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SUMMARY:Machine learning the formation of dark matter halos - Luisa Lucie-
 Smith (UCL)
DTSTART:20200129T110000Z
DTEND:20200129T113000Z
UID:TALK138970@talks.cam.ac.uk
CONTACT:Will Handley
DESCRIPTION:Dark matter halos are the fundamental building blocks of cosmi
 c large-scale structure. Improving our theoretical understanding of their 
 structure\, evolution and formation is an essential step towards understan
 ding how galaxies form. I will present a machine learning approach which a
 ims to provide new physical insights into the physics driving halo formati
 on. We train a machine learning algorithm to learn the relationship betwee
 n the initial conditions and the final dark matter halos directly from N-b
 ody simulations. We evaluate the predictive performance of the algorithm w
 hen provided with different types of information about the initial conditi
 ons\, allowing us to infer which aspects of the early-Universe density fie
 ld impact the formation of the final dark matter halos. I will also presen
 t 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.
LOCATION:Battcock Tea area
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