Exploring deep learning as an event classification method for the Cherenkov Telescope Array

Abstract

Telescopes based on the imaging atmospheric Cherenkov technique (IACTs) detect images of the atmospheric showers generated by gamma rays and cosmic rays as they are absorbed by the atmosphere. The much more frequent cosmic-ray events form the main background when looking for gamma-ray sources, and therefore IACT sensitivity is significantly driven by the capability to distinguish between these two types of events. Supervised learning algorithms, like random forests and boosted decision trees, have been shown to effectively classify IACT events. In this contribution we present results from exploratory work using deep learning as an event classification method for the Cherenkov Telescope Array (CTA). CTA, conceived as an array of tens of IACTs, is an international project for a next-generation ground-based gamma-ray observatory, aiming to improve on the sensitivity of current-generation experiments by an order of magnitude and provide energy coverage from 20 GeV to more than 300 TeV.

Deep Learning for Astrophysics, Understanding the Impact of Attention on Variability Induced by Parameter Initialization

“…Over the past decade, deep learning has emerged as the leading approach in many computer vision tasks. IACT data has not escaped this trend [16,18,20,25].…”

Analysis of the Cherenkov Telescope Array first Large Size Telescope real data using convolutional neural networks

“…Indeed, since the AlexNet revolution [4] CNNs have shown in many fields [5] their capacity to replace and overtake solutions with engineered parameters extraction. They have been applied to IACT event reconstruction in the past on CTA simulated data [6][7][8] and on H.E.S.S. [9,10] data, but with limited success on observations.…”

Reconstruction of stereoscopic CTA events using deep learning with CTLearn

“…Previous works have demonstrated the potential application of these algorithms for IACT event reconstruction [8][9][10][11][12][13]. DCN-based monoscopic telescope performance and the application of DCNs on observational data from the first Large-Sized Telescope (LST-1 prototype) of CTA North is discussed in these proceedings elsewhere [14,15].…”