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DTSTART;TZID=America/Los_Angeles:20220114T120000
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UID:1138-1642161600-1642165200@inpa.lbl.gov
SUMMARY:VIRTUAL INPA SEMINAR | Matthew Ho (Carnegie Mellon)
DESCRIPTION:Speaker: Matthew Ho (Carnegie Mellon) \nTitle: Galaxy Cluster Mass Estimation Using Deep Learning  \nAbstract: \nIn this talk\, I will discuss how we use modern deep learning models to infer galaxy cluster masses with high precision\, reliable uncertainty\, and computational efficiency. I will describe our work in using Convolutional Neural Networks (CNNs) to mitigate systematics in the virial scaling relation to produce dynamical mass estimates of galaxy clusters\, using projected galaxies\, with remarkably low bias and scatter. I will also discuss how we can recover and empirically verify Bayesian uncertainties on deep learning mass predictions using variational weight distributions. I will describe how we’ve validated our methods on real observational systems like the Coma\, CLASH\, and HeCS clusters as well as projections for how we can use these models to study cluster cosmology using data from current and upcoming sky surveys. Lastly\, I will mention results from our ongoing work on combining multi-wavelength observables to produce fully informed observational probes of cluster dark matter.\n\n\n\n\n\n\n\n\n\nJoin Zoom Meeting\n\nhttps://lbnl.zoom.us/j/93651647483 \nMeeting ID: 936 5164 7483
URL:https://inpa.lbl.gov/event/virtual-inpa-seminar-matthew-ho-carnegie-mellon/
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