  We design a neural network to extract and process features from absorption
images taken of one-dimensional Bose gases in the quasi-condensate 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 ripples thermometry. We find the predictions of the two methods
compatible, although the neural network reaches similar precision needing much
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.
