Kavli Affiliate: Zeeshan Ahmed
| First 5 Authors: Shraddha Rajpal, Zeeshan Ahmed, Tyrus Berry, ,
| Summary:
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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