A COMPUTER-GENERATED VISUAL MORPHOLOGY CATALOG OF ∼3,000,000 SDSS GALAXIES

We have applied computer analysis to classify the broad morphological types of ~3 106 Sloan Digital Sky Survey (SDSS) galaxies. For each galaxy, the catalog provides the DR8 object ID, the R.A., the decl., and the certainty for the automatic classification as either spiral or elliptical. The certainty of the classification allows us to control the accuracy of a subset of galaxies by sacrificing some of the least certain classifications. The accuracy of the catalog was tested using galaxies that were classified by the manually annotated Galaxy Zoo catalog. The results show that the catalog contains ~900,000 spiral galaxies and ~600,000 elliptical galaxies with classification certainty that has a statistical agreement rate of ~98% with the Galaxy Zoo debiased "superclean" data set. The catalog also shows that objects assigned by the SDSS pipeline with a relatively high redshift (z > 0.4) can have clear visual spiral morphology. The catalog can be downloaded at http://vfacstaff.ltu.edu/lshamir/data/morph_catalog. The image analysis software that was used to create the catalog is also publicly available.

[1]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[2]  Hideyuki Tamura,et al.  Textural Features Corresponding to Visual Perception , 1978, IEEE Transactions on Systems, Man, and Cybernetics.

[3]  M. Teague Image analysis via the general theory of moments , 1980 .

[4]  Yung-Chang Chen,et al.  Texture features for classification of ultrasonic liver images , 1992, IEEE Trans. Medical Imaging.

[5]  L. Simard Photometric Redshifts and the Luminosity-Size Relation of Galaxies to z=1. 1 , 1999 .

[6]  E. al.,et al.  The Sloan Digital Sky Survey: Technical summary , 2000, astro-ph/0006396.

[7]  Shree K. Nayar,et al.  Spatial information in multiresolution histograms , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[8]  Alexander S. Szalay,et al.  Sloan digital sky survey: Early data release , 2002 .

[9]  L. Ho,et al.  Detailed structural decomposition of galaxy images , 2002, astro-ph/0204182.

[10]  Christopher J. Conselice,et al.  The Relationship between Stellar Light Distributions of Galaxies and Their Formation Histories , 2003 .

[11]  University of Toronto,et al.  A New Approach to Galaxy Morphology. I. Analysis of the Sloan Digital Sky Survey Early Data Release , 2003, astro-ph/0301239.

[12]  S. Okamura,et al.  Morphological Classification of Galaxies Using Photometric Parameters: The Concentration Index versus the Coarseness Parameter , 2005, astro-ph/0507060.

[13]  O. Fèvre,et al.  A robust morphological classification of high-redshift galaxies using support vector machines on seeing limited images I. Method description , 2007, 0709.1359.

[14]  Lior Shamir,et al.  Source Code for Biology and Medicine Open Access Wndchrm – an Open Source Utility for Biological Image Analysis , 2022 .

[15]  C. Lintott,et al.  Galaxy Zoo: morphologies derived from visual inspection of galaxies from the Sloan Digital Sky Survey , 2008, 0804.4483.

[16]  Lior Shamir,et al.  WND-CHARM: Multi-purpose image classification using compound image transforms , 2008, Pattern Recognit. Lett..

[17]  M. Salvato,et al.  A robust morphological classification of high-redshift galaxies using support vector machines on seeing limited images II. Quantifying morphological k-correction in the COSMOS field at 1 < z < 2: Ks band vs. I band , 2008, 0811.1045.

[18]  Donald W. Sweeney,et al.  LSST Science Book, Version 2.0 , 2009, 0912.0201.

[19]  C. Lintott,et al.  Galaxy Zoo: reproducing galaxy morphologies via machine learning★ , 2009, 0908.2033.

[20]  Lior Shamir,et al.  Automatic morphological classification of galaxy images. , 2009, Monthly notices of the Royal Astronomical Society.

[21]  Lior Shamir,et al.  Knee X-Ray Image Analysis Method for Automated Detection of Osteoarthritis , 2009, IEEE Transactions on Biomedical Engineering.

[22]  C. Lintott,et al.  Galaxy Zoo 1: data release of morphological classifications for nearly 900 000 galaxies , 2010, 1007.3265.

[23]  Roberto G. Abraham,et al.  A CATALOG OF DETAILED VISUAL MORPHOLOGICAL CLASSIFICATIONS FOR 14,034 GALAXIES IN THE SLOAN DIGITAL SKY SURVEY , 2010, 1001.2401.

[24]  Lior Shamir,et al.  Impressionism, expressionism, surrealism: Automated recognition of painters and schools of art , 2010, TAP.

[25]  Yannick Mellier,et al.  The EFIGI catalogue of 4458 nearby galaxies with detailed morphology , 2011, 1103.5734.

[26]  Lior Shamir,et al.  GANALYZER: A TOOL FOR AUTOMATIC GALAXY IMAGE ANALYSIS , 2011, 1105.3214.

[27]  Marc Huertas-Company,et al.  Revisiting the Hubble sequence in the SDSS DR7 spectroscopic sample: a publicly available Bayesian automated classification , 2010, 1010.3018.

[28]  Luc Simard,et al.  A CATALOG OF BULGE+DISK DECOMPOSITIONS AND UPDATED PHOTOMETRY FOR 1.12 MILLION GALAXIES IN THE SLOAN DIGITAL SKY SURVEY , 2011, 1107.1518.

[29]  S. Djorgovski,et al.  Sky Surveys , 2012, 1203.5111.

[30]  Lior Shamir,et al.  Automatic detection of peculiar galaxies in large datasets of galaxy images , 2012, J. Comput. Sci..

[31]  C. Lintott,et al.  Galaxy Zoo 2: detailed morphological classifications for 304,122 galaxies from the Sloan Digital Sky Survey , 2013, 1308.3496.

[32]  L. Shamir,et al.  Automatic quantitative morphological analysis of interacting galaxies , 2013, Astron. Comput..

[33]  K. Borne Virtual Observatories, Data Mining, and Astroinformatics , 2013 .

[34]  Lior Shamir,et al.  WND-CHARM: Multi-purpose image classifier , 2013 .

[35]  Terry D. Oswalt,et al.  Planets, Stars and Stellar Systems , 2013 .

[36]  Lior Shamir,et al.  CHLOE: A tool for automatic detection of peculiar galaxies , 2014 .

[37]  Lior Shamir,et al.  Automatic detection and quantitative assessment of peculiar galaxy pairs in Sloan Digital Sky Survey , 2014, 1407.5000.

[38]  Lior Shamir,et al.  Quantitative analysis of spirality in elliptical galaxies , 2013, 1310.0387.

[39]  Lior Shamir,et al.  Combining Human and Machine Learning for Morphological Analysis of Galaxy Images , 2014, ArXiv.

[40]  Wayne B. Hayes,et al.  SpArcFiRe: SCALABLE AUTOMATED DETECTION OF SPIRAL GALAXY ARM SEGMENTS , 2014, 1402.1910.

[41]  Sander Dieleman,et al.  Rotation-invariant convolutional neural networks for galaxy morphology prediction , 2015, ArXiv.

[42]  Santiago,et al.  A CATALOG OF VISUAL-LIKE MORPHOLOGIES IN THE 5 CANDELS FIELDS USING DEEP LEARNING , 2015, 1509.05429.

[43]  Lior Shamir,et al.  Galaxy morphology - An unsupervised machine learning approach , 2015, Astron. Comput..

[44]  M. Huertas-Company,et al.  THE MORPHOLOGIES OF MASSIVE GALAXIES FROM z ∼ 3—WITNESSING THE TWO CHANNELS OF BULGE GROWTH , 2015, 1506.03084.

[45]  Lior Shamir,et al.  Leveraging Pattern Recognition Consistency Estimation for Crowdsourcing Data Analysis , 2016, IEEE Transactions on Human-Machine Systems.