A neural network-based synthetic diagnostic of laser-accelerated proton energy spectra

Tools

McQueen, Christopher J.G. and Wilson, Robbie and Frazer, Timothy P. and King, Martin and Alderton, Matthew and Bacon, Ewan F.J. and Dolier, Ewan J. and Dzelzainis, Thomas and Patel, Jesel K. and Peat, Maia P. and Torrance, Ben C. and Gray, Ross J. and McKenna, Paul (2025) A neural network-based synthetic diagnostic of laser-accelerated proton energy spectra. Communications Physics, 8. 66. ISSN 2399-3650 (https://doi.org/10.1038/s42005-025-01984-8)

[thumbnail of McQueen-etal-CP-2025-A-neural-network-based-synthetic-diagnostic-of-laser-accelerated-proton-energy-spectra]
Preview
 Text. Filename: McQueen-etal-CP-2025-A-neural-network-based-synthetic-diagnostic-of-laser-accelerated-proton-energy-spectra.pdf
Final Published Version
License: Creative Commons Attribution 4.0 logo
Download (1MB)| Preview

Abstract

Machine learning can revolutionize the development of laser-plasma accelerators by enabling real-time optimization, predictive modeling and experimental automation. Given the broad range of laser and plasma parameters and shot-to-shot variability in laser-driven ion acceleration at present, continuous monitoring with real-time, non-disruptive ion diagnostics is crucial for consistent operation. Machine learning provides effective solutions for this challenge. We present a synthetic diagnostic method using deep neural networks to predict the energy spectrum of laser-accelerated protons. This model combines variational autoencoders for dimensionality reduction with feed-forward networks for predictions based on secondary diagnostics of the laser-plasma interactions. Trained on data from fewer than 700 laser-plasma interactions, the model achieves an error level of 13.5%, and improves with more data. This non-destructive diagnostic enables high-repetition laser operations with the approach extendable to a fully surrogate model for predicting realistic ion beam properties, unlocking potential for diverse applications of these promising sources.

ORCID iDs

McQueen, Christopher J.G. ORCID logoORCID: https://orcid.org/0009-0000-5372-5257, Wilson, Robbie, Frazer, Timothy P., King, Martin ORCID logoORCID: https://orcid.org/0000-0003-3370-6141, Alderton, Matthew ORCID logoORCID: https://orcid.org/0000-0002-4043-0393, Bacon, Ewan F.J., Dolier, Ewan J. ORCID logoORCID: https://orcid.org/0000-0002-2744-7406, Dzelzainis, Thomas, Patel, Jesel K. ORCID logoORCID: https://orcid.org/0009-0006-5579-4903, Peat, Maia P., Torrance, Ben C., Gray, Ross J. ORCID logoORCID: https://orcid.org/0000-0003-0610-9595 and McKenna, Paul ORCID logoORCID: https://orcid.org/0000-0001-8061-7091;
CORE (COnnecting REpositories)