A Telescope System for Charge and Position Measurement of High Energy Nuclei

TL;DR

This paper presents a high-precision silicon telescope system with a hybrid machine learning algorithm for charge and position measurement of high-energy nuclei, achieving unprecedented resolution in a compact design.

Contribution

The development of a high-granularity silicon telescope with hybrid machine learning for precise charge and position measurement of high-energy nuclei.

Findings

Spatial resolution of ~1 micron achieved.

Charge resolution better than 0.16 units for Z=1 to 29.

Most precise charge and spatial resolution simultaneously achieved by a silicon telescope.

Abstract

A high-granularity telescope system with a large sensitive area and low material budget has been developed for high-energy heavy ion beam tests. The telescope consists of nine layers of silicon microstrip detectors (SSDs), whose performance was validated through a heavy ion beam test at the CERN SPS. A hybrid machine learning algorithm is proposed to address the challenges of nuclear charge measurement with SSDs. The system achieves a spatial resolution of $\mathcal{O}(1) \,$\SI{}{\micro\metre} and a charge resolution better than 0.16 charge units for nuclei from $Z = 1$ to $Z = 29$, with a sensitive area of $8 \times 8 \, \mathrm{cm}^2$. To the best of our knowledge, this represents the most precise charge and spatial resolution simultaneously achieved by a silicon telescope to date.