PoS - Proceedings of Science
Volume 358 - 36th International Cosmic Ray Conference (ICRC2019) - CRD - Cosmic Ray Direct
Neural Networks for Electron Identification with DAMPE
D. Droz,* A. Tykhonov, X. Wu on behalf of the DAMPE Collaboration
*corresponding author
Full text: pdf
Pre-published on: July 22, 2019
Published on:
Abstract
The Dark Matter Particle Explorer (DAMPE) is a space-borne particle detector and cosmic ray observatory in operation since 2015, equipped with alongside other instruments a deep calorimeter able to detect electrons up to an energy of 10 TeV and cosmic hadrons up to 100 TeV. The large proton and ion background in orbit requires a powerful electron identification method. In recent years, the field of machine learning has provided such tools. We explore here a neural network based approach to an on-orbit particle identification problem. We present the issues that arise from the constraints of particle physics, notably the difference between training set based on simulated data, and the application set based on real unlabeled data, leading to a trade-off between performances and general usability. We finally compare the neural network discrimination power with the more traditional cut-based analysis.
DOI: https://doi.org/10.22323/1.358.0064
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.