# Automated control and optimisation of laser driven ion acceleration

arXiv: https://arxiv.org/abs/2303.00823 · HTML: https://mlsanity.com/paper/2303.00823/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 65 references that are in the map.
- First posted on arXiv 1 Mar 2023.

## Built on by (most cited first, 2 of 2)

- [Shaping Laser Pulses with Reinforcement Learning](https://mlsanity.com/paper/2503.00499/) (2025)
- [TempoRL: laser pulse temporal shape optimization with Deep Reinforcement Learning](https://mlsanity.com/paper/2304.12187/) (2023)

## Abstract (arXiv)

The interaction of relativistically intense lasers with opaque targets represents a highly non-linear, multi-dimensional parameter space. This limits the utility of sequential 1D scanning of experimental parameters for the optimisation of secondary radiation, although to-date this has been the accepted methodology due to low data acquisition rates. High repetition-rate (HRR) lasers augmented by machine learning present a valuable opportunity for efficient source optimisation. Here, an automated, HRR-compatible system produced high fidelity parameter scans, revealing the influence of laser intensity on target pre-heating and proton generation. A closed-loop Bayesian optimisation of maximum proton energy, through control of the laser wavefront and target position, produced proton beams with equivalent maximum energy to manually-optimized laser pulses but using only 60% of the laser energy. This demonstration of automated optimisation of laser-driven proton beams is a crucial step towards deeper physical insight and the construction of future radiation sources.