Evaluating probabilistic and data-driven inference models for fiber-coupled NV-diamond temperature sensors
This work addresses temperature sensing for quantum technologies, offering incremental improvements in robustness and generalizability for fiber-coupled NV-diamond sensors.
The paper tackles temperature inference from ODMR spectra in NV-diamond sensors, achieving a prediction uncertainty of ±1 K across 243-323 K. It shows that data-driven methods like PCR and CNN can reduce uncertainties by up to 0.67 K within the training range, but the probabilistic model outperforms them in extrapolation, with data-driven methods showing up to ten times worse uncertainties outside the training range.
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.