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
Primordial black holes(PBHs) offer a compelling candidate for dark matter. The production of PBHs through well-tested and accepted physical processes is highly worthy of investigation. This work highlights the role of...Primordial black holes(PBHs) offer a compelling candidate for dark matter. The production of PBHs through well-tested and accepted physical processes is highly worthy of investigation. This work highlights the role of turbulences in the very early universe in sustaining intense and persistent fluctuations in energy or mass density,which could provide a natural mechanism for PBH formation in the primordial universe. We analyze the mass range and abundance of PBHs produced in the magnetohydrodynamic turbulence induced by the electroweak phase transition. Remarkably, we find that the mass range of the produced PBHs falls within the most viable“asteroid mass” window from the present-day observations, and within natural parameter regions their abundance can be sufficiently large. These findings suggest that PBHs produced during magnetohydrodynamic turbulence in the very early universe may comprise a dominant part of dark matter.展开更多
This paper addresses the challenges of insufficient navigation accuracy,low path-planning efficiency,and poor environmental adaptability faced by deep space rovers in complex extraterrestrial environments(e.g.,the Moo...This paper addresses the challenges of insufficient navigation accuracy,low path-planning efficiency,and poor environmental adaptability faced by deep space rovers in complex extraterrestrial environments(e.g.,the Moon and Mars).A novel autonomous navigation scheme is proposed that integrates laser Doppler velocimetry(LDV)with star trackers(ST)and inertial navigation system(INS).The scheme suppresses slip errors from wheel odometry through non-contact,high-precision laser speed measurement(accuracy better than 0.1%).By deeply fusing multi-source data via a Kalman filter algorithm,high-precision positioning is realized under extreme extraterrestrial conditions such as weak illumination and dust coverage.This solution features high accuracy,non-contact measurement,and anti-interference capabilities,significantly improving the navigation accuracy and autonomy of deep space rovers in complex environments.展开更多
Gravitational wave data analysis(GWDA)faces significant challenges due to high-dimensional parameter spaces and non-Gaussian,non-stationary artifacts in the interferometer background,which traditional methods have mad...Gravitational wave data analysis(GWDA)faces significant challenges due to high-dimensional parameter spaces and non-Gaussian,non-stationary artifacts in the interferometer background,which traditional methods have made significant progress in addressing but continue to face limitations.Artificial intelligence(AI),particularly deep learning(DL)algorithms,offers potential advantages,including computational efficiency,scalability,and adaptability,which may complement traditional approaches in tackling these challenges more effectively.In this review,we explore AI-driven approaches to GWDA,covering every stage of the pipeline and presenting first explorations in waveform modeling and parameter estimation.This work represents the most comprehensive review to date,integrating the latest AI advancements with practical GWDA applications.Our meta-analysis reveals insights and trends,highlighting the transformative potential of AI in revolutionizing gravitational wave research and paving the way for future discoveries.展开更多
Gravitational wave(GW) astronomy is witnessing a transformative shift from terrestrial to space-based detection, with missions like Taiji at the forefront. While the transition brings unprecedented opportunities for e...Gravitational wave(GW) astronomy is witnessing a transformative shift from terrestrial to space-based detection, with missions like Taiji at the forefront. While the transition brings unprecedented opportunities for exploring massive black hole binaries(MBHBs), it also imposes complex challenges in data analysis, particularly in parameter estimation amidst confusion noise.Addressing this gap, we utilize scalable normalizing flow models to achieve rapid and accurate inference within the Taiji environment. Innovatively, our approach simplifies the data's complexity, employs a transformation mapping to overcome the year-period time-dependent response function, and unveils additional multimodality in the arrival time parameter. Our method estimates MBHBs several orders of magnitude faster than conventional techniques, maintaining high accuracy even in complex backgrounds. These findings significantly enhance the efficiency of GW data analysis, paving the way for rapid detection and alerting systems and enriching our ability to explore the universe through space-based GW observation.展开更多
Using the AdS/CFT correspondence,this paper investigates the holographic images of a charged black hole within the context of Lorentz symmetry breaking massive gravity.The photon rings,luminosity-deformed rings,or lig...Using the AdS/CFT correspondence,this paper investigates the holographic images of a charged black hole within the context of Lorentz symmetry breaking massive gravity.The photon rings,luminosity-deformed rings,or light points from various observational perspectives are obtained.We also study the influences of both the chemical potential and temperature on the Einstein ring.Unlike the previous work,which primarily examines the effect of chemical potential on ring radius at high temperatures and find no change in the radius with varying chemical potential,we also investigate the effect of chemical potential on the ring radius at low temperature besides at high temperature.Our findings indicate that at low temperatures,the photon ring radius decreases with increasing of chemical potential,while at high temperatures,the results are consistent with previous studies.Additionally,we explore the impact of the model parameterλon the Einstein ring radius and find the the ring radius increases as the model parameterλincreases.More interestingly,for the large chemical potential,u=1,the temperature dependence of the photon ring radius is reversed forλ=2 andλ=4.Conversely,for a small chemical potential u=0.1,the temperature dependence of the Einstein ring stays the same asλ=2 andλ=4.展开更多
As a generalization of Einstein's theory,Horava-Lifshitz gravity has attracted significant interest owing to its healthy ultraviolet behavior.In this paper,we analyze the impact of the Horava-Lifshitz corrections ...As a generalization of Einstein's theory,Horava-Lifshitz gravity has attracted significant interest owing to its healthy ultraviolet behavior.In this paper,we analyze the impact of the Horava-Lifshitz corrections on the gravitomagnetic field.We propose a new measurement method for the planetary gravitomagnetic field based on space-based laser interferometry,which is further used to constrain the Horava-Lifshitz parameters.Our analysis shows that high-precision laser gradiometers can indeed limit the parameters in Horava-Lifshitz gravity and improve the results by one or two orders of magnitude compared with the existing theories.Our novel method also provides insights into how to constrain the parameters in the modified gravitational theory to gain deeper understanding of this complex framework and pave the way for potential technological advancements in the field.展开更多
The characteristics and images of Einstein-Maxwell-dilaton(EMD)black holes are examined in this paper,focusing on their effective potential,photon trajectories,and images with both thin and thick accretion disks.We fi...The characteristics and images of Einstein-Maxwell-dilaton(EMD)black holes are examined in this paper,focusing on their effective potential,photon trajectories,and images with both thin and thick accretion disks.We find that the shadow and photon sphere radii decrease as the dilaton charge increases.As the observation inclination increases,the direct and secondary images become distinct,with the direct image appearing hat-shaped.Simulations indicate that the brightness of the shadow and photon ring is higher in static spherical accretion flows compared to infalling ones.The study also shows that in thin disk accretion flows,direct emission predominantly influences the observed luminosity,while photon ring emission is less significant.Additionally,the appearance of black hole images varies with the observer’s inclination angle.展开更多
In this study,we investigate the shadow and observational image of the Kerr-like Loop Quantum Gravity(LQG)inspired black bounce with the help of the celestial light and thin disk sources by employing the backward ray-...In this study,we investigate the shadow and observational image of the Kerr-like Loop Quantum Gravity(LQG)inspired black bounce with the help of the celestial light and thin disk sources by employing the backward ray-tracing method.The results indicate that both the LQG parameterαand rotation parameter a contribute to a reduction in the shadow size.However,the influence of a is predominant,whereas that ofαis supplementary.For the accretion disk model,we extend its inner edge to the black hole's event horizon,and the motion of particles is different in the regions inside and outside the innermost stable circular orbit.We find that the correlation parameters(α,α),along with the observer’s inclination angle,affect the image’s asymmetry and the distortion of the inner shadow.As the inclination increases,the direct and lensed images diverge,creating a structure resembling a hat.Moreover,we investigate the redshift distribution of the direct lensed images of the accretion disk under different parameters and observation angles.The results show that the redshift distribution and observed intensity are evidently related to the behavior of accretion flow.These results may provide a potential approach for limiting black hole parameters,detecting quantum gravity effects,and distinguishing the LQG black hole from other black hole models.展开更多
Within the framework of AdS/CFT correspondence,this paper studies the holographic shadow images of charged Phantom AdS black holes.Using a Gaussian oscillator source on the AdS boundary,the test waves generated by thi...Within the framework of AdS/CFT correspondence,this paper studies the holographic shadow images of charged Phantom AdS black holes.Using a Gaussian oscillator source on the AdS boundary,the test waves generated by this source propagate through the black hole spacetime are detected by the response function on the other side of the boundary.The results show that the amplitude of the response function differs for different wave sources and gravitational parameters.From an optical system with a convex lens,we successfully constructed the shadow image of the black hole.When the wave source is located at the South Pole and the observation inclination is zero,a series of axially symmetric concentric circular patterns are always displayed on the screen.As the observation inclination increases,the brightest ring transforms into a ring with distorted brightness,Eventually collapsing to a bright spot.Additionally,the research finds that the shadow image depends not only on the black hole’s temperature and chemical potential but also on the frequency of the wave source.Based on the geometric optics,the incidence angle of the photon ring is also discussed,and finds that it Matches the angular distance of the Einstein ring obtained by the holographic framework,which validates the effectiveness of studying Einstein rings through AdS/CFT correspondence.展开更多
In this study,we develop a modeling framework based on spatio-temporal generalized random fields to simulate the time-evolving accretion flows and their associated imaging signatures around rotating regular black hole...In this study,we develop a modeling framework based on spatio-temporal generalized random fields to simulate the time-evolving accretion flows and their associated imaging signatures around rotating regular black holes.We extend the Matérn field formalism to the spatio-temporal domain and introduce a locally anisotropic tensor structureΛ(x),which encodes direction-dependent correlation scales motivated by Keplerian velocity fields,thereby generating physically informed perturbation structures.Coupled with a computationally efficient light ray-tracing scheme,this framework produces a sequence of time-resolved images of regular black hole shadows and accretion structures.By incorporating light-travel time effects,we identify significant temporal smearing of features within strongly lensed regions and rapidly varying sources,thus enhancing the physical realism of the modeling.Comparison with existing general relativistic magnetohydrodynamic simulations demonstrates that our stochastic generative model maintains statistical consistency while offering substantial computational efficiency.Moreover,the simulated results reproduce the dynamic positional shift of the bright ring structure observed in M87*,providing theoretical support for interpreting its time-variable images.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
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 International Partnership Program of the Chinese Academy of Sciences (Grant No.025GJHZ2023106GC)。
文摘Primordial black holes(PBHs) offer a compelling candidate for dark matter. The production of PBHs through well-tested and accepted physical processes is highly worthy of investigation. This work highlights the role of turbulences in the very early universe in sustaining intense and persistent fluctuations in energy or mass density,which could provide a natural mechanism for PBH formation in the primordial universe. We analyze the mass range and abundance of PBHs produced in the magnetohydrodynamic turbulence induced by the electroweak phase transition. Remarkably, we find that the mass range of the produced PBHs falls within the most viable“asteroid mass” window from the present-day observations, and within natural parameter regions their abundance can be sufficiently large. These findings suggest that PBHs produced during magnetohydrodynamic turbulence in the very early universe may comprise a dominant part of dark matter.
文摘This paper addresses the challenges of insufficient navigation accuracy,low path-planning efficiency,and poor environmental adaptability faced by deep space rovers in complex extraterrestrial environments(e.g.,the Moon and Mars).A novel autonomous navigation scheme is proposed that integrates laser Doppler velocimetry(LDV)with star trackers(ST)and inertial navigation system(INS).The scheme suppresses slip errors from wheel odometry through non-contact,high-precision laser speed measurement(accuracy better than 0.1%).By deeply fusing multi-source data via a Kalman filter algorithm,high-precision positioning is realized under extreme extraterrestrial conditions such as weak illumination and dust coverage.This solution features high accuracy,non-contact measurement,and anti-interference capabilities,significantly improving the navigation accuracy and autonomy of deep space rovers in complex environments.
基金supported in part by the National Key Research and Development Program of China(Grant No.2021YFC2203001)in part by the National Natural Science Foundation of China(NSFC)(Nos.11920101003 and 12021003)。
文摘Gravitational wave data analysis(GWDA)faces significant challenges due to high-dimensional parameter spaces and non-Gaussian,non-stationary artifacts in the interferometer background,which traditional methods have made significant progress in addressing but continue to face limitations.Artificial intelligence(AI),particularly deep learning(DL)algorithms,offers potential advantages,including computational efficiency,scalability,and adaptability,which may complement traditional approaches in tackling these challenges more effectively.In this review,we explore AI-driven approaches to GWDA,covering every stage of the pipeline and presenting first explorations in waveform modeling and parameter estimation.This work represents the most comprehensive review to date,integrating the latest AI advancements with practical GWDA applications.Our meta-analysis reveals insights and trends,highlighting the transformative potential of AI in revolutionizing gravitational wave research and paving the way for future discoveries.
基金supported by the National Key Research and Development Program of China (Grant Nos. 2021YFC2203004, and 2021YFC2201903)supported by the National Natural Science Foundation of China (Grant Nos. 12147103, and 12247187)the Fundamental Research Funds for the Central Universities。
文摘Gravitational wave(GW) astronomy is witnessing a transformative shift from terrestrial to space-based detection, with missions like Taiji at the forefront. While the transition brings unprecedented opportunities for exploring massive black hole binaries(MBHBs), it also imposes complex challenges in data analysis, particularly in parameter estimation amidst confusion noise.Addressing this gap, we utilize scalable normalizing flow models to achieve rapid and accurate inference within the Taiji environment. Innovatively, our approach simplifies the data's complexity, employs a transformation mapping to overcome the year-period time-dependent response function, and unveils additional multimodality in the arrival time parameter. Our method estimates MBHBs several orders of magnitude faster than conventional techniques, maintaining high accuracy even in complex backgrounds. These findings significantly enhance the efficiency of GW data analysis, paving the way for rapid detection and alerting systems and enriching our ability to explore the universe through space-based GW observation.
基金supported by the National Natural Science Foundation of China(Grant Nos.11675140,11705005,and 12375043)Innovation and Development Joint Foundation of Chongqing Natural Science Foundation(Grant No.CSTB2022NSCQ-LZX0021)Basic Research Project of Science and Technology Committee of Chongqing(Grant No.CSTB2023NSCQ-MSX0324)。
文摘Using the AdS/CFT correspondence,this paper investigates the holographic images of a charged black hole within the context of Lorentz symmetry breaking massive gravity.The photon rings,luminosity-deformed rings,or light points from various observational perspectives are obtained.We also study the influences of both the chemical potential and temperature on the Einstein ring.Unlike the previous work,which primarily examines the effect of chemical potential on ring radius at high temperatures and find no change in the radius with varying chemical potential,we also investigate the effect of chemical potential on the ring radius at low temperature besides at high temperature.Our findings indicate that at low temperatures,the photon ring radius decreases with increasing of chemical potential,while at high temperatures,the results are consistent with previous studies.Additionally,we explore the impact of the model parameterλon the Einstein ring radius and find the the ring radius increases as the model parameterλincreases.More interestingly,for the large chemical potential,u=1,the temperature dependence of the photon ring radius is reversed forλ=2 andλ=4.Conversely,for a small chemical potential u=0.1,the temperature dependence of the Einstein ring stays the same asλ=2 andλ=4.
文摘As a generalization of Einstein's theory,Horava-Lifshitz gravity has attracted significant interest owing to its healthy ultraviolet behavior.In this paper,we analyze the impact of the Horava-Lifshitz corrections on the gravitomagnetic field.We propose a new measurement method for the planetary gravitomagnetic field based on space-based laser interferometry,which is further used to constrain the Horava-Lifshitz parameters.Our analysis shows that high-precision laser gradiometers can indeed limit the parameters in Horava-Lifshitz gravity and improve the results by one or two orders of magnitude compared with the existing theories.Our novel method also provides insights into how to constrain the parameters in the modified gravitational theory to gain deeper understanding of this complex framework and pave the way for potential technological advancements in the field.
基金supported by the National Natural Science Foundation of China(Grant Nos.11675140,11705005,and 12375043)the Innovation and Development Joint Foundation of Chongqing Natural Science Foundation(Grant No.CSTB2022NSCQ-LZX0021)+1 种基金the Basic Research Project of Science and Technology Committee of Chongqing(Grant No.CSTB2023NSCQ-MSX0324)the Fund Project of Chongqing Normal University(Grant No.24XLB033).
文摘The characteristics and images of Einstein-Maxwell-dilaton(EMD)black holes are examined in this paper,focusing on their effective potential,photon trajectories,and images with both thin and thick accretion disks.We find that the shadow and photon sphere radii decrease as the dilaton charge increases.As the observation inclination increases,the direct and secondary images become distinct,with the direct image appearing hat-shaped.Simulations indicate that the brightness of the shadow and photon ring is higher in static spherical accretion flows compared to infalling ones.The study also shows that in thin disk accretion flows,direct emission predominantly influences the observed luminosity,while photon ring emission is less significant.Additionally,the appearance of black hole images varies with the observer’s inclination angle.
基金the National Natural Science Foundation of China(12375043)。
文摘In this study,we investigate the shadow and observational image of the Kerr-like Loop Quantum Gravity(LQG)inspired black bounce with the help of the celestial light and thin disk sources by employing the backward ray-tracing method.The results indicate that both the LQG parameterαand rotation parameter a contribute to a reduction in the shadow size.However,the influence of a is predominant,whereas that ofαis supplementary.For the accretion disk model,we extend its inner edge to the black hole's event horizon,and the motion of particles is different in the regions inside and outside the innermost stable circular orbit.We find that the correlation parameters(α,α),along with the observer’s inclination angle,affect the image’s asymmetry and the distortion of the inner shadow.As the inclination increases,the direct and lensed images diverge,creating a structure resembling a hat.Moreover,we investigate the redshift distribution of the direct lensed images of the accretion disk under different parameters and observation angles.The results show that the redshift distribution and observed intensity are evidently related to the behavior of accretion flow.These results may provide a potential approach for limiting black hole parameters,detecting quantum gravity effects,and distinguishing the LQG black hole from other black hole models.
文摘Within the framework of AdS/CFT correspondence,this paper studies the holographic shadow images of charged Phantom AdS black holes.Using a Gaussian oscillator source on the AdS boundary,the test waves generated by this source propagate through the black hole spacetime are detected by the response function on the other side of the boundary.The results show that the amplitude of the response function differs for different wave sources and gravitational parameters.From an optical system with a convex lens,we successfully constructed the shadow image of the black hole.When the wave source is located at the South Pole and the observation inclination is zero,a series of axially symmetric concentric circular patterns are always displayed on the screen.As the observation inclination increases,the brightest ring transforms into a ring with distorted brightness,Eventually collapsing to a bright spot.Additionally,the research finds that the shadow image depends not only on the black hole’s temperature and chemical potential but also on the frequency of the wave source.Based on the geometric optics,the incidence angle of the photon ring is also discussed,and finds that it Matches the angular distance of the Einstein ring obtained by the holographic framework,which validates the effectiveness of studying Einstein rings through AdS/CFT correspondence.
基金supported by the National Natural Science Foundation of China(Grant No.12133003)the Fapesq-PB of Brazil+1 种基金the Fund Project of Chongqing Normal University(Grant No.24XLB033)the Key Project of Sichuan Science and Technology Education Joint Fund(Grant No.25LHJJ0097)。
文摘In this study,we develop a modeling framework based on spatio-temporal generalized random fields to simulate the time-evolving accretion flows and their associated imaging signatures around rotating regular black holes.We extend the Matérn field formalism to the spatio-temporal domain and introduce a locally anisotropic tensor structureΛ(x),which encodes direction-dependent correlation scales motivated by Keplerian velocity fields,thereby generating physically informed perturbation structures.Coupled with a computationally efficient light ray-tracing scheme,this framework produces a sequence of time-resolved images of regular black hole shadows and accretion structures.By incorporating light-travel time effects,we identify significant temporal smearing of features within strongly lensed regions and rapidly varying sources,thus enhancing the physical realism of the modeling.Comparison with existing general relativistic magnetohydrodynamic simulations demonstrates that our stochastic generative model maintains statistical consistency while offering substantial computational efficiency.Moreover,the simulated results reproduce the dynamic positional shift of the bright ring structure observed in M87*,providing theoretical support for interpreting its time-variable images.
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.
