340 citations · 2.7k across the 45 of their papers we have counts for
6 papers · 1 filter
Learning the Evolution of the Universe in N-body Simulations
Chang Chen, Yin Li, Francisco Villaescusa-Navarro +2
Understanding the physics of large cosmological surveys down to small (nonlinear) scales will significantly improve our knowledge of the Universe. Large N-body simulations have bee…
Fast and Accurate Non-Linear Predictions of Universes with Deep Learning
Renan Alves de Oliveira, Yin Li, Francisco Villaescusa-Navarro +2
Cosmologists aim to model the evolution of initially low amplitude Gaussian density fluctuations into the highly non-linear "cosmic web" of galaxies and clusters. They aim to compa…
Constraining the Halo Mass of Damped Ly Absorption Systems (DLAs) at using the Quasar-CMB Lensing Cross-correlation
Xiaojing Lin, Zheng Cai, Yin Li +2
We study the cross correlation of damped Ly systems (DLAs) and their background quasars, using the most updated DLA catalog and the Planck 2018 CMB lensing convergence field. Ou…
Cosmological simulation in tides: power spectra and halo shape responses, and shape assembly bias
Kazuyuki Akitsu, Yin Li, Teppei Okumura
The well-developed separate universe technique enables accurate calibration of the response of any observable to an isotropic long-wavelength density fluctuation. The large-scale e…
AI-assisted super-resolution cosmological simulations
Yin Li, Yueying Ni, Rupert A. C. Croft +3
Cosmological simulations of galaxy formation are limited by finite computational resources. We draw from the ongoing rapid advances in Artificial Intelligence (specifically Deep Le…
The CAMELS project: Cosmology and Astrophysics with MachinE Learning Simulations
Francisco Villaescusa-Navarro, Daniel Anglés-Alcázar, Shy Genel +19
We present the Cosmology and Astrophysics with MachinE Learning Simulations --CAMELS-- project. CAMELS is a suite of 4,233 cosmological simulations of volu…