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<p>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.</p>

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年2月12日

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  • Christopher J. G. McQueen,
  • Robbie Wilson,
  • Timothy P. Frazer,
  • Martin King,
  • Matthew Alderton,
  • Ewan F. J. Bacon,
  • Ewan J. Dolier,
  • Thomas Dzelzainis,
  • Jesel K. Patel,
  • Maia P. Peat,
  • Ben C. Torrance,
  • Ross J. Gray,
  • Paul McKenna

摘要

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.