评估用于光纤耦合NV金刚石温度传感器的概率与数据驱动推理模型
Evaluating probabilistic and data-driven inference models for fiber-coupled NV-diamond temperature sensors
AI总结：
本研究评估了连续波ODMR测量温度时推理模型对不确定性的影响，提出概率前馈推理模型，在243-323K范围内实现±1K预测不确定性，外推超出训练温度范围时优于数据驱动方法。
摘要：
我们评估了在使用连续波光探测磁共振（ODMR）测量推断温度时，推理模型对不确定性的影响。我们的方法利用概率前馈推理模型，通过自动微分最大化观测ODMR光谱的似然性。该模型有效利用自旋哈密顿量参数的温度依赖性，从ODMR数据的光谱特征中推断温度。在243 K至323 K的温度范围内，我们实现了±1 K的预测不确定性。为了对我们的概率模型进行基准测试，我们将其与非参数峰值查找技术以及主成分回归（PCR）和一维卷积神经网络（CNN）等数据驱动方法进行了比较。我们发现，当针对与训练数据集温度范围相同的样本外数据集进行验证时，数据驱动方法在不结合光谱-温度关系专业级理解的情况下，不确定性可降低多达0.67 K。然而，我们的结果表明，当任务是外推超出训练集使用的温度范围时，概率模型优于PCR和CNN，表明其具有稳健性和通用性。相比之下，PCR和CNN等数据驱动方法在外推超出其训练数据范围时，不确定性表现差多达十倍。
英文摘要：
We evaluate the impact of inference model on uncertainties when using continuous wave Optically Detected Magnetic Resonance (ODMR) measurements to infer temperature. Our approach leverages a probabilistic feedforward inference model designed to maximize the likelihood of observed ODMR spectra through automatic differentiation. This model effectively utilizes the temperature dependence of spin Hamiltonian parameters to infer temperature from spectral features in the ODMR data. We achieve prediction uncertainty of $\pm$ 1 K across a temperature range of 243 K to 323 K. To benchmark our probabilistic model, we compare it with a non-parametric peak-finding technique and data-driven methodologies such as Principal Component Regression (PCR) and a 1D Convolutional Neural Network (CNN). We find that when validated against out-of-sample dataset that encompasses the same temperature range as the training dataset, data driven methods can show uncertainties that are as much as 0.67 K lower without incorporating expert-level understanding of the spectroscopic-temperature relationship. However, our results show that the probabilistic model outperforms both PCR and CNN when tasked with extrapolating beyond the temperature range used in training set, indicating robustness and generalizability. In contrast, data-driven methods like PCR and CNN demonstrate up to ten times worse uncertainties when tasked with extrapolating outside their training data range.
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