Speaker
Breeana Pritchard
(RadiaSoft (United States))
Description
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.
Funding Agency
This work is supported by DOE Office of Science Office of Basic Energy Science award number DE-SC0021555.
Author
Breeana Pritchard
(RadiaSoft (United States))
Co-authors
Jonathan Edelen
(RadiaSoft (United States))
Morgan Henderson
(RadiaSoft (United States))
