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arXiv · 2608.11628

Reconstructive AI Spectroscopy of Charged Particle Beams

Vasily Kozhevnikov·Andrey Kozyrev·Elena Klepalova·Victor Tarasenko·Evgenii Baksht
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Abstract

This work introduces a physics-informed neural network (PINN) framework for reconstructing electron energy spectra from sparsely sampled attenuation-curve data. Leveraging the NVIDIA PhysicsNeMo platform, the proposed mesh-free methodology operates directly on raw experimental datasets while explicitly incorporating all experimental uncertainties. Validation on subnanosecond electron beam measurements demonstrates that the approach accurately resolves complex, multi-peaked spectral features of energy distribution. The framework enforces physical consistency through embedded governing principles and exhibits substantial predictive capability for energy spectrum reconstruction from noisy, low-precision, and sparse experimental data.

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BibTeXRIS

Vasily Kozhevnikov, Andrey Kozyrev, Elena Klepalova, Victor Tarasenko, Evgenii Baksht. 2026-08-12. Reconstructive AI Spectroscopy of Charged Particle Beams. https://arxiv.org/abs/2608.11628

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