Automation of submicron resolution x-ray spectroscopy measurements and analysis using supervised and unsupervised machine learning algorithms

Rebecca A. Coles; Biays Bowerman; Martin Schoonen; Juergen Thieme; Andrew Duffin

doi:10.1117/12.2633459

3 October 2022 Automation of submicron resolution x-ray spectroscopy measurements and analysis using supervised and unsupervised machine learning algorithms

Rebecca A. Coles, Biays Bowerman, Martin Schoonen, Juergen Thieme, Andrew Duffin

Author Affiliations +

Proceedings Volume 12227, Applications of Machine Learning 2022; 122270K (2022) https://doi.org/10.1117/12.2633459
Event: SPIE Optical Engineering + Applications, 2022, San Diego, California, United States

Abstract

We describe the methods for automating the workflow for rapidly measuring and producing elemental maps of large-area samples using the Submicron Resolution X-ray Spectroscopy Beamline (SRX) at the National Synchrotron Light Source II, Brookhaven National Laboratory, through a novel combination of supervised (support vector machine) and unsupervised (cluster analysis) machine learning algorithms. SRX has the capability to create centimeter area full spectrum x-ray fluorescence (XRF) maps non-destructively with special detector and beam configurations. To facilitate the automation of this process, we discuss the development of the Synchrotron Network Automation Program in Python (SnapPy) software package that automates measurements such that SnapPy will control everything from beamline machine control to data acquisition and analysis. The only intervention that will need to be performed by beamline staff will be to physically install and remove samples. This will allow us to run measurements overnight or during times when beamline staff would not otherwise be available.

Conference Presentation

Citation Download Citation

Rebecca A. Coles, Biays Bowerman, Martin Schoonen, Juergen Thieme, and Andrew Duffin "Automation of submicron resolution x-ray spectroscopy measurements and analysis using supervised and unsupervised machine learning algorithms", Proc. SPIE 12227, Applications of Machine Learning 2022, 122270K (3 October 2022); https://doi.org/10.1117/12.2633459

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