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

TL;DR

This paper introduces a machine-learning correction method for DAMPE's calorimeter saturation, enabling accurate cosmic-ray ion energy measurements up to the PeV scale, surpassing previous limitations.

Contribution

A novel machine-learning technique that corrects calorimeter saturation effects, extending the energy detection range of DAMPE to PeV energies.

Findings

Successfully corrected energy measurements up to PeV scale.

Generalized correction across different ion types.

Improved primary energy reconstruction accuracy.

Abstract

The Dark MAtter Particle Explorer (DAMPE) instrument is a space-borne 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 starts occurring above $\sim$100 TeV of incident energy, and can significantly affect the primary energy reconstruction. Different techniques -- analytical and machine-learning based -- were 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 benefits from 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. This work is a continuation of the results presented in [1].