Current progress and challenges from the Cosmology and Astrophysics with MachinE Learning Simul ...
Current progress and challenges from the Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) project (Daniel Angles-Alcazar) -- https://indico.iap.fr/event/1/contrib...
Large-volume cosmological hydrodynamic simulations have become a primary tool to understand supermassive black holes (SMBHs), galaxies, and the large-scale structure of the Universe. However, current uncertainties in sub-grid models for core physical processes such as feedback from massive stars and SMBHs limit their predictive power and plausible use to extract information from extragalactic surveys. In this talk, I will present an overview of the Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) project, containing thousands of simulations implementing different cosmological and astrophysical parameters, sub-grid galaxy formation implementation, and hydrodynamics solver, and designed to train machine learning algorithms to maximize the extraction of information from cosmological surveys while marginalizing over uncertainties in sub-grid physics. I will show illustrative examples of the broad range of possible applications of CAMELS, discuss recent progress and challenges building robust simulation-based inference models for cosmology, and advertise the latest additions to the ever-growing CAMELS public data repository.
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