Improvements to monoscopic analysis for imaging atmospheric Cherenkov telescopes: Application to H.E.S.S.

¹ Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen Centre for Astroparticle Physics, Nikolaus-Fiebiger-Str. 2, 91058 Erlangen, Germany
² Max-Planck-Institut für Kernphysik, Saupfercheckweg 1, 69117 Heidelberg, Germany
³ Indian Institute of Science Education and Research (IISER) Tirupati, Tirupati 517507, Andhra Pradesh, India

^★ Corresponding authors; tim.unbehaun@fau.de; rodrigo.lang@fau.de; lars.mohrmann@mpi-hd.mpg.de

Received: 8 November 2024
Accepted: 8 January 2025

Abstract

Imaging atmospheric Cherenkov telescopes (IACTs) detect γ rays by measuring the Cherenkov light emitted by secondary particles in the air shower when the γ rays hit the atmosphere of the Earth. Given usual distances between telescopes in IACT arrays, at low energies (≲100 GeV), the limited amount of Cherenkov light produced typically implies that the event is registered by one IACT only. Such events are called monoscopic events, and their analysis is particularly difficult. Challenges include the reconstruction of the event’s arrival direction, energy, and the rejection of background events due to charged cosmic rays. Here, we present a set of improvements, including a machine-learning algorithm to determine the correct orientation of the image in the camera frame, an intensity-dependent selection cut that ensures optimal performance across all energies, and a collection of new image parameters. To quantify these improvements, we make use of simulations and data from the 28-m diameter central telescope of the H.E.S.S. IACT array. Knowing the correct image orientation, which corresponds to the arrival direction of the photon in the camera frame, is especially important for the angular reconstruction, which could be improved in resolution by 57% at 100 GeV. The event selection cut, which now depends on the total measured intensity of the events, leads to a reduction of the low-energy threshold for source analyses by ~50%. The new image parameters characterize the intensity and time distribution within the recorded images and complement the traditionally used Hillas parameters in the machine learning algorithms. We evaluate their importance to the algorithms in a systematic approach and carefully evaluate associated systematic uncertainties. We find that including subsets of the new variables in machine-learning algorithms improves the reconstruction and background rejection, resulting in a sensitivity improved by 41% at the low-energy threshold. Finally, we apply the new analysis to data from the Crab Nebula and estimate systematic uncertainties introduced by the new method.