Analysis of Atomic Charge State and Atomic Number for VAMOS++ Magnetic Spectrometer using Deep Neural Networks and Fractionally Labelled Events
arXiv:2507.07109v2 Announce Type: cross 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
The increasing complexity of scientific instrumentation and the maturity of deep neural network techniques allows for their application in detailed data analysis for established physics experiments.
This development indicates a growing trend of AI applications enhancing precision and efficiency in specialized scientific analysis, potentially accelerating discovery and experimental output.
The accuracy and speed of analyzing complex data from systems like magnetic spectrometers are improved through AI integration, moving beyond traditional methods.
- · Experimental Nuclear Physics
- · Deep Neural Network Developers
- · Scientific Instrumentation Manufacturers
- · Manual data analysis specialists
- · Older, less efficient data processing techniques
Deep neural networks are adopted for more precise and faster analysis of atomic charge states in high-energy physics experiments.
This method could be generalized to improve data interpretation across various scientific fields using complex sensor arrays.
Accelerated discovery of new particles or states of matter due to enhanced data processing capabilities becomes plausible, requiring less experimental time.
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