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基于机器学习的X射线光谱从过堆光谱仪数据的展开
M Alvarado Alvarez1, B T Wolfe1, C-S Wong1
1Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA.
The Review of scientific instruments
|August 1, 2024
概括
神经网络准确地从波器堆谱仪中展开X射线光谱. 这种方法显示出对错误的稳定性和高重复率应用的潜力.
科学领域:
- 频谱学是一种光谱学.
- 机器学习 机器学习
- 在X射线物理中,X射线物理
背景情况:
- 过堆光谱仪通过光刺激发光 (PSL) 测量X射线能量沉积.
- 精确的X射线光谱展开对于各种科学和工业应用至关重要.
研究的目的:
- 应用神经网络用于X射线光谱的展开使用过堆光谱仪数据.
- 评估神经网络方法的准确性,稳定性和速度.
主要方法:
- 在合成X射线数据 (<1 MeV) 上训练神经网络,使用马克斯韦尔和高斯分布.
- 使用来自五种不同的过堆光谱仪设计的PSL测量.
- 测试网络的性能与地面真相光谱和模拟的实验错误.
主要成果:
- 神经网络的预测与单个分布的基本真相光谱密切匹配,高能量的差异<20%.
- 该网络表现出对实验误差 (<5%) 的稳定性,以及对混合分布展开的一些能力.
- 展开速度超过1Hz,适用于高重复率系统.
结论:
- 神经网络提供了一个强大而高效的工具,用于用波器堆谱仪展开X射线光谱.
- 开发的方法是准确的,强大的实验噪声,并足够快的要求应用程序.
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