Datasets and code from "Applying Machine Learning Methods to Laser Acceleration of Protons: Synthetic Data for Exploring the High Repetition Rate Regime"

Advances in ultra-intense laser technology have increased repetition rates and average power for chirped-pulse laser
systems which is promising for many applications including energetic proton sources. An important challenge is the need
to optimize and control the proton source by changing the details of the laser-plasma interaction, which is where machine
learning can play an important role. Building upon our earlier work in Desai et al. 2024, we generate a large synthetic
data set for proton acceleration using a physics-informed analytic model that we improved to include pre-pulse physics
and we train different machine learning methods on this data set to determine which methods perform efficiently and
accurately. Generally we find that quasi-real time training of these models using single shot data from a kHz repetition
rate ultra-intense laser system should typically be feasible on a single GPU. We also find that less sophisticated models
can be trained even faster, and that the accuracy of these models is still good enough to be useful. We provide our source
code and example synthetic data for others to test new machine learning methods and approaches to automated learning
in this regime.