How the Galaxy–Halo Connection Depends on Large-scale Environment

Wu, John F.; Jespersen, Christian Kragh; Wechsler, Risa H.

doi:10.3847/1538-4357/ad7bb3

How the Galaxy–Halo Connection Depends on Large-scale Environment

Journal Article · Tue Nov 12 00:00:00 EST 2024 · The Astrophysical Journal

DOI:https://doi.org/10.3847/1538-4357/ad7bb3· OSTI ID:2574617

^[1]; ^[2]; ^[3]

Space Telescope Science Institute, Baltimore, MD (United States); Johns Hopkins University, Baltimore, MD (United States)
Princeton University, NJ (United States)
Stanford University, CA (United States); SLAC National Accelerator Laboratory (SLAC), Menlo Park, CA (United States)

We investigate the connection between galaxies, dark matter halos, and their large-scale environments at z = 0 with Illustris TNG300 hydrodynamic simulation data. We predict stellar masses from subhalo properties to test two types of machine learning (ML) models: explainable boosting machines (EBMs) with simple galaxy environment features and E(3)-invariant graph neural networks (GNNs). The best-performing EBM models leverage spherically averaged overdensity features on 3 Mpc scales. Interpretations via SHapley Additive exPlanations also suggest that in the context of the TNG300 galaxy–halo connection, simple spherical overdensity on ∼3 Mpc scales is more important than cosmic web distance features measured using the DisPerSE algorithm. Meanwhile, a GNN with connectivity defined by a fixed linking length, L, outperforms the EBM models by a significant margin. As we increase the linking length scale, GNNs learn important environmental contributions up to the largest scales we probe (L = 10 Mpc). We conclude that 3 Mpc distance scales are most critical for describing the TNG galaxy–halo connection using the spherical overdensity parameterization, but that information on larger scales, which is not captured by simple environmental parameters or cosmic web features, can further augment these models. Our study highlights the benefits of using interpretable ML algorithms to explain models of astrophysical phenomena, and the power of using GNNs to flexibly learn complex relationships directly from data while imposing constraints from physical symmetries.

View Accepted Manuscript (DOE)

Research Organization:: SLAC National Accelerator Laboratory (SLAC), Menlo Park, CA (United States)

Sponsoring Organization:: National Science Foundation (NSF); USDOE

Grant/Contract Number:: AC02-76SF00515

OSTI ID:: 2574617

Journal Information:: The Astrophysical Journal, Journal Name: The Astrophysical Journal Journal Issue: 1 Vol. 976; ISSN 0004-637X; ISSN 1538-4357

Publisher:: IOP PublishingCopyright Statement

Country of Publication:: United States

Language:: English

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Mangrove: Learning Galaxy Properties from Merger Trees Jespersen, Christian Kragh; Cranmer, Miles; Melchior, Peter The Astrophysical Journal, Vol. 941, Issue 1 https://doi.org/10.3847/1538-4357/ac9b18	journal	December 2022
Inpainting Hydrodynamical Maps with Deep Learning Mohammad, Faizan G.; Villaescusa-Navarro, Francisco; Genel, Shy The Astrophysical Journal, Vol. 941, Issue 2 https://doi.org/10.3847/1538-4357/ac9f14	journal	December 2022
Searching for Conformity Across Cosmic Time with Local Group and Local Volume Star Formation Histories Olsen, Charlotte; Gawiser, Eric The Astrophysical Journal, Vol. 943, Issue 1 https://doi.org/10.3847/1538-4357/acaa39	journal	January 2023
Revealing the Galaxy–Halo Connection through Machine Learning Hausen, Ryan; Robertson, Brant E.; Zhu, Hanjue The Astrophysical Journal, Vol. 945, Issue 2 https://doi.org/10.3847/1538-4357/acb25c	journal	March 2023
A New Stellar Mass Proxy for Subhalo Abundance Matching Chuang, Chen-Yu; Lin, Yen-Ting The Astrophysical Journal, Vol. 944, Issue 2 https://doi.org/10.3847/1538-4357/acb5f3	journal	February 2023
Environment Matters: Predicted Differences in the Stellar Mass–Halo Mass Relation and History of Star Formation for Dwarf Galaxies Christensen, Charlotte R.; Brooks, Alyson M.; Munshi, Ferah The Astrophysical Journal, Vol. 961, Issue 2 https://doi.org/10.3847/1538-4357/ad0c5a	journal	January 2024
Leaving No Branches Behind: Predicting Baryonic Properties of Galaxies from Merger Trees Chuang, Chen-Yu; Jespersen, Christian Kragh; Lin, Yen-Ting The Astrophysical Journal, Vol. 965, Issue 2 https://doi.org/10.3847/1538-4357/ad2b6c	journal	April 2024
symfind : Addressing the Fragility of Subhalo Finders and Revealing the Durability of Subhalos Mansfield, Philip; Darragh-Ford, Elise; Wang, Yunchong The Astrophysical Journal, Vol. 970, Issue 2 https://doi.org/10.3847/1538-4357/ad4e33	journal	July 2024
Filaments of the Slime Mold Cosmic Web and How They Affect Galaxy Evolution Hasan, Farhanul; Burchett, Joseph N.; Hellinger, Douglas The Astrophysical Journal, Vol. 970, Issue 2 https://doi.org/10.3847/1538-4357/ad4ee2	journal	July 2024
PyTorch: An Imperative Style, High-Performance Deep Learning Library Paszke, Adam; Gross, Sam; Massa, Francisco arXiv https://doi.org/10.48550/arXiv.1912.01703	preprint	January 2019

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