Constructing a general-purpose framework for mapping between dark matter simulations and observable hydrodynamical simulation outputs is a long-standing problem in modern astrophysics. In this work, we present a new approach utilizing stochastic interpolants to map between cheap fast particle mesh simulations and baryonic quantities in three dimensions, requiring a total of 7 GPU minutes per 256^3 grid size simulation. Using the CAMELS multifield dataset, we are able to condition our mapping on both cosmological and astrophysical properties. We focus this work on hydrodynamical quantities suitable for Lya observables finding excellent agreement up to small spatial scales, k ~ 10.0 (h^(-1) Mpc) at z=2.0, for Lya flux statistics. Our approach is fully convolutional, allowing training on comparatively small volumes and application to larger volumes, which was tested on TNG50.
@article{arxiv.2510.19224,
title = {BaryonBridge: Stochastic Interpolant Model for Fast Hydrodynamical Simulations},
author = {Benjamin Horowitz and Carolina Cuesta-Lazaro and Omar Yehia},
journal= {arXiv preprint arXiv:2510.19224},
year = {2025}
}
Comments
Abstract accepted to the 3rd Machine Learning for Astrophysics Workshop at ICML (July 2025), 3 figures, 6 pages