Automation of sample alignment for neutron scattering experiments
Year: 2024
Type: conference-paper
Abstract: Sample alignment in neutron scattering experiments is critical to ensuring high quality data for the users. This process typically involves a skilled operator or beamline scientist. Machine learning has been demonstrated as an effective tool for a wide range of automation tasks. RadiaSoft in particular has been developing ML tools for a range of accelerator applications including beamline automation. In this poster we will present recent developments for selecting and aligning multiple samples at the HB-2A powder diffractometer at HFIR.
Source: JACOW
Authors: Pritchard,Breeana, Edelen,Jonathan, Henderson,Morgan
Institution: RadiaSoft (United States)
Language: English
Subfield: Radiation
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W6962679247
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