Deep learning study of an electromagnetic calorimeter

TL;DR

This paper demonstrates that deep learning can effectively analyze electromagnetic calorimeter data, achieving high accuracy and revealing that much information can be extracted from limited detector regions, aiding detector design.

Contribution

It introduces a deep learning approach tailored for electromagnetic calorimeter data analysis, improving extraction accuracy and understanding detector mechanisms.

Findings

Biases of about 2% in measurements

Most information obtainable from a small detector fraction

Deep learning enhances detector data interpretation

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.