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

arXiv: https://arxiv.org/abs/2405.17566 · HTML: https://mlsanity.com/paper/2405.17566/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 80 references that are in the map.
- First posted on arXiv 27 May 2024.

## Builds on (most cited first, 5 of 5)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [Rotation-invariant convolutional neural networks for galaxy morphology prediction](https://mlsanity.com/paper/1503.07077/) (2015)
- [On the dissection of degenerate cosmologies with machine learning](https://mlsanity.com/paper/1810.11027/) (2018)

## Abstract (arXiv)

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 σrm DM/m = 0.1cm2/g or with σDM/m = 1cm2/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 < 0.01cm2/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.