Work

Convolutional Neural Network Energy Reconstruction Method of Non-Fiducial Electrons Cosmic-Rays Using the DAMPE Experiment

Year: 2025

Type: conference-paper

Abstract: The Dark Matter Particle Explorer (DAMPE) is a space-based cosmic-ray 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 increase the acceptance by selecting events outside of the fiducial volume. Non-fiducial events, with their complex topology, require special treatment with sophisticated analysis tools. We propose therefore a Convolutional Neural Network to recover an accurate estimation of the initial energy of non-fiducial CREs. By leveraging deep learning, our method significantly improves the energy estimation over traditional algorithms, enabling a more precise measurement of high-energy CREs. We will demonstrate the CNN’s effectiveness in mitigating shower leakage effects and discuss its potential for enhancing DAMPE’s cosmic-ray observations.

Source: International Cosmic Ray Conference

DOI: 10.22323/1.501.0115

Authors: Enzo Putti-Garcia, Andrii Tykhonov, Manbing Li, Xin Wu

Institution: University of Geneva

Language: English

Topic: Astrophysics and Cosmic Phenomena

Subfield: Nuclear and High Energy Physics

Field: Physics and Astronomy

Domain: Physical Sciences

SDG: Affordable and clean energy

Open Access status: hybrid