.png){kind=icon}
返回
BayesCLUMPY: BAYESIAN INFERENCE WITH CLUMPY DUSTY TORUS MODELS
.png){kind=icon}
OA
AI
DOI：10.1088/0004-637X/696/2/2075.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
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 of 10(-4) of the original size of the database and a factor of 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 six filters. The code is freely available from the authors.
Keyword:
galaxies: nuclei
galaxies: Seyfert
infrared: galaxies
methods: data analysis
methods: statistical
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
5.4
论文数:
8.3W
被引数:
32.0W
机构
引用论文
.png){kind=icon}
Chronic Pain and the Emotional Brain: Specific Brain Activity Associated with Spontaneous Fluctuations of Intensity of Chronic Back Pain慢性疼痛和情绪大脑: 与慢性背痛强度的自发性波动相关的特定大脑活动OAAI
OAAI分享收藏Real-time and offline techniques for identifying obstructive sleep apnea patients用于识别阻塞性睡眠呼吸暂停患者的实时和离线技术
PREAI
分享收藏Arsenic Removal from Natural Water Using Low Cost Granulated Adsorbents: A Review使用低成本颗粒状吸附剂从天然水中去除砷: 综述
OAAI
OAAI分享收藏分享收藏
查看更多内容