| Issue |
EPJ Web Conf.
Volume 225, 2020
ANIMMA 2019 – Advancements in Nuclear Instrumentation Measurement Methods and their Applications
|
|
|---|---|---|
| Article Number | 01004 | |
| Number of page(s) | 6 | |
| Section | Fundamental Physics | |
| DOI | https://doi.org/10.1051/epjconf/202022501004 | |
| Published online | 20 January 2020 | |
https://doi.org/10.1051/epjconf/202022501004
Deep Learning Methods On Neutron Scattering Data
Institut Laue-Langevin Grenoble,
France
* Email: songg@ill.fr
† Email: mutti@ill.fr
Published online: 20 January 2020
Recently, by using deep learning methods, a computer is able to surpass or come close to matching human performance on image analysis and recognition. This advanced methods could also help extracting features from neutron scattering experimental data. Those data contain rich scientific information about structure and dynamics of materials under investigation. Deep learning could help researchers better understand the link between experimental data and materials properties. Moreover,it could also help to optimize neutron scattering experiment by predicting the best possible instrument configuration. Among all possible experimental methods, we begin our study on the small-angle neutron scattering (SANS) data and by predicting the structure geometry of the sample material at an early stage. This step is a keystone to predict the experimental parameters to properly setup the instrument as well as the best measurement strategy. In this paper, we propose to use transfer learning to retrain a convolutional neural networks (CNNs) based pre rained model to adapt the scattering images classification, which could predict the structure of the materials at an early stage in the SANS experiment. This deep neural network is trained and validated on simulated database, and tested on real scattering images.
Key words: artificial intelligence / deep learning / convolutional neural network / transfer learning / small-angle neutron scattering
© The Authors, published by EDP Sciences, 2020
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.