Mathematical morphology: star/galaxy differentiation & galaxy morphology classification

Jason Moore, Kevin Pimbblet, Michael Drinkwater

Research output: Contribution to journalArticleResearchpeer-review

10 Citations (Scopus)

Abstract

We present an application of Mathematical Morphology (MM) for the classification of astronomical objects, both for star/galaxy differentiation and galaxy morphology classification. We demonstrate that, for CCD images, 99.3 A? 3.8 of galaxies can be separated from stars using MM, with 19.4 A? 7.9 of the stars being misclassified. We demonstrate that, for photographic plate images, the number of galaxies correctly separated from the stars can be increased using our MM diffraction spike tool, which allows 51.0 A? 6.0 of the high-brightness galaxies that are inseparable in current techniques to be correctly classified, with only 1.4 A? 0.5 of the high-brightness stars contaminating the population. We demonstrate that elliptical (E) and late-type spiral (Sc-Sd) galaxies can be classified using MM with an accuracy of 91.4 A? 7.8 . It is a method involving fewer free parameters than current techniques, especially automated machine learning algorithms. The limitation of MM galaxy morphology classification based on seeing and distance is also presented. We examine various star/galaxy differentiation and galaxy morphology classification techniques commonly used today, and show that our MM techniques compare very favourably.
Original languageEnglish
Pages (from-to)135 - 146
Number of pages12
JournalPublications of the Astronomical Society of Australia
Volume23
Issue number4
DOIs
Publication statusPublished - 2006
Externally publishedYes

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