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VERSION:2.0
CALSCALE:GREGORIAN
PRODID:UW-Madison-Physics-Events
BEGIN:VEVENT
SEQUENCE:7
UID:UW-Physics-Event-8197
DTSTART:20230303T200000Z
DTEND:20230303T210000Z
DTSTAMP:20260414T054152Z
LAST-MODIFIED:20230303T200034Z
LOCATION:CH4274/Join Zoom Meeting https://uwmadison.zoom.us/j/93014897
 761?pwd=U2c5d3dSWHNETHdRWkVBL3REYXBzQT09
SUMMARY:Machine Learning and its Applications in IceCube\, NPAC (Nucle
 ar/Particle/Astro/Cosmo) Forum\, Claudio Kopper\, Michigan State Unive
 rsity/Friedrich-Alexander-Universität Erlangen-Nürnberg
DESCRIPTION:In this talk\, I will be discussing the fascinating world 
 of machine learning (ML) and its applications to the IceCube neutrino 
 telescope. The field of machine learning has become increasingly impor
 tant over the last years and now constitutes a vital contribution to t
 he physics output of experiments such as IceCube. I will present recen
 t IceCube results that were made possible by machine learning techniqu
 es and highlight the challenges we face when applying ML to IceCube da
 ta.
\n
\nThe key challenges to be solved in IceCube are background sup
 pression\, particle identification\, and event reconstruction\, all of
  which can benefit from the implementation of ML techniques. I will be
  showcasing the ways in which ML can help with these challenges\, and 
 how it has been widely adopted within IceCube\, not only to tackle the
 se issues but also in the development of analysis methodology. Overall
 \, the talk will provide an overview of ML techniques\, how they are a
 pplied in IceCube\, and the exciting recent results based on ML.
URL:https://www.physics.wisc.edu/events/?id=8197
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