A neural network classifier for electron identification on the DAMPE experiment. (arXiv:2102.05534v2 [astro-ph.IM] UPDATED)
<a href="http://arxiv.org/find/astro-ph/1/au:+Droz_D/0/1/0/all/0/1">David Droz</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Tykhonov_A/0/1/0/all/0/1">Andrii Tykhonov</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Wu_X/0/1/0/all/0/1">Xin Wu</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Alemanno_F/0/1/0/all/0/1">Francesca Alemanno</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Ambrosi_G/0/1/0/all/0/1">Giovanni Ambrosi</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Catanzani_E/0/1/0/all/0/1">Enrico Catanzani</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Santo_M/0/1/0/all/0/1">Margherita Di Santo</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Kyratzis_D/0/1/0/all/0/1">Dimitrios Kyratzis</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Zimmer_S/0/1/0/all/0/1">Stephan Zimmer</a>
The Dark Matter Particle Explorer (DAMPE) is a space-borne particle detector
and cosmic ray observatory in operation since 2015, designed to probe electrons
and gamma rays from a few GeV to 10 TeV energy, as well as cosmic protons and
nuclei up to 100 TeV. Among the main scientific objectives is the precise
measurement of the cosmic electron+positron flux, which due to the very large
proton background in orbit requires a powerful particle identification method.
In the past decade, the field of machine learning has provided us the needed
tools. This paper presents a neural network based approach to cosmic electron
identification and proton rejection and showcases its performances based on
simulated Monte Carlo data. The neural network reaches significantly lower
background than the classical, cut-based method for the same detection
efficiency, especially at highest energies. A good matching between simulations
and real data completes the picture.
The Dark Matter Particle Explorer (DAMPE) is a space-borne particle detector
and cosmic ray observatory in operation since 2015, designed to probe electrons
and gamma rays from a few GeV to 10 TeV energy, as well as cosmic protons and
nuclei up to 100 TeV. Among the main scientific objectives is the precise
measurement of the cosmic electron+positron flux, which due to the very large
proton background in orbit requires a powerful particle identification method.
In the past decade, the field of machine learning has provided us the needed
tools. This paper presents a neural network based approach to cosmic electron
identification and proton rejection and showcases its performances based on
simulated Monte Carlo data. The neural network reaches significantly lower
background than the classical, cut-based method for the same detection
efficiency, especially at highest energies. A good matching between simulations
and real data completes the picture.
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