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Title: Advances in utilizing event based data structures for neutron scattering experiments

Abstract

This article strives to expand on existing work to demonstrate advancements in data processing made available using event mode measurements. Most spallation neutron sources in the world have data acquisition systems that provide event recording. The new science that is enabled by utilizing event mode has only begun to be explored. In the past, these studies were difficult to perform because histograms forced dealing with either large chunks of time or a large number of files. With event based data collection, data can be explored and rebinned long after the measurement has completed. This article will review some of the principles of event data and how the method opens up new possibilities for in situ measurements, highlighting techniques that can be used to explore changes in the data. We also demonstrate the statistical basis for determining data quality and address the challenge of determining how long to measure mid-measurement. Lastly, we demonstrate a model independent method of grouping data via hierarchical clustering methods that can be used to improve calibration, reduction, and data exploration.

Authors:
ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [1]
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  1. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Basic Energy Sciences (BES)
OSTI Identifier:
1486961
Alternate Identifier(s):
OSTI ID: 1471794
Grant/Contract Number:  
AC05-00OR22725
Resource Type:
Accepted Manuscript
Journal Name:
Review of Scientific Instruments
Additional Journal Information:
Journal Volume: 89; Journal Issue: 9; Journal ID: ISSN 0034-6748
Publisher:
American Institute of Physics (AIP)
Country of Publication:
United States
Language:
English
Subject:
74 ATOMIC AND MOLECULAR PHYSICS; neutron scattering; Poisson statistics; clustering; machine learning; diffraction

Citation Formats

Peterson, Peter F., Olds, Daniel, Savici, Andrei T., and Zhou, Wenduo. Advances in utilizing event based data structures for neutron scattering experiments. United States: N. p., 2018. Web. https://doi.org/10.1063/1.5034782.
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Peterson, Peter F., Olds, Daniel, Savici, Andrei T., & Zhou, Wenduo. Advances in utilizing event based data structures for neutron scattering experiments. United States. https://doi.org/10.1063/1.5034782
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Peterson, Peter F., Olds, Daniel, Savici, Andrei T., and Zhou, Wenduo. Fri . "Advances in utilizing event based data structures for neutron scattering experiments". United States. https://doi.org/10.1063/1.5034782. https://www.osti.gov/servlets/purl/1486961.
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@article{osti_1486961,
title = {Advances in utilizing event based data structures for neutron scattering experiments},
author = {Peterson, Peter F. and Olds, Daniel and Savici, Andrei T. and Zhou, Wenduo},
abstractNote = {This article strives to expand on existing work to demonstrate advancements in data processing made available using event mode measurements. Most spallation neutron sources in the world have data acquisition systems that provide event recording. The new science that is enabled by utilizing event mode has only begun to be explored. In the past, these studies were difficult to perform because histograms forced dealing with either large chunks of time or a large number of files. With event based data collection, data can be explored and rebinned long after the measurement has completed. This article will review some of the principles of event data and how the method opens up new possibilities for in situ measurements, highlighting techniques that can be used to explore changes in the data. We also demonstrate the statistical basis for determining data quality and address the challenge of determining how long to measure mid-measurement. Lastly, we demonstrate a model independent method of grouping data via hierarchical clustering methods that can be used to improve calibration, reduction, and data exploration.},
doi = {10.1063/1.5034782},
journal = {Review of Scientific Instruments},
number = 9,
volume = 89,
place = {United States},
year = {2018},
month = {9}
}
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Journal Article:
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Publisher's Version of Record
https://doi.org/10.1063/1.5034782
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Figures / Tables:

FIG. 1 FIG. 1: The data presented is from a water (in a Al holder) sample measured on the instrument VISION12 as measured through the diffraction detectors as a function of measurement time with constant incident flux. The different wall clock tolerances are plotted for the detector pixels kept separately (a), andmore » for the detectors time focused into two back-scattering and 90-degree "banks" (b). At a value of one, the uncompressed line (not visible in (a)) represents storing all of the events separately. The black dashed compressed line is the minimum amount of weighted events needed to store the information that comes from ignoring the wall clock. The compression factors are different between (a) and (b) because there are not enough events in a single pixel to make the compression worthwhile. This was modified from figure 3 of reference 11.« less
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