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PRODID:UW-Madison-Physics-Events
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SEQUENCE:1
UID:UW-Physics-Event-9337
DTSTART:20250808T160000Z
DTEND:20250808T180000Z
DTSTAMP:20260413T084229Z
LAST-MODIFIED:20250807T180020Z
LOCATION:1310 Sterling or https://uwmadison.zoom.us/j/9808598851?pwd=h
 tNBYtuyBKa4NEelCNpYMKID9XMyL7.1&omn=94039156074
SUMMARY:Discovery and Characterization of Strong Gravitational Lenses 
 in the Dark Energy Survey with Machine Learning\, Thesis Defense\, Jim
 ena Gonzalez\, Physics PhD Graduate Student
DESCRIPTION:Strong gravitational lensing\, where a massive galaxy bend
 s and magnifies the light from a more distant source\, offers a unique
  window into the underlying cosmology of the universe and serves as a 
 powerful tool for studying dark energy. Yet identifying and analyzing 
 these rare systems within the vast datasets produced by modern surveys
  remains a major challenge. In this talk\, I will present my thesis wo
 rk developing new machine learning and hybrid techniques to make stron
 g lens discovery and modeling more efficient. First\, I introduce a ma
 chine learning–based search that uncovered hundreds of strong lensin
 g candidates in the Dark Energy Survey. Next\, I compare three indepen
 dent machine learning searches applied to the same dataset and show ho
 w combining them improves performance and reduces missed discoveries. 
 Finally\, I describe a new pipeline that integrates machine learning w
 ith traditional parametric modeling\, dramatically reducing the time r
 equired to model individual systems while maintaining accuracy. Togeth
 er\, these projects provide scalable\, automated approaches for discov
 ering and characterizing strong gravitational lenses\, maximizing thei
 r potential as cosmological probes in the new era of large-scale surve
 ys such as LSST and Euclid.
URL:https://www.physics.wisc.edu/events/?id=9337
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