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Fast and Accurate Pixel Calibration of Tof Neutron Diffractometers with Machine Learning

Journal Article · · Quantum Beam Science
DOI:https://doi.org/10.3390/qubs10010001· OSTI ID:3011556
 [1];  [2]
  1. Northwestern Univ., Evanston, IL (United States); Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
  2. Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
+ Show Author Affiliations
At a spallation neutron source, neutron pulses of varying energies are generated, and the detection of neutrons by instrument detectors is recorded as time-of-flight from the emission of the neutron pulse to its arrival at specific detector pixels with high time resolution. The flight path of neutrons from the moderator to the sample and then to the detector must be precisely calibrated at the detector-pixel level using standard powders, so the neutron events from all pixels can be time-focused to produce high-resolution diffraction patterns. Modern time-of-flight neutron diffractometers at spallation neutron sources are equipped with two-dimensional detectors with millimeter-scale pixelations. The number of pixels in a diffraction instrument can reach millions, which makes a single-pixel-level calibration process time-consuming or even impossible with conventional refinement or fitting approaches. Here we present a machine-learning-aided calibration process using a train-and-predict approach, in which machine learning models are trained on the relationship between an individual pixel time-of-flight diffraction pattern and its diffraction constant. These models use a portion of the available pixels for training, and a good model then predicts the diffraction constants precisely and rapidly for large sets of 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). Scientific User Facilities (SUF)
Grant/Contract Number:
AC05-00OR22725
OSTI ID:
3011556
Journal Information:
Quantum Beam Science, Journal Name: Quantum Beam Science Journal Issue: 1 Vol. 10; ISSN 2412-382X
Publisher:
MDPICopyright Statement
Country of Publication:
United States
Language:
English

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Related Subjects

detector pixel calibration
machine learning
neutron diffraction
time-of-flight