Disentangling Feedback and Variance in 1,024 Milky Way-Mass DREAMS Simulations

          @misc{ scivideos_PIRSA:26060032,
            doi = {10.48660/26060032},
            url = {https://pirsa.org/26060032},
            author = {Rose, Jonah},
            keywords = {Cosmology},
            language = {en},
            title = {Disentangling Feedback and Variance in 1,024 Milky Way-Mass DREAMS Simulations},
            publisher = {Perimeter Institute for Theoretical Physics},
            year = {2026},
            month = {jun},
            note = {PIRSA:26060032 see, \url{https://scivideos.org/pirsa/26060032}}
          }
          

Abstract

We introduce a novel framework for simulation-based inference using the DREAMS Project, a suite of 1,024 cosmological hydrodynamical zoom-in simulations of Milky Way-mass halos. This suite is designed to systematically disentangle theoretical uncertainties in galaxy formation physics from intrinsic halo-to-halo variance by varying key astrophysical parameters governing supernova wind energy, wind speed, and AGN feedback efficiency within the IllustrisTNG model [arXiv:2512.00148]. To overcome the computational bottleneck of evaluating this high-dimensional parameter space, we utilize a hierarchical generative machine learning framework. By incorporating conditional normalizing flows and Variational Diffusion Models, we accurately emulate both central host properties and variable-length satellite populations [arXiv:2409.02980]. We then introduce a novel observational weighting scheme constrained by the empirical stellar mass-halo mass relation [arXiv:2602.03613]. This approach yields pseudo-posterior constraints that reveal broad degeneracies in fiducial feedback parameters, demonstrating that standard single-model tuning misses complex parameter interdependencies. Applying this inference framework allows us to robustly assess the impact of feedback variations versus accretion history. For central galaxies, we demonstrate that specific structural shifts are driven by specific merger histories, such as the Gaia-Sausage-Enceladus event, though immense halo-to-halo scatter persists. For satellites, we show that intrinsic variance overwhelmingly dominates population statistics, while identifying a persistent tension regarding extended half-light radii observed in the SAGA survey [arXiv:2512.02095]. Finally, we outline ongoing extensions of this inference framework to multi-code simulations (FIRE3, RAMSES, ChaNGa) along with new simulation designs that incorporate mass-varied and resolution-varied suites spanning $10^9$ to $10^{14}$ solar masses; establishing a new method to understand parameter and resolution variations across a full range of halo masses.