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arXiv · 2503.10521

Energy Reconstruction of Non-fiducial Electron-Positron Events in the DAMPE Experiment Using Convolutional Neural Networks

Enzo Putti-Garcia·Andrii Tykhonov·Andrii Kotenko·Hugo Boutin·Manbing Li·Paul Coppin·Andrea Serpolla·Jennifer Maria Frieden·Chiara Perrina·Xin Wu
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Abstract

The Dark Matter Particle Explorer (DAMPE) is a space-based Cosmic-Ray (CR) observatory with the aim, among others, to study Cosmic-Ray Electrons (CREs) up to 10 TeV. Due to the low CRE rate at multi-TeV energies, we aim to increasing the acceptance by selecting events outside the fiducial volume. The complex topology of non-fiducial events requires the development of a novel energy reconstruction method. We propose the usage of Convolutional Neural Networks for a regression task to recover an accurate estimation of the initial energy.

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Enzo Putti-Garcia, Andrii Tykhonov, Andrii Kotenko, Hugo Boutin, Manbing Li, Paul Coppin, Andrea Serpolla, Jennifer Maria Frieden, Chiara Perrina, Xin Wu. 2025-03-13. Energy Reconstruction of Non-fiducial Electron-Positron Events in the DAMPE Experiment Using Convolutional Neural Networks. https://doi.org/10.1088/1748-0221%2F20%2F09%2Fp09033

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