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
公開日: 2025/3/13
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.