Extreme-mass-ratio inspiral(EMRI)signals pose significant challenges to gravitational wave(GW)data analysis,mainly owing to their highly complex waveforms and high-dimensional parameter space.Given their extended time...Extreme-mass-ratio inspiral(EMRI)signals pose significant challenges to gravitational wave(GW)data analysis,mainly owing to their highly complex waveforms and high-dimensional parameter space.Given their extended timescales of months to years and low signal-to-noise ratios,detecting and analyzing EMRIs with confidence generally relies on long-term observations.Besides the length of data,parameter estimation is particularly challenging due to non-local parameter degeneracies,arising from multiple local maxima,as well as flat regions and ridges inherent in the likelihood function.These factors lead to exceptionally high time complexity for parameter analysis based on traditional matched filtering and random sampling methods.To address these challenges,the present study explores a machine learning approach to Bayesian posterior estimation of EMRI signals,leveraging the recently developed flow matching technique based on ordinary differential equation neural networks.To our knowledge,this is also the first instance of applying continuous normalizing flows to EMRI analysis.Our approach demonstrates an increase in computational efficiency by several orders of magnitude compared to the traditional Markov chain Monte Carlo(MCMC)methods,while preserving the unbiasedness of results.However,we note that the posterior distributions generated by FMPE may exhibit broader uncertainty ranges than those obtained through full Bayesian sampling,requiring subsequent refinement via methods such as MCMC.Notably,when searching from large priors,our model rapidly approaches the true values while MCMC struggles to converge to the global maximum.Our findings highlight that machine learning has the potential to efficiently handle the vast EMRI parameter space of up to seventeen dimensions,offering new perspectives for advancing space-based GW detection and GW astronomy.展开更多
One of the primary goals of space-borne gravitational wave detectors is to detect and analyze extreme-mass-ratio inspirals(EM-RIs).This task is particularly challenging because EMRI signals are complex,lengthy,and fai...One of the primary goals of space-borne gravitational wave detectors is to detect and analyze extreme-mass-ratio inspirals(EM-RIs).This task is particularly challenging because EMRI signals are complex,lengthy,and faint.In this work,we introduce a 2-layer convolutional neural network(CNN)approach to detect EMRI signals for space-borne detectors,achieving a true positive rate(TPR)of 96.9%at a 1%false positive rate(FPR)for signal-to-noise ratio(SNR)from 50 to 100.Especially,the key intrinsic parameters of EMRIs such as the mass,spin of the supermassive black hole(SMBH)and the initial eccentricity of the orbit can also be inferred directly by employing a neural network.The mass and spin of the SMBH can be determined at 99%and 92%respectively.This will greatly reduce the parameter spaces and computing cost for the following Bayesian parameter estimation.Our model also has a low dependency on the accuracy of the waveform model.This study underscores the potential of deep learning methods in EMRI data analysis,enabling the rapid detection of EMRI signals and efficient parameter estimation.展开更多
In this study,we investigate the detectability of the secondary spin in an extreme mass ratio inspiral(EMRI) system within a modified gravity model coupled with a scalar field.The central black hole,which reduces to a...In this study,we investigate the detectability of the secondary spin in an extreme mass ratio inspiral(EMRI) system within a modified gravity model coupled with a scalar field.The central black hole,which reduces to a Kerr one,is circularly spiralled by a scalar-charged spinning secondary body on the equatorial plane.The analysis reveals that the presence of the scalar field amplifies the secondary spin effect,allowing for a lower limit of the detectability and an improved resolution of the secondary spin when the scalar charge is sufficiently large.Our findings suggest that secondary spin detection is more feasible when the primary mass is not large,and TianQin is the optimal choice for detection.展开更多
基金supported by the National Key Research and Development Program of China(Grant Nos.2021YFC2201901,2021YFC2203004,2020YFC2200100 and 2021YFC2201903)International Partnership Program of the Chinese Academy of Sciences(Grant No.025GJHZ2023106GC)+4 种基金the financial support from Brazilian agencies Funda??o de AmparoàPesquisa do Estado de S?o Paulo(FAPESP)Funda??o de Amparoà Pesquisa do Estado do Rio Grande do Sul(FAPERGS)Fundacao de Amparoà Pesquisa do Estado do Rio de Janeiro(FAPERJ)Conselho Nacional de Desenvolvimento Científico e Tecnológico(CNPq)Coordenacao de Aperfeicoamento de Pessoal de Nível Superior(CAPES)。
文摘Extreme-mass-ratio inspiral(EMRI)signals pose significant challenges to gravitational wave(GW)data analysis,mainly owing to their highly complex waveforms and high-dimensional parameter space.Given their extended timescales of months to years and low signal-to-noise ratios,detecting and analyzing EMRIs with confidence generally relies on long-term observations.Besides the length of data,parameter estimation is particularly challenging due to non-local parameter degeneracies,arising from multiple local maxima,as well as flat regions and ridges inherent in the likelihood function.These factors lead to exceptionally high time complexity for parameter analysis based on traditional matched filtering and random sampling methods.To address these challenges,the present study explores a machine learning approach to Bayesian posterior estimation of EMRI signals,leveraging the recently developed flow matching technique based on ordinary differential equation neural networks.To our knowledge,this is also the first instance of applying continuous normalizing flows to EMRI analysis.Our approach demonstrates an increase in computational efficiency by several orders of magnitude compared to the traditional Markov chain Monte Carlo(MCMC)methods,while preserving the unbiasedness of results.However,we note that the posterior distributions generated by FMPE may exhibit broader uncertainty ranges than those obtained through full Bayesian sampling,requiring subsequent refinement via methods such as MCMC.Notably,when searching from large priors,our model rapidly approaches the true values while MCMC struggles to converge to the global maximum.Our findings highlight that machine learning has the potential to efficiently handle the vast EMRI parameter space of up to seventeen dimensions,offering new perspectives for advancing space-based GW detection and GW astronomy.
基金supported by the National Key R&D Program of China(Grant No.2021YFC2203002)the National Natural Science Foundation of China(Grant Nos.12173071,and 12473075)。
文摘One of the primary goals of space-borne gravitational wave detectors is to detect and analyze extreme-mass-ratio inspirals(EM-RIs).This task is particularly challenging because EMRI signals are complex,lengthy,and faint.In this work,we introduce a 2-layer convolutional neural network(CNN)approach to detect EMRI signals for space-borne detectors,achieving a true positive rate(TPR)of 96.9%at a 1%false positive rate(FPR)for signal-to-noise ratio(SNR)from 50 to 100.Especially,the key intrinsic parameters of EMRIs such as the mass,spin of the supermassive black hole(SMBH)and the initial eccentricity of the orbit can also be inferred directly by employing a neural network.The mass and spin of the SMBH can be determined at 99%and 92%respectively.This will greatly reduce the parameter spaces and computing cost for the following Bayesian parameter estimation.Our model also has a low dependency on the accuracy of the waveform model.This study underscores the potential of deep learning methods in EMRI data analysis,enabling the rapid detection of EMRI signals and efficient parameter estimation.
基金Supported by the National Key Research and Development Program of China (2020YFC2201400)Yun-Gui Gong acknowledges the support by the National Key Research and Development Program of China (2020YFC2201504)+4 种基金Chao Zhang was supported by the China Postdoctoral Science Foundation (2023M742297)Brazilian agencies Funda??o de AmparoàPesquisa do Estado de S?o Paulo (FAPESP)Funda??o de AmparoàPesquisa do Estado do Rio de Janeiro (FAPERJ)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Coordena??o de Aperfei?oa-mento de Pessoal de Nível Superior (CAPES)。
文摘In this study,we investigate the detectability of the secondary spin in an extreme mass ratio inspiral(EMRI) system within a modified gravity model coupled with a scalar field.The central black hole,which reduces to a Kerr one,is circularly spiralled by a scalar-charged spinning secondary body on the equatorial plane.The analysis reveals that the presence of the scalar field amplifies the secondary spin effect,allowing for a lower limit of the detectability and an improved resolution of the secondary spin when the scalar charge is sufficiently large.Our findings suggest that secondary spin detection is more feasible when the primary mass is not large,and TianQin is the optimal choice for detection.
