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

Abstract

The Cherenkov Telescope Array (CTA) is the future ground-based gamma-ray observatory and will be composed of two arrays of imaging atmospheric Cherenkov telescopes (IACTs) located in the Northern and Southern hemispheres respectively. The first CTA prototype telescope built on-site, the Large-Sized Telescope (LST-1), is under commissioning in La Palma and has already taken data on numerous known sources. IACTs detect the faint flash of Cherenkov light indirectly produced after a very energetic gamma-ray photon has interacted with the atmosphere and generated an atmospheric shower. Reconstruction of the characteristics of the primary photons is usually done using a parameterization up to the third order of the light distribution of the images. In order to go beyond this classical method, new approaches are being developed using state-of-the-art methods based on convolutional neural networks (CNN) to reconstruct the properties of each event (incoming direction, energy and particle type) directly from the telescope images. While promising, these methods are notoriously difficult to apply to real data due to differences (such as different levels of night sky background) between Monte Carlo (MC) data used to train the network and real data. The GammaLearn project, based on these CNN approaches, has already shown an increase in sensitivity on MC simulations for LST-1 as well as a lower energy threshold. This work applies the GammaLearn network to real data acquired by LST-1 and compares the results to the classical approach that uses random forests trained on extracted image parameters. The improvements on the background rejection, event direction, and energy reconstruction are discussed in this contribution.

TeV Instrumentation: current and future

“…telescopes data, DL methods were shown to provide a boost in the gamma/hadron separation of actual data, however slight worsening in the angular resolution has been observed as well [86]. Similarly, in the case of LST-1 telescope commissioning data, DL methods provided an improvement in the gamma/hadron separation, but not in the angular resolution [91]. CNN method applied to cleaned images of showers in MAGIC data resulted in similar sensitivity to the standard approach based on decision trees [92].…”

TeV Instrumentation: Current and Future

“…telescopes' data, DL methods were shown to provide a boost in the gamma/hadron separation of actual data; however, slight worsening in the angular resolution has been observed as well [86]. Similarly, in the case of LST-1 telescope commissioning data, DL methods provided an improvement in the gamma/hadron separation, but not in the angular resolution [91]. The CNN method applied to cleaned images of showers in MAGIC data resulted in similar sensitivity to the standard approach based on decision trees [92].…”

Machine Learning Techniques for the EUSO-SPB2 Fluorescence Telescope

“…On the other hand, this task has been attracting attention of experiments in gamma-astronomy for quite a while now [9][10][11][12][13][14][15][16][17][18]. In this case, the main instruments are Cherenkov telescopes (Imaging Atmosphere Cherenkov Telescopes, IACTs), which register tracks produced by cascades initiated by gamma rays or by hadrons.…”