Optimal cold atom thermometry using adaptive Bayesian strategies
Jonas Glatthard, Jes\'us Rubio, Rahul Sawant, Thomas Hewitt, Giovanni, Barontini, Luis A. Correa

TL;DR
This paper introduces an adaptive Bayesian approach to improve the efficiency and accuracy of temperature measurements in ultracold atom systems, reducing measurement resources and increasing reliability especially with limited data.
Contribution
The authors develop a platform-independent adaptive Bayesian framework that optimizes measurement strategies for cold atom thermometry, outperforming conventional methods.
Findings
Adaptive measurement selection reduces the number of measurements needed.
The Bayesian approach yields more reliable and faster convergence of temperature estimates.
A simpler non-adaptive method with prior information performs competitively on real data.
Abstract
Precise temperature measurements on systems of few ultracold atoms is of paramount importance in quantum technologies, but can be very resource-intensive. Here, we put forward an adaptive Bayesian framework that substantially boosts the performance of cold atom temperature estimation. Specifically, we process data from real and simulated release--recapture thermometry experiments on few potassium atoms cooled down to the microkelvin range in an optical tweezer. From simulations, we demonstrate that adaptively choosing the release--recapture times to maximise information gain does substantially reduce the number of measurements needed for the estimate to converge to a final reading. Unlike conventional methods, our proposal systematically avoids capturing and processing uninformative data. We also find that a simpler non-adaptive method exploiting all the a priori information can yield…
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Taxonomy
TopicsHemodynamic Monitoring and Therapy · Cold Atom Physics and Bose-Einstein Condensates
