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

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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Acknowledgment

The Research at Oak Ridge National Laboratory’s Spallation Neutron Source was sponsored by the Scientific User Facilities Division, Office of Basic Energy Sciences, U.S. Department of Energy.

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Correspondence to Changwoo Do.

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Chang, MC., Wei, Y., Chen, WR. et al. Deep learning-based super-resolution for small-angle neutron scattering data: attempt to accelerate experimental workflow. MRS Communications 10, 11–17 (2020). https://doi.org/10.1557/mrc.2019.166

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