Speaker
Description
Dark matter accounts for 85% of all matter in the Universe, yet its nature remains elusive. Next-generation telescopes are providing a wealth of observations of dark matter-dominated galaxy clusters, which contain subtle clues to its nature. However, traditional methods either compress the data into summary statistics or require computationally expensive forward modelling. We present a machine learning framework, designed to provide robust and interpretable constraints on dark matter self-interactions from observations informed by simulations. Our method is trained on multiple simulation suites alongside weak gravitational lensing observations from Euclid and the Hubble Space Telescope. It learns to align shared physical features and marginalise over the differences found in simulations. In addition, we use confidence metrics of the learned features to determine whether the model successfully learned the physical relationships or if simulation-observation mismatch would still lead to unreliable constraints. Finally, we will present preliminary results on the nature of dark matter from the first application of this methodology to a large sample of observed galaxy clusters.