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:20170312T100000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0700
TZOFFSETTO:-0800
TZNAME:PST
DTSTART:20171105T090000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20171027T120000
DTEND;TZID=America/Los_Angeles:20171027T130000
DTSTAMP:20260527T185720
CREATED:20171024T173237Z
LAST-MODIFIED:20171024T173314Z
UID:297-1509105600-1509109200@inpa.lbl.gov
SUMMARY:Xavier Prochaska (UCSD) - Deep Learning of Quasar Spectra
DESCRIPTION:I will describe our development of a convolutional neural network (CNN) to learn to search for and characterize absorption lines in quasar spectra. Specifically\, the algorithm discovers and measures the redshift and Hydrogen column density of damped Lya systems (DLAs). These systems dominate the neutral hydrogen gas of the universe\, trace the interstellar medium of distant galaxies\, and offer cosmological constraints on the build up of gas and heavy elements across cosmic time. I will discuss the lessons learned employing CNN techniques on large spectral datasets and the prospects for future analysis.
URL:https://inpa.lbl.gov/event/xavier-prochaska-ucsd-deep-learning-of-quasar-spectra/
LOCATION:50A-5132- Sessler\, 50A-5132 Sessler Conference Room\, CA
END:VEVENT
END:VCALENDAR