Detector pixel calibration of time-of-flight neutron diffractometers accelerated by machine learning

Technical Report · Sun Jun 01 04:00:00 EDT 2025

DOI:https://doi.org/10.2172/2573098· OSTI ID:2573098

Song, Albert ^[1]; An, Ke ^[2]

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1. Northwest University, Chicago, Illinois (United States)
2. Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Modern time-of-flight neutron diffractometers at spallation neutron source are equipped with two dimensional detectors with fine pixelations. The flight path of neutrons from the moderator to the sample and to the detector needs to be precisely calibrated at detector pixel level using standard powders so the diffraction data from all the detector pixels can be correctly time-focused to produce high resolution diffraction peaks. The number of pixels can reach to millions which makes a single-pixel calibration process time-consuming, or even impossible, with conventional fitting routine. Here we presented a machine learning aided calibration process via a “training and predict” process by training machine learning models with the relations between the individual pixel time-of-flight diffraction pattern and fitted diffraction constant. The training models take a portion of the available pixels to predict the diffraction constants precisely and rapidly for massive pixel diffraction patterns.

• Research Organization: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
• Sponsoring Organization: USDOE Office of Science (SC), Basic Energy Sciences (BES)
• DOE Contract Number: AC05-00OR22725
• OSTI ID: 2573098
• Report Number(s): ORNL-TM--2025-3948
• Country of Publication: United States
• Language: English

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