BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//LBNL Physics Division Research Progress Meetings - ECPv6.8.3//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:LBNL Physics Division Research Progress Meetings
X-ORIGINAL-URL:https://rpm.physics.lbl.gov
X-WR-CALDESC:Events for LBNL Physics Division Research Progress Meetings
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:UTC
BEGIN:STANDARD
TZOFFSETFROM:+0000
TZOFFSETTO:+0000
TZNAME:UTC
DTSTART:20200101T000000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=UTC:20200312T160000
DTEND;TZID=UTC:20200312T170000
DTSTAMP:20260417T211813
CREATED:20200309T155048Z
LAST-MODIFIED:20200312T152356Z
UID:1374-1584028800-1584032400@rpm.physics.lbl.gov
SUMMARY:Ka Vang Tsang (SLAC) "Imaging Neutrinos: Machine Learning in LArTPC"
DESCRIPTION:Abstract:\n\nEver since the discovery\, neutrinos have proven to be one of the most intriguing subatomic particles. In the past two decades\, we have made tremendous progress in the establishment of the neutrino oscillation phenomenon. We are now able to reveal the nature using neutrinos\, such as CP violation in the lepton sector\, the neutrino mass hierarchy\, and the possible existence of the sterile neutrinos.\nLiquid argon time projection chamber (LArTPC) is a novel technology for neutrino detection because of its excellent imaging capability of charged particles. However\, it is challenging to reconstruct and analyze LArTPC events efficiently in large scale detectors.  In this talk\, I will review some revolutionary ideas in machine learning\, and demonstrate the use of these techniques to tackle the challenge in LArTPC event reconstruction.
URL:https://rpm.physics.lbl.gov/event/ka-vang-tsang-slac-imaging-neutrinos-machine-learning-in-lartpc/
LOCATION:50-Auditorium
END:VEVENT
END:VCALENDAR