论文标题
基于神经网络的图像重建与天体物理先验
Neural network based image reconstruction with astrophysical priors
论文作者
论文摘要
随着在VLTI的4个望远镜和Chara的6个望远镜的干涉仪器的出现，科学的可能性常规获得了观察到的目标的毫米尺度图像。这种图像重建过程通常在贝叶斯框架中执行，在贝叶斯框架中，要最小化的功能由两个术语组成：datalikelihood和贝叶斯先验。此先验应基于我们对观察到的来源的先验知识。到目前为止，此先验是从一组通用和任意功能（例如总变化）中选择的。在这里，我们使用生成对抗网络提出了图像重建框架，其中使用目标对象的最新辐射传输模型来定义贝叶斯先验。我们验证了具有增加噪声的合成数据上的这种新图像重建算法。生成的图像显示了人工制品的急剧减少，并允许更直接的天体物理解释。结果可以看作是神经网络如何为各种天体物理来源的图像重建提供重大改进的第一个说明。
With the advent of interferometric instruments with 4 telescopes at the VLTI and 6 telescopes at CHARA, the scientific possibility arose to routinely obtain milli-arcsecond scale images of the observed targets. Such an image reconstruction process is typically performed in a Bayesian framework where the function to minimize is made of two terms: the datalikelihood and the Bayesian prior. This prior should be based on our prior knowledge of the observed source. Up to now,this prior was chosen from a set of generic and arbitrary functions, such as total variation for example. Here, we present an image reconstruction framework using generative adversarial networks where the Bayesian prior is defined using state-of-the-art radiative transfer models of the targeted objects. We validate this new image reconstruction algorithm on synthetic data with added noise. The generated images display a drastic reduction of artefacts and allow a more straight forward astrophysical interpretation. The results can be seen as a first illustration of how neural networks can provide significant improvements to the image reconstruction of a variety of astrophysical sources.
