19 citations · 43 across the 18 of their papers we have counts for
18 papers
FUSE: FK-Steered Multi-Modal Flow Matching for Efficient Simulation-Based Posterior Estimation
Weichen Qin, Yufan Xie, Peihao Wang +8
Simulation-Based Inference (SBI) is critical for scientific discovery, with generative models offering a promising path toward efficient inference. However, existing methods strugg…
Ringdown Signatures of Dehnen Dark Matter Halos: Fluid Modes and Detectability with Space-Based Detectors
Manjia Liang, Minghui Du, Qing Diao +5
In this work, we investigate the feasibility of using ringdown waveforms from supermassive black holes immersed in dark-matter halos to extract both the intrinsic black-hole parame…
Physics informed operator learning of parameter dependent spectra
Haohao Gu, Sensen He, Hanlin Song +6
Spectral problems governed by differential operators underpin a wide range of physical systems, yet remain computationally challenging because their spectra depend sensitively on c…
High-Precision Ground Characterization of Test-Mass Magnetic Properties for the Taiji Gravitational Wave Mission via a Physics-Informed Neural Framework
Chang Liu, Qiong Deng, Huadong Li +12
Taiji is a gravitational wave detection mission in space initiated by the Chinese Academy of Sciences, which will open the millihertz window through a heliocentric triangular const…
Robust parameter inference for Taiji via time-frequency contrastive learning and normalizing flows
Tian-Yang Sun, Bo Liang, Ji-Yu Song +6
Transient noise artifacts, commonly referred to as glitches, pose a major challenge to parameter inference for space-based gravitational-wave (GW) observations. We develop a glitch…
A Dynamical Equilibrium Linking Nanohertz Stochastic Gravitational Wave Background to Cosmic Structure Formation
Manjia Liang, Peng Xu, Ruijun Shi +6
The stochastic gravitational wave background (SGWB) is conventionally treated as a passive relic of its astrophysical and cosmological sources, with negligible back-reaction on the…
FluxMC: Rapid and High-Fidelity Inference for Space-Based Gravitational-Wave Observations
Bo Liang, Chang Liu, Hanlin Song +11
Bayesian inference in the physical sciences faces a fundamental challenge: the imperative for high-fidelity physical modeling often clashes with the intrinsic limitations of stocha…
Estimating Orbital Parameters of Direct Imaging Exoplanet Using Neural Network
Bo Liang, Hanlin Song, Chang Liu +9
In this work, we propose a flow-matching Markov chain Monte Carlo (FM-MCMC) algorithm for estimating the orbital parameters of exoplanetary systems, especially for those only one e…
Toward Efficient and Accurate EMRI Parameter Estimation: A Machine Learning-Enhanced MCMC Framework
Bo Liang, Chang Liu, Hanlin Song +12
The detection of gravitational waves from extreme-mass-ratio inspirals (EMRIs) in space-based antennas like Taiji and Laser Interferometer Space Antenna promises deep insights into…
Recent Advances in Simulation-based Inference for Gravitational Wave Data Analysis
Bo Liang, He Wang
The detection of gravitational waves by the LIGO-Virgo-KAGRA collaboration has ushered in a new era of observational astronomy, emphasizing the need for rapid and detailed paramete…
Towards Realistic Detection Pipelines of Taiji: New Challenges in Data Analysis and High-Fidelity Simulations of Space-Based Gravitational Wave Antenna
Minghui Du, Pengcheng Wang, Ziren Luo +23
Taiji, a Chinese space-based gravitational wave (GW) detection project, aims to explore the millihertz GW universe with unprecedented sensitivity. By observing astrophysical and co…
Accelerating Stochastic Gravitational Wave Backgrounds Parameter Estimation in Pulsar Timing Arrays with Flow Matching
Bo Liang, Chang Liu, Tianyu Zhao +9
\Acp{PTA} are essential tools for detecting the \ac{SGWB}, but their analysis faces significant computational challenges. Traditional methods like \ac{MCMC} struggle with high-dime…
Fluctuation-dissipation relation of test masses in classical stochastic gravitational wave background
Manjia Liang, Peng Xu, Congfeng Qiao +4
Research on pulsar timing arrays has provided preliminary evidence for the existence of a stochastic gravitational background, which, either being primordial or of astrophysical or…
Gravitational Wave Signal Denoising and Merger Time Prediction By Deep Neural Network
Yuxiang Xu, He Wang, Minghui Du +2
The mergers of massive black hole binaries could generate rich electromagnetic emissions, which allow us to probe the environments surrounding these massive black holes and gain de…
Rapid Parameter Estimation for Extreme Mass Ratio Inspirals Using Machine Learning
Bo Liang, Hong Guo, Tianyu Zhao +11
Extreme-mass-ratio inspiral (EMRI) signals pose significant challenges in gravitational wave (GW) astronomy owing to their low-frequency nature and highly complex waveforms, which…
Rapid Parameter Estimation for Merging Massive Black Hole Binaries Using Continuous Normalizing Flows
Bo Liang, Minghui Du, He Wang +6
Detecting the coalescences of massive black hole binaries (MBHBs) is one of the primary targets for space-based gravitational wave observatories such as LISA, Taiji, and Tianqin. T…
Gravitational Wave Signal Extraction Against Non-Stationary Instrumental Noises with Deep Neural Network
Yuxiang Xu, Minghui Du, Peng Xu +2
Sapce-borne gravitational wave antennas, such as LISA and LISA-like mission (Taiji and Tianqin), will offer novel perspectives for exploring our Universe while introduce new challe…
Advancing Space-Based Gravitational Wave Astronomy: Rapid Parameter Estimation via Normalizing Flows
Minghui Du, Bo Liang, He Wang +3
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 br…