4 papers
Learning and Interpreting Gravitational-Wave Features from CNNs with a Random Forest Approach
Jun Tian, He Wang, Jibo He +3
Convolutional neural networks (CNNs) have become widely adopted in gravitational wave (GW) detection pipelines due to their ability to automatically learn hierarchical features fro…
A Kalman-smoother based data imputation strategy to data gaps in spaceborne gravitational wave detectors
Tingyang Shen, He Wang, Jibo He
Massive black hole binaries (MBHBs) and other sources within the frequency band of spaceborne gravitational wave observatories like the Laser Interferometer Space Antenna (LISA), T…
The future of gravitational wave science unlocking LIGO potential: AI-driven data analysis and exploration
Yong Xiao, Li, Zin Nandar Win +3
The advent of gravitational wave astronomy (GW) has revolutionized the observation of cataclysmic cosmic events, such as black hole mergers and neutron star collisions. The Laser I…
Accelerating Bayesian Sampling for Massive Black Hole Binaries with Prior Constraints from Conditional Variational Autoencoder
Hui Sun, He Wang, Jibo He
A Conditional Variational Autoencoder (CVAE) model is employed for parameter inference on gravitational waves (GW) signals of massive black hole binaries, considering joint observa…