<ul><li>Researchers evaluate the impact of inference models on uncertainties in using continuous wave Optically Detected Magnetic Resonance (ODMR) measurements to infer temperature.</li><li>A probabilistic feedforward inference model is developed to maximize the likelihood of observed ODMR spectra by leveraging the temperature dependence of spin Hamiltonian parameters.</li><li>The probabilistic model achieves a prediction uncertainty of ±1 K across a temperature range of 243 K to 323 K.</li><li>When extrapolating beyond the training data range, the probabilistic model outperforms data-driven techniques such as Principal Component Regression (PCR) and a 1D Convolutional Neural Network (CNN), demonstrating robustness and generalizability.</li></ul>

Evaluating probabilistic and data-driven inference models for fiber-coupled NV-diamond temperature sensors

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