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

T Taguchi; T Minami; T Hihara; F Nikaido; T Asai; K Sakai; Y Abe; A Yogo; Y Arikawa; H Kohri; A O Tokiyasu; C M Chu; W Y Woon; S Kodaira; M Kanasaki; Y Fukuda; Y Kuramitsu

doi:10.1063/5.0172202

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

Rev Sci Instrum. 2024 Mar 1;95(3):033301. doi: 10.1063/5.0172202.

Authors

T Taguchi¹, T Minami^{1

2}, T Hihara¹, F Nikaido¹, T Asai^{2

3}, K Sakai^{1

4}, Y Abe^{1

5}, A Yogo⁵, Y Arikawa⁵, H Kohri⁶, A O Tokiyasu⁷, C M Chu⁸, W Y Woon⁸, S Kodaira⁹, M Kanasaki³, Y Fukuda², Y Kuramitsu^{1

5}

Affiliations

¹ Graduate School of Engineering, Osaka University, 2-1 Yamadaoka, Suita, Osaka 565-0871, Japan.
² Kansai Institute for Photon Science (KPSI), National Institutes for Quantum Science and Technology (QST), 8-1-7 Umemidai, Kizugawa, Kyoto 619-0215, Japan.
³ Graduate School of Maritime Sciences, Kobe University, Kobe 658-0022, Japan.
⁴ National Institute for Fusion Science, 322-6 Oroshicho, Toki, Gifu, 509-5292, Japan.
⁵ Institute of Laser Engineering, Osaka University, 2-6 Yamadaoka, Suita, Osaka 565-0871, Japan.
⁶ Research Center for Nuclear Physics, Osaka University, 10-1 Mihogaoka, Ibaraki, Osaka 567-0047, Japan.
⁷ Research Center for Electron Photon Science, Tohoku University, 1-2-1 Mikamine, Taihaku-ku, Sendai, Miyagi 982-0826, Japan.
⁸ Department of Physics, National Central University, No. 300, Jhongda Rd., Jhongli, Taoyuan 320, Taiwan.
⁹ Institute for Radiological Science (NIRS), National Institutes for Quantum Science and Technology (QST), Inage, Chiba 263-8555, Japan.

PMID: 38436451
DOI: 10.1063/5.0172202

Abstract

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.