A graph-based Neural Network surrogate model for accelerating semi-analytical model of galaxy formation and evolution
astro-ph.GA
Submitted: 2026-04-25
Updated: 2026-09-16
Comments: 26 pages, 16 figures, 2 tables. Updated to match the accepted version
Code: https://github.com/MutongCat/sam2galaxy-gnn
License: http://creativecommons.org/licenses/by/4.0/
The gist: Understanding how galaxy populations emerge and evolve from the growth of dark matter structure is a central challenge in galaxy formation theory.
Terminology
Abstract
Understanding how galaxy populations emerge and evolve from the growth of dark matter structure is a central challenge in galaxy formation theory. Semi-analytic models (SAMs) provide an efficient framework to address this problem, but exploring large ensembles of merger trees across broad parameter spaces remains computationally demanding. We develop a conditional graph neural network surrogate model that combines merger tree information with SAM parameters to predict galaxy properties across cosmic time. Using merger trees of dark matter halos from the Uchuu simulation and the Galacticus SAM, the model predicts stellar mass, luminosity, angular momentum, gas metal mass, and specific star formation rate across the wide redshift range of 0 <= z <= 5. For instance, the model can predict stellar mass at 0 <= z <= 3 with a scatter of 0.19-0.28 dex and coefficient of determination R squared of 0.946-0.973 (R squared close to 1 indicates prediction closely matching the truth). The results show that a single graph based model can reproduce these galaxy properties with good accuracy over multiple SAM realizations, merger trees and redshifts. This catalog-level model provides a practical route for accelerating SAM based studies of galaxy formation to enable a more detailed investigation of the model parameter space. The inference code, trained models, and example data products are publicly available at https://github.com/MutongCat/sam2galaxy-gnn.
Sources
- Fast and Accurate Non-Linear Predictions of Universes with Deep Learning
- Galaxy Phase-Space and Field-Level Cosmology: The Strength of Semi-Analytic Models
- Gaussian Error Linear Units (GELUs)
- Galaxies on graph neural networks: towards robust synthetic galaxy catalogs with deep generative models
- Galaxies and Halos on Graph Neural Networks: Deep Generative Modeling Scalar and Vector Quantities for Intrinsic Alignment
- Decoupled Weight Decay Regularization
- Wide-Field InfrarRed Survey Telescope-Astrophysics Focused Telescope Assets WFIRST-AFTA 2015 Report
- Clustering analysis of BOSS-CMASS galaxies with semi-analytical model for galaxy formation and halo occupation distribution
Related papers
- Apparent Stability in Self-Gravitating Turbulence and the Evolution of Molecular Clouds
- Two sets of potential-density basis pairs for the study of radial perturbations in collisionless spherical stellar systems
- Constraining reionization-era Ly alpha escape with JELS-MUSE: a highly complete H alpha-selected sample at z about6.1
- Deriving volume density profiles of filaments from observed surface densities
- Little Red Dots and Supermassive Black Hole Seed Formation in Ultralight Dark Matter Halos
- MEGATRON: how the first stars can create an iron metallicity plateau in the smallest dwarf galaxies