Machine-learning-based automated loading of strontium isotopes into magneto-optical trap

Abstract

We implemented optimization techniques of machine learning (ML) to obtain the mutually exclusive sets of experimental parameters that maximize the number of strontium atoms of different isotopes (88Sr, 86Sr, and 87Sr) in a magneto-optical trap (MOT). Machine learning optimization techniques are significantly faster than conventional manual optimization. While optimizing the parameters, these algorithms efficiently tackle the problem of being confined in one of the local maxima in the parametric space. Thus, ML can be implemented to automate the loading of different isotopes into MOT to perform multiple experiments in a single setup.