Machine learning-based method of calorimeter saturation correction for helium flux analysis with DAMPE experiment. (1st June 2022)

• Record Type: Journal Article
• Authors: Stolpovskiy, M.; Wu, X.; Tykhonov, A.; Deliyergiyev, M.; Perrina, C.; Muñoz Salinas, M.; Droz, D.; Ruina, A.; Catanzani, E.
• Is Part Of: Journal of instrumentation. Volume 17:Number 6(2022)
• Publication Date: 2022-06-01
• Subjects: Calorimeters -- Particle detectors -- Performance of High Energy Physics Detectors; Scientific apparatus and instruments -- Periodicals; 502.84
• DOI: 10.1088/1748-0221/17/06/P06031
• Languages: English
• ISSNs: 1748-0221

Abstract

Abstract: 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.