"Decoding Dark Matter Substructure without Supervision" Michael Toomey (Brown) - CFPU SMLI

Center for the Fundamental Physics of the Universe (CFPU) Student Machine Learning Initiative (SMLI) - Recorded July 21, 2020
https://cfpu.brown.edu

"Decoding Dark Matter Substructure without Supervision"

The identity of dark matter remains one of the most pressing questions in physics today. While many promising dark matter candidates have been put forth over the last half-century, to date the true identity of dark matter remains elusive. While it is possible that one of the many proposed candidates may turn out to be dark matter, it is at least equally likely that the correct physical description has yet to be proposed. To address this challenge, novel applications of machine learning can help physicists gain insight into the dark sector from a theory agnostic perspective. In this work we demonstrate the use of unsupervised machine learning techniques to infer the presence of substructure in dark matter halos using galaxy-galaxy strong lensing simulations in a proof-of-principle application.

Видео "Decoding Dark Matter Substructure without Supervision" Michael Toomey (Brown) - CFPU SMLI канала Brown University Department of Physics