Thermometry of one-dimensional Bose gases with neural networks

We design a neural network to extract and process features from absorption images taken of one-dimensional Bose gases in the quasicondensate regime. Specifically, the network is trained to predict both the temperature of single realizations of the system and the uncertainty thereof. For multiple realizations, the individual predictions can be combined in an estimate of the mean temperature, improving precision. We benchmark our model on both simulated and experimentally measured data and compare it to the established method of density ripple thermometry. We find the predictions of the two methods compatible, although the neural network reaches similar precision needing many fewer realizations, thus highlighting the efficiency gain achievable when incorporating neural networks into analysis of data from cold gas experiments. Further, we study feature maps to reveal which local features of the condensate are extracted by the network and how said features correlate with properties of the system. A similar analysis could be employed to uncover physical relations in more complex systems.

Automated summary

A neural network model is designed to extract and process features from absorption images of one-dimensional Bose gases in the quasicondensate regime, predicting system temperature and uncertainty. The network achieves similar precision to established methods with fewer realizations, highlighting efficiency gains in cold gas experiments. Feature maps reveal local condensate features and their correlation with system properties, with potential applications in uncovering physical relations in complex systems.