PaperPanorama

Nuclear Experiment·nucl-ex

Wed·Jul 1, 2026

2 papers0 primary·2 cross-listed

  1. 01

    Seven-dimensional Trajectory Reconstruction for VAMOS++

    M. Rejmund🇫🇷 · A. Lemasson🇫🇷

    The VAMOS++ magnetic spectrometer is characterized by a large angular and momentum acceptance and highly non-linear ion optics properties requiring the use of software ion trajectory reconstruction methods to measure the ion magnetic rigidity and the trajectory length between the beam interaction point and the focal plane of the spectrometer. Standard measurements, involving the use of a thin target and a narrow beam spot, allow the assumption of a point-like beam interaction volume for ion trajectory reconstruction. However, this represents a limitation for the case of large beam spot size or extended gaseous target volume. To overcome this restriction, a seven-dimensional reconstruction method incorporating the reaction position coordinates was developed, making use of artificial deep neural networks. The neural networks were trained on a theoretical dataset generated by standard magnetic ray-tracing code. Future application to a voluminous gas target, necessitating the explicit inclusion of the three-dimensional position of the beam interaction point within the target in the trajectory reconstruction method, is discussed. The performances of the new method are presented along with a comparison of mass resolution obtained with previously reported model for the case of thin-target experimental data.

    physics.ins-detcs.LGnucl-exNucl.Instrum.Meth.A(2025)·2 citations
  2. 02

    Analysis of Atomic Charge State and Atomic Number for VAMOS++ Magnetic Spectrometer using Deep Neural Networks and Fractionally Labelled Events

    M. Rejmund🇫🇷 · A. Lemasson🇫🇷

    The VAMOS++ magnetic spectrometer is a multi-parametric system that integrates ion optical magnetic elements with a multi-detector stack. The magnetic elements, along with the tracking and timing detectors and the trajectory reconstruction method, provide the analysis of the magnetic rigidity, the trajectory length between the beam interaction point and the focal plane of the spectrometer, and the related velocity and mass-over-charge ratio. The segmented ionization chamber provides the energy measurements necessary to analyze the atomic charge state and atomic number. However, this analysis critically suffers from inherent limitations due to the variable thickness and non-uniformity of the entrance window of the ionization chamber and other detector imperfections. Conventionally, this meticulous, detailed analysis is exceptionally tedious, often requiring several months to complete. We present a novel method utilizing deep neural networks, trained on an experimental dataset with only a small fraction of precisely labeled events for the lowest and best-resolved atomic charge states or numbers. This innovative approach enables the networks to autonomously and accurately classify the remaining events. This method drastically accelerates the acquisition of high-resolution atomic charge state and atomic number spectra, reducing analysis time from months to mere hours. Crucially, by discarding human bias, this approach ensures standardized, optimal, and reproducible results with unprecedented efficiency.

    physics.ins-detcs.LGnucl-exphysics.atom-ph+1JINST(2025)·2 citations

Affiliations

first authorsco-authorsvia INSPIRE