论文标题
可伸缩的贝叶斯推断，以在天文图像中检测和脱并列
Scalable Bayesian Inference for Detection and Deblending in Astronomical Images
论文作者
论文摘要
我们提出了一种新的概率方法，用于检测称为贝叶斯光源分离器（Bliss）的天文来源，融合和编目。 Bliss基于深层生成模型，该模型将神经网络嵌入贝叶斯模型中。对于后推断，Bliss使用一种新形式的变分推断，称为正向摊销变异推断。 Bliss推断例程很快，一旦训练了编码器网络，就需要GPU上编码网络的单个正向通行证。 Bliss可以在几秒钟内对百万像素图像进行完全贝叶斯的推断，并产生高度准确的目录。 Bliss是高度可扩展的，除了产生概率目录外，还可以直接回答下游科学问题。
We present a new probabilistic method for detecting, deblending, and cataloging astronomical sources called the Bayesian Light Source Separator (BLISS). BLISS is based on deep generative models, which embed neural networks within a Bayesian model. For posterior inference, BLISS uses a new form of variational inference known as Forward Amortized Variational Inference. The BLISS inference routine is fast, requiring a single forward pass of the encoder networks on a GPU once the encoder networks are trained. BLISS can perform fully Bayesian inference on megapixel images in seconds, and produces highly accurate catalogs. BLISS is highly extensible, and has the potential to directly answer downstream scientific questions in addition to producing probabilistic catalogs.
