From Images to Features: Unbiased Morphology Classification via Variational Auto-Encoders and Domain Adaptation

Abstract

We present a novel approach for the dimensionality reduction of galaxy images by leveraging a combination of variational auto-encoders (VAE) and domain adaptation (DA). We demonstrate the effectiveness of this approach using a sample of low redshift galaxies with detailed morphological type labels from the Galaxy-Zoo DECaLS project. We show that 40-dimensional latent variables can effectively reproduce most morphological features in galaxy images. To further validate the effectiveness of our approach, we utilised a classical random forest (RF) classifier on the 40-dimensional latent variables to make detailed morphology feature classifications. This approach performs similarly to a direct neural network application on galaxy images. We further enhance our model by tuning the VAE network via DA using galaxies in the overlapping footprint of DECaLS and BASS+MzLS, enabling the unbiased application of our model to galaxy images in both surveys. We observed that DA led to even better morphological feature extraction and classification performance. Overall, this combination of VAE and DA can be applied to achieve image dimensionality reduction, defect image identification, and morphology classification in large optical surveys.

arXiv commentAccepted by MNRAS 2023 October 12. 10 pages, 8 figures

References in corpus (31)

• The Seventh Data Release of the Sloan Digital Sky Survey
• Spectroscopic Target Selection in the Sloan Digital Sky Survey: The Main Galaxy Sample
• Overview of the DESI Legacy Imaging Surveys
• Color Separation of Galaxy Types in the Sloan Digital Sky Survey Imaging Data
• The Ninth Data Release of the Sloan Digital Sky Survey: First Spectroscopic Data from the SDSS-III Baryon Oscillation Spectroscopic Survey
• Galaxy Zoo 2: detailed morphological classifications for 304,122 galaxies from the Sloan Digital Sky Survey
• Project Overview of the Beijing-Arizona Sky Survey
• Galaxy Zoo DECaLS: Detailed Visual Morphology Measurements from Volunteers and Deep Learning for 314,000 Galaxies
• Finding Strong Gravitational Lenses in the Kilo Degree Survey with Convolutional Neural Networks
• CANDELS Visual Classifications: Scheme, Data Release, and First Results
• Classifying Radio Galaxies with Convolutional Neural Network
• Galaxy Morphology Classification with Deep Convolutional Neural Networks
• Galaxy Zoo: Quantitative Visual Morphological Classifications for 48,000 galaxies from CANDELS
• An automatic taxonomy of galaxy morphology using unsupervised machine learning
• Deep Learning Identifies High-z Galaxies in a Central Blue Nugget Phase in a Characteristic Mass Range
• Galaxy morphological classification in deep-wide surveys via unsupervised machine learning
• Beyond the Hubble Sequence -- Exploring Galaxy Morphology with Unsupervised Machine Learning
• Galaxy Morphological Classification Catalogue of the Dark Energy Survey Year 3 data with Convolutional Neural Networks
• Photometry of high-redshift blended galaxies using deep learning
• Morphological classification of galaxies with deep learning: comparing 3-way and 4-way CNNs
• Deep Generative Models for Galaxy Image Simulations
• DeepMerge II: Building Robust Deep Learning Algorithms for Merging Galaxy Identification Across Domains
• Machine learning technique for morphological classification of galaxies from the SDSS. I. Photometry-based approach
• Bars formed in galaxy merging and their classification with deep learning
• GAlaxy Light profile convolutional neural NETworks (GaLNets). I. fast and accurate structural parameters for billion galaxy samples
• Unsupervised Galaxy Morphological Visual Representation with Deep Contrastive Learning
• Eigengalaxies: describing galaxy morphology using principal components in image space
• Precision of The Chinese Space Station Telescope (CSST) Stellar Radial Velocities
• GaMPEN: A Machine Learning Framework for Estimating Bayesian Posteriors of Galaxy Morphological Parameters
• Galaxy Deblending using Residual Dense Neural networks
• A General Approach to Domain Adaptation with Applications in Astronomy

Cited by in corpus (6)

• A Robust Study of High-Redshift Galaxies: Unsupervised Machine Learning for Characterising morphology with JWST up to z ~ 8
• Uncovering Tidal Treasures: Automated Classification of Faint Tidal Features in DECaLS Data
• Galaxy Morphological Classification with Manifold Learning
• Deciphering galaxy images using machine vision -- Combining variational autoencoder and principal component analysis for feature extraction
• The contribution of the color space in LSST-like photometry for the selection of extragalactic globular cluster candidates
• Conditional variational autoencoders for cosmological model discrimination and anomaly detection in cosmic microwave background power spectra