A method for classification of red, blue and green galaxies using fuzzy set theory

TL;DR

This paper introduces a fuzzy set theory-based method for classifying galaxies into red, blue, and green categories, capturing classification uncertainty more effectively than traditional cut-based methods.

Contribution

It presents a novel fuzzy set approach for galaxy classification, utilizing SDSS data to model degrees of redness and relationships among galaxy properties.

Findings

Fuzzy sets effectively model galaxy color uncertainties.

Fuzzy operations derive blue and green galaxy sets from red galaxy set.

Fuzzy relations reveal property correlations among galaxy types.

Abstract

Red and blue galaxies are traditionally classified using some specific cuts in colour or other galaxy properties, which are supported by empirical arguments. The vagueness associated with such cuts are likely to introduce a significant contamination in these samples. Fuzzy sets are vague boundary sets which can efficiently capture the classification uncertainty in the absence of any precise boundary. We propose a method for classification of galaxies according to their colours using fuzzy set theory. We use data from the SDSS to construct a fuzzy set for red galaxies with its members having different degrees of `redness'. We show that the fuzzy sets for the blue and green galaxies can be obtained from it using different fuzzy operations. We also explore the possibility of using fuzzy relation to study the relationship between different galaxy properties and discuss its strengths and limitations.