Spatial Resolution of Double-Sided Silicon Microstrip Detectors for the PAMELA Apparatus

TL;DR

This paper evaluates the spatial resolution and performance of silicon microstrip detectors in the PAMELA satellite experiment, including simulation, data comparison, and optimized position reconstruction methods for cosmic ray analysis.

Contribution

It presents a detailed simulation and analysis of silicon microstrip detectors, introducing optimized impact point reconstruction methods based on incidence angle and cluster data.

Findings

Achieved position measurement accuracy better than 3 micrometers.

Developed a simulation model validated against experimental data.

Proposed impact point reconstruction methods tailored to particle incidence angles.

Abstract

The PAMELA apparatus has been assembled and it is ready to be launched in a satellite mission to study mainly the antiparticle component of cosmic rays. In this paper the performances obtained for the silicon microstrip detectors used in the magnetic spectrometer are presented. This subdetector reconstructs the curvature of a charged particle in the magnetic field produced by a permanent magnet and consequently determines momentum and charge sign, thanks to a very good accuracy in the position measurements (better than 3 um in the bending coordinate). A complete simulation of the silicon microstrip detectors has been developed in order to investigate in great detail the sensor's characteristics. Simulated events have been then compared with data gathered from minimum ionizing particle (MIP) beams during the last years in order to tune free parameters of the simulation. Finally some either widely used or original position finding algorithms, designed for such kind of detectors, have been applied to events with different incidence angles. As a result of the analysis, a method of impact point reconstruction can be chosen, depending on both the particle's incidence angle and the cluster multiplicity, so as to maximize the capability of the spectrometer in antiparticle tagging.