Uncovering Tidal Treasures: Automated Classification of Faint Tidal Features in DECaLS Data

TL;DR

This paper develops a CNN-based automated method to classify faint tidal features in galaxies from DECaLS data, enabling efficient analysis of large survey datasets for galaxy formation studies.

Contribution

It introduces the first CNN approach for classifying multiple tidal feature categories in galaxy images, achieving high accuracy and interpretability.

Findings

Median classification accuracy over 97% for individual features.

High precision and recall scores for feature detection.

Effective visualization of important image regions using Grad-CAM.

Abstract

Tidal features are a key observable prediction of the hierarchical model of galaxy formation and contain a wealth of information about the properties and history of a galaxy. Modern wide-field surveys such as LSST and Euclid will revolutionise the study of tidal features. However, the volume of data will prohibit visual inspection to identify features, thereby motivating a need to develop automated detection methods. This paper presents a visual classification of $\sim2,000$ galaxies from the DECaLS survey into different tidal feature categories: arms, streams, shells, and diffuse. We trained a Convolutional Neural Network (CNN) to reproduce the assigned visual classifications using these labels. Evaluated on a testing set where galaxies with tidal features were outnumbered $\sim1:10$, our network performed very well and retrieved a median $98.7\pm0.3$, $99.1\pm0.5$, $97.0\pm0.8$, and $99.4^{+0.2}_{-0.6}$ per cent of the actual instances of arm, stream, shell, and diffuse features respectively for just 20 per cent contamination. A modified version that identified galaxies with any feature against those without achieved scores of $0.981^{+0.001}_{-0.003}$, $0.834^{+0.014}_{-0.026}$, $0.974^{+0.008}_{-0.004}$, and $0.900^{+0.073}_{-0.015}$ for the accuracy, precision, recall, and F1 metrics, respectively. We used a Gradient-weighted Class Activation Mapping analysis to highlight important regions on images for a given classification to verify the network was classifying the galaxies correctly. This is the first demonstration of using CNNs to classify tidal features into sub-categories, and it will pave the way for the identification of different categories of tidal features in the vast samples of galaxies that forthcoming wide-field surveys will deliver.