19 citations · 43 across the 18 of their papers we have counts for
6 papers · 1 filter
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…