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Title: Deep learning-based super-resolution for small-angle neutron scattering data: attempt to accelerate experimental workflow

Abstract

In this paper, 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.

Authors:
 [1];  [1]; ORCiD logo [2]; ORCiD logo [2]
  1. State Univ. of New York (SUNY), Albany, NY (United States)
  2. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Spallation Neutron Source (SNS)
Sponsoring Org.:
USDOE Office of Science (SC), Basic Energy Sciences (BES). Scientific User Facilities Division
OSTI Identifier:
1633142
Grant/Contract Number:  
AC05-00OR22725
Resource Type:
Accepted Manuscript
Journal Name:
MRS Communications
Additional Journal Information:
Journal Volume: 10; Journal Issue: 1; Journal ID: ISSN 2159-6859
Publisher:
Materials Research Society - Cambridge University Press
Country of Publication:
United States
Language:
English
Subject:
36 MATERIALS SCIENCE

Citation Formats

Chang, Ming-Ching, Wei, Yi, Chen, Wei-Ren, and Do, Changwoo. Deep learning-based super-resolution for small-angle neutron scattering data: attempt to accelerate experimental workflow. United States: N. p., 2020. Web. https://doi.org/10.1557/mrc.2019.166.
Chang, Ming-Ching, Wei, Yi, Chen, Wei-Ren, & Do, Changwoo. Deep learning-based super-resolution for small-angle neutron scattering data: attempt to accelerate experimental workflow. United States. https://doi.org/10.1557/mrc.2019.166
Chang, Ming-Ching, Wei, Yi, Chen, Wei-Ren, and Do, Changwoo. Tue . "Deep learning-based super-resolution for small-angle neutron scattering data: attempt to accelerate experimental workflow". United States. https://doi.org/10.1557/mrc.2019.166. https://www.osti.gov/servlets/purl/1633142.
@article{osti_1633142,
title = {Deep learning-based super-resolution for small-angle neutron scattering data: attempt to accelerate experimental workflow},
author = {Chang, Ming-Ching and Wei, Yi and Chen, Wei-Ren and Do, Changwoo},
abstractNote = {In this paper, 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.},
doi = {10.1557/mrc.2019.166},
journal = {MRS Communications},
number = 1,
volume = 10,
place = {United States},
year = {2020},
month = {1}
}

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