65 citations · 129 across the 5 of their papers we have counts for
5 papers
Parameter inference for coalescing massive black hole binaries using deep learning
Wen-Hong Ruan, He Wang, Chang Liu +1
In the 2030s, a new era of gravitational-wave (GW) observations will dawn as multiple space-based GW detectors, such as the Laser Interferometer Space Antenna, Taiji and TianQin, o…
WaveFormer: transformer-based denoising method for gravitational-wave data
He Wang, Yue Zhou, Zhoujian Cao +2
With the advent of gravitational-wave astronomy and the discovery of more compact binary coalescences, data quality improvement techniques are desired to handle the complex and ove…
MLGWSC-1: The first Machine Learning Gravitational-Wave Search Mock Data Challenge
Marlin B. Schäfer, Ondřej Zelenka, Alexander H. Nitz +20
We present the results of the first Machine Learning Gravitational-Wave Search Mock Data Challenge (MLGWSC-1). For this challenge, participating groups had to identify gravitationa…
Space-based gravitational wave signal detection and extraction with deep neural network
Tianyu Zhao, Ruoxi Lyu, He Wang +2
Space-based gravitational wave (GW) detectors will be able to observe signals from sources that are otherwise nearly impossible from current ground-based detection. Consequently, t…
Rapid search for massive black hole binary coalescences using deep learning
Wen-Hong Ruan, He Wang, Chang Liu +1
The coalescences of massive black hole binaries are one of the main targets of space-based gravitational wave observatories. Such gravitational wave sources are expected to be acco…