基于深度学习的宽带光源X射线吸收光谱使用光子计数探测器
Zheng Fang1,2, Jingxuan Xu1, Kang Fan1
1Department of Instrumental & Electrical Engineering, Xiamen University, Xiamen 361102, China.
Analytical chemistry
|February 1, 2026
概括
光谱变压器纠正高光子流量引起的X射线光谱学扭曲. 这种深度学习框架提高了光谱真实性,提高了材料分类的准确性.
科学领域:
- 物理 物理学 物理
- 材料科学 材料科学 材料科学
- 计算机科学 计算机科学
背景情况:
- 光子计数探测器提供高分辨率,多能X射线光谱.
- 在X射线光谱学中,高光子流量会导致光谱扭曲 (堆积,极化).
- 准确的光谱数据对于材料分析至关重要.
研究的目的:
- 开发一个深度学习框架来纠正宽带光源X射线吸收光谱 (BL-XAS) 中的光谱扭曲.
- 为了提高在高光子流量条件下获得的X射线吸收光谱数据的光谱完整性.
主要方法:
- 拟议的光谱变压器,这是一个深度学习模型,融合了光谱和管流数据.
- 利用双式映射机制和基于物理的损失函数进行扭曲校正.
- 开发了一种级联模拟框架,用于生成高保真训练数据,并结合堆积和极化效应.
主要成果:
- 光谱变压器框架成功地纠正了光谱扭曲.
- 经过校正的光谱显示出异常的真实性,将KL分歧减少到1.0×10−4.
- 使用更正的光谱,材料分类的准确性从86.2%大幅提高到95.5%.
结论:
- 光谱变压器是一种有效的深度学习解决方案,用于BL-XAS中的光谱扭曲校正.
- 增强的光谱完整性可以提高分析性能和材料分类.
- 这种方法促进了光子计数探测器在高流量X射线光谱学中的应用.
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