PoS - Proceedings of Science
Volume 398 - The European Physical Society Conference on High Energy Physics (EPS-HEP2021) - T01: Astroparticle and Gravitational Waves
Neural networks for TeV cosmic electrons identification on the DAMPE experiment
D. Droz*, A. Tykhonov, X. Wu and M. Deliyergiyev
Full text: Not available
Abstract
The past decades have witnessed the deployment of a new generation of cosmic ray (CR) observatories with unprecedented sensitivity and complexity, pushing towards ever-higher energies. To face the challenges of the multi-TeV domain, such instruments must be accompanied by equally powerful analysis techniques, able to exploit as much information as available. For example, the machine learning tool set may provide the needed techniques. We present a neural network optimised for the identification of multi-TeV electrons on DAMPE, a calorimetric spaceborne CR observatory with among other objectives the measurement of cosmic electrons up to 10 TeV. This constitutes a particularly challenging endeavour due to both the soft electron spectrum and the large proton background. The developed neural network significantly outperforms the more traditional cut-based approach, achieving a much lower proton contamination in the multi-TeV domain with a high signal efficiency, and retains its accuracy when transposed from Monte Carlo to real data.
How to cite

Metadata are provided both in "article" format (very similar to INSPIRE) as this helps creating very compact bibliographies which can be beneficial to authors and readers, and in "proceeding" format which is more detailed and complete.

Open Access
Creative Commons LicenseCopyright owned by the author(s) under the term of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.