Inferring Structural Parameters of Low-Surface-Brightness-Galaxies with Uncertainty Quantification using Bayesian Neural Networks
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Abstract (arXiv)
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.
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References · 5
- 2015Weight Uncertainty in Neural Networkscited by 554
- 2020Hands-on Bayesian Neural Networks -- a Tutorial for Deep Learning Userscited by 336
- 2020Hierarchical Inference With Bayesian Neural Networks: An Application to Strong Gravitational Lensingcited by 10
- 2019Self-supervised Learning with Physics-aware Neural Networks I: Galaxy Model Fittingcited by 5
- 2021Inferring Black Hole Properties from Astronomical Multivariate Time Series with Bayesian Attentive Neural Processescited by 4
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