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| Paper: |
Reconstruction of IACT Events Using Deep Learning Techniques with CTLearn |
| Volume: |
532, ASTRONOMICAL DATA ANALYSIS SOFTWARE AND SYSTEMS XXX |
| Page: |
191 |
| Authors: |
Nieto, D.; Miener, T.; Brill, A.; Contreras, J. L.; Humensky, T. B.; Mukherjee, R. |
| 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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