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

Published online by Cambridge University Press:  07 January 2020

Ming-Ching Chang ,
Yi Wei ,
Wei-Ren Chen  and
Changwoo Do Open the ORCID record for Changwoo Do [Opens in a new window]
Ming-Ching Chang
Affiliation:
University at Albany, State University of New York, New York, NY, USA
Yi Wei
Affiliation:
University at Albany, State University of New York, New York, NY, USA
Wei-Ren Chen
Affiliation:
Neutron Scattering Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA
Changwoo Do*
Affiliation:
Neutron Scattering Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA
*
Address all correspondence to Changwoo Do at doc1@ornl.gov
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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.

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Type
Research Letters
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MRS Communications , Volume 10 , Issue 1 , March 2020 , pp. 11 - 17
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Copyright © Materials Research Society 2020

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