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arXiv · 2101.07626

Reconstruction of IACT events using deep learning techniques with CTLearn

D. Nieto·T. Miener·A. Brill·J. L. Contreras·T. B. Humensky·R. Mukherjee
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Abstract

Arrays of imaging atmospheric Cherenkov telescopes (IACT) are superb instruments to probe the very-high-energy gamma-ray sky. This type of telescope focuses the Cherenkov light emitted from air showers, initiated by very-high-energy gamma rays and cosmic rays, onto the camera plane. Then, a fast camera digitizes the longitudinal development of the air shower, recording its spatial, temporal, and calorimetric information. The properties of the primary very-high-energy particle initiating the air shower can then be inferred from those images: the primary particle can be classified as a gamma ray or a cosmic ray and its energy and incoming direction can be estimated. This so-called full-event reconstruction, crucial to the sensitivity of the array to gamma rays, can be assisted by machine learning techniques. We present a deep-learning driven, full-event reconstruction applied to simulated IACT events using CTLearn. CTLearn is a Python package that includes modules for loading and manipulating IACT data and for running deep learning models with TensorFlow, using pixel-wise camera data as input.

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D. Nieto, T. Miener, A. Brill, J. L. Contreras, T. B. Humensky, R. Mukherjee. 2021-01-19. Reconstruction of IACT events using deep learning techniques with CTLearn. https://arxiv.org/abs/2101.07626

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