Painting baryons onto N-body simulations of galaxy clusters with image-to-image deep learning

TL;DR

This paper introduces a U-net deep learning model that can efficiently add baryonic physics to dark matter-only simulations of galaxy clusters, enabling large-scale, realistic cluster catalog generation for cosmological studies.

Contribution

The authors develop a U-net based image-to-image translation method to accurately 'paint' baryonic properties onto dark matter simulations, streamlining the creation of realistic galaxy cluster models.

Findings

The model reproduces key scaling relations and distribution functions of cluster X-ray luminosity and gas mass.

It predicts baryonic properties for thousands of clusters in under two minutes.

Performance remains robust across different simulation resolutions, with some bias at lower resolutions.

Abstract

Galaxy cluster mass functions are a function of cosmology, but mass is not a direct observable, and systematic errors abound in all its observable proxies. Mass-free inference can bypass this challenge, but it requires large suites of simulations spanning a range of cosmologies and models for directly observable quantities. In this work, we devise a U-net - an image-to-image machine learning algorithm - to ``paint'' the IllustrisTNG model of baryons onto dark-matter-only simulations of galaxy clusters. Using 761 galaxy clusters with $M_{200c} \gtrsim 10^{14}M_\odot$ from the TNG-300 simulation at $z<1$, we train the algorithm to read in maps of projected dark matter mass and output maps of projected gas density, temperature, and X-ray flux. The models train in under an hour on two GPUs, and then predict baryonic images for $\sim2700$ dark matter maps drawn from the TNG-300 dark-matter-only (DMO) simulation in under two minutes. Despite being trained on individual images, the model reproduces the true scaling relation and scatter for the $M_{DM}-L_X$, as well as the distribution functions of the cluster X-ray luminosity and gas mass. For just one decade in cluster mass, the model reproduces three orders of magnitude in $L_X$. The model is biased slightly high when using dark matter maps from the DMO simulation. The model performs well on inputs from TNG-300-2, whose mass resolution is 8 times coarser; further degrading the resolution biases the predicted luminosity function high. We conclude that U-net-based baryon painting is a promising technique to build large simulated cluster catalogs which can be used to improve cluster cosmology by combining existing full-physics and large $N$-body simulations.