2024 workshop program
Probing Self-Interacting Dark Matter with Interpretable Neural Networks
The work
Abstract
While the ΛCDM model has proven successful in cosmology, it faces challenges in explaining observations of dwarf galaxies and galaxy clusters. Often, these issues are attributed to unrealistic models of black hole feedback - often known as baryonic feedback. However, it can also be evidence for self-interacting dark matter (SIDM).
Unfortunately, the impact of black holes and SIDM on classical observations of galaxy clusters is degenerate, making it difficult to disentangle one from the other.
Here, we present a semi-supervised clustering neural network that can discern between ΛCDM and SIDM and break the degeneracy between SIDM and baryonic feedback models in galaxy clusters. We leverage data from the BAHAMAS hydro-dynamical simulations, incorporating three models of black hole feedback and four self-interaction cross-sections.
Our neural network achieves comparable classification accuracy to previous methods while yielding an interpretable latent space. This latent space not only incorporates the physicalised cross-section but also captures additional features in the data, such as baryonic feedback effects or simulation nuances, ultimately providing a direct validation check between observations and simulations.