Studies of Gamma-Ray Shower Reconstruction Using Deep Learning

Tomas Bylund, Gašper Kukec Mezek, Mohanraj Senniappan, Yvonne Becherini, Michael Punch, Satyendra Thoudam, Jean Pierre Ernenwein

Research output: Contribution to journalConference articlepeer-review


The Cosmic Multiperspective Event Tracker (CoMET) R&D project aims to optimize the techniques for the detection of soft-spectrum sources through very-high-energy gamma-ray observations using particle detectors (called ALTO detectors), and atmospheric Cherenkov light collectors (called CLiC detectors). The accurate reconstruction of the energies and maximum depths of gamma-ray events using a surface array only, is an especially challenging problem at low energies, and the focus of the project. In this contribution, we leverage Convolutional Neural Networks (CNNs) using the ALTO detectors only, to try to improve reconstruction performance at lower energies ( < 1 TeV) as compared to the SEMLA analysis procedure, which is a more traditional method using manually derived features.

Original languageBritish English
Article number758
JournalProceedings of Science
StatePublished - 18 Mar 2022
Event37th International Cosmic Ray Conference, ICRC 2021 - Virtual, Berlin, Germany
Duration: 12 Jul 202123 Jul 2021


Dive into the research topics of 'Studies of Gamma-Ray Shower Reconstruction Using Deep Learning'. Together they form a unique fingerprint.

Cite this