Speaker
Description
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.
Keywords
protons; IACT; random forest; energy reconstruction; air shower
| Subcategory | Experimental Methods & Instrumentation |
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