12-23 July 2021
Online
Europe/Berlin timezone

Reconstruction of stereoscopic CTA events using deep learning with CTLearn

13 Jul 2021, 12:00
1h 30m
04

04

Poster GAI | Gamma Ray Indirect Discussion

Speaker

Tjark Miener (IPARCOS, UCM)

Description

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
Collaboration CTA

Primary author

Tjark Miener (IPARCOS, UCM)

Co-authors

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