43 citations · 73 across the 6 of their papers we have counts for
14 papers
Robust marginalization of baryonic effects for cosmological inference at the field level
Francisco Villaescusa-Navarro, Shy Genel, Daniel Angles-Alcazar +11
We train neural networks to perform likelihood-free inference from 2D maps containing the total mass surface density from thousands of hydrodynamic simula…
Multifield Cosmology with Artificial Intelligence
Francisco Villaescusa-Navarro, Daniel Anglés-Alcázar, Shy Genel +10
Astrophysical processes such as feedback from supernovae and active galactic nuclei modify the properties and spatial distribution of dark matter, gas, and galaxies in a poorly und…
Inferring Black Hole Properties from Astronomical Multivariate Time Series with Bayesian Attentive Neural Processes
Ji Won Park, Ashley Villar, Yin Li +5
Among the most extreme objects in the Universe, active galactic nuclei (AGN) are luminous centers of galaxies where a black hole feeds on surrounding matter. The variability patter…
AI-assisted super-resolution cosmological simulations II: Halo substructures, velocities and higher order statistics
Yueying Ni, Yin Li, Patrick Lachance +4
In this work, we expand and test the capabilities of our recently developed super-resolution (SR) model to generate high-resolution (HR) realizations of the full phase-space matter…
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…