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

Journal Article · · MRS Communications

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.

Research Organization:
Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States). Spallation Neutron Source (SNS)
Sponsoring Organization:
USDOE Office of Science (SC), Basic Energy Sciences (BES). Scientific User Facilities Division
Grant/Contract Number:
AC05-00OR22725
OSTI ID:
1633142
Journal Information:
MRS Communications, Vol. 10, Issue 1; ISSN 2159-6859
Publisher:
Materials Research Society - Cambridge University PressCopyright Statement
Country of Publication:
United States
Language:
English
Citation Metrics:
Cited by: 7 works
Citation information provided by
Web of Science

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