2.3k citations · 36.1k across the 179 of their papers we have counts for
181 papers
The CAMELS-CROCODILE Simulation Suite: A New Cosmology--Astrophysics Playground for Machine Learning
Kentaro Nagamine, Yuri Oku, Atsushi J. Nishizawa +4
We present CAMELS-CROCODILE, a suite of cosmological hydrodynamic simulations that extends the CAMELS framework with the \gadget{} smoothed particle hydrodynamics code and the Osak…
BINDing the lightcone: A suite of astrophysical ray-traced weak lensing and SZ maps
Max E. Lee, Shy Genel, Zoltan Haiman +2
Recent multiwavelength observations of galaxy group and cluster gas suggest stronger baryonic feedback than our best-calibrated hydrodynamical simulations produce, while modeling t…
BIND (Baryonic INpainting with Deep learning): A Field-level Emulator for Galaxy Groups and Clusters
Max E. Lee, Shy Genel, Zoltan Haiman +3
Baryonic feedback is a dominant source of systematic uncertainty for upcoming weak-lensing surveys, but current tools for modeling its effect rely on spherical approximations and d…
Inductive Biases in Field-Level Cosmological Inference from Galaxy Catalogs
James O. Baldwin, Shy Genel, Francisco Villaescusa-Navarro
We perform field-level likelihood-free inference of the matter density parameter from simulated galaxy catalogs using machine learning models with differing inductive biases.…
Evaluating the flexibility of the MillenniumTNG galaxy formation model with multi-zoom re-simulations
Francisco Maion, Raul E. Angulo, Volker Springel +2
In this study we introduce a new simulation campaign designed to understand how parameters that control star-formation and AGN feedback processes in cosmological hydrodynamical sim…
Learning the Universe with cosmological rescaling of merger trees and semi-analytic galaxy formation models
Richard Stiskalek, Lucia A. Perez, Shy Genel +3
Learning cosmology from galaxy surveys requires large suites of simulations spanning the cosmological and astrophysical parameter space, yet hydrodynamical simulations of galaxy fo…