Inferring Structural Parameters of Low-Surface-Brightness-Galaxies with Uncertainty Quantification using Bayesian Neural Networks
This work addresses the need for efficient and accurate galaxy parameter estimation in astrophysics, particularly for large surveys, though it is incremental as it applies an existing method (BNNs) to a specific domain problem.
The authors tackled the problem of measuring structural parameters of low-surface-brightness galaxies by using Bayesian Neural Networks for inference with uncertainty quantification from simulated images, showing that their method provides comparable uncertainties, better-calibrated results, and faster processing compared to traditional methods.
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.