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A Bayesian approach to analyze water tank signals in the Pierre Auger experiment| old_uid | 3518 |
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| title | A Bayesian approach to analyze water tank signals in the Pierre Auger experiment |
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| start_date | 2007/11/26 |
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| schedule | 13h30 |
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| online | no |
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| location_info | 1er sous-sol, amphi |
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| summary | The objective of the Pierre Auger experiment 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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| responsibles | Stoltz |
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