Morphological Classification of Galaxies Through Structural and Star Formation Parameters Using Machine Learning

TL;DR

This study employs machine learning, specifically XGBoost, to classify galaxy morphologies using a comprehensive set of structural and star formation parameters, achieving high accuracy and interpretability.

Contribution

The paper introduces a multi-parameter XGBoost classification framework for galaxy morphology, including hierarchical classification and SHAP-based feature analysis, with performance comparable to or better than previous methods.

Findings

Achieved 88% accuracy for two-class galaxy classification.

Achieved 65% accuracy for five-class galaxy classification.

Hierarchical classification performs comparably to direct five-class approach.

Abstract

We employ the XGBoost machine learning (ML) method for the morphological classification of galaxies into two (early-type, late-type) and five (E, S0--S0a, Sa--Sb, Sbc--Scd, Sd--Irr) classes, using a combination of non-parametric ($C,\,A,\,S,\,A_S,\,\mathrm{Gini},\,M_{20},\,c_{5090}$), parametric (S\'ersic index, $n$), geometric (axial ratio, $BA$), global colour ($g-i,\,u-r,\,u-i$), colour gradient ($\Delta (g - i)$), and asymmetry gradient ($\Delta A_{9050}$) information, all estimated for a local galaxy sample ($z<0.15$) compiled from the Sloan Digital Sky Survey (SDSS) imaging data. We train the XGBoost model and evaluate its performance through multiple standard metrics. Our findings reveal better performance when utilizing all fourteen parameters, achieving accuracies of 88\% and 65\% for the two-class and five-class classification tasks, respectively. In addition, we investigate a hierarchical classification approach for the five-class scenario, combining three XGBoost classifiers. We observe comparable performance to the ``direct'' five-class classification, with discrepancies of only up to 3\%. Using SHAP (an advanced interpretation tool), we analyse how galaxy parameters impact the model's classifications, providing valuable insights into the influence of these features on classification outcomes. Finally, we compare our results with previous studies and find them consistently aligned.