BEGIN:VCALENDAR
VERSION:2.0
CALSCALE:GREGORIAN
PRODID:UW-Madison-Physics-Events
BEGIN:VEVENT
SEQUENCE:0
UID:UW-Physics-Event-1660
DTSTART:20091023T193000Z
DURATION:PT1H0M0S
DTSTAMP:20260506T172207Z
LAST-MODIFIED:20091019T144644Z
LOCATION:5280 Chamberlin
SUMMARY:Quantifying the Unknown in Astronomy:  A Bayesian Approach\, N
 PAC (Nuclear/Particle/Astro/Cosmo) Forum\, Brian Connolly\, University
  of Pennsylvania
DESCRIPTION:Over the last few years the Bayesian statistics has played
  an increasingly important role in astronomical data analyses\, from c
 lassifying quasars to fitting cosmological models to WMAP data.  One a
 spect of Bayesian statistical methods used to classify astronomical ob
 jects is that traditionally they assume that the object being classifi
 ed falls into a finite set of modeled (or known) astronomical objects.
   However\, astronomical research continually reveals new\, unexplaine
 d phenomena\; new large-scale surveys coming up in the next decade (su
 ch as the Dark Energy Survey\, the Large Synoptic Survey Telescope\, t
 he Joint Dark Energy Mission\, etc.) are expected to greatly enrich ou
 r catalog of understood (or at least modeled) astronomical objects.   
 In my<br>
\ntalk\, I will first review the two approaches to statistic
 s\, Bayesian and Frequentist.  I will describe how and why the Bayesia
 n approach has been so successful in astronomy in general\, and in par
 ticular how the Bayesian approach can be extended to account for as ye
 t unmodeled objects.  I will then show how this method can be used to 
 quantify the differences in the spectra of <br>
\nUltra-Luminous Infra
 -Red Galaxies (ULIRGs) and aid in the identification of Type Ia supern
 ovae\, a staple of modern cosmological research.  I will also discuss 
 how network diagrams and graph theory can be used in conjunction with 
 these Bayesian methods to enhance our understanding of the evolution o
 f ULRGs <br>
\nand Type Ia supernovae.
URL:https://www.physics.wisc.edu/events/?id=1660
END:VEVENT
END:VCALENDAR