基于肖特基谱的稳健电子感应加速器调谐测量：经典与深度学习互补范式
Robust Betatron-Tune Measurement from Schottky Spectra: Complementary Classical and Deep-Learning Paradigms
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中文总结 AI 辅助
针对紧凑型医用质子同步加速器调谐测量难题，开发经典与深度学习互补的调谐估计器。经典方法通过多宽度匹配滤波器组检测边带并进行子区间估计；深度学习利用卷积神经网络将光谱转换为调谐似然图。两种方法在不同数据上表现良好，支持实时调谐测量。
中文摘要 AI 辅助
肖特基谱提供关键束流诊断，电子感应加速器边带编码分数调谐。可靠的调谐测量对于紧凑型医用质子同步加速器中的三阶共振慢引出尤为重要，传统方法在低信噪比和有限频率分辨率下会受影响。本文开发了两种互补的调谐估计器，具有共享光谱前端但不同时间表示。经典估计器通过多宽度匹配滤波器组检测边带并进行子区间估计；深度学习估计器利用卷积神经网络将光谱转换为调谐似然图。在合成动态调谐基准上，深度学习估计器性能优于已发表基线，经典估计器超过延迟补偿基线且无需训练数据和GPU加速。在近静止的SAPT束流数据上，两种方法都能端到端运行，深度学习模型无需重新训练，在商用硬件上每帧延迟中位数低于1ms，支持紧凑型医用同步加速器实时调谐测量。
英文摘要
Schottky spectra provide key beam diagnostics, with betatron sidebands encoding the fractional tune. Reliable tune measurement is particularly important for third-order resonance slow extraction in compact medical proton synchrotrons, where low signal-to-noise ratios and limited frequency resolution can compromise conventional peak-detection and curve-fitting methods. This work develops two complementary tune estimators with a shared spectral front-end but different temporal representations. The classical estimator coherently pools motion-compensated spectra, detects the sideband using a multi-width matched-filter bank, and performs sub-bin estimation through local argmax and an adaptive MAD-gated centroid. The deep-learning estimator converts each spectrum into a tune-likelihood map using a convolutional neural network with FFT-based global convolutions, then propagates the posterior with a discrete two-dimensional (q,v) Bayesian tracker under a Gaussian motion model while also reporting posterior uncertainty. On a synthetic dynamic-tune benchmark, the deep-learning estimator outperforms published baselines across the operating range, while the classical estimator exceeds the latency-compensated baseline and requires neither training data nor GPU acceleration. On near-stationary SAPT beam data, both methods operate end-to-end, with the deep-learning model requiring no retraining. Median per-frame latency remains below 1 ms on commodity hardware, supporting real-time-capable tune measurement in compact medical synchrotrons.
