Application of deep learning methods to analysis of imaging atmospheric Cherenkov telescopes data

• Published in: Astroparticle physics Vol. 105; pp. 44 - 53
• Main Authors: Shilon, I., Kraus, M., Büchele, M., Egberts, K., Fischer, T., Holch, T.L., Lohse, T., Schwanke, U., Steppa, C., Funk, S.
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.02.2019
• Subjects: Analysis technique; Convolutional neural networks; Deep learning; Gamma-ray astronomy; IACT; Recurrent neural networks; Deep learning; Analysis technique; Recurrent neural networks; IACT; Convolutional neural networks; Gamma-ray astronomy

Ground based γ-ray observations with Imaging Atmospheric Cherenkov Telescopes (IACTs) play a significant role in the discovery of very high energy (E > 100 GeV) γ-ray emitters. The analysis of IACT data demands a highly efficient background rejection technique, as well as methods to accurately de...