arXiv subjects

Jennifer Maria Frieden

Publications and source records attributed to Jennifer Maria Frieden.

4 recordsLinked to original sources

arXiv2026

Development, Configuration and Performance Characterization of a Scalable BETA ASIC-Based Readout System for Multi-Channel SiPM Detectors

We present the design, configuration, and performance characterization of a scalable readout system based on the BETA application-specific integrated circuit (ASIC), developed to meet stringent requirements on noise, linearity, dynamic range, and power consumption for multi-channel silicon photomultiplier (SiPM) detectors in spaceborne instrumentation. The readout electronics consists of modular interface boards (FIBs) hosting multiple BETA ASICs and controlled by a field-programmable gate array (FPGA), which provides configuration, data acquisition, and global trigger generation. Multiple BETA FIBs were tested in a dedicated optical setup enabling simultaneous readout of a large number of channels. The system performance was evaluated using three S13552-10 SiPM arrays manufactured by Hamamatsu for the FIT detector, a scintillating-fiber tracker developed for charged cosmic-ray particle tracking and charge measurement in the HERD mission. We describe the configuration procedures and performance measurements of the readout system, including gain calibration, linearity characterization, and threshold response. In addition, we present the development of a global internal trigger logic for the identification of ionizing particles in the FIT detector. The results demonstrate the stability, scalability, and suitability of the developed BETA-based readout system for large-scale multi-channel SiPM detector applications in space experiments.

astro-ph.IM↗

arXiv2026

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

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].

astro-ph.HE↗

arXiv2025

Energy Reconstruction of Non-fiducial Electron-Positron Events in the DAMPE Experiment Using Convolutional Neural Networks

The Dark Matter Particle Explorer (DAMPE) is a space-based Cosmic-Ray (CR) observatory with the aim, among others, to study Cosmic-Ray Electrons (CREs) up to 10 TeV. Due to the low CRE rate at multi-TeV energies, we aim to increasing the acceptance by selecting events outside the fiducial volume. The complex topology of non-fiducial events requires the development of a novel energy reconstruction method. We propose the usage of Convolutional Neural Networks for a regression task to recover an accurate estimation of the initial energy.

astro-ph.IM↗

arXiv2022

A deep learning method for the trajectory reconstruction of cosmic rays with the DAMPE mission

A deep learning method for the particle trajectory reconstruction with the DAMPE experiment is presented. The developed algorithms constitute the first fully machine-learned track reconstruction pipeline for space astroparticle missions. Significant performance improvements over the standard hand-engineered algorithms are demonstrated. Thanks to the better accuracy, the developed algorithms facilitate the identification of the particle absolute charge with the tracker in the entire energy range, opening a door to the measurements of cosmic-ray proton and helium spectra at extreme energies, towards the PeV scale, hardly achievable with the standard track reconstruction methods. In addition, the developed approach demonstrates an unprecedented accuracy in the particle direction reconstruction with the calorimeter at high deposited energies, above several hundred GeV for hadronic showers and above a few tens of GeV for electromagnetic showers.

astro-ph.IM↗