Dwarfs from the Dark (Energy Survey): a machine learning approach to classify dwarf galaxies from multi-band images

TL;DR

This paper develops a machine learning approach using convolutional neural networks to classify different types of low-surface brightness dwarf galaxies from multi-band images, significantly reducing manual effort in large astronomical surveys.

Contribution

It introduces a neural network architecture capable of classifying dwarf galaxy types with high accuracy, building upon previous work and addressing the challenge of morphological diversity.

Findings

Achieved 85% accuracy for spiral galaxies

Achieved 94% accuracy for dwarf ellipticals

Achieved 52% accuracy for dwarf irregulars

Abstract

Countless low-surface brightness objects - including spiral galaxies, dwarf galaxies, and noise patterns - have been detected in recent large surveys. Classically, astronomers visually inspect those detections to distinguish between real low-surface brightness galaxies and artefacts. Employing the Dark Energy Survey (DES) and machine learning techniques, Tanoglidis et al. (2020) have shown how this task can be automatically performed by computers. Here, we build upon their pioneering work and further separate the detected low-surface brightness galaxies into spirals, dwarf ellipticals, and dwarf irregular galaxies. For this purpose, we have manually classified 5567 detections from multi-band images from DES and searched for a neural network architecture capable of this task. Employing a hyperparameter search, we find a family of convolutional neural networks achieving similar results as with the manual classification, with an accuracy of 85% for spiral galaxies, 94% for dwarf ellipticals, and 52% for dwarf irregulars. For dwarf irregulars - due to their diversity in morphology - the task is difficult for humans and machines alike. Our simple architecture shows that machine learning can reduce the workload of astronomers studying large data sets by orders of magnitudes, as will be available in the near future with missions such as Euclid.