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The measurement of accelerator tubes often employs direct probe methods; however, these methods frequently introduce perturbations, leading to inaccurate results, especially at high frequencies. This study presents a novel approach utilizing a Convolutional Neural Network (CNN) combined with a Long Short-Term Memory (LSTM) network to address these challenges. By deeply learning the reflection coefficient curves of cavity chains in microwave networks, our method enables an effective diagnosis of high-frequency accelerator cavities through indirect detuning techniques. The proposed algorithm accurately identifies discrepancies between the actual single-cavity frequency and the design specifications, thereby enhancing the precision of measurements in the high-frequency domain. This research contributes significantly to the field of accelerator tube diagnostics by offering a robust, non-intrusive alternative to traditional direct probing methods.
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