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

Aug 5, 2026

The Dark MAtter Particle Explorer (DAMPE) is a space-borne cosmic-ray detector measuring ion fluxes up to ~500 TeV/n via its deep calorimeter. Saturation begins above ~100 TeV in incident energy, distorting primary energy reconstruction and motivating recovery methods for deposited energy. This work introduces a new machine-learning approach using a dedicated model to correct total deposited energy, enabling generalization across different ions and extending the maximum detectable incident energy to the PeV scale, building on prior results.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages11

Reading Time28 min

AccessFree to download

Introduction

DAMPE detector and the saturation problem

The BGO calorimeter of DAMPE

Detector geometry and readout channels

Photomultiplier dynodes and gain ranges

Nuclear Instruments and Methods in Physics Research A 1085 (2026) 171306 | Full Length Article
Machine-learning correction for the calorimeter saturation of cosmic-ray ions with the Dark Matter Particle Explorer: Towards the PeV scale | | | | | --- | --- | --- | --- | | Andrea Serpollaa ,∗, Andrii Tykhonova, Paul Coppina, Manbing Lia, Andrii Kotenkoa, Enzo Putti-Garcia a, Hugo Valentin Boutina, Mikhail Stolpovskiy c, Jennifer Maria Frieden b, Chiara Perrinab, Xin Wu a
a Département de physique nucléaire et corpusculaire (DPNC), Université de Genève (UniGE), Quai Ernest-Ansermet 24, Geneva, CH-1205, Switzerland b Institute of Physics, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, CH-1015, Switzerland
c International Space Science Institute (ISSI), Hallerstrasse 6, Bern, CH-3012, Switzerland | | | | | A R T I C L E I N F O | | A B S T R A C T | | | Keywords: Calorimeters Cosmic rays Machine learning Energy reconstruction DAMPE | | The Dark MAtter Particle Explorer (DAMPE) instrument is a space-borne cosmic-ray detector, capable of measuring ion fluxes up to ∼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 ∼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 Stolpovskiyet al. (2022). | | 1. Introduction Galactic cosmic rays (GCRs) are accelerated high-energy particles wandering in our galaxy. Their origin and interaction with the interstellar medium (ISM) are crucial topics in astrophysics [1]. One of the main challenges in GCR studies is the accurate measurement of their composition and energy, which are essential to probe their origin in our galaxy and propagation in the ISM. More specifically, a key open question is the behaviour of heavy nuclei fluxes at the 100 TeV to PeV scale; this work is a necessary and important step to achieve such goal. The DArk Matter Particle Explorer (DAMPE) is a space-based detector operating since its launch in December 2015 [2]. DAMPE is capable of detecting CR nuclei, electrons/positrons and 􀀍-rays, thanks to its four sub-detectors: a plastic scintillator (PSD) [3], a silicon-tungsten trackerconverter (STK) [4], a bismuth germanium oxide (BGO) electromagnetic calorimeter [5], and a neutron detector (NUD) [6]. The DAMPEinstru∼ment allows to detect and study 􀀍-rays and electr∼ons/positrons from 5 GeV up to several TeVs [7,8], and nuclei from 50 GeV up to several hundreds of TeVs [9–11]. DAMPE stands out over other spacebased instruments because of its calorimeter, the deepest currently in orbit w∼ith its 32 radiation lengths, or 1.6 nuclear interaction lengths. Above 100 TeV of kinetic energy, the readout channels can saturate and a significant fraction of the deposited particle energy can be lost. ∗ Corresponding author. E-mail address: [andrea.serpolla@cern.ch](andrea.serpolla@cern.ch) (A. Serpolla). Previous studies showed the possibility of recovering the lost energy due to saturation using analytical [12], or machine-learning (ML) techniques [13]. However, these methods start losing accuracy and precision for heavy nuclei, and for incident energies above ∼500 TeV. Therefore, the development of techniques that can help correcting for saturation at higher energies and for heavy nuclei is crucial for the measurement of GCRs spectra beyo

FAQs

Why does calorimeter saturation affect DAMPE energy reconstruction?

Above ~100 TeV incident energy, DAMPE calorimeter readout channels saturate, causing a significant fraction of deposited energy to be lost. This distortion impacts the accuracy of reconstructed primary energy.

What limitation do previous analytical and machine-learning techniques face?

Analytical and earlier ML methods lose accuracy and precision for heavy nuclei and for incident energies above ~500 TeV. This motivates improved corrections at higher energies and for heavier ions.

How does the proposed machine-learning method extend DAMPE measurements?

The method uses a unique model to correct the total deposited energy in DAMPE’s calorimeter, generalizing across different ions. It enables reliable corrections up to a few PeV of primary energy, extending the detectable incident energy scale.

Aug 5, 2026

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