2 citations · 5 across the 8 of their papers we have counts for
8 papers
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…
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…
Calibration of key parameters during the in-orbit phase for the Taiji-2 gravitational reference sensor
Haoyue Zhang, Chang Liu, Xiaotong Wei +4
The Taiji mission, a pioneering Chinese space-borne gravitational wave observatory, requires ultra-precise calibration of its gravitational reference sensors (GRSs) to achieve its…
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…
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…
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…