A new versatile method for the reconstruction of scintillator-based muon telescope events

TL;DR

This paper introduces a novel particle trajectory reconstruction method using RANSAC for muon telescopes, enhancing signal/noise separation and improving muon radiography of volcanic structures.

Contribution

The paper develops a RANSAC-based data processing technique specifically designed for scintillator-based muon telescopes, improving event reconstruction accuracy.

Findings

RANSAC algorithm effectively discriminates noise hits from true muon signals.

Enhanced angular resolution and reconstruction efficiency demonstrated via simulations.

Promising initial results from real volcanic data analysis.

Abstract

This paper presents a new method to process the data recorded with muon telescopes. We have developed this processing method for the plastic scintillator-based hodoscopes located around the volcano La Soufri\`ere de Guadeloupe, in the French Lesser Antilles, in order to perform muon radiographies of the lava dome region, strongly impacted by the volcanic hydrothermal activity. Our method relies on particle trajectory reconstruction, performing a fit of the recorded hits in the impacted scintillator bars using a Random Sample Consensus algorithm. This algorithm is specifically built to discriminate outlier points, usually due to noise hits, in the data. Thus, it is expected to significantly improve the signal/noise separation in muon track hits and to obtain higher quality estimates of the particles' incident trajectories in our detectors. The first analysis of the RANSAC-reconstructed events offers promising results in terms of average density maps. To illustrate the performances of this algorithm, we provide angular resolution and reconstruction efficiency estimates using a GEANT4 simulation of a telescope equipped with four detection matrices. In addition, we also show preliminary results from open-sky data recorded with such telescope at La Soufri\`ere de Guadeloupe volcano.