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• W3082694231 abstract "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." @default.
• W3082694231 created "2020-09-08" @default.
• W3082694231 creator A5017562780 @default.
• W3082694231 creator A5025571888 @default.
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• W3082694231 creator A5071962308 @default.
• W3082694231 creator A5082903712 @default.
• W3082694231 creator A5087038361 @default.
• W3082694231 date "2020-08-28" @default.
• W3082694231 modified "2023-09-27" @default.
• W3082694231 title "Decoding Dark Matter Substructure without Supervision" @default.
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