Characterization and automated optimization of laser-driven proton beams from converging liquid sheet jet targets

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Glenn, G. D. and Treffert, F. and Ahmed, H. and Astbury, S. and Borghesi, M. and Bourgeois, N. and Curry, C. B. and Dann, S. J. D. and Diiorio, S. and Dover, N. P. and Dzelzainis, T. and Ettlinger, O. and Gauthier, M. and Giuffrida, L. and Gray, R. J. and Green, J. S. and Hicks, G. S. and Hyland, C. and Istokskaia, V. and King, M. and Loughran, B. and Margarone, D. and McCusker, O. and McKenna, P. and Najmudin, Z. and Parisuaña, C. and Parsons, P. and Spindloe, C. and Streeter, M. J. V. and Symes, D. R. and Thomas, A. G. R. and Xu, N. and Glenzer, S. H. and Palmer, C. A. J. (2026) Characterization and automated optimization of laser-driven proton beams from converging liquid sheet jet targets. Physical Review Research, 8. 013101. ISSN 2643-1564 (https://doi.org/10.1103/ggsf-hkm1)

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Abstract

Compact, stable, and versatile laser-driven ion sources hold great promise for applications ranging from medicine to materials science and fundamental physics. While single-shot sources have demonstrated favorable beam properties, including the peak fluxes necessary for several applications, high-repetition-rate operation will be necessary to generate and sustain the high average flux needed for many of the most exciting applications of laser-driven ion sources. Further, to navigate through the high-dimensional space of laser and target parameters toward experimental optima, it is essential to develop ion acceleration platforms compatible with machine learning techniques and capable of autonomous real-time optimization. Here, we present a multi-Hz ion acceleration platform employing a liquid sheet jet target. We characterize the laser-plasma interaction and the laser-driven proton beam across a variety of key parameters governing the interaction using an extensive suite of online diagnostics. We also demonstrate real-time, closed-loop optimization of the ion beam maximum energy by tuning the laser wave front using a Bayesian optimization scheme. This approach increased the maximum proton energy by 11% compared to a manually optimized wave front by enhancing the energy concentration within the laser focal spot, demonstrating the potential for closed-loop optimization schemes to tune future ion accelerators for robust high-repetition-rate operation.

ORCID iDs

Glenn, G. D., Treffert, F., Ahmed, H., Astbury, S., Borghesi, M., Bourgeois, N., Curry, C. B., Dann, S. J. D., Diiorio, S., Dover, N. P., Dzelzainis, T., Ettlinger, O., Gauthier, M., Giuffrida, L., Gray, R. J. ORCID logoORCID: https://orcid.org/0000-0003-0610-9595, Green, J. S., Hicks, G. S., Hyland, C., Istokskaia, V., King, M. ORCID logoORCID: https://orcid.org/0000-0003-3370-6141, Loughran, B., Margarone, D., McCusker, O., McKenna, P. ORCID logoORCID: https://orcid.org/0000-0001-8061-7091, Najmudin, Z., Parisuaña, C., Parsons, P., Spindloe, C., Streeter, M. J. V., Symes, D. R., Thomas, A. G. R., Xu, N., Glenzer, S. H. and Palmer, C. A. J.;
CORE (COnnecting REpositories)