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Cosmic web-type classification using decision theory
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OA
AI
DOI：10.1051/0004-6361/201526006.png)
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Abstract
En 中文
Aims. We propose a decision criterion for segmenting the cosmic web into different structure types (voids, sheets, filaments, and clusters) on the basis of their respective probabilities and the strength of data constraints. Methods. Our approach is inspired by an analysis of games of chance where the gambler only plays if a positive expected net gain can be achieved based on some degree of privileged information. Results. The result is a general solution for classification problems in the face of uncertainty, including the option of not committing to a class for a candidate object. As an illustration, we produce high-resolution maps of web-type constituents in the nearby Universe as probed by the Sloan Digital Sky Survey main galaxy sample. Other possible applications include the selection and labelling of objects in catalogues derived from astronomical survey data.
Keywords:
large-scale structure of Universe
methods: statistical
catalogs