首页|A non-invasive diagnostic method of cavity detuning based on a convolutional neural network
A non-invasive diagnostic method of cavity detuning based on a convolutional neural network
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As modern accelerator technologies advance toward more compact sizes,conventional invasive diag-nostic methods of cavity detuning introduce negligible interference in measurements and run the risk of harming structural surfaces.To overcome these difficulties,this study developed a non-invasive diagnostic method using knowledge of scattering parameters with a convolutional neural network and the interior point method.Meticulous construction and training of the neural network led to remarkable results on three typical acceleration structures:a 13-cell S-band standing-wave linac,a 12-cell X-band traveling-wave linac,and a 3-cell X-band RF gun.The trained networks significantly reduced the burden of the tuning process,freed researchers from tedious tuning tasks,and provided a new perspective for the tuning of side-coupling,semi-enclosed,and total-enclosed structures.
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