Deep learning study of an electromagnetic calorimeter

2022/02/03 by Elihu Sela, Shan Huang, Sela, Elihu +3
Computer Science · Physics and Astronomy · #Computational Physics and Python Applications #Data Analysis #FOS: Physical sciences #High Energy Physics - Experiment (hep-ex) #Particle Detector Development and Performance #Radiation Detection and Scintillator Technologies #Statistics and Probability (physics.data-an)

paper · pdf · doi:10.48550/arxiv.2202.01532

openalex publication_date 2022/02/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

The accurate and precise extraction of information from a modern particle physics detector, such as an electromagnetic calorimeter, may be complicated and challenging. In order to overcome the difficulties we propose processing the detector output using the deep-learning methodology. Our algorithmic approach makes use of a known network architecture, which is being modified to fit the problems at hand. The results are of high quality (biases of order 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 understanding the essential mechanism of the detector and should be performed as a part of its designing procedure.

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