[Lisa_seminaires] NEW LOCATION: UdeM-McGill-MITACS machine learning seminar, PAA 1360, Fri Nov 30, 11:30am

Hugo Larochelle larocheh at IRO.UMontreal.CA
Jeu 29 Nov 15:58:45 EST 2007


This week's seminar (see 
http://www.iro.umontreal.ca/article.php3?id_article=107&lang=en):


A Bayesian approach to analyze water tank signals in the Pierre Auger 
experiment


by Balazs Kegl,
Laboratoire de l’Accélérateur Linéaire
Université Paris-Sud 11

Location: Pavillon André-Aisenstadt (UdeM), room 1360
Time: November 30th 2007, 11h30

The objective of the Pierre Auger experiment (auger.org) is to study the 
properties of ultra-high energy cosmic ray particles. When one if these 
particles collides with the atmosphere, it generates a huge shower of 
atmospheric particles that covers several square kilometers on the 
Earth’s surface. The surface detector of the Auger Observatory (built on 
the pampas of Argentina) consists of 1600 water tanks spaced at 1.5 km 
on a regular hexagonal grid that detect atmospheric shower particles 
through their interaction with water. To analyze the water tank signals, 
we have built an elaborate generative model based on physical laws and 
simulations of both the showers and the detector. To estimate the model 
parameters, we use a Bayesian approach based on reversible jump Monte 
Carlo Markov chains. This technique allows us to estimate the number of 
muonic mixture components in the signal which is one of the best 
indicators of the nature of the original cosmic ray particle. In the 
first part of the talk we introduce the basic physics behind the 
experiment and describe the statistical model. In the second part we 
show the Bayesian technique that we are using to estimate the parameters 
and present some preliminary results.
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