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Simulation of Imaging Atmospheric Cherenkov Telescopes with CORSIKA and sim_telarray
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Simulation of Imaging Atmospheric Cherenkov Telescopes with CORSIKA and sim_telarray
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Imaging Atmospheric Cherenkov Telescopes (IACTs) have resulted in a breakthrough in very-high energy (VHE) gamma-ray astrophysics. While early IACT installations faced the problem of detecting any sources at all, current instruments are able to see many sources, often over more than two orders of magnitude in energy. As instruments and analysis methods have matured, the requirements for calibration and modelling of physical and instrumental effects have increased. In this article, a set of Monte Carlo simulation tools is described that attempts to include all relevant effects for IACTs in great detail but aims to achieve this in an efficient and flexible way. These tools were originally developed for the HEGRA IACT system and later adapted for the H.E.S.S. experiment. Their inherent flexibility to describe quite arbitrary IACT systems makes them also an ideal tool for evaluating the potential of future installations. It is in use for design studies of CTA and other projects.
Forward citations
Cited by 3 Pith papers
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Prospects for the Use of Photosensor Timing Information with Machine Learning Techniques in Background Rejection
Proposes feeding seven 2D histograms of waveform parameters into ML algorithms alongside integrated charge images to better reject background in IACT observations.
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The Cherenkov Telescope Array Performance in Divergent Mode
Monte Carlo simulations provide the first performance estimates for CTA in divergent pointing mode to increase field of view.
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Machine Learning for Event Reconstruction in Imaging Atmospheric Cherenkov Telescopes
This review describes the IACT event reconstruction pipeline and the role of machine learning for classification and regression, highlighting timing features and ensemble methods as improvements over baseline approaches.
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