8 citations · 21 across the 10 of their papers we have counts for
10 papers
Self-supervised reconstruction of transients in data from space-borne gravitational-wave detectors
Yuxiang Xu, Minghui Du, Bo Liang +3
Space-based gravitational-wave (GW) data may contain transient signals whose waveform morphologies are not known in advance. Extracting these signals is important for characterizin…
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…
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…
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…