A deep-learning algorithm to disentangle self-interacting dark matter and AGN feedback models

TL;DR

This paper introduces a machine learning approach using CNNs to distinguish between self-interacting dark matter and astrophysical feedback effects in galaxy cluster images, improving dark matter inference accuracy.

Contribution

It presents a novel CNN-based method trained on simulations to break degeneracies between dark matter models and feedback mechanisms, with high accuracy and robustness.

Findings

80% accuracy in identifying dark matter types in simulations

Adding X-ray maps improves feedback model differentiation

Model remains accurate with realistic noise, bias, and redshift errors

Abstract

Different models of dark matter can alter the distribution of mass in galaxy clusters in a variety of ways. However, so can uncertain astrophysical feedback mechanisms. Here we present a Machine Learning method that ''learns'' how the impact of dark matter self-interactions differs from that of astrophysical feedback in order to break this degeneracy and make inferences on dark matter. We train a Convolutional Neural Network on images of galaxy clusters from hydro-dynamic simulations. In the idealised case our algorithm is 80% accurate at identifying if a galaxy cluster harbours collisionless dark matter, dark matter with ${\sigma}_{\rm DM}/m = 0.1$cm$^2/$g or with ${\sigma}_{DM}/m = 1$cm$^2$/g. Whilst we find adding X-ray emissivity maps does not improve the performance in differentiating collisional dark matter, it does improve the ability to disentangle different models of astrophysical feedback. We include noise to resemble data expected from Euclid and Chandra and find our model has a statistical error of < 0.01cm$^2$/g and that our algorithm is insensitive to shape measurement bias and photometric redshift errors. This method represents a new way to analyse data from upcoming telescopes that is an order of magnitude more precise and many orders faster, enabling us to explore the dark matter parameter space like never before.