Publications (9)
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…
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…
Accelerating Stochastic Gravitational Wave Backgrounds Parameter Estimation in Pulsar Timing Arrays with Flow Matching
Bo Liang, Chang Liu, Tianyu Zhao +9
Pulsar timing arrays (PTAs) are essential tools for detecting the stochastic gravitational wave background (SGWB), but their analysis faces significant computational challenges. Tr…
Unlocking New Paths for Science with Extreme-Mass-Ratio Inspirals: Machine Learning-Enhanced MCMC for Accurate Parameter Inversion
Bo Liang, Chang Liu, Hanlin Song +11
The detection of gravitational waves from extreme-mass-ratio inspirals (EMRIs) in space-borne antennas like Taiji and LISA promises deep insights into strong-field gravity and blac…
5-dimensional Brans-Dicke Theory and Cosmic Acceleration
Li-e Qiang, Yongge Ma, Muxin Han +1
We consider a 5-dimensional scalar-tensor theory which is a direct generalization of the original 4-dimensional Brans-Dicke theory to 5-dimensions. By assuming that there is a hype…
Cosmological Implications of 5-dimensional Brans-Dicke Theory
Li-e Qiang, Yan Gong, Yongge Ma +1
The five dimensional Brans-Dicke theory naturally provides two scalar fields by the Killing reduction mechanism. These two scalar fields could account for the accelerated expansion…