Machine learning technique for morphological classification of galaxies from the SDSS. I. Photometry-based approach

Abstract

Methods. We used different galaxy classification techniques: human labeling, multi-photometry diagrams, Naive Bayes, Logistic Regression, Support Vector Machine, Random Forest, k-Nearest Neighbors, and k-fold validation. Results. We present results of a binary automated morphological classification of galaxies conducted by human labeling, multiphotometry, and supervised Machine Learning methods. We applied its to the sample of galaxies from the SDSS DR9 with 0.02 < z < 0.1 and 24m < Mr < 19.4m. To study the classifier, we used absolute magnitudes: Mu, Mg, Mr , Mi, Mz, Mu-Mr , Mg-Mi, Mu-Mg, Mr-Mz, and inverse concentration index to the center R50/R90. Using the Support vector machine classifier and the data on color indices, absolute magnitudes, inverse concentration index of galaxies with visual morphological types, we were able to classify 316 031 galaxies from the SDSS DR9 with unknown morphological types. Conclusions. The methods of Support Vector Machine and Random Forest with Scikit-learn machine learning in Python provide the highest accuracy for the binary galaxy morphological classification: 96.4% correctly classified (96.1% early E and 96.9% late L types) and 95.5% correctly classified (96.7% early E and 92.8% late L types), respectively. Applying the Support Vector Machine for the sample of 316 031 galaxies from the SDSS DR9 at z < 0.1, we found 141 211 E and 174 820 L types among them.

References in corpus (33)

• Sloan Digital Sky Survey IV: Mapping the Milky Way, Nearby Galaxies, and the Distant Universe
• The Sixteenth Data Release of the Sloan Digital Sky Surveys: First Release from the APOGEE-2 Southern Survey and Full Release of eBOSS Spectra
• HyperLEDA. III. The catalogue of extragalactic distances
• Classifying Radio Galaxies with Convolutional Neural Network
• Surveying the reach and maturity of machine learning and artificial intelligence in astronomy
• Optimising Automatic Morphological Classification of Galaxies with Machine Learning and Deep Learning using Dark Energy Survey Imaging
• Galaxy Zoo: Quantitative Visual Morphological Classifications for 48,000 galaxies from CANDELS
• Big Universe, Big Data: Machine Learning and Image Analysis for Astronomy
• Machine and Deep Learning Applied to Galaxy Morphology -- A Comparative Study
• Virgo cluster early-type dwarf galaxies with the Sloan Digital Sky Survey. IV. The color-magnitude relation
• Galaxy morphological classification in deep-wide surveys via unsupervised machine learning
• The Catalog of Edge-on Disk Galaxies from SDSS. Part I: the catalog and the Structural Parameters of Stellar Disks
• A catalog of merging dwarf galaxies in the local universe
• Morphological classification of radio galaxies: Capsule Networks versus Convolutional Neural Networks
• Integrating human and machine intelligence in galaxy morphology classification tasks
• Galaxy Formation as a Cosmological Tool. I: The Galaxy Merger History as a Measure of Cosmological Parameters
• Radio Galaxy Zoo: Machine learning for radio source host galaxy cross-identification
• Radio Galaxy Zoo: Unsupervised Clustering of Convolutionally Auto-encoded Radio-astronomical Images
• Prediction of galaxy halo masses in SDSS DR7 via a machine learning approach
• KiDS-SQuaD II: Machine learning selection of bright extragalactic objects to search for new gravitationally lensed quasars
• Computer-generated visual morphology catalog of ~3,000,000 SDSS galaxies
• Star Formation in Nearby Early-Type Galaxies: The Radio Continuum Perspective
• Galaxy And Mass Assembly: Automatic Morphological Classification of Galaxies Using Statistical Learning
• Low Surface Brightness Galaxy catalogue selected from the alpha.40-SDSS DR7 Survey and Tully-Fisher relation
• Validity of abundances derived from spaxel spectra of the MaNGA survey
• Inferring physical properties of galaxies from their emission line spectra
• Predicting the Neutral Hydrogen Content of Galaxies From Optical Data Using Machine Learning
• deepSIP: Linking Type Ia Supernova Spectra to Photometric Quantities with Deep Learning
• Machine-learning computation of distance modulus for local galaxies
• Cold gas and dust: Hunting spiral-like structures in early-type galaxies
• Labeling Bias in Galaxy Morphologies
• Visual Survey of 18020 Objects from the 2MFGC Catalog
• H-alpha Images of Ultra-Flat Edge-On Spiral Galaxies

Cited by in corpus (26)

• The Planck clusters in the LOFAR sky. I. LoTSS-DR2: new detections and sample overview
• What drives the scatter of local star-forming galaxies in the BPT diagrams? A Machine Learning based analysis
• Diffuse radio emission from non-Planck galaxy clusters in the LoTSS-DR2 fields
• From Images to Features: Unbiased Morphology Classification via Variational Auto-Encoders and Domain Adaptation
• Morphological Classification of Radio Galaxies with wGAN-supported Augmentation
• The luminosity function of ringed galaxies
• Galaxy mass-size segregation in the cosmic web from the CAVITY parent sample
• Machine learning prediction for mean motion resonance behaviour -- The planar case
• Machine learning technique for morphological classification of galaxies from SDSS. II. The image-based morphological catalogs of galaxies at 0.02<z<0.1
• Machine learning technique for morphological classification of galaxies from the SDSS. III. Image-based inference of detailed features
• WISE2MBH: A scaling-based algorithm for probing supermassive black hole masses through WISE catalogues
• Hunting for exocomet transits in the TESS database using the Random Forest method
• Image feature extraction and galaxy classification: a novel and efficient approach with automated machine learning
• Discovery of the Polar Ring Galaxies with deep learning
• Automated quasar continuum estimation using neural networks: a comparative study of deep-learning architectures
• NGC 3521 as the Milky Way near twin: spectral energy distribution from UV to radio decameter ranges
• Large-step neural network for learning the symplectic evolution from partitioned data
• Multiwavelength properties of the low-redshift isolated galaxies with active nuclei modelled with CIGALE
• Impact of PSF misestimation and galaxy population bias on precision shear measurement using a CNN
• NGC3521 as the Milky Way analogue: spectral energy distributon from UV to Radio and photometric variability
• Applying Vision Transformers on Spectral Analysis of Astronomical Objects
• The Next Generation Fornax Survey (NGFS).VIII. A Support Vector Machine Approach for Disentangling Globular Clusters from other Sources
• Can AI Dream of Unseen Galaxies? Conditional Diffusion Model for Galaxy Morphology Augmentation
• Milky Way galaxy-analogs and isolated galaxies with bars: environmental density in the Local Volume
• Quantifying the Poor Purity and Completeness of Morphological Samples Selected by Galaxy Colour
• Visual inspection of potential exocomet transits identified through machine learning and statistical methods