Analysis of Atomic Charge State and Atomic Number for VAMOS++ Magnetic Spectrometer using Deep Neural Networks and Fractionally Labelled Events
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Physics > Instrumentation and Detectors
arXiv:2507.07109 (physics)
[Submitted on 20 Jun 2025 (v1), last revised 13 Aug 2025 (this version, v2)]
Title:Analysis of Atomic Charge State and Atomic Number for VAMOS++ Magnetic Spectrometer using Deep Neural Networks and Fractionally Labelled Events
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Abstract: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.
| Comments: | Update figures 5 and 6 |
| Subjects: | Instrumentation and Detectors (physics.ins-det); Machine Learning (cs.LG); Nuclear Experiment (nucl-ex); Atomic Physics (physics.atom-ph); Data Analysis, Statistics and Probability (physics.data-an) |
| Cite as: | arXiv:2507.07109 [physics.ins-det] |
| (or arXiv:2507.07109v2 [physics.ins-det] for this version) | |
| https://doi.org/10.48550/arXiv.2507.07109
arXiv-issued DOI via DataCite
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| Journal reference: | JINST 20 P08022 (2025) |
| Related DOI: | https://doi.org/10.1088/1748-0221/20/08/P08022
DOI(s) linking to related resources
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Submission history
From: Antoine Lemasson [view email][v1] Fri, 20 Jun 2025 14:52:22 UTC (4,619 KB)
[v2] Wed, 13 Aug 2025 08:51:58 UTC (4,587 KB)
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View a PDF of the paper titled Analysis of Atomic Charge State and Atomic Number for VAMOS++ Magnetic Spectrometer using Deep Neural Networks and Fractionally Labelled Events, by M. Rejmund and A. Lemasson
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