Constraining Galaxy-Halo Connection Using Machine Learning

Abstract

We investigate the potential of machine learning (ML) methods to model small-scale galaxy clustering for constraining Halo Occupation Distribution (HOD) parameters. Our analysis reveals that while many ML algorithms report good statistical fits, they often yield likelihood contours that are significantly biased in both mean values and variances relative to the true model parameters. This highlights the importance of careful data processing and algorithm selection in ML applications for galaxy clustering, as even seemingly robust methods can lead to biased results if not applied correctly. ML tools offer a promising approach to exploring the HOD parameter space with significantly reduced computational costs compared to traditional brute-force methods if their robustness is established. Using our ANN-based pipeline, we successfully recreate some standard results from recent literature. Properly restricting the HOD parameter space, transforming the training data, and carefully selecting ML algorithms are essential for achieving unbiased and robust predictions. Among the methods tested, artificial neural networks (ANNs) outperform random forests (RF) and ridge regression in predicting clustering statistics, when the HOD prior space is appropriately restricted. We demonstrate these findings using the projected two-point correlation function ($w_{\mathrm{p}}(r_{\mathrm{p}})$), angular multipoles of the correlation function (${\xi }_{\ell }(r)$), and the void probability function (VPF) of Luminous Red Galaxies from Dark Energy Spectroscopic Instrument mocks. Our results show that while combining $w_{\mathrm{p}}(r_{\mathrm{p}})$ and VPF improves parameter constraints, adding the multipoles ${\xi }_0$, ${\xi }_2$, and ${\xi }_4$ to $w_{\mathrm{p}}(r_{\mathrm{p}})$ does not significantly improve the constraints.

19 pages, 14 figures, final revisions suggested by Astronomy & Computing

References in corpus (12)

• Deep Learning in Neural Networks: An Overview
• The EAGLE project: Simulating the evolution and assembly of galaxies and their environments
• The DESI Experiment Part I: Science,Targeting, and Survey Design
• Simba: Cosmological Simulations with Black Hole Growth and Feedback
• Wide-Field InfrarRed Survey Telescope-Astrophysics Focused Telescope Assets WFIRST-AFTA 2015 Report
• Galaxy Evolution from Halo Occupation Distribution Modeling of DEEP2 and SDSS Galaxy Clustering
• AbacusSummit: A Massive Set of High-Accuracy, High-Resolution $N$-Body Simulations
• Corrfunc --- A Suite of Blazing Fast Correlation Functions on the CPU
• The $\text{Abacus}$ Cosmological $N$-body Code
• A machine learning approach to galaxy properties: joint redshift-stellar mass probability distributions with Random Forest
• A Hybrid Deep Learning Approach to Cosmological Constraints From Galaxy Redshift Surveys
• Modeling assembly bias with machine learning and symbolic regression