Assessment of normalizing flows for parameter estimation on time-frequency representations of gravitational-wave data
Universidad de Oviedo（奥维耶多大学）；Instituto Universitario de Ciencias y Tecnologías Espaciales de Asturias (ICTEA)（阿斯图里亚斯宇宙科学与技术大学研究所）；Universitat de València（瓦伦西亚大学）；Centro de Física das Universidades do Minho e do Porto (CF-UM-UP), Universidade do Minho（米尼奥与波尔图大学物理中心（CF-UM-UP），米尼奥大学）
Daniel Lanchares, Osvaldo G. Freitas, Lysiane Mornas, José A. Font, Joaquín González-Nuevo, Luigi Toffolatti, Pietro Vischia
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15 pages, 11 figures; Accepted for publication in Physical Review D
The speed-up of parameter estimation is an active field of research in gravitational-wave data analysis. In this paper we present GP15, a deep-learning method that merges residual networks and normalizing flows into a general-purpose, image-based estimator of binary black hole (BBH) parameters. Building on our early work, we map BBH spectrograms from the Advanced LIGO and Advanced Virgo detectors to color channels in an RGB image amenable to be processed with residual networks. GP15 is trained on simulated data for BBH mergers obtained with the \texttt{IMRPhenomXPHM} waveform approximant and tested for all three-detector events from the GWTC-3 and GWTC-2.1 catalogs reported by the LIGO-Virgo-KAGRA (LVK) collaboration. Overall, our model yields good agreement with the LVK results over most parameters. Our simple model can produce large amounts of posterior samples in the order of a second, complementing existing approaches with normalizing flows based on time or frequency representation of gravitational-wave data. We also discuss current shortcomings of our model and possible improvements for future extensions (e.g. including noise conditioning from the detectors' PSD or splitting the parameter space into intrinsic and extrinsic subspaces).
Accelerated Bayesian parameter estimation and model selection for gravitational waves with normalizing flows
Mullard Space Science Laboratory（穆拉德空间科学实验室）；University College London（伦敦大学学院）；Utrecht University（乌得勒支大学）；Nikhef（荷兰国家亚原子物理研究所）；Center for Computational Astrophysics（计算天体物理中心）；Flatiron Institute（弗拉蒂伦研究所）；Johns Hopkins University（约翰斯·霍普金斯大学）；Alan Turing Institute（阿兰·图灵研究所）
Efficient parameter inference for gravitational wave signals in the presence of transient noises using temporal and time-spectral fusion normalizing flow
Incorporating neutron star physics into gravitational wave inference with physics-informed priors using normalizing flows
Utrecht University（乌得勒支大学）；Nikhef（尼黑国家粒子物理研究所）；Universität Potsdam（波茨坦大学）；Max Planck Institute for Gravitational Physics (Albert Einstein Institute)（马克斯·普朗克引力物理研究所（爱因斯坦研究所））
Fast Parameter Inference on Pulsar Timing Arrays with Normalizing Flows
Rutgers University（罗格斯大学）；Cornell University（康奈尔大学）；Vanderbilt University（范德堡大学）；University of Florida（佛罗里达大学）；University of California Santa Cruz（加州大学圣克鲁兹分校）
Scaling RIFT 1: Extending RIFT to third-generation gravitational-wave analyses
Center for Computational Relativity and Gravitation, Rochester Institute of Technology（罗切斯特理工学院相对论与引力计算中心）；University of Texas, Austin（德克萨斯大学奥斯汀分校）；Cardiff University（卡迪夫大学）
Robust parameter inference for Taiji via time-frequency contrastive learning and normalizing flows
Northeastern University（东北大学）；Institute of Mechanics, Chinese Academy of Sciences（中国科学院力学研究所）；University of Chinese Academy of Sciences (UCAS)（中国科学院大学）；International Centre for Theoretical Physics Asia-Pacific (ICTP-AP)（亚太理论物理中心）
Emulating compact binary population synthesis simulations with uncertainty quantification and model comparison using Bayesian normalizing flows
Center for Interdisciplinary Exploration and Research in Astrophysics（跨学科天体物理探索与研究中心）；Northwestern University（西北大学）；The NSF-Simons AI Institute for the Sky (NSF-Simons SkAI)（NSF-模拟斯诺人工智能天空研究所）
Bayesian evidence estimation from posterior samples with normalizing flows
SISSA（国际高等研究学院）；INFN Sezione di Trieste（的里雅斯特国家核物理研究所）；IFPU - Institute for Fundamental Physics of the Universe（宇宙基础物理研究所）；TAPIR, Division of Physics, Mathematics, and Astronomy, California Institute of Technology（加州理工学院）；Dipartimento di Fisica, Università di Pisa（比萨大学）；Department of Physics, Imperial College London（帝国理工学院）；Italian Research Center on High Performance Computing, Big Data and Quantum Computing（意大利高性能计算、大数据与量子计算研究中心）
A new model for the continuum spectra of AM CVn binaries and multi-messenger inference with normalizing flows
University of Manitoba（曼尼托巴大学）；Winnipeg Institute for Theoretical Physics（温尼伯理论物理研究所）；University of Texas Rio Grande Valley（德克萨斯大学里奥格兰德河谷分校）；South Texas Space Science Institute（南德克萨斯空间科学研究所）；Carnegie Mellon University（卡内基梅隆大学）；Texas Tech University（德克萨斯理工大学）；Flatiron Institute（平顿研究所）
Adapting to noise distribution shifts in flow-based gravitational-wave inference
Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）；University of Nottingham（诺丁汉大学）；Max Planck Institute for Gravitational Physics (Albert Einstein Institute)（马克斯·普朗克引力物理研究所（阿尔伯特·爱因斯坦研究所））；University of Rhode Island（罗德岛大学）；University of Tübingen（图宾根大学）；University of Maryland, College Park（马里兰大学帕克分校）
A fast deep-learning approach to probing primordial black hole populations in gravitational wave events
School of Physical Sciences, University of Chinese Academy of Sciences（中国科学院大学物理科学学院）；School of Fundamental Physics and Mathematical Sciences, Hangzhou Institute for Advanced Study, UCAS（中国科学院大学杭州高等研究院基础物理与数学学院）；Department of Physics and Chongqing Key Laboratory for Strongly Coupled Physics, Chongqing University（重庆大学物理系强耦合物理重庆市重点实验室）；International Center for Theoretical Physics Asia-Pacific, Beijing/Hangzhou, China（亚太理论物理中心）；Taiji Laboratory for Gravitational Wave Universe (Beijing/Hangzhou), UCAS（中国科学院大学太极引力波宇宙实验室）；Institute of Theoretical Physics, Chinese Academy of Sciences（中国科学院理论物理研究所）
Model-Agnostic Population Inference for Gravitational-Wave Astronomy: From LVK to LISA
State Key Laboratory of Radio Astronomy and Technology, Xinjiang Astronomical Observatory, CAS（中国科学院新疆天文台射电天文与技术重点实验室）；School of Astronomy and Space Science, University of Chinese Academy of Sciences（中国科学院大学天文与空间科学学院）；Institute of Fundamental Physics and Quantum Technology, Ningbo University（宁波大学基础物理与量子技术学院）；School of Physical Science and Technology, Ningbo University（宁波大学物理科学与技术学院）
Astrophysical or Terrestrial: Machine learning classification of gravitational-wave candidates using multiple-search information
University of Minnesota（明尼苏达大学）；The University of British Columbia（不列颠哥伦比亚大学）；NASA Goddard Space Flight Center（美国国家航空航天局戈达德太空飞行中心）；Joint Space-Science Institute, University of Maryland（马里兰大学联合空间科学研究所）
Search for Gravitational Wave Memory in PPTA and EPTA Data: A Complete Signal Model
Max Planck Institute for Gravitational Physics (Albert Einstein Institute)（马克斯·普朗克引力物理研究所（爱因斯坦研究所））；Leibniz Universität Hannover（汉诺威莱布尼茨大学）；SISSA（的里雅斯特国际高等研究学院）；INFN Sezione di Trieste（意大利国家核物理研究所的里雅斯特分部）；IFPU - Institute for Fundamental Physics of the Universe（宇宙基础物理研究所）；Xinjiang Astronomical Observatories, Chinese Academy of Sciences（中国科学院新疆天文台）；Institute of Optoelectronic Technology, Lishui University（丽水学院光电技术研究所）；Institute of Astrophysics, Foundation for Research & Technology – Hellas (FORTH)（希腊研究与技术基金会天体物理研究所）
Recent Advances in Simulation-based Inference for Gravitational Wave Data Analysis
Center for Gravitational Wave Experiment, National Microgravity Laboratory, Institute of Mechanics, Chinese Academy of Sciences（引力波实验中心，国家微重力实验室，力学研究所，中国科学院）；Taiji Laboratory for Gravitational Wave Universe (Beijing/Hangzhou), University of Chinese Academy of Sciences (UCAS)（引力波宇宙 Taiji 实验室（北京/杭州），中国科学院大学）；International Centre for Theoretical Physics Asia-Pacific (ICTP-AP), University of Chinese Academy of Sciences (UCAS)（亚洲太平洋理论物理中心（ICTP-AP），中国科学院大学）
