Deep learning-based super-resolution for small-angle neutron scattering data: attempt to accelerate experimental workflow. (March 2020)

• Record Type: Journal Article
• Authors: Chang, Ming-Ching; Wei, Yi; Chen, Wei-Ren; Do, Changwoo
• Is Part Of: MRS communications. Volume 10:Number 1(2020)
• Page Start: 11
• Page End: 17
• Publication Date: 2020-03
• Subjects: Materials -- Periodicals; 620.11
• DOI: 10.1557/mrc.2019.166
• Languages: English
• ISSNs: 2159-6859

Abstract

Abstract: Abstract : The authors propose an alternative route to circumvent the limitation of neutron flux using the recent deep learning super-resolution technique. The feasibility of accelerating data collection has been demonstrated by using small-angle neutron scattering (SANS) data collected from the EQ-SANS instrument at Spallation Neutron Source (SNS). Data collection time can be reduced by increasing the size of binning of the detector pixels at the sacrifice of resolution. High-resolution scattering data is then reconstructed by using a deep learning-based super-resolution method. This will allow users to make critical decisions at a much earlier stage of data collection, which can accelerate the overall experimental workflow.