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