使用概率自动编码器的银河系多频率天空的贝叶斯分解
Bayesian decomposition of the Galactic multi-frequency sky using probabilistic autoencoders
摘要：
对银河系的全天观测显示了银河系和非银河系的漫射发射，例如来自星际物质或宇宙微波背景（CMB）的漫射发射。不同的发射体在测量中部分重叠，部分它们彼此模糊，有时它们在一定的光谱范围内占主导地位。从光谱数据中分解潜在的辐射成分是一个信号重建问题，通常与详细的物理建模和大量的计算工作有关。我们的目标是建立一个有效的和自我指导的算法检测银河系全天空数据覆盖从$γ$射线到无线电波的光谱波段所包含的基本光谱信息。利用信息论的原理，我们开发了一个国家的最先进的变分自动编码器专门适应高斯噪声统计。我们首先推导出一个通用的生成过程，导致从一个低维的一组发射功能所观察到的高维数据。我们制定了这些功能的后验分布使用贝叶斯方法和近似后变分推理。该算法有效地编码的35个银河系的发射数据集的信息在10个潜在的特征地图。这些包含重建高保真初始数据所需的基本信息，并根据其对数据再生的重要性由算法进行排名。三个最重要的特征图对天体物理成分进行了编码：（1）致密星际介质（ISM），（2）ISM的热和稀区，（3）CMB。光谱数据的机器辅助和数据驱动的降维能够揭示编码输入数据的物理特征。我们的算法是能够提取的密集和稀释的银河系区域，以及CMB，从天空亮度值。
英文摘要：
All-sky observations of the Milky Way show both Galactic and non-Galactic diffuse emission, for example from interstellar matter or the cosmic microwave background (CMB). The different emitters are partly superimposed in the measurements, partly they obscure each other, and sometimes they dominate within a certain spectral range. The decomposition of the underlying radiative components from spectral data is a signal reconstruction problem and often associated with detailed physical modeling and substantial computational effort. We aim to build an effective and self-instructing algorithm detecting the essential spectral information contained Galactic all-sky data covering spectral bands from $γ$-ray to radio waves. Utilizing principles from information theory, we develop a state-of-the-art variational autoencoder specialized on the adaption to Gaussian noise statistics. We first derive a generic generative process that leads from a low-dimensional set of emission features to the observed high-dimensional data. We formulate a posterior distribution of these features using Bayesian methods and approximate this posterior with variational inference. The algorithm efficiently encodes the information of 35 Galactic emission data sets in ten latent feature maps. These contain the essential information required to reconstruct the initial data with high fidelity and are ranked by the algorithm according to their significance for data regeneration. The three most significant feature maps encode astrophysical components: (1) The dense interstellar medium (ISM), (2) the hot and dilute regions of the ISM and (3) the CMB. The machine-assisted and data-driven dimensionality reduction of spectral data is able to uncover the physical features encoding the input data. Our algorithm is able to extract the dense and dilute Galactic regions, as well as the CMB, from the sky brightness values only.
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