Machine-learning-based automated loading of strontium isotopes into magneto-optical trap
Source abstract
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.
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Research context
OpenAlex topics: Advanced Frequency and Time Standards · Cold Atom Physics and Bose-Einstein Condensates · Atomic and Subatomic Physics Research
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Physics, Indian Institute of Science Education and Research 1 , Pune 411008, Maharashtra, India
I-HUB Quantum Technology Foundation, Indian Institute of Science Education and Research 2 , Pune 411008, Maharashtra, India
Funding
Department of Science and Technology, Ministry of Science and Technology, India · Department of Science and Technology, Ministry of Science and Technology, India, National Supercomputing Mission
References (19)
Show source-provided references
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