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Title: Inferring Structural Parameters of Low-Surface-Brightness-Galaxies with Uncertainty Quantification using Bayesian Neural Networks
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.  more » « less
Award ID(s):
2006340
NSF-PAR ID:
10340974
Author(s) / Creator(s):
; ;
Date Published:
Journal Name:
39th International Conference on Machine Learning (ICML 2022)
Page Range / eLocation ID:
22
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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