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

by Ming-ching Chang, Yi Wei, Wei-ren Chen, Changwoo Do
Publication Type
Journal Name
MRS Communications
Publication Date
Page Numbers
11 to 17

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.