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    Machine Learning Enhances Synchrotron Performance

    Article obtained from Photonics RSS Feed.

    A team of researchers from Lawrence Berkeley National Laboratory (Berkeley Lab) and the University of California, Berkeley (UC Berkeley) have demonstrated how machine learning can improve the stability of synchrotron light beam performance.

    Synchrotrons, such as the Advanced Light Source (ALS) at Berkeley Lab, are a type of particle accelerator that accelerate electrons to emit light in controlled beams. They allow scientists to explore samples using a variety of colors and wavelengths, and many synchrotron facilities deliver different types of light for dozens of experiments happening simultaneously.
    This chart shows how vertical beam-size stability greatly improves when a neural network is implemented during Advanced Light…

    Nov, 11 2019 |

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