Speaker
Description
Machine learning has become a powerful tool for characterizing the high-dimensional structure of cosmological simulations, yet identifying rare or physically distinct objects in a statistically principled way remains challenging. In this work, we develop a normalizing flow based framework to detect low-probability dark matter halos based on their intrinsic properties. Using simulations from the Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) project, we train separate models on three suites: two hydrodynamical (IllustrisTNG and SIMBA) and one $N$-body. For each trained model, we define anomalous halos as those lying in the low-probability tail of the learned distribution within the corresponding simulation. This suite-calibrated probability threshold is then applied across simulations, enabling a systematic comparison of halo populations under different physical assumptions. We show that halos identified as anomalous in one suite are not necessarily rare in another, revealing structured shifts in halo property distributions driven by baryonic physics. Our results demonstrate that normalizing flows provide a robust and transferable statistical framework for identifying physically distinct halo populations and for quantifying differences between cosmological models, representing a step towards robustness in machine learning based analyses of structure formation.