The upcoming generation of cosmological surveys such as LSST will aim to map the Universe in great detail and on an unprecedented scale. This of course implies new and outstanding challenges at all levels of the scientific analysis, from pixel level data reduction to cosmological inference. In this talk, I will illustrate how recent advances in deep learning and associated automatic differentiation frameworks, can help us tackle these challenges and rethink our approach to data analysis for cosmological surveys. We will see how at the pixel level, combining physical models of the instrument (which account for noise/PSF) with deep generative models (which account for complex galaxy morphologies) can allow us to solve a number of astronomical inverse problems ranging from deconvolution to deblending galaxy images. At the cosmological analysis level, I will present our efforts to implement N-body simulations directly in TensorFlow, opening the door to a range of novel and efficient inference techniques, and allowing for fast hybrid physical/ml simulations. Finally, I will highlight how even without any machine learning, automatic differentiation can make tasks like Fisher forecasts, data compression, or survey optimization trivial, and allow for fast inference via Hamiltonian Monte-Carlo.
Organizer: Virginia AJANI
