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PRODID:UW-Madison-Physics-Events
BEGIN:VEVENT
SEQUENCE:1
UID:UW-Physics-Event-9094
DTSTART:20250226T220000Z
DTEND:20250226T230000Z
DTSTAMP:20260413T165755Z
LAST-MODIFIED:20250204T133806Z
LOCATION:5280 CH & https://uwmadison.zoom.us/j/93056807183?pwd=bmRBTnF
 pQTZSYk1QSUVLb3BBY1M0QT09
SUMMARY:Beyond the Observable: A Machine Learning perspective on moder
 n Cosmology\, NPAC (Nuclear/Particle/Astro/Cosmo) Forum\, Dr. Carolina
  Cuesta Lazaro\, Institute for Artificial Intelligence and Fundamental
  Interactions\, MIT
DESCRIPTION:Our observations have painted a simple portrait of cosmic 
 evolution\, yet fundamental questions remain unanswered: What is the n
 ature of dark matter\, the invisible substance that makes up most of t
 he matter in the Universe? What is driving the accelerated expansion o
 f the cosmos? How did it begin? In this talk\, I will present machine 
 learning frameworks that bridge the gap between numerical simulations 
 and increasingly precise astronomical observations to decode these inv
 isible components. I will demonstrate how generative models can serve 
 as both efficient emulators and tools for parameter estimation\, enabl
 ing direct inference from high-dimensional astronomical data without r
 elying on simplified summary statistics. I will then show how we can p
 robabilistically reconstruct the dark matter distribution from observe
 d galaxy clustering\, while being robust to uncertainties in galaxy fo
 rmation physics. Finally\, I will discuss how machine learning enables
  us to reconstruct the primordial Universe by inferring the initial co
 nditions that evolve into our observed cosmos. Through these methods\,
  we can also identify potential anomalies in the cosmic web that might
  signal departures from our standard cosmological model. These advance
 s come at a pivotal moment\, as we enter an era of extremely precise c
 osmological surveys that may transform current statistical tensions in
 to discoveries of new fundamental physics.
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URL:https://www.physics.wisc.edu/events/?id=9094
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