Machine learning-based method of calorimeter saturation correction for helium flux analysis with DAMPE experiment

Andrii Tykhonov; Arshia Ruina; Chiara Perrina; David Droz; Enrico Catanzani (on behalf of the DAMPE collaboration); Maksym Deliyergiyev; Maria Munoz; Mikhail Stolpovskiy; Xin Wu

arxiv: 2201.12185 · v3 · pith:3EGLMOR2new · submitted 2022-01-28 · 🌌 astro-ph.HE

Machine learning-based method of calorimeter saturation correction for helium flux analysis with DAMPE experiment

Mikhail Stolpovskiy , Xin Wu , Andrii Tykhonov , Maksym Deliyergiyev , Chiara Perrina , Maria Munoz , David Droz , Arshia Ruina

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Enrico Catanzani (on behalf of the DAMPE collaboration)

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keywords dampecalorimeterenergiesenergyexperimentmachinesaturationabove

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DAMPE is a space-borne experiment for the measurement of the cosmic-ray fluxes at energies up to around 100 TeV per nucleon. At energies above several tens of TeV, the electronics of DAMPE calorimeter would saturate, leaving certain bars with no energy recorded. In the present work we discuss the application of machine learning techniques for the treatment of DAMPE data, to compensate the calorimeter energy lost by saturation.

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Machine learning-based method of calorimeter saturation correction for helium flux analysis with DAMPE experiment

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