5 papers
Robust parameter inference for Taiji via time-frequency contrastive learning and normalizing flows
Tian-Yang Sun, Bo Liang, Ji-Yu Song +6
Transient noise artifacts, commonly referred to as glitches, pose a major challenge to parameter inference for space-based gravitational-wave (GW) observations. We develop a glitch…
Gravitational wave standard sirens: A brief review of cosmological parameter estimation
Shang-Jie Jin, Ji-Yu Song, Tian-Yang Sun +5
Gravitational wave (GW) observations are expected to serve as a powerful and independent probe of the expansion history of the universe. By providing direct and calibration-free me…
Conditional variational autoencoders for cosmological model discrimination and anomaly detection in cosmic microwave background power spectra
Tian-Yang Sun, Tian-Nuo Li, He Wang +2
The cosmic microwave background power spectra are a primary window into the early universe. However, achieving interpretable compression and fast inference diagnostics under weak m…
Search for exotic gravitational wave signals beyond general relativity using deep learning
Yu-Xin Wang, Xiaotong Wei, Chun-Yue Li +6
The direct detection of gravitational waves by LIGO has confirmed general relativity (GR) and sparked rapid growth in gravitational wave (GW) astronomy. However, subtle post-Newton…
Deep learning-driven likelihood-free parameter inference for 21-cm forest observations
Tian-Yang Sun, Yue Shao, Yichao Li +3
The hyperfine structure absorption lines of neutral hydrogen in spectra of high-redshift radio sources, known collectively as the 21-cm forest, have been demonstrated as a sensitiv…