Feasibility Studies on improved Proton Energy Reconstruction with IACTs

Discussion timeslot (ZOOM-Meeting): 16. July 2021 - 18:00
ZOOM-Meeting URL: https://icrc2021.desy.de/pf_access_abstracts
Corresponding Session: https://icrc2021-venue.desy.de/channel/Presenter-Forum-1-Evening-All-Categories/48
Abstract:
'Air showers induced by cosmic protons and heavier nuclei constitute the dominant background for very high energy gamma-ray observations of Imaging Air Cherenkov Telescopes (IACTs). Even for strong very high energy gamma-ray sources the signal-to-background ratio in the raw data is typically less than 1:5000. Therefore, a very large statistic of events, induced by cosmic protons and heavier nuclei, is easily available as a byproduct of gamma-ray source observations. In this contribution, we present a feasibility study on improved reconstruction of the energy of primary protons. For the latter purpose, we used a random forest method trained and tested by using Monte Carlo simulations of the MAGIC telescopes, for energies above 70GeV. We employ the aict-tools framework, including machine learning methods for the energy reconstruction. The open-source Python project aict-tools was developed at TU Dortmund and its reconstruction tools are based on scikit-learn predictors. Here, we report on the performance of the proton energy regression with the well-tested and robust random forest approach.'

Authors: Alicia Fattorini
Co-Authors: Wolfgang Rhode | Dominik Elsaesser | Dominik Baack | Maximilian Noethe
Indico-ID: 821
Proceeding URL: https://pos.sissa.it/395/237