论文标题
使用贝叶斯神经网络进行不确定性定量的低表面 - 亮度 - 半山脉的结构参数
Inferring Structural Parameters of Low-Surface-Brightness-Galaxies with Uncertainty Quantification using Bayesian Neural Networks
论文作者
论文摘要
测量星系的结构参数（大小，总亮度，光浓度等）是朝着不同星系种群定量描述的重要第一步。在这项工作中，我们证明了贝叶斯神经网络（BNN）可用于通过不确定性定量的推断，从模拟的低表面亮度星系图像中进行了这种形态学参数。与传统的配置拟合方法相比，我们表明使用BNN获得的不确定性在大小，校准良好，参数的点估计值更接近真实值。我们的方法也大大更快，这在大型星系调查和天体物理学中的大数据的时代的出现非常重要。
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.
