Preprint/Report

PUBDB-2018-00839

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Exploring deep learning as an event classification method for the Cherenkov Telescope Array

Nieto, D. (Corresponding author) ; Brill, A. ; Kim, B. ; Humensky, T. B. ; Array ; Cherenkov Telescope Array Consortium (Collaboration author)DESY*

2017

This record in other databases:    

Please use a persistent id in citations: doi:10.3204/PUBDB-2018-00839

Report No.: arXiv:1709.05889

Abstract: Telescopes based on the imaging atmospheric Cherenkov technique (IACTs) detect images of the atmospheric showers generated by gamma rays and cosmic rays as they are absorbed by the atmosphere. The much more frequent cosmic-ray events form the main background when looking for gamma-ray sources, and therefore IACT sensitivity is significantly driven by the capability to distinguish between these two types of events. Supervised learning algorithms, like random forests and boosted decision trees, have been shown to effectively classify IACT events. In this contribution we present results from exploratory work using deep learning as an event classification method for the Cherenkov Telescope Array (CTA). CTA, conceived as an array of tens of IACTs, is an international project for a next-generation ground-based gamma-ray observatory, aiming to improve on the sensitivity of current-generation experiments by an order of magnitude and provide energy coverage from 20 GeV to more than 300 TeV.

Note: Proceedings of the 35th International Cosmic Ray Conference (ICRC 2017), Busan, Korea. All CTA contributions at arXiv:1709.03483

---

Contributing Institute(s):

1. Cherenkov Telescope Array (ZEU-CTA)

Research Program(s):

1. 613 - Matter and Radiation from the Universe (POF3-613) (POF3-613)

Experiment(s):

1. Cherenkov Telescope Array

Appears in the scientific report 2017

---

Database coverage:
;

---