Machine-learning correction for the calorimeter saturation of cosmic-rays ions with the Dark Matter Particle Explorer: towards the PeV scale

Rapport Preprint Machine-learning correction for the calorimeter saturation of cosmic-rays ions with the Dark Matter Particle Explorer: towards the PeV scale eng 2025 author Serpolla Andrea 0000-0002-4122-6298 412179 Département de physique nucléaire et corpusculaire (FINUC) author Tykhonov Andrii 813906 Département de physique nucléaire et corpusculaire (FINUC) author Coppin Paul 989099 Département de physique nucléaire et corpusculaire (FINUC) author Li Manbing 404387 Département de physique nucléaire et corpusculaire (FINUC) author Kotenko Andrii 939793 author Putti-Garcia Enzo 832837 author Boutin Hugo Valentin 0009-0004-6010-9486 832805 Département de physique nucléaire et corpusculaire (FINUC) author Stolpovskiy Mikhail author Frieden Jennifer Maria author Perrina Chiara author Wu Xin 27612 Département de physique nucléaire et corpusculaire (FINUC) 10.48550/arxiv.2507.06626 The Dark MAtter Particle Explorer (DAMPE) instrument is a space-born cosmic-ray detector, capable of measuring ion fluxes up to $\sim$500 TeV/n. This energy scale is made accessible through its calorimeter, which is the deepest currently operating in orbit. Saturation of the calorimeter readout channels start occurring above $\sim$100 TeV of incident energy, and can significantly affect the primary energy reconstruction. Different techniques, analytical and machine-learning based, have been developed to tackle this issue, focusing on the recovery of single-bar deposits, up to several hundreds of TeV. In this work, a new machine-learning technique is presented, which profit of a unique model to correct the total deposited energy in DAMPE calorimeter. The described method is able to generalise its corrections for different ions and extend the maximum detectable incident energy to the PeV scale. High Energy Astrophysical Phenomena (astro-ph.HE) FOS: Physical sciences Dewey 500.2 34 Département de physique nucléaire et corpusculaire 132 false 792 Groupe Wu 81f2294f-21a5-4bc8-9ee6-26cf31152018 Preprint Public Preprint_from_arxiv.pdf 1543213 application/pdf f37c3787eed952ede904e31970b44bf87c0643a48759b41f9d2a32d2f0d92eec openAccess Faculté des sciences / Section de physique / Département de physique nucléaire et corpusculaire

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Machine-learning correction for the calorimeter saturation of cosmic-rays ions with the Dark Matter Particle Explorer: towards the PeV scale				2025	14	3

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