On the application of dimensionality reduction and clustering algorithms for the classification of kinematic morphologies of galaxies

TL;DR

This paper presents an unsupervised machine learning approach combining dimensionality reduction and clustering to classify galaxy kinematic morphologies from simulated data, effectively distinguishing different galaxy types and orientations.

Contribution

It introduces a novel method that automatically classifies galaxy morphologies using kinematic maps and evaluates its effectiveness across various input configurations.

Findings

Successfully separates slow and fast rotator galaxies.

Differentiates galaxy shapes based on added dispersion and flux data.

Effective across different viewing angles and galaxy subsamples.

Abstract

The morphological classification of galaxies is considered a relevant issue and can be approached from different points of view. The increasing growth in the size and accuracy of astronomical data sets brings with it the need for the use of automatic methods to perform these classifications. The aim of this work is to propose and evaluate a method for automatic unsupervised classification of kinematic morphologies of galaxies that yields a meaningful clustering and captures the variations of the fundamental properties of galaxies. We obtain kinematic maps for a sample of 2064 galaxies from the largest simulation of the EAGLE project that mimics integral field spectroscopy (IFS) images. These maps are the input of a dimensionality reduction algorithm followed by a clustering algorithm. We analyse the variation of physical and observational parameters among the clusters obtained from the application of this procedure to different inputs. The inputs studied in this paper are (a) line-of-sight velocity maps for the whole sample of galaxies observed at fixed inclinations, (b) line-of-sight velocity, dispersion, and flux maps together for the whole sample of galaxies observed at fixed inclinations, (c) line-of-sight velocity, dispersion, and flux maps together for two separate subsamples of edge-on galaxies with similar amount of rotation, and (d) line-of-sight velocity, dispersion, and flux maps together for galaxies from different observation angles mixed. The application of the method to solely line-of-sight velocity maps achieves a clear division between slow rotators (SRs) and fast rotators (FRs) and can differentiate rotation orientation. By adding the dispersion and flux information at the input, low rotation edge-on galaxies are separated according to their shapes. Abridged.