Deep Learning Study of an Electromagnetic Calorimeter

Elihu Sela, Shan Huang, David Horn*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Article number115
Issue number4
StatePublished - Apr 2022


  • deep learning
  • electromagnetic calorimetry
  • high-energy physics


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