Applying Gaussian Mixture Models to Track Reconstruction in Inelastic Scattering Experiments with Active Targets

TL;DR

This paper introduces a Gaussian Mixture Model-based method for reconstructing low-energy particle tracks in active target experiments, improving the analysis of inelastic scattering data for unstable nuclei.

Contribution

It presents a novel GMM-based track reconstruction approach tailored for active target experiments, emphasizing low-energy track treatment and demonstrated on simulated data.

Findings

Effective reconstruction of low-energy tracks demonstrated

Improved separation of resonance modes shown

Method validated on simulated GANIL data

Abstract

Active targets such as ACTAR TPC are well suited for studying giant resonances in unstable nuclei via inelastic scattering in inverse kinematics. A key challenge in such measurements is the detection of low-energy ejectiles emitted at small angles relative to the beam direction. Accurate reconstruction of these tracks is essential for disentangling different resonance modes. Probabilistic models such as the Gaussian Mixture Model (GMM) are particularly effective in capturing the complex covariance structures characteristic of the beam-recoil interface in narrow-angle events. In this work, we present a track reconstruction approach based on the GMM, specifically designed for inelastic scattering experiments with active targets. Special emphasis is placed on the treatment of low-energy tracks. The proposed method is demonstrated on simulated data of the $^{58}\mathrm{Ni}(\alpha,\alpha')^{58}\mathrm{Ni}$ reaction at an incident energy of $E=49$~MeV/nucleon, generated under conditions representative of the experiment carried out at GANIL for the same reaction.