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Deep Learning Methods On Neutron Scattering Data

Authors :
Song Guanghan
Porcar Lionel
Boehm Martin
Cecillon Franck
Dewhurst Charles
Le Goc Yannick
Locatelli Jérome
Mutti Paolo
Weber Tobias
Source :
EPJ Web of Conferences, Vol 225, p 01004 (2020)
Publication Year :
2020
Publisher :
EDP Sciences, 2020.

Abstract

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.

Details

Language :
English
ISSN :
2100014X
Volume :
225
Database :
Directory of Open Access Journals
Journal :
EPJ Web of Conferences
Publication Type :
Academic Journal
Accession number :
edsdoj.9b92cb07699b4a5a89644c418111fa4d
Document Type :
article
Full Text :
https://doi.org/10.1051/epjconf/202022501004