340 citations · 2.7k across the 45 of their papers we have counts for
9 papers · 1 filter
NECOLA: Towards a Universal Field-level Cosmological Emulator
Neerav Kaushal, Francisco Villaescusa-Navarro, Elena Giusarma +3
We train convolutional neural networks to correct the output of fast and approximate N-body simulations at the field level. Our model, Neural Enhanced COLA --NECOLA--, takes as inp…
Super-resolving Dark Matter Halos using Generative Deep Learning
David Schaurecker, Yin Li, Jeremy Tinker +2
Generative deep learning methods built upon Convolutional Neural Networks (CNNs) provide a great tool for predicting non-linear structure in cosmology. In this work we predict high…
The ASTRID simulation: the evolution of Supermassive Black Holes
Yueying Ni, Tiziana Di Matteo, Simeon Bird +6
We present the evolution of black holes (BHs) and their relationship with their host galaxies in Astrid, a large-volume cosmological hydrodynamical simulation with box size 250 $h^…
Weak gravitational lensing shear measurement with FPFS: analytical mitigation of noise bias and selection bias
Xiangchong Li, Yin Li, Richard Massey
Dedicated 'Stage IV' observatories will soon observe the entire extragalactic sky, to measure the 'cosmic shear' distortion of galaxy shapes by weak gravitational lensing. To measu…
The CAMELS Multifield Dataset: Learning the Universe's Fundamental Parameters with Artificial Intelligence
Francisco Villaescusa-Navarro, Shy Genel, Daniel Angles-Alcazar +25
We present the Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) Multifield Dataset, CMD, a collection of hundreds of thousands of 2D maps and 3D grids containi…
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…