Elena Hernandez-Martinez
Elena Hernandez Martinez is an AI Researcher at the appliedAI Institute for Europe (TransferLab). She holds a Ph.D. from LMU Munich, where she worked on cosmological simulations of large-scale structure formation in the Computational Astrophysics Research Group. During a research stay at the Flatiron Institute (Simons Foundation), she developed machine learning methods for cosmological parameter inference from galaxy cluster data. Her interests span simulation-based inference, neural networks for scientific applications, and high-performance computing.
AppliedAI Institute for Europe
Session
Galaxy clusters are the largest gravitationally bound structures in the universe, shaped by both the overall composition of the cosmos and the complex physics of the gas within them. Disentangling these two influences is a central challenge in modern astrophysics, and key to resolving cases where different experiments measuring the same fundamental properties of the universe yield conflicting results, pointing to new physics.
Telescopes such as eROSITA, the Simons Observatory, CMB-S4, Euclid, and the Rubin Observatory are now observing clusters across multiple wavelengths at unprecedented depth, making this challenge urgent and tractable.
We present a likelihood-free inference framework combining Gaussian process emulation (CARPoolGP) with neural networks to jointly infer 28 cosmological and astrophysical parameters from stacked cluster profiles. The emulator, trained on 768 hydrodynamic zoom-in simulations (CAMELS-zoomGZ), generates low-variance predictions across the full parameter space. Neural networks, optimized via Optuna, map emulated multiwavelength profiles to posterior moments.
We achieve correlation coefficients above 0.97 for all cosmological parameters and above 0.90 for all astrophysical ones, with robustness to realistic noise levels. This accuracy across a full 28-dimensional parameter space is unprecedented, allowing us to separate cosmology from internal cluster physics, enabling more reliable cosmological measurements and better-calibrated simulations.