Speaker: Luisa Lucie-Smith, Research Fellow, Max-Planck-Institue for Astrophysics, Munich
Dark matter halos are the fundamental building blocks of cosmic large-scale structure. Improving our theoretical understanding of their evolution and formation is an essential step towards understanding how galaxies form. I will present a deep learning model, based on 3D convolutional neural networks (CNNs), trained to learn the mapping between the initial conditions and the final dark matter halos of an N-body simulation. The CNN is able to extract directly from the initial density field the features that are relevant to halo formation. Our goal is to utilize machine learning for knowledge extraction: we aim to gain new physical understanding of halo formation by extracting information from the deep learning model regarding the underlying physics of halo collapse. To do this, we first compare the performance of the deep learning algorithm to that of machine learning models whose predictions depend only on physically-motivated features extracted from the initial conditions. I will then present ongoing work on interpreting the features learnt by the deep learning model in relation to physical properties of the initial conditions.
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