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VERSION:2.0
CALSCALE:GREGORIAN
PRODID:UW-Madison-Physics-Events
BEGIN:VEVENT
SEQUENCE:2
UID:UW-Physics-Event-9480
DTSTART:20251029T170000Z
DTEND:20251029T180000Z
DTSTAMP:20260413T102705Z
LAST-MODIFIED:20251026T141558Z
LOCATION:5280 Chamberlin & Zoom: https://uwmadison.zoom.us/j/930568071
 83?pwd=bmRBTnFpQTZSYk1QSUVLb3BBY1M0QT09
SUMMARY:Universe under the Lens: Exploring Galaxy Evolution and Cosmol
 ogy through Strong Lensing and Machine Learning\, NPAC (Nuclear/Partic
 le/Astro/Cosmo) Forum\, Sreevani Jarugula\, Fermilab
DESCRIPTION:Strong gravitational lensing is a powerful probe that prov
 ides high-resolution views of the early universe and valuable insights
  into the nature of dark matter and dark energy. In this talk\, I will
  present my analysis of high-redshift\, lensed dusty star-forming gala
 xies observed with Atacama Large Millimetre/submillimetre array (ALMA)
 . These early galaxies offer valuable insights into early galaxy evolu
 tion. I will discuss detections of molecular water and carbon monoxide
  emission lines\, used to infer key physical properties of these galax
 ies. To recover intrinsic source properties\, I will introduce an imag
 e-plane lens-modeling framework tailored for interferometric data from
  radio and submillimeter telescopes. The second part of my talk will f
 ocus on machine learning applications for cosmology and telescope oper
 ations. I will present simulation-based inference with neural ratio es
 timators to constrain cosmological parameters from lensing images\, es
 sential for analyzing thousands of systems expected from surveys such 
 as Rubin LSST. I will also highlight machine learning models that iden
 tify and filter defective images to ensure reliable Rubin data process
 ing. Together\, these studies demonstrate how strong lensing and machi
 ne learning can jointly advance our understanding of galaxy formation 
 and the dark universe.
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URL:https://www.physics.wisc.edu/events/?id=9480
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