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

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 $\sim 2,000$ 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 $\sim 1:10$, our network performed very well and retrieved a median $98.7\pm 0.3$, $99.1\pm 0.5$, $97.0\pm 0.8$, and $99.4_{-0.6}^{+0.2}$ 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 $0.981_{-0.003}^{+0.001}$, $0.834_{-0.026}^{+0.014}$, $0.974_{-0.004}^{+0.008}$, and $0.900_{-0.015}^{+0.073}$ 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.

22 pages, 14 figures, 5 tables, accepted for publication in MNRAS

References in corpus (44)

• Array Programming with NumPy
• The Astropy Project: Sustaining and Growing a Community-oriented Open-source Project and the Latest Major Release (v5.0) of the Core Package
• Shortcut Learning in Deep Neural Networks
• Rotation-invariant convolutional neural networks for galaxy morphology prediction
• Tracing Galaxy Formation with Stellar Halos II: Relating Substructure in Phase- and Abundance-Space to Accretion Histories
• Introducing the NewHorizon simulation: Galaxy properties with resolved internal dynamics across cosmic time
• Galaxy Zoo DECaLS: Detailed Visual Morphology Measurements from Volunteers and Deep Learning for 314,000 Galaxies
• The shape of the inner Milky Way halo from observations of the Pal 5 and GD-1 stellar streams
• Surveying the reach and maturity of machine learning and artificial intelligence in astronomy
• LinKS: Discovering galaxy-scale strong lenses in the Kilo-Degree Survey using Convolutional Neural Networks
• Galaxy And Mass Assembly (GAMA): the effect of close interactions on star formation in galaxies
• The DAWES review 10: The impact of deep learning for the analysis of galaxy surveys
• An automatic taxonomy of galaxy morphology using unsupervised machine learning
• Galaxy Merger Rates up to z $\sim$ 3 using a Bayesian Deep Learning Model $-$ A Major-Merger classifier using IllustrisTNG Simulation data
• Identifying Galaxy Mergers in Observations and Simulations with Deep Learning
• Probing the merger history of red early-type galaxies with their faint stellar substructures
• Galaxy morphological classification in deep-wide surveys via unsupervised machine learning
• On the lifetime of merger features of equal-mass disk mergers
• Census and classification of low-surface-brightness structures in nearby early-type galaxies from the MATLAS survey
• Preparing for low surface brightness science with the Vera C. Rubin Observatory: characterisation of tidal features from mock images
• Catalog of Visually Classified Galaxies in the Local ($z\sim 0.01$) Universe
• Minor vs Major Mergers: The Stellar Mass Growth of Massive Galaxies from z=3 using Number Density Selection Techniques
• Hidden depths in the local Universe: The Stellar Stream Legacy Survey
• Action-space clustering of tidal streams to infer the Galactic potential
• Galaxy Formation as a Cosmological Tool. I: The Galaxy Merger History as a Measure of Cosmological Parameters
• Characterization of Low Surface Brightness structures in annotated deep images
• Tracing satellite planes in the Sculptor group: I. Discovery of three faint dwarf galaxies around NGC 253
• AstroVaDEr: Astronomical Variational Deep Embedder for Unsupervised Morphological Classification of Galaxies and Synthetic Image Generation
• Pushing automated morphological classifications to their limits with the Dark Energy Survey
• Euclid preparation: XVI. Exploring the ultra low-surface brightness Universe with Euclid/VIS
• Discovery and analysis of low-surface-brightness galaxies in the environment of NGC 1052
• Mapping Dark Matter with Extragalactic Stellar Streams: the Case of Centaurus A
• Star Formation Suppresion by Tidal Removal of Cold Molecular Gas from an Intermediate-Redshift Massive Post-Starburst Galaxy
• Classifying Galaxy Morphologies with Few-Shot Learning
• Constraining the Gravitational Potential from the Projected Morphology of Extragalactic Tidal Streams
• Identification of tidal features in deep optical galaxy images with Convolutional Neural Networks
• Frequency of Tidal Features Correlates with Age and Internal Structure of Early-type Galaxies
• Evidence for Impact of Galaxy Mergers on Stellar Kinematics of Early-type Galaxies
• A stream come true: Connecting tidal tails, shells, streams, and planes with galaxy kinematics and formation history
• Galaxy And Mass Assembly (GAMA): Comparing Visually and Spectroscopically Identified Galaxy Merger Samples
• From Images to Features: Unbiased Morphology Classification via Variational Auto-Encoders and Domain Adaptation
• A Comparison of Deep Learning Architectures for Optical Galaxy Morphology Classification
• Merger identification through photometric bands, colours, and their errors
• The Classification of Optical Galaxy Morphology Using Unsupervised Learning Techniques

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