Reconstruction of stereoscopic CTA events using deep learning with CTLearn

Abstract

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 a factor of five to ten 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 veryhigh-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.

Event reconstruction using pattern spectra and convolutional neural networks for the Cherenkov Telescope Array

“…The work presented here is preliminary and results in a lower performance compared to other existing works applying CTA images on a CNN, e.g. in [14]. The final results, including the performance on the Alpha Configuration of CTA, are discussed in [30].…”

“…Other reconstruction methods are based on semi-analytical or Gaussian photosphere shower models, such as model analysis [9] and 3D model analysis [10]. Lately, convolutional neural networks (CNNs) have been applied to IACT data [11][12][13][14][15] motivated by their success in other image classification and regression tasks [16]. However, one of the main drawbacks of this method is that the training of CNNs is computationally very expensive [17].…”

Event reconstruction using pattern spectra and convolutional neural networks for the Cherenkov Telescope Array

“…The work presented here is preliminary and results in a lower performance compared to other existing works applying CTA images on a CNN, e.g. in [14]. The final results, including the performance on the Alpha Configuration of CTA, are discussed in [30].…”

The next generation cameras for the Large-Sized Telescopes of the Cherenkov Telescope Array Observatory

“…The fully digital architecture will offer the upgradability required to maintain the highest level of performance for the future LST cameras. The stereo analysis of the simulated data is already ongoing both with a classical approach [13] and with novel analysis methods based on deep-learning [9].…”