The Cherenkov Telescope Array (CTA), conceived as an array of tens of imaging atmospheric
Cherenkov telescopes (IACTs), is an international project for a next-generation ground-based
gamma-ray observatory, aiming to improve on the sensitivity of current-generation instruments
by an order of magnitude and provide energy coverage from 20 GeV to more than 300 TeV.
Arrays of IACTs probe the very-high-energy gamma-ray sky. Their working
principle consists of the simultaneous observation of air showers initiated by
the interaction of very-high-energy gamma rays and cosmic rays with the atmosphere.
Cherenkov photons induced by a given shower are focused onto the camera plane
of the telescopes in the array, producing a multi-stereoscopic record of the event. This
image contains the longitudinal development of the air shower, together
with its spatial, temporal, and calorimetric information. The properties of
the originating very-high-energy particle (type, energy and incoming direction)
can be inferred from those images by reconstructing the full event using machine
learning techniques. In this contribution, we present a purely deep-learning
driven, full-event reconstruction of simulated, stereoscopic IACT events
using CTLearn. CTLearn is a package that includes modules for loading
and manipulating IACT data and for running deep learning models,
using pixel-wise camera data as input.
|Subcategory||Experimental Methods & Instrumentation|