Speaker
Description
The internal properties of individual galaxies carry information about the cosmological environment in which they formed. Extracting this information robustly, while marginalizing over uncertain astrophysical processes, remains an open challenge. We present an amortized simulation-based inference framework using conditional normalizing flows to estimate joint posteriors over cosmological and astrophysical parameters directly from galaxy observables. Training on the CAMELS-ASTRID SBOb suite of 1024 hydrodynamic simulations (which simultaneously varies Omega_m, Omega_b, sigma_8 and four sub-grid feedback parameters to disentangle their degenerate effects on baryonic observables), we condition the flow on 14 properties of central galaxies spanning stellar, gas, black hole, and kinematic quantities. Calibration is rigorously assessed via TARP coverage tests, simulation-based calibration rank histograms, and reduced chi^2 statistics, confirming well-calibrated posteriors on the ASTRID test set. On held-out simulations, S8 and Omega_m are recovered with percent-level mean bias, with posteriors showing meaningful compression relative to the prior. Crucially, this performance generalises to IllustrisTNG simulations, which use a different subgrid physics. This suggests that central galaxy properties encode cosmological information in a manner robust to baryonic modelling choices.