Identifying galaxies, quasars, and stars with machine learning: A new catalogue of classifications for 111 million SDSS sources without spectra

TL;DR

This paper develops a machine learning classifier trained on SDSS data to accurately categorize 111 million celestial sources into galaxies, quasars, and stars, significantly expanding the catalog of classified objects without requiring spectra.

Contribution

The study introduces an optimized random forest model and a comprehensive catalog of classifications for unlabelled SDSS sources, utilizing photometry and transfer learning techniques.

Findings

Classified 111 million sources with high confidence probabilities.

Achieved strong agreement between UMAP visualizations and classifier labels.

Analyzed the impact of class imbalance and magnitude errors on classification performance.

Abstract

We used 3.1 million spectroscopically labelled sources from the Sloan Digital Sky Survey (SDSS) to train an optimised random forest classifier using photometry from the SDSS and the Widefield Infrared Survey Explorer (WISE). We applied this machine learning model to 111 million previously unlabelled sources from the SDSS photometric catalogue which did not have existing spectroscopic observations. Our new catalogue contains 50.4 million galaxies, 2.1 million quasars, and 58.8 million stars. We provide individual classification probabilities for each source, with 6.7 million galaxies (13%), 0.33 million quasars (15%), and 41.3 million stars (70%) having classification probabilities greater than 0.99; and 35.1 million galaxies (70%), 0.72 million quasars (34%), and 54.7 million stars (93%) having classification probabilities greater than 0.9. Precision, Recall, and F1 score were determined as a function of selected features and magnitude error. We investigate the effect of class imbalance on our machine learning model and discuss the implications of transfer learning for populations of sources at fainter magnitudes than the training set. We used a non-linear dimension reduction technique, Uniform Manifold Approximation and Projection (UMAP), in unsupervised, semi-supervised, and fully-supervised schemes to visualise the separation of galaxies, quasars, and stars in a two-dimensional space. When applying this algorithm to the 111 million sources without spectra, it is in strong agreement with the class labels applied by our random forest model.