Naoya Tamaki

Generating 100-MeV protons is pivotal to developing laser-driven ion sources for many applications that range from medicine to laboratory astrophysics. An ultra-thin target is a way to increase the ion energy; however, such a target is easily destroyed by the interaction with laser prepulses. We have developed a large-area suspended graphene (LSG) target and demonstrated its durability by generating energetic protons and carbons by irradiating the thinnest target ever (2 nm) with an ultra-intense laser without a plasma mirror. Here, we show the experimental results of proton energies reaching 132 MeV by optimizing the laser conditions for the ultra-thin LSG targets. Ion diagnostics were primarily performed using a CR-39 stack detector combined with machine-learning-assisted analysis. Numerical simulations support the experimental results, where the radiation pressure of a relatively long-duration, low-intensity laser pulse slowly pushes the plasma, leading to the formation of a propagating electrostatic wave that efficiently accelerates protons, as in surfing acceleration.

Recent advances in laser-driven ion acceleration have brought GeV-class carbon acceleration closer to realization. For high-energy ion measurements, we have used a stack detector, which consists of plastic nuclear track detectors, such as CR-39 and aluminum energy absorbers. In such experiments, etch pit analysis from a large number of microscope images is required. To handle these massive datasets efficiently, the etch pit analysis was automated with the aid of machine learning. However, as the ion energy increases, secondary fragments generated within the CR-39 stack detector make a non-negligible contribution, and their impact on the measured energy spectra remains unclear. In this study, secondary fragments produced by GeV-class carbon ions incident on a CR-39 stack detector were examined at a conventional accelerator. The results show that the ratio of secondary fragments to incident carbon increases with depth, reaching more than 20%. In addition, comparison of linear energy transfer distributions with a Monte Carlo nuclear particle transport simulation revealed the production of Li, Be, and B. These findings highlight the importance of accounting for secondary fragments in precise high-energy heavy-ion measurements using CR-39 stack detectors.

Diagnosing turbulent electromagnetic fields in laser-driven plasmas is challenging because ion radiography cannot easily resolve the electric and magnetic field components. To address this limitation, we propose and numerically investigate a reconstruction method based on encoder–decoder convolutional neural networks. We construct a simulation dataset for training and testing the neural networks by pairing electric fields generated in hydrodynamic simulations with corresponding synthetic ion radiographs obtained from test particle simulations. Using the resulting dataset, we systematically investigate the influence of several key factors on the reconstruction, including the addition of synthetic noise, variations in the probing ion flux, and the use of different input data types.