Powellsnakes II: a fast Bayesian approach to discrete object detection in multi-frequency astronomical data sets
Powellsnakes II: a fast Bayesian approach to discrete object detection in multi-frequency astronomical data sets
复制标题
Powellsnakes II：多频率天文数据集中离散物体检测的快速贝叶斯方法
DOI：
10.1111/j.1365-2966.2012.22033.x
复制
发表时间：
2011
影响因子：
4.8
通讯作者：
A. Lasenby
中科院分区：
文献类型：
--
作者：
P. Carvalho;G. Rocha;G. Rocha;M. Hobson;A. Lasenby
鲍尔蛇(PWS；Carvalho等人)是一种贝叶斯算法，用于检测弥漫背景中嵌入的致密物体，并被普朗克财团(Planck Collaboration 2011，a)选择并成功用于制作其第一个公开交付产品：早期发布的紧凑源目录(Planck Collaboration 2011，b)(ERCSC)。我们给出了PWS进一步发展的关键基础和主要方向，它在形式正确性和在一致的统一框架内优化利用所有可用信息方面进行了扩展，其中点源(未分辨对象)、SZ簇、单通道或多通道检测之间不存在差异。强调了为了达到最优，多频率、多模型检测算法的必要性。
Powellsnakes (PwS; Carvalho et al. 2009) is a Bayesian algorithm for detecting compact objects embedded in a diffuse background, and was selected and successfully employed by the Planck consortium (Planck Collaboration 2011, a) in the production of its first public deliverable: the Early Release Compact Source Catalogue (Planck Collaboration 2011, b) (ERCSC). We present the critical foundations and main directions of further development of PwS, which extend it in terms of formal correctness and the optimal use of all the available information in a consistent unified framework, where no dist inction is made between point sources (unresolved objects), SZ clusters, single or multi -channel detection. An emphasis is placed on the necessity of a multi-frequency, multi-model detection algorithm in order to achieve optimality.