BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//INPA - ECPv6.8.3//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-ORIGINAL-URL:https://inpa.lbl.gov
X-WR-CALDESC:Events for INPA
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:America/Los_Angeles
BEGIN:DAYLIGHT
TZOFFSETFROM:-0800
TZOFFSETTO:-0700
TZNAME:PDT
DTSTART:20200308T100000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0700
TZOFFSETTO:-0800
TZNAME:PST
DTSTART:20201101T090000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20201218T120000
DTEND;TZID=America/Los_Angeles:20201218T130000
DTSTAMP:20260429T063830
CREATED:20201213T231230Z
LAST-MODIFIED:20201213T231230Z
UID:937-1608292800-1608296400@inpa.lbl.gov
SUMMARY:VIRTUAL INPA SEMINAR | Patrick Komiske (MIT)
DESCRIPTION:Speaker: Patrick Komiske (MIT) \nTitle: Optimizing Particle Physics With Machine Learning \nAbstract: \nExciting new advances in particle physics\, particularly in the area of jet physics at colliders such as the LHC\, are being driven by machine learning (ML) methods. For example\, in just a few short years\, the state of the art for important tasks such as jet classification has progressed from cutting on single observables to hyper-variate classifiers that can be trained directly on data. In this talk\, I will discuss these and other developments that demonstrate broad synergy between ML and particle physics\, including training and calibrating jet classifiers directly on data\, visualizing and quantifying jets from the CMS Open Data with a recently proposed metric between collider events\, and simultaneously unfolding multiple observables with the OmniFold method. \nJoin Zoom Meeting\nhttps://lbnl.zoom.us/j/96028254060?pwd=OUxQbmNzVXpFNkk0YTZLdUtTczhGQT09 \nMeeting ID: 960 2825 4060\nPasscode: 456994
URL:https://inpa.lbl.gov/event/virtual-inpa-seminar-patrick-komiske-mit/
END:VEVENT
END:VCALENDAR