Automation of sample alignment for neutron scattering experiments

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.