论文标题
通过快速自适应贝叶斯估计的稳健自旋松弛计
Robust spin relaxometry with fast adaptive Bayesian estimation
论文作者
论文摘要
用钻石中的氮散发(NV)中心的自旋松弛计提供了对微波频率磁噪声的频谱选择性,原子定位的和校准的测量,为凝结的物质和生物系统提供了多功能探头。通常,松弛率是通过不提供最佳灵敏度的曲线拟合技术估计的,通常会导致长期获取时间在容易漂移或其他感兴趣的动态的系统中特别有害。在这里,我们表明自适应贝叶斯估计非常适合此问题,从而产生动态弛豫脉冲序列,从而迅速找到最佳的工作状态。在许多情况下(包括我们采用的系统),这种方法可以通过数量级加速获取。我们还提出了一个四信号测量方案,该方案在自旋读数对比度,极化和微波脉冲保真度中具有鲁棒性,同时仍然达到了近乎最佳的灵敏度。该组合技术为广泛的NV松弛计应用程序提供了一种实用的,硬件的方法。
Spin relaxometry with nitrogen-vacancy (NV) centers in diamond offers a spectrally selective, atomically localized, and calibrated measurement of microwave-frequency magnetic noise, presenting a versatile probe for condensed matter and biological systems. Typically, relaxation rates are estimated with curve-fitting techniques that do not provide optimal sensitivity, often leading to long acquisition times that are particularly detrimental in systems prone to drift or other dynamics of interest. Here we show that adaptive Bayesian estimation is well suited to this problem, producing dynamic relaxometry pulse sequences that rapidly find an optimal operating regime. In many situations (including the system we employ), this approach can speed the acquisition by an order of magnitude. We also present a four-signal measurement protocol that is robust to drifts in spin readout contrast, polarization, and microwave pulse fidelity while still achieving near-optimal sensitivity. The combined technique offers a practical, hardware-agnostic approach for a wide range of NV relaxometry applications.