Energy Reconstruction of Non-fiducial Electron-Positron Events in the DAMPE Experiment Using Convolutional Neural Networks
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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915510165176320 |
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| author | Putti-Garcia, Enzo Tykhonov, Andrii Kotenko, Andrii Boutin, Hugo Li, Manbing Coppin, Paul Serpolla, Andrea Frieden, Jennifer Maria Perrina, Chiara Wu, Xin |
| author_facet | Putti-Garcia, Enzo Tykhonov, Andrii Kotenko, Andrii Boutin, Hugo Li, Manbing Coppin, Paul Serpolla, Andrea Frieden, Jennifer Maria Perrina, Chiara Wu, Xin |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2503_10521 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Energy Reconstruction of Non-fiducial Electron-Positron Events in the DAMPE Experiment Using Convolutional Neural Networks Putti-Garcia, Enzo Tykhonov, Andrii Kotenko, Andrii Boutin, Hugo Li, Manbing Coppin, Paul Serpolla, Andrea Frieden, Jennifer Maria Perrina, Chiara Wu, Xin Instrumentation and Methods for Astrophysics High Energy Astrophysical Phenomena High Energy Physics - Experiment Instrumentation and Detectors 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. |
| title | Energy Reconstruction of Non-fiducial Electron-Positron Events in the DAMPE Experiment Using Convolutional Neural Networks |
| topic | Instrumentation and Methods for Astrophysics High Energy Astrophysical Phenomena High Energy Physics - Experiment Instrumentation and Detectors |
| url | https://arxiv.org/abs/2503.10521 |