Automation of etch pit analyses on solid-state nuclear track detectors with machine learning for laser-driven ion acceleration.

Solid-state nuclear track detectors (SSNTDs) are often used as ion detectors in laser-driven ion acceleration experiments and are considered to be the most reliable ion diagnostics since they are sensitive only to ions and measure ions one by one. However, ion pit analyses require tremendous time and effort in chemical etching, microscope scanning, and ion pit identification by eyes. From a laser-driven ion acceleration experiment, there are typically millions of microscopic images, and it is practically impossible to analyze all of them by hand. This research aims to improve the efficiency and automation of SSNTD analyses for laser-driven ion acceleration. We use two sets of data obtained from calibration experiments with a conventional accelerator where ions with known nuclides and energies are generated and from actual laser experiments using SSNTDs. After chemical etching and scanning the SSNTDs with an optical microscope, we use machine learning to distinguish the ion etch pits from noises. From the results of the calibration experiment, we confirm highly accurate etch-pit detection with machine learning. We are also able to detect etch pits with machine learning from the laser-driven ion acceleration experiment, which is much noisier than calibration experiments. By using machine learning, we successfully identify ion etch pits ∼105 from more than 10 000 microscopic images with a precision of ≳95%. A million microscopic images can be examined with a recent entry-level computer within a day with high precision. Machine learning tremendously reduces the time consumption on ion etch pit analyses detected on SSNTDs.

Mass-resolved ion measurement by particle counting analysis for characterizing relativistic ion beams driven by lasers.

Particle counting analysis is a possible way to characterize GeV-scale, multi-species ions produced in laser-driven experiments. We present a multi-layered scintillation detector to differentiate multi-species ions of different masses and energies. The proposed detector concept offers potential advantages over conventional diagnostics in terms of (1) high sensitivity to GeV ions, (2) realtime analysis, and (3) the ability to differentiate ions with the same charge-to-mass ratio. A novel choice of multiple scintillators with different ion stopping powers results in a significant difference in energy deposition between the scintillators, allowing accurate particle identification in the GeV range. Here, we report a successful demonstration of particle identification for heavy ions, performed at the Heavy Ion Medical Accelerator in Chiba. In the experiment, the proposed detector setup showed the ability to differentiate particles with similar atomic numbers, such as C6+ and O8+ ions, and provided an excellent energy resolution of 0.41%-1.2% (including relativistic effect, 0.51%--1.6%).