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arXiv · 1907.02428

Probing Neural Networks for the Gamma/Hadron Separation of the Cherenkov Telescope Array

Etienne Lyard·Roland Walter·Vitalii Sliusar·Nicolas Produit
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Abstract

We compared convolutional neural networks to the classical boosted decision trees for the separation of atmospheric particle showers generated by gamma rays from the particle-induced background. We conduct the comparison of the two techniques applied to simulated observation data from the Cherenkov Telescope Array. We then looked at the Receiver Operating Characteristics (ROC) curves produced by the two approaches and discuss the similarities and differences between both. We found that neural networks overperformed classical techniques under specific conditions.

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Etienne Lyard, Roland Walter, Vitalii Sliusar, Nicolas Produit. 2019-07-04. Probing Neural Networks for the Gamma/Hadron Separation of the Cherenkov Telescope Array. https://doi.org/10.1088/1742-6596/1525/1/012084

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