Neutron Research: Efficient Use of Measurement Time Through Machine Learning
Jülich scientists have developed a new approach to improve the efficiency of neutron scattering experiments at the Heinz Maier-Leibnitz Zentrum, and have successfully tested the method at the Paul Scherrer Institute in Switzerland. The scientists were able to optimise the data collection using an active learning approach from the field of artificial intelligence. In this way, the time needed for each experiment can be reduced and the scarce resource of measurement time can be used more effectively.
