Galaxy classification: a deep learning approach for classifying Sloan Digital Sky Survey images
arXiv:2211.00397 · doi:10.1093/mnras/stac457
Abstract
In recent decades, large-scale sky surveys such as Sloan Digital Sky Survey (SDSS) have resulted in generation of tremendous amount of data. The classification of this enormous amount of data by astronomers is time consuming. To simplify this process, in 2007 a volunteer-based citizen science project called Galaxy Zoo was introduced, which has reduced the time for classification by a good extent. However, in this modern era of deep learning, automating this classification task is highly beneficial as it reduces the time for classification. For the last few years, many algorithms have been proposed which happen to do a phenomenal job in classifying galaxies into multiple classes. But all these algorithms tend to classify galaxies into less than six classes. However, after considering the minute information which we know about galaxies, it is necessary to classify galaxies into more than eight classes. In this study, a neural network model is proposed so as to classify SDSS data into 10 classes from an extended Hubble Tuning Fork. Great care is given to disc edge and disc face galaxies, distinguishing between a variety of substructures and minute features which are associated with each class. The proposed model consists of convolution layers to extract features making this method fully automatic. The achieved test accuracy is 84.73 per cent which happens to be promising after considering such minute details in classes. Along with convolution layers, the proposed model has three more layers responsible for classification, which makes the algorithm consume less time.
arXiv commentPublished in MNRAS
References in corpus (22)
- Very Deep Convolutional Networks for Large-Scale Image Recognition
- Star Formation in Galaxies Along the Hubble Sequence
- Deep Learning using Rectified Linear Units (ReLU)
- Galaxy Zoo : Morphologies derived from visual inspection of galaxies from the Sloan Digital Sky Survey
- Activation Functions: Comparison of trends in Practice and Research for Deep Learning
- Structure and Formation of Elliptical and Spheroidal Galaxies
- Galaxy Zoo 2: detailed morphological classifications for 304,122 galaxies from the Sloan Digital Sky Survey
- Palomar/Las Campanas Imaging Atlas of Blue Compact Dwarf galaxies: I. Images and integrated photometry
- Dwarf Elliptical Galaxies
- Multicomponent Decompositions for a Sample of S0 galaxies
- The Evolution of Galaxy Number Density at z < 8 and its Implications
- Pooling Methods in Deep Neural Networks, a Review
- Galaxy Zoo: Probabilistic Morphology through Bayesian CNNs and Active Learning
- Galaxy Zoo: Quantitative Visual Morphological Classifications for 48,000 galaxies from CANDELS
- Physical Properties of Spectroscopically-Confirmed Galaxies at z >= 6. II. Morphology of the Rest-Frame UV Continuum and Lyman-alpha Emission
- Effects of galaxy interactions in different environments
- Morphological classification of galaxies with deep learning: comparing 3-way and 4-way CNNs
- Effective and Efficient Dropout for Deep Convolutional Neural Networks
- The Properties of Spiral Galaxies: Confronting Hierarchical Galaxy Formation Models with Observations
- A morphology revaluation of galaxies in common from the Catalog of Isolated Galaxies and the Sloan Digital Sky Survey (DR6)
- A galaxy classification grid that better recognises early-type galaxy morphology
- A catalogue of structural and morphological measurements for DES Y1