340 citations · 2.7k across the 45 of their papers we have counts for
45 papers
One latent to fit them all: a unified representation of baryonic feedback on matter distribution
Shurui Lin, Yin Li, Shy Genel +4
Accurate and parsimonious quantification of baryonic feedback on matter distribution is of crucial importance for understanding both cosmology and galaxy formation from observation…
Hybrid-bias and displacement emulators for field-level modelling of galaxy clustering in real and redshift space
Marcos Pellejero Ibanez, Raul E. Angulo, Drew Jamieson +1
Recently, hybrid bias expansions have emerged as a powerful approach to modelling the way in which galaxies are distributed in the Universe. Similarly, field-level emulators have r…
Quadratic shape biases in three-dimensional halo intrinsic alignments
Kazuyuki Akitsu, Yin Li, Teppei Okumura
Understanding the nonlinear relation between the shapes of halos or galaxies and the surrounding matter distribution is essential in accurate modeling of their intrinsic alignments…
AI-assisted super-resolution cosmological simulations III: Time evolution
Xiaowen Zhang, Patrick Lachance, Yueying Ni +5
In this work, we extend our recently developed super-resolution (SR) model for cosmological simulations to produce fully time consistent evolving representations of the particle ph…
Emulating cosmological growth functions with B-Splines
Ngai Pok Kwan, Chirag Modi, Yin Li +1
In the light of GPU accelerations, sequential operations such as solving ordinary differential equations can be bottlenecks for gradient evaluations and hinder potential speed gain…
RSD measurements from BOSS galaxy power spectrum using the halo perturbation theory model
Byeonghee Yu, Uros Seljak, Yin Li +1
We present growth of structure constraints from the cosmological analysis of the power spectrum multipoles of SDSS-III BOSS DR12 galaxies. We use the galaxy power spectrum model of…
Particle clustering in turbulence: Prediction of spatial and statistical properties with deep learning
Yan-Mong Chan, Natascha Manger, Yin Li +4
We investigate the utility of deep learning for modeling the clustering of particles that are aerodynamically coupled to turbulent fluids. Using a Lagrangian particle module within…
Gravitational wave fossils in nonlinear regime: halo tidal bias and intrinsic alignments from gravitational wave separate universe simulations
Kazuyuki Akitsu, Yin Li, Teppei Okumura
We investigate impacts of long-wavelength gravitational waves (GWs) on nonlinear structure formation by utilizing the tidal separate universe simulations. Based on the equivalence…
Field Level Neural Network Emulator for Cosmological N-body Simulations
Drew Jamieson, Yin Li, Renan Alves de Oliveira +3
We build a field level emulator for cosmic structure formation that is accurate in the nonlinear regime. Our emulator consists of two convolutional neural networks trained to outpu…
Simple lessons from complex learning: what a neural network model learns about cosmic structure formation
Drew Jamieson, Yin Li, Siyu He +4
We train a neural network model to predict the full phase space evolution of cosmological N-body simulations. Its success implies that the neural network model is accurately approx…
Cosmology with one galaxy?
Francisco Villaescusa-Navarro, Jupiter Ding, Shy Genel +10
Galaxies can be characterized by many internal properties such as stellar mass, gas metallicity, and star-formation rate. We quantify the amount of cosmological and astrophysical i…
The CAMELS project: public data release
Francisco Villaescusa-Navarro, Shy Genel, Daniel Anglés-Alcázar +44
The Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) project was developed to combine cosmology with astrophysics through thousands of cosmological hydrodynami…
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…
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…