BEGIN:VCALENDAR
VERSION:2.0
CALSCALE:GREGORIAN
PRODID:UW-Madison-Physics-Events
BEGIN:VEVENT
SEQUENCE:3
UID:UW-Physics-Event-9180
DTSTART:20250501T193000Z
DTEND:20250501T203000Z
DTSTAMP:20260413T151543Z
LAST-MODIFIED:20250501T145134Z
LOCATION:Chamberlin 5280   (Will end in time to get to the Astronomy/P
 hysics Colloquium at 3:30.)
SUMMARY:Unlocking High Redshift Hydrogen Cosmology with Machine Learni
 ng (Differentiable Bayesian Forward Models for Detecting Cosmic Dawn)\
 , NPAC (Nuclear/Particle/Astro/Cosmo) Forum\, Nick Kern\, MIT & Univer
 sity of Michigan
DESCRIPTION:The frontier of modern astrophysics and cosmology lies at 
 high redshifts\, where new generations of experiments are tapping into
  uncharted astrophysical information to shed light on longstanding pro
 blems with LCDM and the birth of the first stars at Cosmic Dawn. In pa
 rticular\, over the next decade\, radio telescopes using the 21 cm hyp
 erfine transition of neutral hydrogen will yield unprecedentedly large
 -volume and statistically-powerful measurements of the high redshift u
 niverse. Although widely recognized as a transformative cosmological p
 robe\, these radio telescopes face intense foreground contamination an
 d instrumental systematics that have to date precluded a direct measur
 ement of the 21 cm cosmological signal. However\, a new era of ML-acce
 lerated analysis is enabling deeper foreground subtraction\, faster pa
 rameter inference\, and improved systematics mitigation that will be k
 ey to finally unlocking the potential of 21 cm science. In this talk\,
  I will discuss how current limits of the 21 cm signal at Cosmic Dawn 
 are already providing novel insights into the formation of the first s
 tars and galaxies at z > 6. I will then discuss work on new differenti
 able Bayesian forward models that will bridge the field into a new era
  where 21 cm experiments can fully realize their promise as a cosmolog
 ical probe. Along the way\, I'll share insights into how we are addres
 sing systematics-plagued analyses\, (very) high-dimensional inference\
 , and how GPUs make all of this work at scale.
URL:https://www.physics.wisc.edu/events/?id=9180
END:VEVENT
END:VCALENDAR