Abstract
The accurate and precise extraction of information from a modern particle detector, such as an electromagnetic calorimeter, may be complicated and challenging. In order to overcome the difficulties, we process the simulated detector outputs using the deep-learning methodology. Our algorithmic approach makes use of a known network architecture, which has been modified to fit the problems at hand. The results are of high quality (biases of order 1 to 2%) and, moreover, indicate that most of the information may be derived from only a fraction of the detector. We conclude that such an analysis helps us understand the essential mechanism of the detector and should be performed as part of its design procedure.
Funder
Israel Science Foundation
German Israeli Foundation
Subject
Computational Mathematics,Computational Theory and Mathematics,Numerical Analysis,Theoretical Computer Science
Cited by
2 articles.
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