  Measuring the structural parameters (size, total brightness, light
concentration, etc.) of galaxies is a significant first step towards a
quantitative description of different galaxy populations. In this work, we
demonstrate that a Bayesian Neural Network (BNN) can be used for the inference,
with uncertainty quantification, of such morphological parameters from
simulated low-surface-brightness galaxy images. Compared to traditional
profile-fitting methods, we show that the uncertainties obtained using BNNs are
comparable in magnitude, well-calibrated, and the point estimates of the
parameters are closer to the true values. Our method is also significantly
faster, which is very important with the advent of the era of large galaxy
surveys and big data in astrophysics.
