在固态核轨道探测器上的蚀坑分析自动化,用于激光驱动的离子加速的机器学习
T Taguchi1, T Minami1,2, T Hihara1
1Graduate School of Engineering, Osaka University, 2-1 Yamadaoka, Suita, Osaka 565-0871, Japan.
The Review of scientific instruments
|March 4, 2024
概括
机器学习自动化了用于激光驱动离子加速的固态核轨道探测器 (SSNTD) 的分析. 这种方法显著加快了从数百万微观图像中识别离子蚀刻坑的速度.
科学领域:
- 核物理 核物理是核物理的.
- 材料科学是一种材料科学.
- 数据科学是数据科学.
背景情况:
- 固态核轨道探测器 (SSNTD) 在激光驱动的离子加速中对离子检测至关重要.
- 由于化学蚀刻,扫描和对数百万个离子坑的视觉识别,SSNTD的手动分析是耗时的.
研究的目的:
- 为了提高SSNTD分析的效率和自动化,用于激光驱动的离子加速实验.
- 开发一种机器学习方法,以快速准确地识别离子蚀刻坑.
主要方法:
- 利用机器学习算法来区分离子蚀刻坑与微观图像中的噪音.
- 使用已知离子能量的校准实验和实际激光实验数据进行训练和验证的模型.
- 处理了SSNTDs的10,000多张显微镜图像.
主要成果:
- 在校准实验中实现了高精度的蚀刻坑检测.
- 在激光驱动加速实验中的噪音数据中成功识别了离子蚀刻坑,精度>95%.
- 确定了大约10^5的离子蚀刻坑,证明了该方法的有效性.
结论:
- 机器学习显著减少了SSNTD分析所需的时间和精力.
- 使用当前的计算能力,在一天内对数百万张图像进行自动分析是可行的.
- 这种方法为激光驱动加速中的离子诊断提供了精确有效的解决方案.
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