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SUMMARY:Deep Learning in Astroparticle Physics
DTSTART:20250128T160000Z
DTEND:20250128T164500Z
DTSTAMP:20260312T111900Z
UID:indico-contribution-10694@events.icecube.wisc.edu
DESCRIPTION:Speakers: Jonas Glombitza (RWTH AACHEN UNIVERSITY)\n\nDeep Lea
 rning in Astroparticle Physics\n\nAlgorithms based on machine learning hav
 e been extraordinarily successful across many domains\, including computer
  vision\, machine translation\, engineering\, and science.\nMoreover\, in 
 the field of physics\, the importance of machine learning is growing quick
 ly\, driven by the need for precise and efficient algorithms that can effe
 ctively handle vast amounts of complex and high-dimensional data.\nRecentl
 y\, with the help of these novel algorithms\, providing improved reconstru
 ctions\, new insights into astroparticle physics could be gained.\nCould i
 t even become a new paradigm for data-driven knowledge discovery?\n\nIn th
 is review\, we explore the current state of machine learning in astroparti
 cle physics after introducing its fundamental concepts.\nWe outline the im
 mense potential of this emerging technology\, illustrate the wide variety 
 of possible applications in the context of astroparticle physics\, and deb
 ate the latest breakthroughs.\nFinally\, we present novel approaches and t
 echniques and discuss future applications and challenges in the field.\n\n
 https://events.icecube.wisc.edu/event/243/contributions/10694/
LOCATION:Clayton Hall (University of Delaware)
RELATED-TO:indico-event-243@events.icecube.wisc.edu
URL:https://events.icecube.wisc.edu/event/243/contributions/10694/
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