Investigating the effect of hadronic models on IACT images
Benedetta Bruno, Rodrigo Guedes Lang, Luan Bonneau Arbeletche, Vitor, de Souza, Stefan Funk

TL;DR
This study explores how different hadronic interaction models affect IACT images of cosmic-ray air showers, revealing model-dependent differences that can be identified using image variables and machine learning techniques.
Contribution
It introduces a novel approach using IACT image analysis and machine learning to compare hadronic models, highlighting specific observable differences and their dependence on primary particle type.
Findings
Significant differences between models for protons, helium, and nitrogen in IACT images.
Variables like pixel count, image size, and density are effective indicators of model discrepancies.
Targeted analysis in specific energy and core distance ranges enhances model discrimination.
Abstract
The predictions of hadronic interaction models for cosmic-ray induced air showers contain inherent uncertainties due to limitations of available accelerator data. This leads to differences in shower simulations using each of those models. Many studies have been carried out to track those differences by investigating the shower development or the particle content. In this work, we propose a new approach to search for discrepancies and similarities between the models, via the IACT images resulting from the observations of hadronic air showers. We use simulations of H.E.S.S. as a show-case scenario and, by investigating variables of the camera images, we find potential indicators to highlight differences between models. Number of pixels, Hillas image size, and density showed the largest difference between the models. We then further explore the (in)compatibility of the models by combining…
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Taxonomy
TopicsParticle physics theoretical and experimental studies · Image Processing and 3D Reconstruction · Superconducting Materials and Applications
