论文标题
人工神经网络的大规模卢瑟福后散射光谱数据的处理
Processing of massive Rutherford Back-scattering Spectrometry data by artificial neural networks
论文作者
论文摘要
卢瑟福反向散射光谱法（RBS）是一项重要技术，可提供高精度和鲁棒性的样品近表面区域的元素信息。但是，该技术缺乏数据处理率有限的吞吐量，并且几乎不常规地使用大量样品（即数百或什至数千个样本）进行研究。对于复杂的样本而言，情况甚至更糟。如果这些样品中存在粗糙度或孔隙率，则这些结构的模拟在计算上是要求的。幸运的是，人工神经网络（ANN）表明，对于大量数据处理，是离子光束数据的大量盟友。在本文中，我们报告了ANN与人类评估的性能比较，并且在批处理模式下运行自动拟合程序。使用恒星W7-X的500个标记层光谱用作研究案例。结果表明，ANN比人类更准确，并且比自动拟合更有效。
Rutherford Backscattering Spectrometry (RBS) is an important technique providing elemental information of the near surface region of samples with high accuracy and robustness. However, this technique lacks throughput by the limited rate of data processing and is hardly routinely applied in research with a massive number of samples (i.e. hundreds or even thousands of samples). The situation is even worse for complex samples. If roughness or porosity is present in those samples the simulation of such structures is computationally demanding. Fortunately, Artificial Neural Networks (ANN) show to be a great ally for massive data processing of ion beam data. In this paper, we report the performance comparison of ANN against human evaluation and an automatic fit routine running on batch mode. 500 spectra of marker layers from the stellarator W7-X were used as study case. The results showed ANN as more accurate than humans and more efficient than automatic fits.
