The process that powers the stars—nuclear fusion—is proposed as a future power source for humanity and could provide clean and renewable energy free of the radioactive waste associated with current nuclear fission plants.
Just like the fusion process that sends energy spilling out from the sun, future nuclear fusion facilities will slam together isotopes of the universe's lightest element, hydrogen, in an ultra-hot gas or "plasma" contained by a powerful magnetic field to create helium with the difference in mass harvested as energy.
One thing that scientists must know before the true advent of fusion power here on Earth is what mix of hydrogen isotopes to use— primarily "standard" hydrogen, with one proton in its atomic nucleus, deuterium with one proton and one neutron in its nucleus, and tritium with a nucleus of one proton and two neutrons. This is currently done with spectroscopy for prototype fusion devices called tokamaks, but this analysis can be time-consuming.
In a new paper published in The European Physical Journal D, author Mohammed Koubiti, associate professor at the Aix-Marseille Universite, France, assesses the use of machine learning in connection with plasma spectroscopy to determine the ratios of hydrogen isotopes for nuclear fusion plasma performance.
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