Cosmic web-type classification using decision theory

TL;DR

This paper introduces a decision-theoretic method for classifying cosmic web structures into voids, sheets, filaments, and clusters, based on probabilities and data constraints, allowing for uncertain or undecided classifications.

Contribution

It presents a novel decision criterion for cosmic web classification that incorporates uncertainty and the option to abstain from classifying, inspired by game theory principles.

Findings

Produced high-resolution web-type maps of the nearby Universe.

Demonstrated the method's application to Sloan Digital Sky Survey data.

Enabled probabilistic and uncertain classifications of cosmic structures.

Abstract

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. 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. 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.