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:20190310T100000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0700
TZOFFSETTO:-0800
TZNAME:PST
DTSTART:20191103T090000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20191108T120000
DTEND;TZID=America/Los_Angeles:20191108T130000
DTSTAMP:20260526T015115
CREATED:20191008T011954Z
LAST-MODIFIED:20191008T012326Z
UID:668-1573214400-1573218000@inpa.lbl.gov
SUMMARY:Speaker: Solène Chabanier - IRFU\, CEA\, Université Paris-Saclay
DESCRIPTION:The title and abstract are forthcoming.
URL:https://inpa.lbl.gov/event/speaker-solene-chabanier-irfu-cea-universite-paris-saclay/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20191122T120000
DTEND;TZID=America/Los_Angeles:20191122T130000
DTSTAMP:20260526T015115
CREATED:20191118T211507Z
LAST-MODIFIED:20191118T211530Z
UID:692-1574424000-1574427600@inpa.lbl.gov
SUMMARY:Speaker: Chirag Modi - University of California\, Berkeley
DESCRIPTION:Title: Reconstruction of Cosmological Fields in Forward Model Framework – Galaxy Clustering and Intensity Mapping \nAbstract:\nIn this talk\, I will outline the forward model approach to reconstruct cosmological fields in a Bayesian framework. I will focus on two examples – galaxy clustering and neutral hydrogen intensity mapping. \nIn galaxy clustering example\, I will use the observations of galaxy surveys to reconstruct the initial Lagrangian field. \nHere\, we develop a novel framework with neural networks to forward model halo masses and positions and demonstrate that our method outperforms standard reconstruction in both real and redshift space.  \nThis reconstructed initial field has enhanced signal for baryon acoustic oscillations and can enhance science returns for surveys like DESI.  \nFor neutral hydrogen surveys\, we lose over 50% of the modes at high redshifts due to foregrounds and it severely hampers their feasibility for cosmological analysis. \nWith a novel bias framework for the forward model\, I will show that we are able to reconstruct over 90% of these modes and this recovers cross-correlations with photo-z surveys like LSST and tracers like CMB lensing. Lastly\, I will briefly touch upon assumptions made in this reconstruction framework regarding noise models and likelihood. I will discuss preliminary ways to improve upon them using deep learning.
URL:https://inpa.lbl.gov/event/speaker-chirag-modi-university-of-california-berkeley/
END:VEVENT
END:VCALENDAR