Title: Merging Deep Learning with Physical Models for the Analysis of Cosmological Surveys
Abstract: As we move towards the next generation of cosmological surveys, our field is facing new and outstanding challenges at all levels of scientific analysis, from pixel-level data reduction to cosmological inference. As powerful as Deep Learning (DL) has proven to be in recent years, in most cases a DL approach alone proves to be insufficient to meet these challenges, and is typically plagued by issues including robustness to covariate shifts, interpretability, and proper uncertainty quantification, impeding their exploitation in scientific analyses.
In this talk, I will instead advocate for a unified approach merging the robustness and interpretability of physical models, the proper uncertainty quantification provided by a Bayesian framework, and the inference methodologies and computational frameworks brought about by the Deep Learning revolution.
In practice this will mean following two main directions: 1. using deep generative models as a practical way to manipulate implicit distributions (either data- or simulation-driven) within a larger Bayesian framework. 2. Developing automatically differentiable physics models amenable to gradient-based optimization and inference.
I will illustrate these concepts in a range of applications in the context of cosmological galaxy surveys, from pixel-level astronomical data processing (e.g. deconvolution), to inferring cosmological parameters through fast and automatically differentiable cosmological N-body simulations. Methodology-wise these examples will involve in particular diffusion generative models, score-enhanced simulation-based inference, and hybrid physical/neural ODEs.
BIO: Dr. Lanusse is an Associate Research Scientist at the Flatiron Institute in New York City and holds a permanent CNRS position at the Astrophysics Department of CEA Paris-Saclay (France). He received his PhD in cosmology and inverse problems in 2015 in Paris, and further developed an interdisciplinary expertise in Deep Learning for cosmology as a postdoctoral researcher at Carnegie Mellon University (2015-2018) and UC Berkeley (2018-2019) through multiple collaborations with their respective Machine Learning and Statistics Departments. He is now broadly interested in developing scientific applications of state of the art Deep Learning techniques, by combining concepts of bayesian inference, deep neural networks, and physical forward modeling.
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