Speaker
Description
Dark matter accounts for 85% of all matter in the Universe, yet its nature remains elusive. Next generation telescopes are providing us with a wealth of observations of dark matter dominated galaxy clusters that have embedded within them 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 for robust inference from cosmological simulations and observations, combining domain adaptation with interpretable latent representations. Our network is trained on multiple simulation suites with known dark matter models alongside observations from Euclid and the Hubble Space Telescope. We use an adversarial network to align physical features, reducing domain mismatch between simulations and observations. In addition, deep clustering and latent-feature confidence metrics allow us to determine whether the network successfully adapted to the observational domain 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.