Constraining Galaxy-Halo connection using machine learning

Journal Article · 14 November 2024 · Astronomy and Computing

DOI: https://doi.org/10.1016/j.ascom.2024.100883 · OSTI ID:3010050

Jana, A.  ^[1]; Samushia, L. ^[2]

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1. Kansas State Univ., Manhattan, KS (United States)
2. Kansas State Univ., Manhattan, KS (United States); Georgian National Astrophysical Observatory (Georgia); Ilia State Univ. (Georgia)

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_p (r_p)), angular multipoles of the correlation function (ξ_ℓ (r)), and the void probability function (VPF) of Luminous Red Galaxies from Dark Energy Spectroscopic Instrument mocks. Our results show that while combining w_p (r_p) and VPF improves parameter constraints, adding the multipoles ξ₀, ξ₂, and ξ₄ to w_p (r_p) does not significantly improve the constraints.

• Research Organization: Kansas State Univ., Manhattan, KS (United States); Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
• Sponsoring Organization: USDOE; USDOE Office of Science (SC), High Energy Physics (HEP)
• Grant/Contract Number: AC02-05CH11231; SC0011840; SC0021165
• OSTI ID: 3010050
• Journal Information: Astronomy and Computing, Journal Name: Astronomy and Computing Vol. 49; ISSN 2213-1337
• Publisher: ElsevierCopyright Statement
• Country of Publication: United States
• Language: English