Teaching neural networks to generate Fast Sunyaev Zel'dovich Maps

TL;DR

This paper introduces deep neural networks trained to generate accurate Sunyaev-Zel'dovich effect maps from dark matter distributions, capturing complex baryonic physics efficiently.

Contribution

It presents a novel deep learning approach using U-Net architecture to produce detailed tSZ and kSZ maps from dark matter data, outperforming existing semi-analytical models.

Findings

Neural networks accurately reproduce power spectrum and bispectrum.

Models outperform semi-analytical approaches in key statistical measures.

Approach enables fast, detailed simulation of baryonic effects in cosmology.

Abstract

The thermal Sunyaev-Zel'dovich (tSZ) and the kinematic Sunyaev-Zel'dovich (kSZ) effects trace the distribution of electron pressure and momentum in the hot Universe. These observables depend on rich multi-scale physics, thus, simulated maps should ideally be based on calculations that capture baryonic feedback effects such as cooling, star formation, and other complex processes. In this paper, we train deep convolutional neural networks with a U-Net architecture to map from the three-dimensional distribution of dark matter to electron density, momentum and pressure at ~ 100 kpc resolution. These networks are trained on a combination of the TNG300 volume and a set of cluster zoom-in simulations from the IllustrisTNG project. The neural nets are able to reproduce the power spectrum, one-point probability distribution function, bispectrum, and cross-correlation coefficients of the simulations more accurately than the state-of-the-art semi-analytical models. Our approach offers a route to capture the richness of a full cosmological hydrodynamical simulation of galaxy formation with the speed of an analytical calculation.