Symbolic regression (SR) is a machine learning-based regression method based on genetic
programming principles that integrates techniques and processes from heterogeneous …

In the last few years, electron microscopy has experienced a new methodological paradigm
aimed to fix the bottlenecks and overcome the challenges of its analytical workflow. Machine …

Symbolic regression (SR) is the study of algorithms that automate the search for analytic
expressions that fit data. While recent advances in deep learning have generated renewed …

We train graph neural networks to perform field-level likelihood-free inference using galaxy
catalogs from state-of-the-art hydrodynamic simulations of the CAMELS project. Our models …

Approximate methods to populate dark-matter haloes with galaxies are of great utility to
galaxy surveys. However, the limitations of simple halo occupation models (HODs) preclude …

We present the first comprehensive halo occupation distribution (HOD) analysis of the Dark
Energy Spectroscopic Instrument (DESI) One-Percent Survey luminous red galaxy (LRG) …

Abstract The Cosmology and Astrophysics with Machine Learning Simulations (CAMELS)
project was developed to combine cosmology with astrophysics through thousands of …

We present CAMELS-ASTRID, the third suite of hydrodynamical simulations in the
Cosmology and Astrophysics with MachinE Learning (CAMELS) project, along with new …

Symbolic Regression (SR) algorithms attempt to learn analytic expressions which fit data
accurately and in a highly interpretable manner. Conventional SR suffers from two …

Modern redshift surveys are tasked with mapping out the galaxy distribution over enormous
distance scales. Existing hydrodynamical simulations, however, do not reach the volumes …