38 citations · 273 across the 17 of their papers we have counts for
18 papers · 1 filter
Consistency of Pantheon+ supernovae with a large-scale isotropic universe
Li Tang, Hai-Nan Lin, Liang Liu +1
We investigate the possible anisotropy of the universe using the most up-to-date type Ia supernovae, i.e. the Pantheon+ compilation. We fit the full Pantheon+ data with the dipole-…
Probing the baryon mass fraction in IGM and its redshift evolution with fast radio bursts using Bayesian inference method
Hai-Nan Lin, Rui Zou
We investigate the fraction of baryon mass in intergalactic medium (), using 18 well-localized FRBs in the redshift range . We construct a five-…
Deep learning method in testing the cosmic distance duality relation
Li Tang, Hai-Nan Lin, Liang Liu
The cosmic distance duality relation (DDR) is constrained from the combination of type-Ia supernovae (SNe Ia) and strong gravitational lensing (SGL) systems using deep learning met…
Search for the correlations between host properties and of fast radio bursts: constraints on the baryon mass fraction in IGM
Hai-Nan Lin, Xin Li, Li Tang
The application of fast radio bursts (FRBs) as probes to investigate astrophysics and cosmology requires the proper modelling of the dispersion measures of Milky Way (${\rm DM_{MW}…
Probing the anisotropic distribution of baryon matter in the Universe using fast radio bursts
Hai-Nan Lin, Yu Sang
We propose that fast radio bursts (FRBs) can be used as the probes to constrain the possible anisotropic distribution of baryon matter in the Universe. Monte Carlo simulations show…
Model-independently calibrating the luminosity correlations of gamma-ray bursts using deep learning
Li Tang, Xin Li, Hai-Nan Lin +1
Gamma-ray bursts (GRBs) detected at high redshift can be used to trace the Hubble diagram of the Universe. However, the distance calibration of GRBs is not as easily as that of typ…