BaryonBridge: Stochastic Interpolant Model for Fast Hydrodynamical Simulations

TL;DR

BaryonBridge introduces a stochastic interpolant model that efficiently maps dark matter simulations to baryonic outputs, enabling fast and accurate hydrodynamical predictions suitable for large-scale astrophysical studies.

Contribution

This work presents a novel stochastic interpolant framework that significantly reduces computational costs for hydrodynamical simulations and can condition on multiple cosmological and astrophysical parameters.

Findings

Achieves accurate Lya flux statistics up to small scales at z=2.0

Requires only 7 GPU minutes per 256^3 simulation

Successfully applied to larger volumes like TNG50

Abstract

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.