BayesCLUMPY：基于离散尘环模型的贝叶斯推断
BayesCLUMPY: Bayesian Inference with Clumpy Dusty Torus Models
摘要：
我们的目标是提出一个快速和通用的贝叶斯推断框架的基础上，机器学习技术和标准的采样方法之间的协同作用，并将其应用于推断cloudspandy环面的物理性质，使用红外光度高空间分辨率观测活动星系核。我们使用Metropolis-Hastings马尔可夫链蒙特卡罗算法对后验分布函数进行采样。这种分布是由关于模型参数的所有先验知识和观测所引入的信息相结合而产生的。主要困难在于，用于解释观测结果的模型计算量很大，采样非常耗时。出于这个原因，我们应用了一组人工神经网络，用于近似和插值模型数据库。因此，可以计算原始数据库中不存在的模型，以确保连续性。我们专注于这个解决方案的应用程序，最近开发的公共数据库的clusterdusty环面模型。本文中使用的机器学习方案允许我们仅使用数据库原始大小的10^-4倍和计算时间的10^-3倍从数据库生成任何模型。为每个模型参数获得的后验分布使我们能够研究观察如何约束参数，以及哪些参数仍然部分或完全不确定，提供统计相关的置信区间。作为一个例子，应用于半人马座A的核区表明，云的光学深度，云的总数和云分布区的径向范围很好地限制使用只有6个过滤器。
英文摘要：
Our aim is to present a fast and general Bayesian inference framework based on the synergy between machine learning techniques and standard sampling methods and apply it to infer the physical properties of clumpy dusty torus using infrared photometric high spatial resolution observations of active galactic nuclei. We make use of the Metropolis-Hastings Markov Chain Monte Carlo algorithm for sampling the posterior distribution function. Such distribution results from combining all a-priori knowledge about the parameters of the model and the information introduced by the observations. The main difficulty resides in the fact that the model used to explain the observations is computationally demanding and the sampling is very time consuming. For this reason, we apply a set of artificial neural networks that are used to approximate and interpolate a database of models. As a consequence, models not present in the original database can be computed ensuring continuity. We focus on the application of this solution scheme to the recently developed public database of clumpy dusty torus models. The machine learning scheme used in this paper allows us to generate any model from the database using only a factor 10^-4 of the original size of the database and a factor 10^-3 in computing time. The posterior distribution obtained for each model parameter allows us to investigate how the observations constrain the parameters and which ones remain partially or completely undetermined, providing statistically relevant confidence intervals. As an example, the application to the nuclear region of Centaurus A shows that the optical depth of the clouds, the total number of clouds and the radial extent of the cloud distribution zone are well constrained using only 6 filters.
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