Predicting the impact of feedback on matter clustering with machine learning in CAMELS

Abstract

Extracting information from the total matter power spectrum with the precision needed for upcoming large cosmological surveys requires unraveling the complex effects of galaxy formation processes on the distribution of matter. In this work, we investigate the impact of baryonic physics on matter clustering at 𝑧 = 0 using a large library of power spectra from the Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) project, containing thousands of (25 ℎ −1 Mpc) 3 volume realizations with varying cosmology, initial random field, stellar and AGN feedback strength, sub-grid model implementation, and (magneto)hydrodynamics methods. We show that baryonic physics can profoundly affect matter clustering on scales 𝑘 0.1 ℎ Mpc −1 and the magnitude of this effect is highly dependent on the details of the galaxy formation implementation and variations of cosmological and astrophysical parameters. Increasing AGN feedback strength decreases halo baryon fractions and yields generally stronger suppression of power relative to N-body simulations, while stronger stellar feedback often results in weaker overall effects by suppressing black hole growth and therefore the impact of AGN feedback. We find a broad correlation between mean baryon fraction of massive halos (𝑀 200c > 10 13.5 M /ℎ) and suppression of matter clustering but with significant scatter compared to previous work owing to wider exploration of feedback parameters and cosmic variance effects. We show that a random forest regressor trained on the baryon content and abundance of halos across the full mass range 10 10 ≤ 𝑀 halo /M ℎ −1 < 10 14 can predict the effect of galaxy formation on the matter power spectrum on scales 𝑘 = 0.5-20 ℎ Mpc −1 , providing access to cosmological information in the highly non-linear regime.

Constraining Cosmology with Machine Learning and Galaxy Clustering: The CAMELS-SAM Suite

“…However, most of these approaches focus on larger scales (R 7 h −1 cMpc or < k h 1 max cMpc −1 ), due to the difficulty of capturing the complexity of clustering on strongly nonlinear scales with these approaches. 12 Smaller nonlinear scales of clustering have been found to hold more cosmological information than larger scales (Contreras et al 2023;Lange et al 2022Lange et al , 2023, and they are affected by the details of feedback and baryonic physics (seen acutely in the CAMELS matter power spectra in Delgado et al 2023). Within the HOD framework and even for the thoroughly studied power spectrum, the chosen prescription for the nonlinear regime strongly affects measured cosmological parameters (e.g., a 5σ bias for a Euclid-like survey; Safi & Farhang 2021); this motivates approaches that inherently reproduce small-scale galaxy clustering while also incorporating baryonic effects.…”

“…Machine learning enables the study of the galaxy-halo connection using nearly any type of galaxy property or feature, in particular those for which relationships are very hard to formulate or model (e.g., Jo et al 2023;Shao et al 2023c;Delgado et al 2023;Rodrigues et al 2023). Machine learning also avoids some of the limitations of "classical" methods, and it notably has the ability to find constraints with fewer samples or over a larger parameter space than a Fisher formalism or a covariance matrix use de Santi & Abramo 2022) as well as for summary statistics for which theoretical descriptions and likelihoods do not exist (Makinen et al 2021).…”