Innovative Approaches to High-Energy Gamma Particle Classification: A Coevolutionary Artificial Neural Network for Cherenkov Telescopes

Date

2025-10-30

Author

Deveci, Ali
Erkan, Mehmet Ali
Medeni, İhsan Tolga
Medeni, Tunç Durmuş

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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This study addresses the challenges in analyzing data from a ground-based atmospheric Cherenkov gamma telescope, aiming to simulate and observe high-energy gamma particles. Notable challenges include differentiating signals from high-energy gamma rays and background noise induced by cosmic-rayinitiated hadronic showers. Robust methodologies, especially for statistical significance amidst varying energy levels, are essential. The study underscores the need for nuanced solutions in effective data analysis, contributing significantly to our understanding of high-energy gamma phenomena. A cooperative coevolution-based artificial neural network model, developed in response to these challenges, achieves a classification accuracy of over 91%. This success highlights the model’s efficacy in addressing scientific problems, effectively separating gamma rays from background noise, and contributing to future research on atmospheric Cherenkov gamma telescopes.

URI

https://hdl.handle.net/11511/117237

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Department of Statistics, Conference / Seminar