Aoki Hiroyuki, Liu Yuwei, Yamashita Takashi
Materials and Life Science Division, J-PARC Center, Japan Atomic Energy Agency, 2-4, Shirakata, Tokai, Ibaraki, 319-1195, Japan.
Institute of Materials Structure Science, High Energy Accelerator Research Organization, 203-1, Shirakata, Tokai, Ibaraki, 319-1106, Japan.
Sci Rep. 2021 Nov 22;11(1):22711. doi: 10.1038/s41598-021-02085-6.
Neutron reflectometry (NR) allows us to probe into the structure of the surfaces and interfaces of various materials such as soft matters and magnetic thin films with a contrast mechanism dependent on isotopic and magnetic states. The neutron beam flux is relatively low compared to that of other sources such as synchrotron radiation; therefore, there has been a strong limitation in the time-resolved measurement and further advanced experiments such as surface imaging. This study aims at the development of a methodology to enable the structural analysis by the NR data with a large statistical error acquired in a short measurement time. The neural network-based method predicts the true NR profile from the data with a 20-fold lower signal compared to that obtained under the conventional measurement condition. This indicates that the acquisition time in the NR measurement can be reduced by more than one order of magnitude. The current method will help achieve remarkable improvement in temporally and spatially resolved NR methods to gain further insight into the surface and interfaces of materials.
中子反射测量技术（NR）使我们能够利用依赖于同位素和磁态的对比度机制，探测诸如软物质和磁性薄膜等各种材料的表面和界面结构。与同步辐射等其他源相比，中子束通量相对较低；因此，在时间分辨测量以及诸如表面成像等进一步的先进实验方面存在很大限制。本研究旨在开发一种方法，以通过在短测量时间内获取的具有大统计误差的NR数据进行结构分析。基于神经网络的方法从信号比传统测量条件下低20倍的数据中预测真实的NR轮廓。这表明NR测量中的采集时间可以减少一个以上数量级。当前方法将有助于在时间和空间分辨的NR方法上实现显著改进，从而更深入地了解材料的表面和界面。
