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

Chang, Ming-Ching; Wei, Yi; Chen, Wei-Ren; Do, Changwoo

doi:10.1557/mrc.2019.166

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

Journal Article · 06 January 2020 · MRS Communications

DOI: https://doi.org/10.1557/mrc.2019.166 · OSTI ID:1633142

Chang, Ming-Ching ^[1]; Wei, Yi ^[1];

^[2];

^[2]

State Univ. of New York (SUNY), Albany, NY (United States)
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

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.

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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, Journal Name: MRS Communications Journal Issue: 1 Vol. 10; ISSN 2159-6859

Publisher:: Materials Research Society - Cambridge University PressCopyright Statement

Country of Publication:: United States

Language:: English

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