7 papers
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…
Informative Priors on Primordial Non-Gaussianity Bias From Galaxy Formation
Anne Moore, Lucia A. Perez, Elisabeth Krause
Constraining primordial non-Gaussianity via its scale-dependent imprint on galaxy clustering requires knowledge of the bias parameter , which is exactly degenerate with $f^{\…
Introducing sapphire: Towards Hybrid Physics-Informed, Data-Driven Modeling of Galaxy Formation
Viraj Pandya, Greg L. Bryan, T. Lucas Makinen +18
Semi-analytic models (SAMs) have been treating galaxy populations as dynamical systems for years, but their evolution equations remain poorly constrained. We introduce…
The Impact of Galaxy Formation on Galaxy Biasing, and Implications for Primordial non-Gaussianity Constraints
Lucia A. Perez, Shy Genel, Elisabeth Krause +1
The parameter measures the local non-Gaussianity in the primordial energy fluctuations of the Universe, with any deviation from providing key…
Galaxy Phase-Space and Field-Level Cosmology: The Strength of Semi-Analytic Models
Natalà S. M. de Santi, Natalí S. M. de Santi, Francisco Villaescusa-Navarro +11
Semi-analytic models are a widely used approach to simulate galaxy properties within a cosmological framework, relying on simplified yet physically motivated prescriptions. They ha…
CosmoBench: A Multiscale, Multiview, Multitask Cosmology Benchmark for Geometric Deep Learning
Ningyuan Huang, Richard Stiskalek, Jun-Young Lee +6
Cosmological simulations provide a wealth of data in the form of point clouds and directed trees. A crucial goal is to extract insights from this data that shed light on the nature…