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Machine learning the formation of dark matter halos

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Dark matter halos are the fundamental building blocks of cosmic large-scale structure. Improving our theoretical understanding of their structure, evolution and formation is an essential step towards understanding how galaxies form. I will present a machine learning approach which aims to provide new physical insights into the physics driving halo formation. We train a machine learning algorithm to learn the relationship between the initial conditions and the final dark matter halos directly from N-body simulations. We evaluate the predictive performance of the algorithm when provided with different types of information about the initial conditions, allowing us to infer which aspects of the early-Universe density field impact the formation of the final dark matter halos. I will also present ongoing work which extends our method to deep learning algorithms, able to extract directly from the initial density field the features that are relevant to halo formation.

This talk is part of the Cavendish Astrophysics Coffee talks series.

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