# Galaxy Phase-Space and Field-Level Cosmology: The Strength of Semi-Analytic Models

arXiv: https://arxiv.org/abs/2512.10222 · HTML: https://mlsanity.com/paper/2512.10222/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 111 references that are in the map.
- First posted on arXiv 11 Dec 2025.

## Builds on (most cited first, 9 of 9)

- [Learning cosmology and clustering with cosmic graphs](https://mlsanity.com/paper/2204.13713/) (2022)
- [The Cosmic Graph: Optimal Information Extraction from Large-Scale Structure using Catalogues](https://mlsanity.com/paper/2207.05202/) (2022)
- [Robust Field-level Likelihood-free Inference with Galaxies](https://mlsanity.com/paper/2302.14101/) (2023)
- [The CAMELS project: public data release](https://mlsanity.com/paper/2201.01300/) (2022)
- [Robust field-level inference with dark matter halos](https://mlsanity.com/paper/2209.06843/) (2022)
- [The CAMELS project: Expanding the galaxy formation model space with new ASTRID and 28-parameter TNG and SIMBA suites](https://mlsanity.com/paper/2304.02096/) (2023)
- [Field-level simulation-based inference with galaxy catalogs: the impact of systematic effects](https://mlsanity.com/paper/2310.15234/) (2023)
- [SWIFT: Using task-based parallelism, fully asynchronous communication, and graph partition-based domain decomposition for strong scaling on more than 100,000 cores](https://mlsanity.com/paper/1606.02738/) (2016)
- [SWIFT: A modern highly-parallel gravity and smoothed particle hydrodynamics solver for astrophysical and cosmological applications](https://mlsanity.com/paper/2305.13380/) (2023)

## Abstract (arXiv)

Semi-analytic models are a widely used approach to simulate galaxy properties within a cosmological framework, relying on simplified yet physically motivated prescriptions. They have also proven to be an efficient alternative for generating accurate galaxy catalogs, offering a faster and less computationally expensive option compared to full hydrodynamical simulations. In this paper, we demonstrate that using only galaxy 3D positions and radial velocities, we can train a graph neural network coupled to a moment neural network to obtain a robust machine learning based model capable of estimating the matter density parameters, Omegarm m, with a precision of approximately 10%. The network is trained on (25 h-1Mpc)3 volumes of galaxy catalogs from L-Galaxies and can successfully extrapolate its predictions to other semi-analytic models (GAEA, SC-SAM, and Shark) and, more remarkably, to hydrodynamical simulations (Astrid, SIMBA, IllustrisTNG, and SWIFT-EAGLE). Our results show that the network is robust to variations in astrophysical and subgrid physics, cosmological and astrophysical parameters, and the different halo-profile treatments used across simulations. This suggests that the physical relationships encoded in the phase-space of semi-analytic models are largely independent of their specific physical prescriptions, reinforcing their potential as tools for the generation of realistic mock catalogs for cosmological parameter inference.