{"title":"从引力波信号推断双星属性","authors":"Javier Roulet, Tejaswi Venumadhav","doi":"10.1146/annurev-nucl-121423-100725","DOIUrl":null,"url":null,"abstract":"This review provides a conceptual and technical survey of methods for parameter estimation of gravitational-wave signals in ground-based interferometers such as Laser Interferometer Gravitational-Wave Observatory (LIGO) and Virgo. We introduce the framework of Bayesian inference and provide an overview of models for the generation and detection of gravitational waves from compact binary mergers, focusing on the essential features that are observable in the signals. Within the traditional likelihood-based paradigm, we describe various approaches for enhancing the efficiency and robustness of parameter inference. This includes techniques for accelerating likelihood evaluations, such as heterodyne/relative binning, reduced-order quadrature, multibanding, and interpolation. We also cover methods to simplify the analysis to improve convergence, via reparameterization, importance sampling, and marginalization. We end with a discussion of recent developments in the application of likelihood-free (simulation-based) inference methods to gravitational-wave data analysis.","PeriodicalId":8090,"journal":{"name":"Annual Review of Nuclear and Particle Science","volume":"17 1","pages":""},"PeriodicalIF":9.1000,"publicationDate":"2024-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Inferring Binary Properties from Gravitational-Wave Signals\",\"authors\":\"Javier Roulet, Tejaswi Venumadhav\",\"doi\":\"10.1146/annurev-nucl-121423-100725\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This review provides a conceptual and technical survey of methods for parameter estimation of gravitational-wave signals in ground-based interferometers such as Laser Interferometer Gravitational-Wave Observatory (LIGO) and Virgo. We introduce the framework of Bayesian inference and provide an overview of models for the generation and detection of gravitational waves from compact binary mergers, focusing on the essential features that are observable in the signals. Within the traditional likelihood-based paradigm, we describe various approaches for enhancing the efficiency and robustness of parameter inference. This includes techniques for accelerating likelihood evaluations, such as heterodyne/relative binning, reduced-order quadrature, multibanding, and interpolation. We also cover methods to simplify the analysis to improve convergence, via reparameterization, importance sampling, and marginalization. We end with a discussion of recent developments in the application of likelihood-free (simulation-based) inference methods to gravitational-wave data analysis.\",\"PeriodicalId\":8090,\"journal\":{\"name\":\"Annual Review of Nuclear and Particle Science\",\"volume\":\"17 1\",\"pages\":\"\"},\"PeriodicalIF\":9.1000,\"publicationDate\":\"2024-06-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Annual Review of Nuclear and Particle Science\",\"FirstCategoryId\":\"101\",\"ListUrlMain\":\"https://doi.org/10.1146/annurev-nucl-121423-100725\",\"RegionNum\":2,\"RegionCategory\":\"物理与天体物理\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"PHYSICS, NUCLEAR\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Annual Review of Nuclear and Particle Science","FirstCategoryId":"101","ListUrlMain":"https://doi.org/10.1146/annurev-nucl-121423-100725","RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"PHYSICS, NUCLEAR","Score":null,"Total":0}
Inferring Binary Properties from Gravitational-Wave Signals
This review provides a conceptual and technical survey of methods for parameter estimation of gravitational-wave signals in ground-based interferometers such as Laser Interferometer Gravitational-Wave Observatory (LIGO) and Virgo. We introduce the framework of Bayesian inference and provide an overview of models for the generation and detection of gravitational waves from compact binary mergers, focusing on the essential features that are observable in the signals. Within the traditional likelihood-based paradigm, we describe various approaches for enhancing the efficiency and robustness of parameter inference. This includes techniques for accelerating likelihood evaluations, such as heterodyne/relative binning, reduced-order quadrature, multibanding, and interpolation. We also cover methods to simplify the analysis to improve convergence, via reparameterization, importance sampling, and marginalization. We end with a discussion of recent developments in the application of likelihood-free (simulation-based) inference methods to gravitational-wave data analysis.
期刊介绍:
The Annual Review of Nuclear and Particle Science is a publication that has been available since 1952. It focuses on various aspects of nuclear and particle science, including both theoretical and experimental developments. The journal covers topics such as nuclear structure, heavy ion interactions, oscillations observed in solar and atmospheric neutrinos, the physics of heavy quarks, the impact of particle and nuclear physics on astroparticle physics, and recent advancements in accelerator design and instrumentation.
One significant recent change in the journal is the conversion of its current volume from gated to open access. This conversion was made possible through Annual Reviews' Subscribe to Open program. As a result, all articles published in the current volume are now freely available to the public under a CC BY license. This change allows for greater accessibility and dissemination of research in the field of nuclear and particle science.