Commission B3 Astroinformatics and Astrostatistics



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Francisco Villaescusa-Navarro (Simons Foundation, NY)

Simulation based inference for cosmology


Abstract: In this talk I will discuss the importance of performing parameter inference in cosmology together with the problems associated with such a task. I will then motivate the usage of deep learning to surpass the problems associated with traditional inference methods. Methods such as simulation-based inference require the usage of very large datasets to properly train the models and capture the full distribution. I will then present the simulations of the CAMELS project, the largest set of state-of-the-art cosmological hydrodynamic simulations run to-date. Next, I will show some examples of how these methods allow us to extract cosmological and astrophysical information from very small scales at the field-level without knowing the likelihood of the data. I will then discuss how these methods may open a new window that will allow us to study cosmology and astrophysics in a unified and more accurate way.



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