Automating galaxy morphology classification using k -nearest neighbours and non-parametric statistics

Mukundan, Kavya; Nair, Preethi; Bailin, Jeremy; Li, Wenhao

doi:10.1093/mnras/stae1684

Title: Automating galaxy morphology classification using k -nearest neighbours and non-parametric statistics

Journal Article · 10 July 2024 · Monthly Notices of the Royal Astronomical Society

DOI: https://doi.org/10.1093/mnras/stae1684 · OSTI ID:2428007

Mukundan, Kavya;

;

; Li, Wenhao

ABSTRACT Morphology is a fundamental property of any galaxy population. It is a major indicator of the physical processes that drive galaxy evolution and in turn the evolution of the entire Universe. Historically, galaxy images were visually classified by trained experts. However, in the era of big data, more efficient techniques are required. In this work, we present a k-nearest neighbours based approach that utilizes non-parametric morphological quantities to classify galaxy morphology in Sloan Digital Sky Survey images. Most previous studies used only a handful of morphological parameters to identify galaxy types. In contrast, we explore 1023 morphological spaces (defined by up to 10 non-parametric statistics) to find the best combination of morphological parameters. Additionally, while most previous studies broadly classified galaxies into early types and late types or ellipticals, spirals, and irregular galaxies, we classify galaxies into 11 morphological types with an average accuracy of $${\sim} 80\!-\!90 \, {{\rm per\, cent}}$$ per T-type. Our method is simple, easy to implement, and is robust to varying sizes and compositions of the training and test samples. Preliminary results on the performance of our technique on deeper images from the Hyper Suprime-Cam Subaru Strategic Survey reveal that an extension of our method to modern surveys with better imaging capabilities might be possible.

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Sponsoring Organization:: USDOE

OSTI ID:: 2428007

Journal Information:: Monthly Notices of the Royal Astronomical Society, Journal Name: Monthly Notices of the Royal Astronomical Society Journal Issue: 1 Vol. 533; ISSN 0035-8711

Publisher:: Oxford University PressCopyright Statement

Country of Publication:: United Kingdom

Language:: English

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