-Wave Candidates from the Third Gravitational-wave Observing Run

Author: Fletcher, C., Wood, J., Hamburg, R., Veres, P., Hui, C. M., Bissaldi, E., Briggs, M. S., Burns, E., Cleveland, W. H., Giles, M. M., Goldstein, A., Hristov, B. A., Kocevski, D., Lesage, S., Mailyan, B., Malacaria, C., Poolakkil, S., von Kienlin, A., Wilson-Hodge, C. A., Team, The Fermi Gamma-ray Burst Monitor, Crnogorčević, M., DeLaunay, J., Tohuvavohu, A., Caputo, R., Cenko, S. B., Laha, S., Parsotan, T., Abbott, R., Abe, H., Acernese, F., Ackley, K., Adhikari, N., Adhikari, R. X., Adkins, V. K., Adya, V. B., Affeldt, C., Agarwal, D., Agathos, M., Agatsuma, K., Aggarwal, N., Aguiar, O. D., Aiello, L., Ain, A., Ajith, P., Akutsu, T., Albanesi, S., Alfaidi, R. A., Allocca, A., Altin, P. A., Amato, A., Anand, C., Anand, S., Ananyeva, A., Anderson, S. B., Anderson, W. G., Ando, M., Andrade, T., Andres, N., Andrés-Carcasona, M., Andríc, T., Angelova, S. V., Ansoldi, S., Antelis, J. M., Antier, S., Apostolatos, T., Appavuravther, E. Z., Appert, S., Apple, S. K., Arai, K., Araya, A., Araya, M. C., Areeda, J. S., Arène, M., Aritomi, N., Arnaud, N., Arogeti, M., Aronson, S. M., Arun, K. G., Asada, H., Asali, Y., Ashton, G., Aso, Y., Assiduo, M., Melo, S. Assis de Souza, Aston, S. M., Astone, P., Aubin, F., AultONeal, K., Austin, C., Babak, S., Badaracco, F., Bader, M. K. M., Badger, C., Bae, S., Bae, Y., Baer, A. M., Bagnasco, S., Bai, Y., Baird, J., Bajpai, R., Baka, T., Ball, M., Ballardin, G., Ballmer, S. W., Balsamo, A., Baltus, G., Banagiri, S., Banerjee, B., Bankar, D., 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., Basak, S., Bassiri, R., Basti, A., Bawaj, M., Bayley, J. C., Bazzan, M., Becher, B. R., Becsy, B., Bedakihale, V. M., Beirnaert, F., Bejger, M., Belahcene, I., Benedetto, V., Beniwal, D., Benjamin, M. G., Bennett, T. F., Bentley, J. D., BenYaala, M., Bera, S., Berbel, M., Bergamin, F., Berger, B. K., Bernuzzi, S., Berry, C. P. L., Bersanetti, D., Bertolini, A., Betzwieser, J., Beveridge, D., Bhandare, R., Bhandari, A. V., Bhardwaj, U., Bhatt, R., Bhattacharjee, D., Bhaumik, S., Bianchi, A., Bilenko, I. A., Billingsley, G., Bini, S., Birney, R., Birnholtz, O., Biscans, S., Bischi, M., Biscoveanu, S., Bisht, A., Biswas, B., Bitossi, M., Bizouard, M. A., Blackburn, J. K., Blair, C. D., Blair, D. G., Blair, R. M., Bobba, F., Bode, N., Boër, M., Bogaert, G., Boldrini, M., Bolingbroke, G. N., Bonavena, L. D., 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., Bucci, F., Bulik, T., Bulten, H. J., Buonanno, A., Burtnyk, K., Buscicchio, R., Buskulic, D., Buy, C., Byer, R. L., Davies, G. S. Cabourn, Cabras, G., Cabrita, R., Cadonati, L., Caesar, M., Cagnoli, G., Cahillane, C., Bustillo, J. Calderòn, Callaghan, J. D., Callister, T. A., Calloni, E., Cameron, J., Camp, J. B., Canepa, M., Canevarolo, S., Cannavacciuolo, M., Cannon, K. C., Cao, H., Cao, Z., Capocasa, E., Capote, E., Carapella, G., Carbognani, F., Carlassara, M., Carlin, J. B., Carney, M. F., Carpinelli, M., Carrillo, G., Carullo, G., Carver, T. L., Díaz, J. Casanueva, Casentini, C., Castaldi, G., Caudill, S., Cavaglià, M., Cavalier, F., Cavalieri, R., Cella, G., Cerdá-Durán, P., Cesarini, E., Chaibi, W., Subrahmanya, S. Chalathadka, Champion, E., Chan, C. H., Chan, C., Chan, C. L., Chan, K., Chan, M., Chandra, K., Chang, I. P., Chanial, P., Chao, S., Chapman-Bird, C., Charlton, P., Chase, E. A., Chassande-Mottin, E., Chatterjee, C., Chatterjee, Debarati, Chatterjee, Deep, Chaturvedi, M., Chaty, S., Chen, C., Chen, D., 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., Chiarini, G., Chierici, R., Chincarini, A., Chiofalo, M. L., Chiummo, A., Choudhary, R. K., Choudhary, S., Christensen, N., Chu, Q., Chu, Y. K., Chua, S. S. Y., Chung, K. W., Ciani, G., Ciecielag, P., Cieślar, M., Cifaldi, M., Ciobanu, A. A., Ciolfi, R., Cipriano, F., Clara, F., Clark, J. A., Clearwater, P., Clesse, S., Cleva, F., Coccia, E., Codazzo, E., Cohadon, P. F., Cohen, D. E., Colleoni, M., Collette, C. G., Colombo, A., 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. J., Corre, D., Corsi, A., Cortese, S., Costa, C. A., Cotesta, R., Cottingham, R., Coughlin, M. W., Coulon, J. P., Countryman, S. T., Cousins, B., Couvares, P., 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., Cunningham, L., Cuoco, E., lo, M. Cury, Dabadie, P., Canton, T. Dal, Dall'Osso, S., Dálya, G., Dana, A., D'Angelo, B., Danilishin, S., D'Antonio, S., Danzmann, K., Darsow-Fromm, C., Dasgupta, A., Datrier, L. E. H., Datta, Sayak, Datta, Sayantani, Dattilo, V., Dave, I., Davier, M., Davis, D., Davis, M. C., 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., Dell'Aquila, D., 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., D'iaz, M. C., Didio, N. A., Dietrich, T., Di Fiore, L., Di Fronzo, C., Di Giorgio, C., Di Giovanni, F., Di Giovanni, M., Di Girolamo, T., Di Lieto, A., Di Michele, A., Ding, B., Di Pace, S., Di Palma, I., Di Renzo, F., Divakarla, A. K., Dmitriev, A., Doctor, Z., Donahue, L., D'Onofrio, L., Donovan, F., Dooley, K. L., Doravari, S., Drago, M., Driggers, J. C., Drori, Y., Ducoin, J. G., Dupej, P., Dupletsa, U., Durante, O., D'Urso, D., Duverne, P. A., Dwyer, S. E., Eassa, C., Easter, P. J., Ebersold, M., Eckhardt, T., 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., Engelby, E., Enomoto, Y., Errico, L., Essick, R. C., Estellés, H., Estevez, D., Etienne, Z., Etzel, T., Evans, M., Evans, T. M., Evstafyeva, T., Ewing, B. E., Fabrizi, F., Faedi, F., Fafone, V., Fair, H., Fairhurst, S., Fan, P. C., Farah, A. M., Farinon, S., Farr, B., Farr, W. M., Fauchon-Jones, E. J., Favaro, G., Favata, M., Fays, M., Fazio, M., Feicht, J., Fejer, M. M., Fenyvesi, E., Ferguson, D. L., Fernandez-Galiana, A., Ferrante, I., Ferreira, T. A., Fidecaro, F., Figura, P., Fiori, A., Fiori, I., Fishbach, M., Fisher, R. P., Fittipaldi, R., Fiumara, V., Flaminio, R., Floden, E., Fong, H. K., Font, J. A., Fornal, B., Forsyth, P. W. F., Franke, A., Frasca, S., Frasconi, F., Freed, J. P., Frei, Z., Freise, A., Freitas, O., Frey, R., Fritschel, P., Frolov, V. V., Fronzé, G. G., Fujii, Y., Fujikawa, Y., Fujimoto, Y., Fulda, P., Fyffe, M., Gabbard, H. A., Gabella, W. E., Gadre, B. U., Gair, J. R., Gais, J., Galaudage, S., Gamba, R., Ganapathy, D., Ganguly, A., Gao, D., Gaonkar, S. G., Garaventa, B., Núñez, C. García, García-Quirós, C., Garufi, F., Gateley, B., Gayathri, V., Ge, G. G., Gemme, G., Gennai, A., George, J., Gerberding, O., Gergely, L., Gewecke, P., Ghonge, S., Ghosh, Abhirup, Ghosh, Archisman, Ghosh, Shaon, Ghosh, Shrobana, Ghosh, Tathagata, Giacomazzo, B., Giacoppo, L., Giaime, J. A., Giardina, K. D., Gibson, D. R., Gier, C., Giesler, M., Giri, P., Gissi, F., Gkaitatzis, S., Glanzer, J., Gleckl, A. E., Godwin, P., Goetz, E., Goetz, R., Gohlke, N., Golomb, J., Goncharov, B., Gonzalez, G., Gosselin, M., Gouaty, R., Gould, D. W., Goyal, S., Grace, B., Grado, A., Graham, V., 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. L., Griggs, H. L., Grignani, G., Grimaldi, A., Grimes, E., Grimm, S. J., Grote, H., Grunewald, S., Gruning, P., Gruson, A. S., Guerra, D., Guidi, G. M., Guimaraes, A. R., Guixe, G., Gulati, H. K., Gunny, A. M., Guo, H. K., Guo, Y., Gupta, Anchal, Gupta, Anuradha, Gupta, I. M., Gupta, P., Gupta, S. K., Gustafson, R., Guzman, F., Ha, S., Hadiputrawan, I. P. W., Haegel, L., 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., Hansen, H., Hansen, T. J., Hanson, J., Harder, T., Haris, K., Harms, J., Harry, G. M., Harry, I. W., Hartwig, D., Hasegawa, K., Haskell, B., Haster, C. J., Hathaway, J. S., Hattori, K., Haughian, K., Hayakawa, H., Hayama, K., Hayes, F. J., Healy, J., Heidmann, A., Heidt, 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., Henshaw, C., Hernandez, A. G., Vivanco, F. Hernandez, Heurs, M., Hewitt, A. L., Higginbotham, S., Hild, S., Hill, P., Himemoto, Y., Hines, A. S., Hirata, N., Hirose, C., Ho, T. C., Hochheim, S., Hofman, D., Hohmann, J. N., Holcomb, D. G., Holland, N. A., Hollows, I. J., Holmes, Z. J., Holt, K., Holz, D. E., Hong, Q., Hough, J., Hourihane, S., Howell, E. J., Hoy, C. G., Hoyland, D., Hreibi, A., Hsieh, B. H., Hsieh, H. F., Hsiung, C., Hsu, Y., Huang, H. Y., Huang, P., Huang, Y. C., Huang, Y. J., Huang, Yiting, Huang, Yiwen, Hübner, M. T., Huddart, A. D., Hughey, B., Hui, D. C. Y., Hui, V., Husa, S., Huttner, S. H., Huxford, R., Huynh-Dinh, T., Ide, S., Idzkowski, B., Iess, A., Inayoshi, K., Inoue, Y., Iosif, P., Isi, M., Isleif, K., Ito, K., Itoh, Y., Iyer, B. R., JaberianHamedan, V., Jacqmin, T., Jacquet, P. E., Jadhav, S. J., Jadhav, S. P., Jain, T., James, A. L., Jan, A. Z., Jani, K., Janquart, J., Janssens, K., Janthalur, N. N., Jaranowski, P., Jariwala, D., Jaume, R., Jenkins, A. C., Jenner, K., Jeon, C., Jia, W., Jiang, J., Jin, H. B., Johns, G. R., Johnston, R., Jones, A. W., Jones, D. I., Jones, P., Jones, R., Joshi, P., Ju, L., Jue, A., Jung, P., Jung, K., Junker, J., Juste, V., 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., Karathanasis, C., Karki, S., Kashyap, R., Kasprzack, M., Kastaun, W., Kato, T., Katsanevas, S., Katsavounidis, E., Katzman, W., Kaur, T., Kawabe, K., Kawaguchi, K., Kéfélian, F., Keitel, D., Key, J. S., Khadka, S., Khalili, F. Y., Khan, S., Khanam, T., Khazanov, E. A., Khetan, N., Khursheed, M., Kijbunchoo, N., Kim, A., Kim, C., Kim, J. C., Kim, J., Kim, K., Kim, W. S., Kim, Y. M., Kimball, C., Kimura, N., Kinley-Hanlon, M., Kirchhoff, R., Kissel, J. S., Klimenko, S., Klinger, T., Knee, A. M., Knowles, T. D., Knust, N., Knyazev, E., Kobayashi, Y., Koch, P., Koekoek, G., Kohri, K., Kokeyama, K., Koley, S., Kolitsidou, P., Kolstein, M., Komori, K., Kondrashov, V., Kong, A. K. H., Kontos, A., Koper, N., Korobko, M., Kovalam, M., Koyama, N., Kozak, D. B., Kozakai, C., Kringel, V., Krishnendu, N. 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B., Ying, M., Yokoyama, J., Yokozawa, T., Yoo, J., Yoshioka, T., Yu, Hang, Yu, Haocun, Yuzurihara, H., Zadrozny, A., Zanolin, M., Zeidler, S., Zelenova, T., Zendri, J. P., Zevin, M., Zhan, M., Zhang, H., Zhang, J., Zhang, L., Zhang, R., Zhang, T., Zhang, Y., Zhao, C., Zhao, G., Zhao, Y., Zhao, Yue, Zhou, R., Zhou, Z., Zhu, X. J., Zhu, Z. H., Zimmerman, A. B., Zucker, M. E., Zweizig, J., Collaboration, The LIGO Scientific, Collaboration, the Virgo, and Collaboration, the KAGRA
Subjects: Astrophysics - High Energy Astrophysical Phenomena
Abstract: We present Fermi Gamma-ray Burst Monitor (Fermi-GBM) and Swift Burst Alert Telescope (Swift-BAT) searches for gamma-ray/X-ray counterparts to gravitational wave (GW) candidate events identified during the third observing run of the Advanced LIGO and Advanced Virgo detectors. Using Fermi-GBM on-board triggers and sub-threshold gamma-ray burst (GRB) candidates found in the Fermi-GBM ground analyses, the Targeted Search and the Untargeted Search, we investigate whether there are any coincident GRBs associated with the GWs. We also search the Swift-BAT rate data around the GW times to determine whether a GRB counterpart is present. No counterparts are found. Using both the Fermi-GBM Targeted Search and the Swift-BAT search, we calculate flux upper limits and present joint upper limits on the gamma-ray luminosity of each GW. Given these limits, we constrain theoretical models for the emission of gamma-rays from binary black hole mergers.
Published: 2023

34. Search for Eccentric Black Hole Coalescences during the Third Observing Run of LIGO and Virgo

Author: The LIGO Scientific Collaboration, the Virgo Collaboration, the KAGRA Collaboration, Abac, A. G., Abbott, R., Abe, H., Acernese, F., Ackley, K., Adamcewicz, C., Adhicary, S., Adhikari, N., Adhikari, R. X., Adkins, V. K., Adya, V. B., Affeldt, C., Agarwal, D., Agathos, M., Aguiar, O. D., Aguilar, I., Aiello, L., Ain, A., Ajith, P., Akutsu, T., Albanesi, S., Alfaidi, R. A., Al-Jodah, A., Alléné, C., Allocca, A., Almualla, M., Altin, P. A., Álvarez-López, S., Amato, A., Amez-Droz, L., Amorosi, A., Anand, S., Ananyeva, A., Andersen, R., Anderson, S. B., Anderson, W. G., Andia, M., Ando, M., Andrade, T., Andres, N., Andrés-Carcasona, M., Andrić, T., Ansoldi, S., Antelis, J. M., Antier, S., Aoumi, M., Apostolatos, T., Appavuravther, E. Z., Appert, S., Apple, S. K., Arai, K., Araya, A., Araya, M. C., Areeda, J. S., Aritomi, N., Armato, F., Arnaud, N., Arogeti, M., Aronson, S. M., Arun, K. G., Ashton, G., Aso, Y., Assiduo, M., Melo, S. Assis de Souza, Aston, S. M., Astone, P., Aubin, F., AultONeal, K., Babak, S., Badalyan, A., Badaracco, F., Badger, C., Bae, S., Bagnasco, S., Bai, Y., Baier, J. G., Bajpai, R., Baka, T., Ball, M., Ballardin, G., Ballmer, S. W., Baltus, G., Banagiri, S., Banerjee, B., Bankar, D., Baral, P., Barayoga, J. C., Barber, J., Barish, B. C., Barker, D., Barneo, P., Barone, F., Barr, B., Barsotti, L., Barsuglia, M., Barta, D., Barthelmy, S. D., Barton, M. A., Bartos, I., Basak, S., Basalaev, A., Bassiri, R., Basti, A., Bawaj, M., Baxi, P., Bayley, J. C., Baylor, A. C., Bazzan, M., Bécsy, B., Bedakihale, V. M., Beirnaert, F., Bejger, M., Bell, A. S., Benedetto, V., Beniwal, D., Benoit, W., Bentley, J. D., Yaala, M. Ben, Bera, S., Berbel, M., Bergamin, F., Berger, B. K., Bernuzzi, S., Beroiz, M., Berry, C. P. L., Bersanetti, D., Bertolini, A., Betzwieser, J., Beveridge, D., Bevins, N., Bhandare, R., Bhandari, A. V., Bhardwaj, U., Bhatt, R., Bhattacharjee, D., Bhaumik, S., Bianchi, A., Bilenko, I. A., Bilicki, M., Billingsley, G., Binetti, A., Bini, S., Birnholtz, O., Biscans, S., Bischi, M., Biscoveanu, S., Bisht, A., Bitossi, M., Bizouard, M. -A., Blackburn, J. K., Blair, C. D., Blair, D. G., Bobba, F., Bode, N., Boër, M., Bogaert, G., Boileau, G., Boldrini, M., Bolingbroke, G. N., Bonavena, L. D., Bondarescu, R., Bondu, F., Bonilla, E., Bonilla, M. S., Bonnand, R., Booker, P., Boschi, V., Bose, S., Bossilkov, V., Boudart, V., Bozzi, A., Bradaschia, C., Brady, P. R., Braglia, M., Branch, A., Branchesi, M., Breschi, M., Briant, T., Brillet, A., Brinkmann, M., Brockill, P., Brooks, A. F., Brown, D. D., Brozzetti, M. L., Brunett, S., Bruno, G., Bruntz, R., Bryant, J., Bucci, F., Buchanan, J., Bulashenko, O., Bulik, T., Bulten, H. J., Buonanno, A., Burtnyk, K., Buscicchio, R., Buskulic, D., Buy, C., Davies, G. S. Cabourn, Cabras, G., Cabrita, R., Cadonati, L., Cagnoli, G., Cahillane, C., Cain III, H. W., Bustillo, J. Calderón, Callaghan, J. D., Callister, T. 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L., Plastino, W., Poggiani, R., Polini, E., Pompili, L., Ponrathnam, S., Poon, J., Porcelli, E., Portell, J., Porter, E. K., Posnansky, C., Poulton, R., Powell, J., Pracchia, M., Pradhan, B. K., Pradier, T., Prajapati, A. K., Prasai, K., Prasanna, R., Prasia, P., Pratten, G., Principe, M., Prodi, G. A., Prokhorov, L., Prosposito, P., Prudenzi, L., Puecher, A., Pullin, J., Punturo, M., Puosi, F., Puppo, P., Pürrer, M., Qi, H., Qin, J., Quetschke, V., Quinonez, P. J., Quitzow-James, R., Raab, F. J., Raaijmakers, G., Radulesco, N., Raffai, P., Rail, S. X., Raja, S., Rajan, C., Ramirez, K. E., Ramos-Buades, A., Rana, D., Randel, E., Rangnekar, P. R., Rapagnani, P., Ray, A., Raymond, V., Razzano, M., Read, J., Payo, M. Recaman, Regimbau, T., Rei, L., Reid, S., Reid, S. W., Reitze, D. H., Relton, P., Renzini, A., Rettegno, P., Revenu, B., Reza, A., Rezac, M., Rezaei, A. S., Ricci, F., Ricci, M., Richards, D., Richardson, J. W., Rijal, A., Riles, K., Riley, H. K., Rinaldi, S., Robertson, C., Robertson, N. A., Robinet, F., Robinson, M., Rocchi, A., Rolland, L., Rollins, J. G., Romanelli, M., Romano, A. E., Romano, R., Romero, A., Romero-Shaw, I. M., Romie, J. H., Ronchini, S., Roocke, T. J., Rosa, L., Rosauer, T. J., Rose, C. A., Rosińska, D., Ross, M. P., Rossello, M., Rowan, S., Roy, S., Royzman, A., Rozza, D., Ruggi, P., Morales, E. Ruiz, Ruiz-Rocha, K., Sachdev, S., Sadecki, T., Sadiq, J., Saffarieh, P., Saha, S. S., Sainrat, T., Menon, S. Sajith, Sakai, K., Sakellariadou, M., Sako, T., Sakon, S., Salafia, O. S., Salces-Carcoba, F., Salconi, L., Saleem, M., Salemi, F., Sallé, M., Salvador, S., Sanchez, A., Sanchez, E. J., Sanchez, J. H., Sanchez, L. E., Sanchis-Gual, N., Sanders, J. R., Sänger, E. M., Saravanan, T. R., Sarin, N., Sasli, A., Sassi, P., Sassolas, B., Satari, H., Sato, R., Sato, S., Sato, Y., Sauter, O., Savage, R. L., Savant, V., Sawada, T., Sawant, H. L., Sayah, S., Schaetzl, D., Scheel, M., Scherf, S. J., Scheuer, J., Schiworski, M. G., Schmidt, P., Schmidt, S., Schmitz, S. J., Schnabel, R., Schneewind, M., Schofield, R. M. S., Schönbeck, A., Schouteden, K., Schuler, H., Schulte, B. W., Schutz, B. F., Schwartz, E., Scott, J., Scott, S. M., Seetharamu, T. C., Seglar-Arroyo, M., Sekiguchi, Y., Sellers, D., Sengupta, A. S., Sentenac, D., Seo, E. G., Seo, J. W., Sequino, V., Servignat, G., Setyawati, Y., Shaffer, T., Shahriar, M. S., Shaikh, M. A., Shams, B., Shao, L., Sharma, P., Sharma-Chaudhary, S., Shawhan, P., Shcheblanov, N. S., Sheela, A., Shen, B., Shepard, K. G., Shikano, Y., Shikauchi, M., Shimode, K., Shinkai, H., Shiota, J., Shoemaker, D. H., Shoemaker, D. M., Short, R. W., ShyamSundar, S., Sider, A., Siegel, H., Sieniawska, M., Sigg, D., Silenzi, L., Simmonds, M., Singer, L. P., Singh, A., Singh, D., Singh, M. K., Singha, A., Sintes, A. M., Sipala, V., Skliris, V., Slagmolen, B. J. J., Slaven-Blair, T. J., Smetana, J., Smith, J. R., Smith, L., Smith, R. J. E., Soldateschi, J., Somala, S. N., Somiya, K., Soni, K., Soni, S., Sordini, V., Sorrentino, F., Sorrentino, N., Soulard, R., Souradeep, T., Sowell, E., Spagnuolo, V., Spencer, A. P., Spera, M., Spinicelli, P., Srivastava, A. K., Srivastava, V., Stachie, C., Stachurski, F., Steer, D. A., Steinlechner, J., Steinlechner, S., Stergioulas, N., Stevens, P., StPierre, M., Strang, L. C., Stratta, G., Strong, M. D., Strunk, A., Sturani, R., Stuver, A. L., Suchenek, M., Sudhagar, S., Sueltmann, N., Suh, H. G., Sullivan, A. G., Summerscales, T. Z., Sun, L., Sunil, S., Sur, A., Suresh, J., Sutton, P. J., Suzuki, Takamasa, Suzuki, Takanori, Swinkels, B. L., Syx, A., Szczepańczyk, M. J., Szewczyk, P., Tacca, M., Tagoshi, H., Tait, S. C., Takahashi, H., Takahashi, R., Takamori, A., Takatani, K., Takeda, H., Takeda, M., Talbot, C. J., Talbot, C., Tamaki, M., Tamanini, N., Tanabe, D., Tanaka, K., Tanaka, S. J., Tanaka, T., Tanasijczuk, A. J., Tanioka, S., Tanner, D. B., Tao, D., Tao, L., Tapia, R. D., Martín, E. N. Tapia San, Tarafder, R., Taranto, C., Taruya, A., Tasson, J. D., Teloi, M., Tenorio, R., Terkowski, L., Themann, H., Thirugnanasambandam, M. P., Thomas, L. M., Thomas, M., Thomas, P., Thompson, J. E., Thondapu, S. R., Thorne, K. A., Thrane, E., Tissino, J., Tiwari, Shubhanshu, Tiwari, Srishti, Tiwari, V., Toivonen, A. M., Tolley, A. E., Tomaru, T., Tomita, K., Tomura, T., Tonelli, M., Toriyama, A., Torres-Forné, A., Torrie, C. I., Toscani, M., Melo, I. Tosta e, Tournefier, E., Trani, A. A., Trapananti, A., Travasso, F., Traylor, G., Trenado, J., Trevor, M., Tringali, M. C., Tripathee, A., Troiano, L., Trovato, A., Trozzo, L., Trudeau, R. J., Tse, M., Tso, R., Tsuchida, S., Tsukada, L., Tsutsui, T., Turbang, K., Turconi, M., Turski, C., Ubach, H., Ubhi, A. S., Uchikata, N., Uchiyama, T., Udall, R. P., Uehara, T., Ueno, K., Unnikrishnan, C. S., Ushiba, T., Utina, A., Vahlbruch, H., Vaidya, N., Vajente, G., Vajpeyi, A., Valdes, G., Valentini, M., Vallejo-Peña, S. A., Vallero, S., Valsan, V., van Bakel, N., van Beuzekom, M., van Dael, M., Brand, J. F. J. van den, Broeck, C. Van Den, Vander-Hyde, D. C., van der Sluys, M., Van de Walle, A., van Dongen, J., van Haevermaet, H., van Heijningen, J. V., Vanosky, J., van Putten, M. H. P. M., van Ranst, Z., van Remortel, N., Vardaro, M., Vargas, A. F., Varma, V., Vasúth, M., Vecchio, A., Vedovato, G., Veitch, J., Veitch, P. J., Venneberg, J., Verdier, P., Verkindt, D., Verma, P., Verma, Y., Vermeulen, S. M., Veske, D., Vetrano, F., Veutro, A., Viceré, A., Vidyant, S., Viets, A. D., Vijaykumar, A., Villa-Ortega, V., Vincent, E. T., Vinet, J. -Y., Viret, S., Virtuoso, A., Vitale, S., Vocca, H., Voigt, D., von Reis, E. R. G., von Wrangel, J. S. A., Vyatchanin, S. P., Wade, L. E., Wade, M., Wagner, K. J., Walet, R. C., Walker, M., Wallace, G. S., Wallace, L., Wang, H., Wang, J. Z., Wang, W. H., Ward, R. L., Warner, J., Was, M., Washimi, T., Washington, N. Y., Watada, K., Watarai, D., Wayt, K. E., Weaver, B., Weaving, C. R., Webster, S. A., Weinert, M., Weinstein, A. J., Weiss, R., Weller, C. M., Weller, R. A., Wellmann, F., Wen, L., Weßels, P., Wette, K., Whelan, J. T., White, D. D., Whiting, B. F., Whittle, C., Wildberger, J. B., Wilk, O. S., Wilken, D., Willetts, K., Williams, D., Williams, M. J., Williamson, A. R., Willis, J. L., Willke, B., Wils, M., Wipf, C. C., Woan, G., Woehler, J., Wofford, J. K., Wong, D., Wong, H. T., Wong, I. C. F., Wright, M., Wu, C., Wu, D. S., Wu, H., Wysocki, D. M., Xiao, L., Xu, V. A., Yadav, N., Yamamoto, H., Yamamoto, K., Yamamoto, M., Yamamoto, T. S., Yamamoto, T., Yamamura, S., Yamazaki, R., Yan, S., Yang, F. W., Yang, K. Z., Yang, L. -C., Yang, Y. -C., Yang, Yang, Yang, Yi, Yap, M. J., Yarbrough, Z., Yeh, S. -W., Yelikar, A. B., Yeung, S. M. C., Yeung, T. Y., Yokoyama, J., Yokozawa, T., Yoo, J., Yu, H., Yuzurihara, H., Zadrożny, A., Zannelli, A. J., Zanolin, M., Zeeshan, M., Zelenova, T., Zendri, J. -P., Zevin, M., Zhang, J., Zhang, L., Zhang, R., Zhang, T., Zhang, Yanqi, Zhang, Ya, Zhao, C., Zhao, Yue, Zhao, Yuhang, Zheng, Y., Zhong, H., Zhou, R., Zhu, Z. -H., Zimmerman, A. B., Zucker, M. E., and Zweizig, J.
Subjects: Astrophysics - High Energy Astrophysical Phenomena
Abstract: Despite the growing number of confident binary black hole coalescences observed through gravitational waves so far, the astrophysical origin of these binaries remains uncertain. Orbital eccentricity is one of the clearest tracers of binary formation channels. Identifying binary eccentricity, however, remains challenging due to the limited availability of gravitational waveforms that include effects of eccentricity. Here, we present observational results for a waveform-independent search sensitive to eccentric black hole coalescences, covering the third observing run (O3) of the LIGO and Virgo detectors. We identified no new high-significance candidates beyond those that were already identified with searches focusing on quasi-circular binaries. We determine the sensitivity of our search to high-mass (total mass $M>70$ $M_\odot$) binaries covering eccentricities up to 0.3 at 15 Hz orbital frequency, and use this to compare model predictions to search results. Assuming all detections are indeed quasi-circular, for our fiducial population model, we place an upper limit for the merger rate density of high-mass binaries with eccentricities $0 < e \leq 0.3$ at $0.33$ Gpc$^{-3}$ yr$^{-1}$ at 90\% confidence level., Comment: 24 pages, 5 figures
Published: 2023

35. Directionality of nuclear recoils in a liquid argon time projection chamber

Author: Collaboration, The DarkSide-20k, Agnes, P., Ahmad, I., Albergo, S., Albuquerque, I. F. M., Alexander, T., Alton, A. K., Amaudruz, P., Corona, M. Atzori, Ave, M., Avetisov, I. Ch., Azzolini, O., Back, H. O., Balmforth, Z., Barrado-Olmedo, A., Barrillon, P., Basco, A., Batignani, G., Bocci, V., Bonivento, W. M., Bottino, B., Boulay, M. G., Busto, J., Cadeddu, M., Caminata, A., Canci, N., Cappello, G., Capra, A., Caprioli, S., Caravati, M., Cargioli, N., Carlini, M., Castello, P., Cataudella, V., Cavalcante, P., Cavuoti, S., Cebrian, S., Ruiz, J. M. Cela, Chashin, S., Chepurnov, A., Chyhyrynets, E., Cifarelli, L., Cintas, D., Citterio, M., Cleveland, B., Cocco, V., Vilda, E. Conde, Consiglio, L., Copello, S., Covone, G., Czubak, M., D'Aniello, M., D'Auria, S., Rolo, M. D. Da Rocha, Davini, S., de Candia, A., De Cecco, S., De Gruttola, D., De Filippis, G., Dell'Aquila, D., De Pasquale, S., De Rosa, G., Dellacasa, G., Derbin, A. V., Devoto, A., Di Capua, F., Di Noto, L., Dionisi, C., Di Stefano, P., Dolganov, G., Dordei, F., Elersich, A., Ellingwood, E., Erjavec, T., Diaz, M. Fernandez, Fiorillo, G., Franchini, P., Franco, D., Funicello, N., Gabriele, F., Gahan, D., Galbiati, C., Gallina, G., Gallus, G., Garbini, M., Abia, P. Garcia, Gendotti, A., Ghiano, C., Giganti, C., Giovanetti, G. K., Casanueva, V. Goicoechea, Gola, A., Grauso, G., di Cortona, G. Grilli, Grobov, A., Gromov, M., Guan, M., Guerzoni, M., Gulino, M., Guo, C., Hackett, B. R., Hallin, A. L., Hamer, A., Haranczyk, M., Hessel, T., Hill, S., Horikawa, S., Hubaut, F., Hucker, J., Hugues, T., Ianni, An., Ippolito, V., Jillings, C., Jois, S., Kachru, P., Kemmerich, N., Kemp, A. A., Kendziora, C. L., Kimura, M., Kochanek, I., Kondo, K., Korga, G., Koulosousas, S., Kubankin, A., Kuss, M., Kuzniak, M., La Commara, M., Lai, M., Guirriec, E. Le, Leason, E., Leoni, A., Li, X., Lidey, L., Lissia, M., Luzzi, L., Lychagina, O., Macfadyen, O., Machulin, I. N., Manecki, S., Manthos, I., Mapelli, L., Margotti, A., Marik, S. M., Mariani, C., Maricic, J., Marini, A., Martínez, M., Martoff, C. J., Matteucci, G., Mavrokoridis, K., McDonald, A. B., Messina, A., Milincic, R., Mitra, A., Moharana, A., Monroe, J., Moretti, E., Morrocchi, M., Mróz, T., Muratova, V. N., Muscas, C., Musico, P., Nania, R., Nessi, M., Nieradka, G., Nikolopoulos, K., Nowak, J., Olchansky, K., Oleinik, A., Oleynikov, V., Organtini, P., de Solórzano, A. Ortiz, Pagani, L., Pallavicini, M., Pandola, L., Pantic, E., Paoloni, E., Paternoster, G., Pegoraro, P. A., Pelczar, K., Pesudo, V., Piacentini, S., Pino, N., Pocar, A., Poehlmann, D. M., Pordes, S., Pralavorio, P., Price, D., Ragusa, F., Ramachers, Y., Razeti, M., Renshaw, A. L., Rescigno, M., Retiere, F., Rignanese, L. P., Ripoli, C., Rivetti, A., Roberts, A., Roberts, C., Rode, J., Rogers, G., Romero, L., Rossi, M., Rubbia, A., Sabia, M. A., Salomone, P., Sandford, E., Sanfilippo, S., Santone, D., Santorelli, R., Savarese, C., Scapparone, E., Schillaci, G., Schuckman II, F. G., Scioli, G., Simeone, M., Skensved, P., Skorokhvatov, M. D., Smirnov, O., Smirnova, T., Smith, B., Sosa, A., Spadoni, F., Spangenberg, M., Stefanizzi, R., Steri, A., Stornelli, V., Stracka, S., Stringer, M., Sulis, S., Sung, A., Suvorov, Y., Szelc, A. M., Tartaglia, R., Taylor, A., Taylor, J., Tedesco, S., Testera, G., Thieme, K., Thorpe, T. N., Tonazzo, A., Tricomi, A., Unzhakov, E. V., John, T. Vallivilayil, Van Uffelen, M., Viant, T., Viel, S., Vogelaar, R. B., Vossebeld, J., Wada, M., Walczak, M. B., Wang, H., Wang, Y., Westerdale, S., Williams, L., Wingerter-Seez, I., Wojaczynski, R., Wojcik, Ma. M., Wright, T., Xie, Y., Yang, C., Zabihi, A., Zakhary, P., Zani, A., Zichichi, A., Zuzel, G., and Zykova, M. P.
Subjects: Physics - Instrumentation and Detectors
Abstract: The direct search for dark matter in the form of weakly interacting massive particles (WIMP) is performed by detecting nuclear recoils (NR) produced in a target material from the WIMP elastic scattering. A promising experimental strategy for direct dark matter search employs argon dual-phase time projection chambers (TPC). One of the advantages of the TPC is the capability to detect both the scintillation and charge signals produced by NRs. Furthermore, the existence of a drift electric field in the TPC breaks the rotational symmetry: the angle between the drift field and the momentum of the recoiling nucleus can potentially affect the charge recombination probability in liquid argon and then the relative balance between the two signal channels. This fact could make the detector sensitive to the directionality of the WIMP-induced signal, enabling unmistakable annual and daily modulation signatures for future searches aiming for discovery. The Recoil Directionality (ReD) experiment was designed to probe for such directional sensitivity. The TPC of ReD was irradiated with neutrons at the INFN Laboratori Nazionali del Sud, and data were taken with 72 keV NRs of known recoil directions. The direction-dependent liquid argon charge recombination model by Cataudella et al. was adopted and a likelihood statistical analysis was performed, which gave no indications of significant dependence of the detector response to the recoil direction. The aspect ratio R of the initial ionization cloud is estimated to be 1.037 +/- 0.027 and the upper limit is R < 1.072 with 90% confidence level, Comment: 20 pages, 10 figures, submitted to Eur. Phys. J. C
Published: 2023

36. Applying Process Mining on Scientific Workflows: a Case Study on High Performance Computing Data

Author: Sadeghibogar, Zahra, Berti, Alessandro, Pegoraro, Marco, and van der Aalst, Wil M. P.
Subjects: Computer Science - Databases
Abstract: Computer-based scientific experiments are becoming increasingly data-intensive, necessitating the use of High-Performance Computing (HPC) clusters to handle large scientific workflows. These workflows result in complex data and control flows within the system, making analysis challenging. This paper focuses on the extraction of case IDs from SLURM-based HPC cluster logs, a crucial step for applying mainstream process mining techniques. The core contribution is the development of methods to correlate jobs in the system, whether their interdependencies are explicitly specified or not. We present our log extraction and correlation techniques, supported by experiments that validate our approach, enabling comprehensive documentation of workflows and identification of performance bottlenecks., Comment: 12 pages, 8 figures, 3 tables, 9 references
Published: 2023

37. Vector Quantile Regression on Manifolds

Author: Pegoraro, Marco, Vedula, Sanketh, Rosenberg, Aviv A., Tallini, Irene, Rodolà, Emanuele, and Bronstein, Alex M.
Subjects: Statistics - Methodology, Computer Science - Machine Learning
Abstract: Quantile regression (QR) is a statistical tool for distribution-free estimation of conditional quantiles of a target variable given explanatory features. QR is limited by the assumption that the target distribution is univariate and defined on an Euclidean domain. Although the notion of quantiles was recently extended to multi-variate distributions, QR for multi-variate distributions on manifolds remains underexplored, even though many important applications inherently involve data distributed on, e.g., spheres (climate and geological phenomena), and tori (dihedral angles in proteins). By leveraging optimal transport theory and c-concave functions, we meaningfully define conditional vector quantile functions of high-dimensional variables on manifolds (M-CVQFs). Our approach allows for quantile estimation, regression, and computation of conditional confidence sets and likelihoods. We demonstrate the approach's efficacy and provide insights regarding the meaning of non-Euclidean quantiles through synthetic and real data experiments.
Published: 2023

38. Attention-Refined Unrolling for Sparse Sequential micro-Doppler Reconstruction

Author: Mazzieri, Riccardo, Pegoraro, Jacopo, and Rossi, Michele
Subjects: Electrical Engineering and Systems Science - Signal Processing, Computer Science - Machine Learning
Abstract: The reconstruction of micro-Doppler signatures of human movements is a key enabler for fine-grained activity recognition wireless sensing. In Joint Communication and Sensing (JCS) systems, unlike in dedicated radar sensing systems, a suitable trade-off between sensing accuracy and communication overhead has to be attained. It follows that the micro-Doppler has to be reconstructed from incomplete windows of channel estimates obtained from communication packets. Existing approaches exploit compressed sensing, but produce very poor reconstructions when only a few channel measurements are available, which is often the case with real communication patterns. In addition, the large number of iterations they need to converge hinders their use in real-time systems. In this work, we propose and validate STAR, a neural network that reconstructs micro-Doppler sequences of human movement even from highly incomplete channel measurements. STAR is based upon a new architectural design that combines a single unrolled iterative hard-thresholding layer with an attention mechanism, used at its output. This results in an interpretable and lightweight architecture that reaps the benefits of both model-based and data driven solutions. STAR is evaluated on a public JCS dataset of 60 GHz channel measurements of human activity traces. Experimental results show that it substantially outperforms state-of-the-art techniques in terms of the reconstructed micro-Doppler quality. Remarkably, STAR enables human activity recognition with satisfactory accuracy even with 90% of missing channel measurements, for which existing techniques fail., Comment: 16 pages, 10 figures, 6 tables
Published: 2023

39. DISC: a Dataset for Integrated Sensing and Communication in mmWave Systems

Author: Pegoraro, Jacopo, Saucedo, Pablo, Lacruz, Jesus Omar, Rossi, Michele, and Widmer, Joerg
Subjects: Electrical Engineering and Systems Science - Signal Processing
Abstract: This paper presents DISC, a dataset of millimeter-wave channel impulse response measurements for integrated human activity sensing and communication. This is the first dataset collected with a software-defined radio testbed that transmits 60 GHz IEEE 802-11ay-compliant packets and estimates the channel response including scattered signals off the moving body parts of subjects moving in an indoor environment. The provided data consists of three parts, for more than 2 hours of channel measurements with high temporal resolution (0.27 ms inter-packet time). DISC contains the contribution of 7 subjects performing 5 different activities, and includes data collected from two distinct environments. Unlike available radar-based millimeter-wave sensing datasets, our measurements are collected using uniform packet transmission times and sparse traffic patterns from real Wi-Fi deployments. We develop, train, and release open-source baseline algorithms based on DISC to perform human sensing tasks. Our results demonstrate that DISC can serve as a multi-purpose benchmarking tool for machine learning-based human activity recognition, radio frequency gait analysis, and sparse sensing algorithms for next-generation integrated sensing and communications., Comment: 7 pages, 4 figures, 1 table
Published: 2023

40. Tracking Power System Events with Accuracy-Based PMU Adaptive Reporting Rate

Author: Frigo, Guglielmo, Pegoraro, Paolo Attilio, and Toscani, Sergio
Subjects: Electrical Engineering and Systems Science - Systems and Control, Electrical Engineering and Systems Science - Signal Processing
Abstract: Fast dynamics and transient events are becoming more and more frequent in power systems, due to the high penetration of renewable energy sources and the consequent lack of inertia. In this scenario, Phasor Measurement Units (PMUs) are expected to track the monitored quantities. Such functionality is related not only to the PMU accuracy (as per the IEC/IEEE 60255-118-1 standard) but also to the PMU reporting rate (RR). High RRs allow tracking fast dynamics, but produce many redundant measurement data in normal conditions. In view of an effective tradeoff, the present paper proposes an adaptive RR mechanism based on a real-time selection of the measurements, with the target of preserving the information content while reducing the data rate. The proposed method has been tested considering real-world datasets and applied to four different PMU algorithms. The results prove the method effectiveness in reducing the average data throughput as well as its scalability at PMU concentrator or storage level., Comment: The paper is the extended version of the conference paper G. Frigo, P. A. Pegoraro and S. Toscani, "Tracking Power Systems Events: PMU, Reporting Rate, Interpolation," 2022 International Conference on Smart Grid Synchronized Measurements and Analytics (SGSMA), Split, Croatia, 2022, pp. 1-6, doi: 10.1109/SGSMA51733.2022.9806003
Published: 2023

41. Geometric Epitope and Paratope Prediction

Author: Pegoraro, Marco, Dominé, Clémentine, Rodolà, Emanuele, Veličković, Petar, and Deac, Andreea
Subjects: Quantitative Biology - Biomolecules, Computer Science - Machine Learning
Abstract: Antibody-antigen interactions play a crucial role in identifying and neutralizing harmful foreign molecules. In this paper, we investigate the optimal representation for predicting the binding sites in the two molecules and emphasize the importance of geometric information. Specifically, we compare different geometric deep learning methods applied to proteins' inner (I-GEP) and outer (O-GEP) structures. We incorporate 3D coordinates and spectral geometric descriptors as input features to fully leverage the geometric information. Our research suggests that surface-based models are more efficient than other methods, and our O-GEP experiments have achieved state-of-the-art results with significant performance improvements.
Published: 2023

42. A Persistence-Driven Edit Distance for Graphs with Abstract Weights

Author: Pegoraro, Matteo
Subjects: Mathematics - Combinatorics, Mathematics - Algebraic Topology
Abstract: In this work we define a novel edit distance for graphs considered with some weights on the edges. The metric is driven by the idea of considering graphs as topological summaries in the context of persistence and topological data analysis. Several examples related to persistent sets are presented. In case the graphs are trees, the metric can be computed with a dynamical binary linear programming approach. This framework is applied and further studied in other works focused on merge trees, where the problems of stability and merge trees estimation are also assessed., Comment: arXiv admin note: substantial text overlap with arXiv:2108.13108
Published: 2023

43. Search for gravitational-lensing signatures in the full third observing run of the LIGO-Virgo network

Author: The LIGO Scientific Collaboration, the Virgo Collaboration, the KAGRA Collaboration, Abbott, R., Abe, H., Acernese, F., Ackley, K., Adhicary, S., Adhikari, N., Adhikari, R. X., Adkins, V. K., Adya, V. B., Affeldt, C., Agarwal, D., Agathos, M., Aguiar, O. D., Aiello, L., Ain, A., Ajith, P., Akutsu, T., Albanesi, S., Alfaidi, R. A., Alléné, C., Allocca, A., Altin, P. A., Amato, A., Anand, S., Ananyeva, A., Anderson, S. B., Anderson, W. G., Ando, M., Andrade, T., Andres, N., Andrés-Carcasona, M., Andrić, T., Ansoldi, S., Antelis, J. M., Antier, S., Apostolatos, T., Appavuravther, E. Z., Appert, S., Apple, S. K., Arai, K., Araya, A., Araya, M. C., Areeda, J. S., Arène, M., Aritomi, N., Arnaud, N., Arogeti, M., Aronson, S. M., Asada, H., Ashton, G., Aso, Y., Assiduo, M., Melo, S. Assis de Souza, Aston, S. M., Astone, P., Aubin, F., AultONeal, K., Babak, S., Badaracco, F., Badger, C., Bae, S., Bae, Y., Bagnasco, S., Bai, Y., Baier, J. G., Baird, J., Bajpai, R., Baka, T., Ball, M., Ballardin, G., Ballmer, S. W., Baltus, G., Banagiri, S., Banerjee, B., Bankar, D., Barayoga, J. 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., Basak, S., Bassiri, R., Basti, A., Bawaj, M., Bayley, J. C., Bazzan, M., Bécsy, B., Bedakihale, V. M., Beirnaert, F., Bejger, M., Belahcene, I., Bell, A. S., Benedetto, V., Beniwal, D., Benoit, W., Bentley, J. D., BenYaala, M., Bera, S., Berbel, M., Bergamin, F., Berger, B. K., Bernuzzi, S., Beroiz, M., Berry, C. P. L., Bersanetti, D., Bertolini, A., Betzwieser, J., Beveridge, D., Bhandare, R., Bhandari, A. V., Bhardwaj, U., Bhatt, R., Bhattacharjee, D., Bhaumik, S., Bianchi, A., Bilenko, I. A., Bilicki, M., Billingsley, G., Bini, S., Birnholtz, O., Biscans, S., Bischi, M., Biscoveanu, S., Bisht, A., Biswas, B., Bitossi, M., Bizouard, M. -A., Blackburn, J. K., Blair, C. D., Blair, D. G., Blair, R. M., Bobba, F., Bode, N., Boër, M., Bogaert, G., Boldrini, M., Bolingbroke, G. N., Bonavena, L. D., Bondarescu, R., Bondu, F., Bonilla, E., Bonnand, R., Booker, P., 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., Bucci, F., Buchanan, J., Bulik, T., Bulten, H. J., Buonanno, A., Burtnyk, K., Buscicchio, R., Buskulic, D., Buy, C., Byer, R. L., Davies, G. S. Cabourn, Cabras, G., Cabrita, R., Cadonati, L., Cagnoli, G., Cahillane, C., Bustillo, J. Calderón, Callaghan, J. D., Callister, T. A., Calloni, E., Camp, J. B., Canepa, M., Caneva, G., Cannavacciuolo, M., Cannon, K. C., Cao, H., Cao, Z., Capistran, L. A., Capocasa, E., Capote, E., Carapella, G., Carbognani, F., Carlassara, M., Carlin, J. B., Carpinelli, M., Carrillo, G., Carter, J. J., Carullo, G., Diaz, J. Casanueva, Casentini, C., Castaldi, G., Caudill, S., Cavaglià, M., Cavalier, F., Cavalieri, R., Cella, G., Cerdá-Durán, P., Cesarini, E., Chaibi, W., Chakalis, W., Subrahmanya, S. Chalathadka, Champion, E., Chan, C. -H., Chan, C., Chan, C. L., Chan, K., Chan, M., Chandra, K., Chang, I. P., Chang, W., Chanial, P., Chao, S., Chapman-Bird, C., Charlton, P., Chassande-Mottin, E., Chatterjee, C., Chatterjee, Debarati, Chatterjee, Deep, Chaturvedi, M., Chaty, S., Chatziioannou, K., Chen, C., Chen, D., Chen, H. Y., Chen, J., Chen, K., Chen, X., Chen, Y. -B., Chen, Y. -R., Chen, Y., Cheng, H., Chessa, P., Cheung, H. Y., Chia, H. Y., Chiadini, F., Chiang, C-Y., Chiarini, G., Chierici, R., Chincarini, A., Chiofalo, M. L., Chiummo, A., Choudhary, R. K., Choudhary, S., Christensen, N., Chu, Q., Chu, Y-K., Chua, S. S. Y., Chung, K. W., Ciani, G., Ciecielag, P., Cieślar, M., Cifaldi, M., Ciobanu, A. A., Ciolfi, R., Clara, F., Clark, J. A., Clarke, T. A., Clearwater, P., Clesse, S., Cleva, F., Coccia, E., Codazzo, E., Cohadon, P. -F., Cohen, D. E., Colleoni, M., Collette, C. G., Colombo, A., Colpi, M., Compton, C. M., Conti, L., Cooper, S. J., Corban, P., Corbitt, T. R., Cordero-Carrión, I., Corezzi, S., Cornish, N. J., Corsi, A., Cortese, S., Coschizza, A. C., Cotesta, R., Cottingham, R., Coughlin, M. W., Coulon, J. -P., Countryman, S. T., Cousins, B., Couvares, P., Coward, D. M., Cowart, M. J., Coyne, D. C., Coyne, R., Craig, K., 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., Dabadie, P., Canton, T. Dal, Dall'Osso, S., Dálya, G., Dana, A., D'Angelo, B., Danilishin, S., D'Antonio, S., Danzmann, K., Darsow-Fromm, C., Dasgupta, A., Datrier, L. E. H., Datta, Sayak, Datta, Sayantani, Dattilo, V., Dave, I., Davier, M., Davis, D., Davis, M. C., Daw, E. J., Dax, M., DeBra, D., Deenadayalan, M., Degallaix, J., De Laurentis, M., Deléglise, S., Del Favero, V., De Lillo, F., De Lillo, N., Dell'Aquila, D., Del Pozzo, W., 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., Diab, R., Díaz, M. C., Didio, N. A., Dietrich, T., Di Fiore, L., Di Fronzo, C., Di Giorgio, C., Di Giovanni, F., Di Giovanni, M., Di Girolamo, T., Diksha, D., Di Lieto, A., Di Michele, A., Di Pace, S., Di Palma, I., Di Renzo, F., Divakarla, A. K., Dmitriev, A., Doctor, Z., Doleva, P. P., Donahue, L., D'Onofrio, L., Donovan, F., Dooley, K. L., Dooney, T., Doravari, S., Dorosh, O., Drago, M., Driggers, J. C., Drori, Y., Ducoin, J. -G., Dunn, L., Dupletsa, U., Durante, O., D'Urso, D., Duverne, P. -A., Dwyer, S. E., Eassa, C., Easter, P. J., Ebersold, M., Eckhardt, T., 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., Engelby, E., Enomoto, Y., Errico, L., Essick, R. C., Estellés, H., Estevez, D., Etzel, T., Evans, M., Evans, T. M., Evstafyeva, T., Ewing, B. E., Ezquiaga, J. M., Fabrizi, F., Faedi, F., Fafone, V., Fair, H., Fairhurst, S., Fan, P. C., Farah, A. M., Farr, B., Farr, W. M., Favaro, G., Favata, M., Fays, M., Fazio, M., Feicht, J., Fejer, M. M., Fenyvesi, E., Ferguson, D. L., Fernandez-Galiana, A., Ferrante, I., Ferreira, T. A., Fidecaro, F., Figura, P., Fiori, A., Fiori, I., Fishbach, M., Fisher, R. P., Fittipaldi, R., Fiumara, V., Flaminio, R., Floden, E., Fong, H. K., Font, J. A., Fornal, B., Forsyth, P. W. F., Franke, A., Frasca, S., Frasconi, F., Freed, J. P., Frei, Z., Freise, A., Freitas, O., Frey, R., Fritschel, P., Frolov, V. V., Fronzé, G. G., Fujii, Y., Fujikawa, Y., Fujimoto, Y., Fulda, P., Fyffe, M., Gabbard, H. A., Gabella, W. E., Gadre, B. U., Gair, J. R., Gais, J., Galaudage, S., Gamba, R., Ganapathy, D., Ganguly, A., Gao, D. -F., Gao, D., Gaonkar, S. G., Garaventa, B., García-Núñez, C., García-Quirós, C., Gardner, K. A., Gargiulo, J., Garufi, F., Gasbarra, C., Gateley, B., Gayathri, V., Ge, G. -G., Gemme, G., Gennai, A., George, J., Gerberding, O., Gergely, L., Ghonge, S., Ghosh, Abhirup, Ghosh, Archisman, Ghosh, Shaon, Ghosh, Shrobana, Ghosh, Tathagata, Giacoppo, L., Giaime, J. A., Giardina, K. D., Gibson, D. R., Gier, C., Giri, P., Gissi, F., Gkaitatzis, S., Glanzer, J., Gleckl, A. E., Godoy, F. G., Godwin, P., Goetz, E., Goetz, R., Golomb, J., Goncharov, B., González, G., Gosselin, M., Gouaty, R., Gould, D. W., Goyal, S., Grace, B., Grado, A., Graham, V., Granata, M., Granata, V., 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. L., Griggs, H. L., Grignani, G., Grimaldi, A., Grimm, S. J., Grote, H., Grunewald, S., Gruson, A. S., Guerra, D., Guidi, G. M., Guimaraes, A. R., Gulati, H. K., Gulminelli, F., Gunny, A. M., Guo, H. -K., Guo, Y., Gupta, Anchal, Gupta, Anuradha, Gupta, P., Gupta, S. K., Gurs, J., Gustafson, R., Gutierrez, N., Guzman, F., Ha, S., Hadiputrawan, I. P. W., Haegel, L., 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., Hansen, H., Hanson, J., Harada, R., Harder, T., Haris, K., Harms, J., Harry, G. M., Harry, I. W., Hartwig, D., Hasegawa, K., Haskell, B., Haster, C. -J., Hathaway, J. S., Hattori, K., Haughian, K., Hayakawa, H., Hayama, K., Hayes, F. J., Healy, J., Heidmann, A., Heidt, 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. -S., Hennig, M., Henshaw, C., Hernandez, A. G., Vivanco, F. Hernandez, Heurs, M., Hewitt, A. L., Higginbotham, S., Hild, S., Hill, P., Himemoto, Y., Hines, A. S., Hirata, N., Hirose, C., Ho, T-C., Hochheim, S., Hofman, D., Hohmann, J. N., Holcomb, D. G., Holland, N. A., Hollows, I. J., Holmes, Z. J., Holt, K., Holz, D. E., Hong, Q., Hough, J., Hourihane, S., Howell, D., Howell, E. J., Hoy, C. G., Hoyland, D., Hreibi, A., Hsieh, B-H., Hsieh, H-F., Hsiung, C., Huang, H-Y., Huang, P., Huang, Y-C., Huang, Y. -J., Huang, Y., Hübner, M. T., Huddart, A. D., Hughey, B., Hui, D. C. Y., Hui, V., Husa, S., Huttner, S. H., Huxford, R., Huynh-Dinh, T., Hyland, J., Iandolo, G. A., Ide, S., Idzkowski, B., Iess, A., Inayoshi, K., Inoue, Y., Iosif, P., Irwin, J., Gupta, Ish, Isi, M., Ito, K., Itoh, Y., Iyer, B. R., JaberianHamedan, V., Jacqmin, T., Jacquet, P. -E., Jadhav, S. J., Jadhav, S. P., Jain, T., James, A. L., Jan, A. Z., Jani, K., Janquart, J., Janssens, K., Janthalur, N. N., Jaranowski, P., Jariwala, D., Jarov, S., Jaume, R., Jenkins, A. C., Jenner, K., Jeon, C., Jia, W., Jiang, J., Jin, H. -B., Johns, G. R., Johnston, R., Johny, N., Jones, A. W., Jones, D. I., Jones, P., Jones, R., Joshi, P., Ju, L., Jung, K., Jung, P., Junker, J., Juste, V., Kaihotsu, K., Kajita, T., Kakizaki, M., Kalaghatgi, C., 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., Kato, T., Katsanevas, S., Katsavounidis, E., Katzman, W., Kaur, T., Kawabe, K., Kawaguchi, K., Kéfélian, F., Keitel, D., Key, J. S., Khadka, S., Khalili, F. Y., Khan, S., Khanam, T., Khazanov, E. A., Khetan, N., Khursheed, M., Kijbunchoo, N., Kim, C., Kim, J. C., Kim, J., Kim, K., Kim, P., Kim, W. S., Kim, Y. -M., Kimball, C., Kimura, N., King, B., Kinley-Hanlon, M., Kirchhoff, R., Kissel, J. S., Klimenko, S., Klinger, T., Knee, A. M., Knust, N., Kobayashi, Y., Koch, P., Koehlenbeck, S. M., Koekoek, G., Kohri, K., Kokeyama, K., Koley, S., Kolitsidou, P., Kolstein, M., Kondrashov, V., Kong, A. K. H., Kontos, A., Korobko, M., Kossak, R. V., Kovalam, M., Koyama, N., Kozak, D. B., Kozakai, C., Kranzhoff, L., Kringel, V., Krishnendu, N. V., Królak, A., Kuehn, G., Kuijer, P., Kulkarni, S., Kumar, A., Kumar, Praveen, Kumar, Prayush, Kumar, Rahul, Kumar, Rakesh, Kume, J., Kuns, K., Kuromiya, Y., Kuroyanagi, S., Kuwahara, S., Kwak, K., Lacaille, G., Lagabbe, P., Laghi, D., Lalande, E., Lalleman, M., Lamberts, A., Landry, M., Lane, B. B., Lang, R. N., Lange, J., Lantz, B., La Rosa, I., Lartaux-Vollard, A., Lasky, P. D., Lawrence, J., Laxen, M., Lazzarini, A., Lazzaro, C., Leaci, P., Leavey, S., LeBohec, S., Lecoeuche, Y. K., Lee, E., Lee, H. M., Lee, K., Lee, R., Legred, I. N., Lehmann, J., Lemaître, A., Lenti, M., Leonardi, M., Leonova, E., Leroy, N., Letendre, N., Levesque, C., Levin, Y., Leviton, J. N., Leyde, K., Li, A. K. Y., Li, B., Li, K. L., Li, P., Li, T. G. F., Li, X., Lin, C-Y., Lin, E. T., Lin, F-K., Lin, F-L., Lin, H. L., Lin, L. C. -C., Linde, F., Linker, S. D., Littenberg, T. B., Liu, G. C., Liu, J., Liu, X., Llamas, F., Lo, R. K. L., Lo, T., London, L. T., Longo, A., Lopez, D., Portilla, M. Lopez, Lorenzini, M., Loriette, V., Lormand, M., Losurdo, G., Lott, T. P., Lough, J. D., Lousto, C. O., Lovelace, G., Lowry, M. J., Lucaccioni, J. F., Lück, H., Lumaca, D., Lundgren, A. P., Lung, Y., Luo, L. -W., Lussier, A. W., Lynam, J. E., Ma'arif, M., Macas, R., MacInnis, M., Macleod, D. M., MacMillan, I. A. O., Macquet, A., Hernandez, I. Magaña, Magazzù, C., Magee, R. M., Maggiore, R., Magnozzi, M., Mahesh, S., Majorana, E., Makarem, C. N., Maksimovic, I., Maliakal, S., Malik, A., Man, N., Mandic, V., Mangano, V., Mannix, B. R., Mansell, G. L., Mansingh, G., Manske, M., Mantovani, M., Mapelli, M., Marchesoni, F., Pina, D. Marín, 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. A., Martinez, V., Martinovic, K., Martynov, D. V., Marx, E. J., Masalehdan, H., Mason, K., Masserot, A., Masso-Reid, M., Mastrogiovanni, S., Matas, A., Mateu-Lucena, M., Matiushechkina, M., Mavalvala, N., McCann, J. J., McCarthy, R., McClelland, D. E., McClincy, P. K., McCormick, S., McCuller, L., McGhee, G. I., McGinn, J., McGuire, S. C., McIsaac, C., McIver, J., McLeod, A., McRae, T., McWilliams, S. T., Meacher, D., Mehmet, M., Mehta, A. K., Meijer, Q., Melatos, A., Mendell, G., Menendez-Vazquez, A., Menoni, C. S., Mercer, R. A., Mereni, L., Merfeld, K., Merilh, E. L., Merritt, J. D., Merzougui, M., Messenger, C., Messick, C., Meyers, P. M., Meylahn, F., Mhaske, A., Miani, A., Miao, H., Michaloliakos, I., Michel, C., Michimura, Y., Middleton, H., Mihaylov, D. P., Miller, A., Miller, A. L., Miller, B., Millhouse, M., Mills, J. C., Milotti, E., Minenkov, Y., Mio, N., Mir, Ll. M., Miravet-Tenés, M., Mishkin, A., Mishra, C., Mishra, T., Mistry, T., Mitchell, A. L., Mitra, S., Mitrofanov, V. P., Mitselmakher, G., Mittleman, R., Miyakawa, O., Miyo, K., Miyoki, S., Mo, Geoffrey, Modafferi, L. M., Moguel, E., Mogushi, K., Mohapatra, S. R. P., Mohite, S. R., Molina-Ruiz, M., Mondal, C., Mondin, M., Montani, M., Moore, C. J., Moragues, J., Moraru, D., Morawski, F., More, A., More, S., Moreno, C., Moreno, G., Mori, Y., Morisaki, S., Morisue, N., Moriwaki, Y., Mours, B., Mow-Lowry, C. M., Mozzon, S., Muciaccia, F., Mukherjee, D., Mukherjee, Soma, Mukherjee, Subroto, Mukherjee, Suvodip, Mukund, N., Mullavey, A., Munch, J., Muñiz, E. A., Murray, P. G., Muusse, S., Nadji, S. L., Nagano, K., Nagar, A., Nagar, T., Nakamura, K., Nakano, H., Nakano, M., Nakayama, Y., Napolano, V., Nardecchia, I., Narola, H., Naticchioni, L., Nayak, R. K., Neil, B. F., Neilson, J., Nelson, A., Nelson, T. J. N., Nery, M., Neubauer, P., Neunzert, A., Ng, K. Y., Ng, S. W. S., Nguyen, C., Nguyen, P., Nguyen, T., Quynh, L. Nguyen, Ni, J., Ni, W. -T., Nichols, S. A., Nieradka, G., Nishimoto, T., Nishizawa, A., Nissanke, S., Nitoglia, E., Niu, W., Nocera, F., Norman, M., North, C., Notte, J., Novak, J., Nozaki, S., Nurbek, G., Nuttall, L. K., Obayashi, Y., Oberling, J., O'Brien, B. D., O'Dell, J., Oelker, E., Oertel, M., Ogaki, W., Oganesyan, G., Oh, J. J., Oh, K., Oh, S. H., O'Hanlon, T., Ohashi, M., Ohashi, T., Ohkawa, M., Ohme, F., Ohta, H., Okutani, Y., Oliveri, R., Olivetto, C., Oohara, K., Oram, R., O'Reilly, B., Ormiston, R. G., Ormsby, N. D., Orselli, M., O'Shaughnessy, R., O'Shea, E., Oshino, S., Ossokine, S., Osthelder, C., Otabe, S., Ottaway, D. J., Overmier, H., Pace, A. E., Pagano, G., Pagano, R., Pagliaroli, G., Pai, A., Pai, S. A., Pal, S., Palamos, J. R., Palashov, O., Palomba, C., Pan, K. -C., Panda, P. K., Pang, P. T. H., Pannarale, F., Pant, B. C., Panther, F. H., Paoletti, F., Paoli, A., Paolone, A., Pappas, G., Parisi, A., Park, J., Parker, W., Pascucci, D., Pasqualetti, A., Passaquieti, R., Passuello, D., Patel, M., Patel, N. R., Pathak, M., Patricelli, B., Patron, A. S., Paul, S., Payne, E., Pedraza, M., Pedurand, R., Pegna, R., Pegoraro, M., Pele, A., Arellano, F. E. Peña, Penano, S., Penn, S., Perego, A., Pereira, A., Pereira, T., Perez, C. J., Périgois, C., Perkins, C. C., Perreca, A., Perriès, S., Perry, J. W., Pesios, D., Petermann, J., Pfeiffer, H. P., Pham, H., Pham, K. A., Phukon, K. S., Phurailatpam, H., Piccinni, O. J., Pichot, M., Piendibene, M., Piergiovanni, F., Pierini, L., Pierra, G., Pierro, V., Pillant, G., Pillas, M., Pilo, F., Pinard, L., Pineda-Bosque, C., Pinto, I. M., Pinto, M., Piotrzkowski, B. J., Piotrzkowski, K., Pirello, M., Pitkin, M. D., Placidi, A., Placidi, E., Planas, M. L., Plastino, W., Poggiani, R., Polini, E., Pong, D. Y. T., Ponrathnam, S., Porter, E. K., Posnansky, C., Poulton, R., Powell, J., Pracchia, M., Pradier, T., Prajapati, A. K., Prasai, K., Prasanna, R., Pratten, G., Principe, M., Prodi, G. A., Prokhorov, L., Prosposito, P., Prudenzi, L., Puecher, A., Punturo, M., Puosi, F., Puppo, P., Pürrer, M., Qi, H., Quartey, N., 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, D., Rana, J., Rangnekar, P. R., Rapagnani, P., Ray, A., Raymond, V., Raza, N., Razzano, M., Read, J., Regimbau, T., Rei, L., Reid, S., Reid, S. W., Reinhard, M., Reitze, D. H., Relton, P., Renzini, A., Rettegno, P., Revenu, B., Reyes, J., Reza, A., Rezac, M., Rezaei, A. S., Ricci, F., Richards, D., Richardson, J. W., Richardson, L., Riles, K., Rinaldi, S., Robertson, C., Robertson, N. A., Robie, R., Robinet, F., Rocchi, A., Rodriguez, S., Rolland, L., Rollins, J. 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S., Wilken, D., Williams, C. E., Williams, D., Williams, M. J., Williamson, A. R., Willis, J. L., Willke, B., Wipf, C. C., Woan, G., Woehler, J., Wofford, J. K., Wojtowicz, I. A., Wong, D., Wong, I. C. F., Wright, M., Wu, C., Wu, D. S., Wu, H., Wysocki, D. M., Xiao, L., Yadav, N., Yamada, T., Yamamoto, H., Yamamoto, K., Yamamoto, T., Yamashita, K., Yamazaki, R., Yang, F. W., Yang, K. Z., Yang, L., Yang, Y. -C., Yang, Y., Yang, Yang, Yap, M. J., Yeeles, D. W., Yeh, S. -W., Yelikar, A. B., Yokoyama, J., Yokozawa, T., Yoo, J., 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., Zhang, Y., Zhao, C., Zhao, G., Zhao, Y., Zhao, Yue, Zheng, Y., Zhou, R., Zhu, X. J., Zhu, Z. -H., Zimmerman, A. B., Zucker, M. E., and Zweizig, J.
Subjects: General Relativity and Quantum Cosmology, Astrophysics - Cosmology and Nongalactic Astrophysics, Astrophysics - High Energy Astrophysical Phenomena
Abstract: Gravitational lensing by massive objects along the line of sight to the source causes distortions of gravitational wave-signals; such distortions may reveal information about fundamental physics, cosmology and astrophysics. In this work, we have extended the search for lensing signatures to all binary black hole events from the third observing run of the LIGO--Virgo network. We search for repeated signals from strong lensing by 1) performing targeted searches for subthreshold signals, 2) calculating the degree of overlap amongst the intrinsic parameters and sky location of pairs of signals, 3) comparing the similarities of the spectrograms amongst pairs of signals, and 4) performing dual-signal Bayesian analysis that takes into account selection effects and astrophysical knowledge. We also search for distortions to the gravitational waveform caused by 1) frequency-independent phase shifts in strongly lensed images, and 2) frequency-dependent modulation of the amplitude and phase due to point masses. None of these searches yields significant evidence for lensing. Finally, we use the non-detection of gravitational-wave lensing to constrain the lensing rate based on the latest merger-rate estimates and the fraction of dark matter composed of compact objects., Comment: 28 pages, 11 figures
Published: 2023

44. JUMP: Joint communication and sensing with Unsynchronized transceivers Made Practical

Author: Pegoraro, Jacopo, Lacruz, Jesus O., Azzino, Tommy, Mezzavilla, Marco, Rossi, Michele, Widmer, Joerg, and Rangan, Sundeep
Subjects: Electrical Engineering and Systems Science - Signal Processing
Abstract: Wideband millimeter-wave communication systems can be extended to provide radar-like sensing capabilities on top of data communication, in a cost-effective manner. However, the development of joint communication and sensing technology is hindered by practical challenges, such as occlusions to the line-of-sight path and clock asynchrony between devices. The latter introduces time-varying timing and frequency offsets that prevent the estimation of sensing parameters and, in turn, the use of standard signal processing solutions. Existing approaches cannot be applied to commonly used phased-array receivers, as they build on stringent assumptions about the multipath environment, and are computationally complex. We present JUMP, the first system enabling practical bistatic and asynchronous joint communication and sensing, while achieving accurate target tracking and micro-Doppler extraction in realistic conditions. Our system compensates for the timing offset by exploiting the channel correlation across subsequent packets. Further, it tracks multipath reflections and eliminates frequency offsets by observing the phase of a dynamically-selected static reference path. JUMP has been implemented on a 60 GHz experimental platform, performing extensive evaluations of human motion sensing, including non-line-of-sight scenarios. In our results, JUMP attains comparable tracking performance to a full-duplex monostatic system and similar micro-Doppler quality with respect to a phase-locked bistatic receiver., Comment: 17 pages, 18 figures
Published: 2023
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45. Wasserstein Principal Component Analysis for Circular Measures

Author: Beraha, Mario and Pegoraro, Matteo
Subjects: Statistics - Methodology, Mathematics - Probability, Mathematics - Statistics Theory
Abstract: We consider the 2-Wasserstein space of probability measures supported on the unit-circle, and propose a framework for Principal Component Analysis (PCA) for data living in such a space. We build on a detailed investigation of the optimal transportation problem for measures on the unit-circle which might be of independent interest. In particular, we derive an expression for optimal transport maps in (almost) closed form and propose an alternative definition of the tangent space at an absolutely continuous probability measure, together with the associated exponential and logarithmic maps. PCA is performed by mapping data on the tangent space at the Wasserstein barycentre, which we approximate via an iterative scheme, and for which we establish a sufficient a posteriori condition to assess its convergence. Our methodology is illustrated on several simulated scenarios and a real data analysis of measurements of optical nerve thickness.
Published: 2023

46. To ChatGPT, or not to ChatGPT: That is the question!

Author: Pegoraro, Alessandro, Kumari, Kavita, Fereidooni, Hossein, and Sadeghi, Ahmad-Reza
Subjects: Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Computation and Language
Abstract: ChatGPT has become a global sensation. As ChatGPT and other Large Language Models (LLMs) emerge, concerns of misusing them in various ways increase, such as disseminating fake news, plagiarism, manipulating public opinion, cheating, and fraud. Hence, distinguishing AI-generated from human-generated becomes increasingly essential. Researchers have proposed various detection methodologies, ranging from basic binary classifiers to more complex deep-learning models. Some detection techniques rely on statistical characteristics or syntactic patterns, while others incorporate semantic or contextual information to improve accuracy. The primary objective of this study is to provide a comprehensive and contemporary assessment of the most recent techniques in ChatGPT detection. Additionally, we evaluated other AI-generated text detection tools that do not specifically claim to detect ChatGPT-generated content to assess their performance in detecting ChatGPT-generated content. For our evaluation, we have curated a benchmark dataset consisting of prompts from ChatGPT and humans, including diverse questions from medical, open Q&A, and finance domains and user-generated responses from popular social networking platforms. The dataset serves as a reference to assess the performance of various techniques in detecting ChatGPT-generated content. Our evaluation results demonstrate that none of the existing methods can effectively detect ChatGPT-generated content.
Published: 2023

47. A survey on hardware-based malware detection approaches

Author: Chenet, Cristiano Pegoraro, Savino, Alessandro, and Di Carlo, Stefano
Subjects: Computer Science - Cryptography and Security
Abstract: This paper delves into the dynamic landscape of computer security, where malware poses a paramount threat. Our focus is a riveting exploration of the recent and promising hardware-based malware detection approaches. Leveraging hardware performance counters and machine learning prowess, hardware-based malware detection approaches bring forth compelling advantages such as real-time detection, resilience to code variations, minimal performance overhead, protection disablement fortitude, and cost-effectiveness. Navigating through a generic hardware-based detection framework, we meticulously analyze the approach, unraveling the most common methods, algorithms, tools, and datasets that shape its contours. This survey is not only a resource for seasoned experts but also an inviting starting point for those venturing into the field of malware detection. However, challenges emerge in detecting malware based on hardware events. We struggle with the imperative of accuracy improvements and strategies to address the remaining classification errors. The discussion extends to crafting mixed hardware and software approaches for collaborative efficacy, essential enhancements in hardware monitoring units, and a better understanding of the correlation between hardware events and malware applications.
Published: 2023
Full Text: View/download PDF

48. Open data from the third observing run of LIGO, Virgo, KAGRA and GEO

Author: The LIGO Scientific Collaboration, the Virgo Collaboration, the KAGRA Collaboration, Abbott, R., Abe, H., Acernese, F., Ackley, K., Adhicary, S., Adhikari, N., Adhikari, R. X., Adkins, V. K., Adya, V. B., Affeldt, C., Agarwal, D., Agathos, M., Aguiar, O. D., Aiello, L., Ain, A., Ajith, P., Akutsu, T., Albanesi, S., Alfaidi, R. A., Al-Jodah, A., Alléné, C., Allocca, A., Almualla, M., Altin, P. A., Amato, A., Amez-Droz, L., Amorosi, A., Anand, S., Ananyeva, A., Andersen, R., Anderson, S. B., Anderson, W. G., Andia, M., Ando, M., Andrade, T., Andres, N., Andrés-Carcasona, M., Andrić, T., Ansoldi, S., Antelis, J. M., Antier, S., Aoumi, M., Apostolatos, T., Appavuravther, E. Z., Appert, S., Apple, S. K., Arai, K., Araya, A., Araya, M. C., Areeda, J. S., Arène, M., Aritomi, N., Arnaud, N., Arogeti, M., Aronson, S. M., Arun, K. G., Asada, H., Ashton, G., Aso, Y., Assiduo, M., Melo, S. Assis de Souza, Aston, S. M., Astone, P., Aubin, F., AultONeal, K., Babak, S., Badalyan, A., Badaracco, F., Badger, C., Bae, S., Bagnasco, S., Bai, Y., Baier, J. G., Baiotti, L., Baird, J., Bajpai, R., Baka, T., Ball, M., Ballardin, G., Ballmer, S. W., Baltus, G., Banagiri, S., Banerjee, B., Bankar, D., Baral, P., Barayoga, J. C., Barber, J., Barish, B. C., Barker, D., Barneo, P., Barone, F., Barr, B., Barsotti, L., Barsuglia, M., Barta, D., Barthelmy, S. D., Barton, M. A., Bartos, I., Basak, S., Basalaev, A., Bassiri, R., Basti, A., Bawaj, M., Bayley, J. C., Baylor, A. C., Bazzan, M., Bécsy, B., Bedakihale, V. M., Beirnaert, F., Bejger, M., Bell, A. S., Benedetto, V., Beniwal, D., Benoit, W., Bentley, J. D., Yaala, M. Ben, Bera, S., Berbel, M., Bergamin, F., Berger, B. K., Bernuzzi, S., Beroiz, M., Berry, C. P. L., Bersanetti, D., Bertolini, A., Betzwieser, J., Beveridge, D., Bevins, N., Bhandare, R., Bhandari, A. V., Bhardwaj, U., Bhatt, R., Bhattacharjee, D., Bhaumik, S., Bianchi, A., Bilenko, I. A., Bilicki, M., Billingsley, G., Bini, S., Birnholtz, O., Biscans, S., Bischi, M., Biscoveanu, S., Bisht, A., Biswas, B., Bitossi, M., Bizouard, M. -A., Blackburn, J. K., Blair, C. D., Blair, D. G., Blair, R. M., Bobba, F., Bode, N., Boër, M., Bogaert, G., Boileau, G., Boldrini, M., Bolingbroke, G. N., Bonavena, L. D., Bondarescu, R., Bondu, F., Bonilla, E., Bonilla, G. S., Bonnand, R., Booker, P., Bork, R., Boschi, V., Bose, N., Bose, S., Bossilkov, V., Boudart, V., Bouffanais, Y., Bozzi, A., Bradaschia, C., Brady, P. R., Braglia, M., Branch, A., Branchesi, M., Brau, J. E., Breschi, M., Briant, T., Brillet, A., Brinkmann, M., Brockill, P., Brooks, A. F., Brooks, J., Brown, D. D., Brunett, S., Bruno, G., Bruntz, R., Bryant, J., Bucci, F., Buchanan, J., Bulashenko, O., Bulik, T., Bulten, H. J., Buonanno, A., Burtnyk, K., Buscicchio, R., Buskulic, D., Buy, C., Byer, R. L., Davies, G. S. Cabourn, Cabras, G., Cabrita, R., Cadonati, L., Caesar, S., Cagnoli, G., Cahillane, C., Bustillo, J. Calderón, Callaghan, J. D., Callister, T. A., Calloni, E., Camp, J. B., Canepa, M., Santoro, G. Caneva, Cannavacciuolo, M., Cannon, K. C., Cao, H., Cao, Z., Capistran, L. A., Capocasa, E., Capote, E., Carapella, G., Carbognani, F., Carlassara, M., Carlin, J. B., Carpinelli, M., Carter, J. J., Carullo, G., Diaz, J. Casanueva, Casentini, C., Castaldi, G., Castro-Lucas, S. Y., Caudill, S., Cavaglià, M., Cavalieri, R., Cella, G., Cerdá-Durán, P., Cesarini, E., Chaibi, W., Chakalis, W., Subrahmanya, S. Chalathadka, Champion, E., Chan, C., Chan, C. L., Chandra, K., Chang, I. P., Chang, W., Chanial, P., Chao, S., Chapman-Bird, C., Charlton, E. L., Charlton, P., Chassande-Mottin, E., Chastain, L., Chatterjee, C., Chatterjee, Debarati, Chatterjee, Deep, Chaturvedi, M., Chaty, S., Chatziioannou, K., Chen, D., Chen, H., Chen, H. Y., Chen, J., Chen, K. H., Chen, X., Chen, Y. -R., Chen, Y., Cheng, H., Chessa, P., Cheung, H. Y., Chia, H. Y., Chiadini, F., Chiang, C-I., Chiang, C., Chiarini, G., Chiba, A., Chiba, R., Chierici, R., Chincarini, A., Chiofalo, M. L., Chiummo, A., Choudhary, S., Christensen, N., Chua, S. S. Y., Chung, K. W., Ciani, G., Ciecielag, P., Cieślar, M., Cifaldi, M., Ciobanu, A. A., Ciolfi, R., Clara, F., Clark, J. A., Clarke, T. A., Clearwater, P., Clesse, S., Cleva, F., Coccia, E., Codazzo, E., Cohadon, P. -F., Colleoni, M., Collette, C. G., Colombo, A., Colpi, M., Compton, C. M., Conti, L., Cooper, S. J., Corban, P., Corbitt, T. R., Cordero-Carrión, I., Corezzi, S., Cornish, N. J., Corsi, A., Cortese, S., Coschizza, A. C., Cottingham, R., Coughlin, M. W., Coulon, J. -P., Countryman, S. T., Coupechoux, J. -F., Cousins, B., Couvares, P., Coward, D. M., Cowart, M. J., Cowburn, B. D., Coyne, D. C., Coyne, R., Craig, K., Creighton, J. D. E., Creighton, T. D., Criswell, A. W., Crockett-Gray, J. C. G., Croquette, M., Crowder, S. G., Cudell, J. R., Cullen, T. J., Cumming, A., Cummings, R., Cuoco, E., Curyło, M., Dabadie, P., Canton, T. Dal, Dall'Osso, S., Dálya, G., D'Angelo, B., Danilishin, S., D'Antonio, S., Danzmann, K., Darroch, K. E., Darsow-Fromm, C., Dasgupta, A., Datrier, L. E. H., Datta, Sayantani, Dattilo, V., Dave, I., Davenport, A., Davier, M., Davis, D., Davis, M. C., Daw, E. J., Dax, M., DeBra, D., Deenadayalan, M., Degallaix, J., De Laurentis, M., Deléglise, S., Del Favero, V., De Lillo, F., De Lillo, N., Dell'Aquila, D., Del Pozzo, W., 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., Diab, R., Diamond, P. Z., Díaz, M. C., Didio, N. A., Dietrich, T., Di Fiore, L., Di Fronzo, C., Di Giorgio, C., Di Giovanni, F., Di Giovanni, M., Di Girolamo, T., Diksha, D., Di Lieto, A., Di Michele, A., Di Pace, S., Di Palma, I., Di Renzo, F., Divyajyoti, Dmitriev, A., Doctor, Z., Dohmen, E., Doleva, P. P., Donahue, L., D'Onofrio, L., Donovan, F., Dooley, K. L., Dooney, T., Doravari, S., Dorosh, O., Drago, M., Driggers, J. C., Drori, Y., Ducoin, J. -G., Dunn, L., Dupletsa, U., Durante, O., D'Urso, D., Duverne, P. -A., Dwyer, S. E., Eassa, C., Easter, P. J., Ebersold, M., Eckhardt, T., Eddolls, G., Edelman, B., Edo, T. B., Edy, O., Effler, A., Eichholz, J., Eisenmann, M., Eisenstein, R. A., Ejlli, A., Engelby, E., Engl, A. J., Errico, L., Essick, R. C., Estellés, H., Estevez, D., Etzel, T., Evans, C., Evans, M., Evans, T. M., Evstafyeva, T., Ewing, B. E., Fabrizi, F., Faedi, F., Fafone, V., Fair, H., Fairhurst, S., Fan, P. C., Fan, X., Farah, A. M., Farr, B., Farr, W. M., Fauchon-Jones, E. J., Favaro, G., Favata, M., Fays, M., Feicht, J., Fejer, M. M., Fenyvesi, E., Ferguson, D. L., Fernandez-Galiana, A., Ferrante, I., Ferreira, T. A., Fidecaro, F., Figura, P., Fiori, A., Fiori, I., Fishbach, M., Fisher, R. P., Fittipaldi, R., Fiumara, V., Flaminio, R., Fleischer, S. M., Fleming, L. 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Subjects: General Relativity and Quantum Cosmology
Abstract: The global network of gravitational-wave observatories now includes five detectors, namely LIGO Hanford, LIGO Livingston, Virgo, KAGRA, and GEO 600. These detectors collected data during their third observing run, O3, composed of three phases: O3a starting in April of 2019 and lasting six months, O3b starting in November of 2019 and lasting five months, and O3GK starting in April of 2020 and lasting 2 weeks. In this paper we describe these data and various other science products that can be freely accessed through the Gravitational Wave Open Science Center at https://gwosc.org. The main dataset, consisting of the gravitational-wave strain time series that contains the astrophysical signals, is released together with supporting data useful for their analysis and documentation, tutorials, as well as analysis software packages., Comment: 27 pages, 3 figures
Published: 2023
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49. Dynamic conditioning of two particle discrete-time quantum walks

Author: Pegoraro, Federico, Held, Philip, Barkhofen, Sonja, Brecht, Benjamin, and Silberhorn, Christine
Subjects: Quantum Physics
Abstract: In real photonic quantum systems losses are an unavoidable factor limiting the scalability to many modes and particles, restraining their application in fields as quantum information and communication. For this reason, a considerable amount of engineering effort has been taken in order to improve the quality of particle sources and system components. At the same time, data analysis and collection methods based on post-selection have been used to mitigate the effect of particle losses. This has allowed for investigating experimentally multi-particle evolutions where the observer lacks knowledge about the system's intermediate propagation states. Nonetheless, the fundamental question how losses affect the behaviour of the surviving subset of a multi-particle system has not been investigated so far. For this reason, here we study the impact of particle losses in a quantum walk of two photons reconstructing the output probability distributions for one photon conditioned on the loss of the other in a known mode and temporal step of our evolution network. We present the underlying theoretical scheme that we have devised in order to model controlled particle losses, we describe an experimental platform capable of implementing our theory in a time multiplexing encoding. In the end we show how localized particle losses change the output distributions without altering their asymptotic spreading properties. Finally we devise a quantum civilization problem, a two walker generalisation of single particle recurrence processes., Comment: 11 pages, 8 figures
Published: 2023
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50. Performance-Preserving Event Log Sampling for Predictive Monitoring

Author: Sani, Mohammadreza Fani, Vazifehdoostirani, Mozhgan, Park, Gyunam, Pegoraro, Marco, van Zelst, Sebastiaan J., and van der Aalst, Wil M. P.
Subjects: Computer Science - Machine Learning, Computer Science - Artificial Intelligence
Abstract: Predictive process monitoring is a subfield of process mining that aims to estimate case or event features for running process instances. Such predictions are of significant interest to the process stakeholders. However, most of the state-of-the-art methods for predictive monitoring require the training of complex machine learning models, which is often inefficient. Moreover, most of these methods require a hyper-parameter optimization that requires several repetitions of the training process which is not feasible in many real-life applications. In this paper, we propose an instance selection procedure that allows sampling training process instances for prediction models. We show that our instance selection procedure allows for a significant increase of training speed for next activity and remaining time prediction methods while maintaining reliable levels of prediction accuracy., Comment: 25 pages, 1 figure, 13 tables, 47 references. arXiv admin note: substantial text overlap with arXiv:2204.01470
Published: 2023

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