Thermometry of one-dimensional Bose gases can be achieved in various ways, which all revolve around comparing measurement statistics to theoretical prediction. As a result, many repetitions of the measurement are required before an accurate comparison can be made. In this work, we train a neural network to estimate the temperature of a one-dimensional Bose gas in the quasi-condensate regime from a single absorption image. We benchmark our model on both simulated and experimentally measured data. Comparing with our standard method of density ripple thermometry based on fitting two-point correlation function shows that the network can achieve the same precision needing only half the amount of images. Our findings highlight the gain in efficiency when incorporating neural networks into analysis of data from cold gas experiments. Further, the neural network architecture presented can easily be reconfigured to extract other parameters from the images.
F. Møller, T. Schweigler, M. Tajik, J. Sabino, F. Cataldini, S.-C. Ji, J. Schmiedmayer, “Thermometry of one-dimensional Bose gases with neural networks”, Phys. Rev. A 104, 043305 (2021).
Related to Project A03