Towards automated analysis for neutron reflectivity

被引：17

作者：

Mironov, Daniil ^{[1
]}

Durant, James H. ^{[1
]}

Mackenzie, Rebecca ^{[2
]}

Cooper, Joshaniel F. K. ^{[1
]}

机构：

[1] Rutherford Appleton Lab, ISIS Neutron & Muon Source, Harwell Campus, Didcot OX11 0QX, Oxon, England

[2] Rutherford Appleton Lab, Div Comp Sci, SciML, Harwell Campus, Didcot OX11 0QX, Oxon, England

来源：

MACHINE LEARNING-SCIENCE AND TECHNOLOGY | 2021年 / 2卷 / 03期

基金：

英国工程与自然科学研究理事会;

关键词：

neutron; x-ray; reflectivity; machine learning; neural network;

D O I：

10.1088/2632-2153/abe7b5

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We describe a neural network-based tool for the automatic estimation of thin film thicknesses and scattering length densities from neutron reflectivity curves. The neural network sits within a data pipeline, that takes raw data from a neutron reflectometer, and outputs data and parameter estimates into a fitting program for end user analysis. Our tool deals with simple cases, predicting the number of layers and layer parameters up to three layers on a bulk substrate. This provides good accuracy in parameter estimation, while covering a large portion of the use case. By automating steps in data analysis that only require semi-expert knowledge, we lower the barrier to on-experiment data analysis, allowing better utility to be made from large scale facility experiments. Transfer learning showed that our tool works for x-ray reflectivity, and all code is freely available on GitHub (neutron-net 2020, available at: https://github.com/xmironov/neutron-net) (Accessed: 25 June 2020).

引用

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