12-20 July 2017
BEXCO
Asia/Seoul timezone
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Contribution

BEXCO - Room F(201/202/203/204)

[GA146] Exploring deep learning as an event classification method for the Cherenkov Telescope Array

Speakers

  • Daniel NIETO

Primary authors

  • Daniel NIETO (Columbia University / Universidad Complutense de Madrid, Department of Atomic, Molecular, and Nuclear Physics)

Co-authors

  • Aryeh BRILL (Columbia University, Department of Physics)
  • Bryan KIM (Columbia University, Department of Physics)
  • Brian HUMENSKY (Columbia University, Department of Physics)

Description

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 of distinguishing between these two types of events. Supervised learning algorithms, like random forests and boosted decision trees, have already demonstrated their effectiveness in the task of classifying IACT events. In this contribution we present results from exploratory works on the consideration of 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.