Machine learning technique for morphological classification of galaxies from SDSS. II. The image-based morphological catalogs of galaxies at 0.02<z<0.1

1Vavilova, IB, 2Khramtsov, V, 1Dobrycheva, DV, 1Vasylenko, MYu., 1Elyiv, AA, 1Melnyk, OV
1Main Astronomical Observatory of the National Academy of Sciences of Ukraine, Kyiv, Ukraine
2V.N. Karazin Kharkiv National University, Kharkiv, Ukraine
Space Sci. & Technol. 2022, 28 ;(1):03-22
https://doi.org/10.15407/knit2022.01.003
Publication Language: English
Abstract: 
We applied the image-based approach with a convolutional neural network (CNN) model to the sample of low-redshift galaxies with –24m<Mr<–19.4m from the SDSS DR9. We divided it into two subsamples, SDSS DR9 galaxy dataset and Galaxy Zoo 2 (GZ2) dataset, considering them as the inference and training datasets, respectively. To determine the principal parameters of galaxy morphology defined within the GZ2 project, we classified the galaxies into five visual types and 34 morphological features of galaxies from the inference dataset, which do not match with GZ2 training dataset. As a result, we created the morphological catalog of 315782 galaxies at 0.02<z<0.1, where these classes and features were defined for the first time for 216148 galaxies by image-based CNN classifier. For the rest of galaxies the initial morphological classification was re-assigned as in the GZ2 project. Main results are presented in the catalog of 27378 completely rounded, 59194 rounded in-between, 18862 cigar-shaped, 7831 edge-on, 23119 spiral in the inference data set of the studied SDSS sample.
        Our method shows the promising performance of morphological classification attaining >93 % of accuracy for five classes morphology prediction except the cigar-shaped (~75 %) and completely rounded (~83 %) galaxies. As for the classification of galaxies by their detailed structural morphological features, our CNN model gives the accuracy in the range of 92–99 % depending on features, a number of galaxies with the given feature in the inference dataset, and the galaxy image quality. As a result, for the first time we assigned 34 morphological detailed features (bar, rings, number of spiral arms, mergers, etc.) for more than 160000 low-redshift galaxies from the SDSS DR9.
        We demonstrate that implication of the CNN model with adversarial validation and adversarial image data augmentation improves classification of smaller and fainter SDSS galaxies with mr <17.7.
Keywords: convolutional neural networks; galaxies: general, galaxy catalogues, large-scale structure of the Universe, machine learning, Methods: data analysis, morphological classification
References: 
1. Abul Hayat Md., Stein G., Harrington P. et al. Self-Supervised Representation Learning for Astronomical Images. eprint arXiv:2012.13083 (2020) https://doi.org/10.3847/2041-8213/abf2c7
2. Ahn C.P., Alexandroff R., Allende Prieto C. et al. The Ninth Data Release of the Sloan Digital Sky Survey: First Spectroscopic Data from the SDSS-III Baryon Oscillation Spectroscopic Survey. Astrophys. J. Suppl., 203, 2, 21 (2012) https:/doi.org/10.1088/0067-0049/203/2/21
3. Amiaux J., Scaramella R., Mellier Y. et al. Euclid mission: building of a reference survey. SPIE Proceedings, vol. 8442, Space Telescopes and Instrumentation: Optical, Infrared, and Millimeter Wave; 84420Z (2012) https://doi.org/10.1117/12.926513
4. Aniyan A.K., Thorat K. Classifying Radio Galaxies with the Convolutional Neural Network. Astrophys. J. Suppl. Ser., 230, 2, 20 (2017). https://doi.org/10.3847/1538-4365/aa7333
5. Babyk I.; Vavilova I. The distant galaxy cluster XLSSJ022403.9-041328 on the LX-TX-M scaling relations using Chandra and XMM-Newton observations. Astrophys. & Space Sci., 353, 2, 613-619 (2014). https://doi.org/10.1007/s10509-014-2057-x
6. Baron Dalya. Machine Learning in Astronomy: a practical overview. eprint arXiv:1904.07248 (2019). https://arxiv.org/pdf/1904.07248.pdf
7. Barchi P.H., de Carvalho R.R., Rosa R.R. et al. Machine and Deep Learning applied to galaxy morphology - A comparative study. Astronomy and Computing, 30, 100334 (2020) https://doi.org/10.1016/j.ascom.2019.100334
8. Barrow J.D., Saich P. Growth of large-scale structure with a cosmological constant. Mon. Not. R. Astron. Soc., 262, 3, 717-725 (1993). https://doi.org/10.1093/mnras/262.3.717
9. Bellm E.C., Kulkarni S.R., Graham M.J. et al. The Zwicky Transient Facility: System Overview, Performance, and First Results. Publications of the Astronomical Society of the Pacific, 131, 995, id. 018002, (2019). https://doi.org/10.1088/1538-3873/aaecbe
10. Blanton M. R., Bershady M.A., Abolfathi B., Sloan Digital Sky Survey IV: Mapping the Milky Way, Nearby Galaxies, and the Distant Universe. Astron. J., 154, 28 (2017). https://doi.org/10.3847/1538-3881/aa7567
11. Bottrell C., Hani M., Teimoorinia H. et al. Deep learning predictions of galaxy merger stage and the importance of observational realism. Mon. Not. R. Astron. Soc., 490, 4, 5390-5413 (2019) https://doi.org/10.1093/mnras/stz2934
12. Bradley A.P. The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recognition, 30, Issue 7, p. 1145-1159 (1997). https://doi.org/10.1016/S0031-3203(96)00142-2
13. Brugere I., Gallagher B., Berger-Wolf T.Y. Network Structure Inference, A Survey: Motivations, Methods, and Applications. eprint arXiv:1610.00782 (2016). https://arxiv.org/pdf/1610.00782.pdf
14. Bundy K., Scarlata C., Carollo C.M. The Rise and Fall of Passive Disk Galaxies: Morphological Evolution Along the Red Sequence Revealed by COSMOS. Astrophys. J., 719, 2, 1969-1983 (2010). https://doi.org/10.1088/0004-637X/719/2/1969
15. Cabayol L., Eriksen M., Amara A. et al. The PAU survey: Estimating galaxy photometry with deep learning. Mon. Not. R. Astron. Soc., 506, 3, 4048-4069 (2021). https://doi.org/10.1093/mnras/stab1909
16. Cabrera-Vives G., Miller C.J., Schneider J. Systematic Labeling Bias in Galaxy Morphologies. Astron. J., 156, 6, 284 (2018) https://doi.org/10.3847/1538-3881/aae9f4
17. Cassata P., Giavalisco M., Guo Y. et al. The Relative Abundance of Compact and Normal Massive Early-type Galaxies and Its Evolution from Redshift z~2 to the Present. Astrophys. J., 743, 1, 96 (2011) https://doi.org/10.1088/0004-637X/743/1/96
18. Chesnok N.G., Sergeev S.G.; Vavilova I.B. Optical and X-ray variability of Seyfert galaxies NGC 5548, NGC 7469, NGC 3227, NGC 4051, NGC 4151, Mrk 509, Mrk 79, and Akn 564 and quasar 1E 0754. Kinematics and Physics of Celestial Bodies, 25, 2, 107-113 (2009). https://doi.org/10.3103/S0884591309020068
19. Chen Bo Han, Goto Tomotsugu, Kim Seong Jin. An active galactic nucleus recognition model based on deep neural network. Mon. Not. R. Astron. Soc., 501, 3, 3951-3961 (2021). https://doi.org/10.1093/mnras/staa3865
20. Cheng Ting-Yun, Conselice C.J., Arag S. Optimizing automatic morphological classification of galaxies with machine learning and deep learning using Dark Energy Survey imaging. Mon. Not. R. Astron. Soc., 493, 3, 4209-4228 (2020) https://doi.org/10.1093/mnras/staa501
21. Chen Yangyao , Mo H. J., Li Cheng. Relating the Structure of Dark Matter Halos to Their Assembly and Environment. Astrophys. J., 899, 1, 81 (2020). https://doi.org/10.3847/1538-4357/aba597
22. Chilingarian I.V., Melchior A.-L., Zolotukhin I.Y. Analytical approximations of K-corrections in optical and near-infrared bands. Mon. Not. R. Astron. Soc., 405, 1409-1420 (2010). https://doi.org/10.1111/j.1365-2966.2010.16506.x
23. Chilingarian I.V., Zolotukhin I.Y. A universal ultraviolet-optical colour-colour-magnitude relation of galaxies. Mon. Not. R. Astron. Soc., 419, 1727-1739 (2012). https://doi.org/10.1111/j.1365-2966.2011.19837.x https://doi.org/10.1111/j.1365-2966.2011.19837.x
24. Ciuca R., Hernandez O.F. A Bayesian framework for cosmic string searches in CMB maps. Journal of Cosmology and Astroparticle Physics, 8, 28, (2017). https://doi.org/10.1088/1475-7516/2017/08/028 https://doi.org/10.1088/1475-7516/2017/08/028
25. Davies R.L., Efstathiou G., Fall S.M. The kinematic properties of faint elliptical galaxies. Astrophys. J., 266, 41-57 (1983) https://doi.org/10.1086/160757 26. Davis M., Efstathiou G., Frenk C. S., White S.D.M. The evolution of large-scale structure in a universe dominated by cold dark matter. Astrophys. J., Part 1, 292, 371-394 (1985). https://doi.org/10.1086/163168
27. Dey A., Schlegel D.J., Lang D. et al. Overview of the DESI Legacy Imaging Surveys. Astron. J., 157, 5, 168 (2019) https://doi.org/10.3847/1538-3881/ab089d
28. Diakogiannis F.I., Lewis G.F., Ibata R.A. Reliable mass calculation in spherical gravitating systems. Mon. Not. Roy. Astron. Soc., 482, 3, 3356-3372 (2019). https://doi.org/10.1093/mnras/sty2931
29. de Diego J.A., Nadolny J., Bongiovanni A. Galaxy classification: deep learning on the OTELO and COSMOS databases. Astron. & Astrophys., 638, A134 (2020). https://doi.org/10.1051/0004-6361/202037697
30. D'Isanto A., Cavuoti S., Gieseke F., Return of the features. Efficient feature selection and interpretation for photometric redshifts. Astron. & Astrophys., 616, A97 (2018). https://doi.org/10.1051/0004-6361/201833103
31. Djorgovski S.G., Graham M.J., Donalek, C. Real-Time Data Mining of Massive Data Streams from Synoptic Sky Surveys. eprint arXiv:1601.04385 (2016). https://arxiv.org/ftp/arxiv/papers/1601/1601.04385.pdf https://doi.org/10.1016/j.future.2015.10.013
32. Dobrycheva D.V., Melnyk O.V., Vavilova I.B., Elyiv A.A. Environmental Properties of Galaxies at z < 0.1 from the SDSS via the Voronoi Tessellation. Odessa Astron. Publ., 27, 26 (2014)
33. Dobrycheva D.V. ,Melnyk O.V., Vavilova I.B. Environmental Density vs. Colour Indices of the Low Redshifts Galaxies. Astrophysics, 58, 2, 168-180 (2015). https://doi.org/10.1007/s10511-015-9373-x
34. Dobrycheva D.V., The New Galaxy Sample from SDSS DR9 at 0.003 < z < 0.1. Odessa Astron. Publ., 26, 187 (2013).
35. Dobrycheva D.V., Morphological content and color indices bimodality of a new galaxy sample at the redshifts z < 0.1. PhD Thesis in Phys.-Math. Sciences, Kyiv, Main Astronomical Observatory, NAS of Ukraine, 132 p. (2017)
36. Dobrycheva D.V., I.B. and Melnyk, O.V., Morphological Type and Color Indices of the SDSS DR9 Galaxies at 0.02 < z < 0.06. Kinematics and Physics of Celestial Bodies, 34, 6, 290-301 (2018). https://doi.org/10.3103/S0884591318060028 37. Dominguez-Sanchez H., Huertas-Company M., Bernardi M. et al. Improving galaxy morphologies for SDSS with Deep Learning. Mon. Not. Roy. Astron. Soc., 476, 3, 3661-3676 (2018) https://doi.org/10.1093/mnras/sty338
38. Domínguez Sánchez H.; Margalef B.; Bernardi M.; Huertas-Company M. SDSS-IV DR17: Final release of MaNGA PyMorph photometric and deep learning morphological catalogs. Mon. Not. R. Astron. Soc., Advance Access. https://doi.org/10.1093/mnras/stab3089
39. Dominguez Sanchez H., Vega-Ferrero J.; Huertas-Company M.; Bernardi M. Constructing the Largest Galaxy Morphological Catalogue with Supervised Deep Learning ... with No Training Sample. American Astronomical Society meeting #238, id. 119.01. Bulletin of the American Astronomical Society, Vol. 53, No. 6 e-id 2021n6i119p01
40. Du Wei, Cheng Cheng, Wu Hong et al. Low Surface Brightness Galaxy catalogue selected from the .40-SDSS DR7 Survey and Tully-Fisher relation. Mon. Not. R. Astron. Soc., 483, 2, 1754-1795 (2019) https://doi.org/10.1093/mnras/sty2976
41. Elyiv A., Melnyk O., Vavilova I. High-order 3D Voronoi tessellation for identifying isolated galaxies, pairs and triplets. Mon. Not. R. Astron. Soc., 394, 3, 1409-1418 (2009). https://doi.org/10.1111/j.1365-2966.2008.14150.x https://doi.org/10.1111/j.1365-2966.2008.14150.x
42. Elyiv A. A., Melnyk O.V., Vavilova, I.B., Machine-learning computation of distance modulus for local galaxies. Astron. & Astrophys., 635, A124 (2020). https://doi.org/10.1051/0004-6361/201936883 https://doi.org/10.1051/0004-6361/201936883
43. Fluke Christopher J. ,Jacobs Colin. Surveying the reach and maturity of machine learning and artificial intelligence in astronomy. WIREs Data Mining and Knowledge Discovery, 10, 2, article id. e134910 (2020). https://doi.org/10.1002/widm.1349
44. Gauthier A., Jain A., Noordeh E. Galaxy Morphology Classification. e-proceedings http://cs229.stanford.edu/proj2016/report/GauthierJainNoordeh-GalaxyMorp..., p. 1-6 (2016)
45. George D., Huerta E.A. Deep neural networks to enable real-time multimessenger astrophysics, Phys. Rev. D 97, 044039 (2018). https://doi.org/10.1103/PhysRevD.97.044039 https://doi.org/10.1103/PhysRevD.97.044039
46. George D., Huerta E.A. Deep Learning for real-time gravitational wave detection and parameter estimation: Results with Advanced LIGO data. Physics Letters B, 778, 64-70 (2018). https://doi.org/10.1016/j.physletb.2017.12.053
47. Huerta E.A., Moore C.J., Kumar P. Eccentric, nonspinning, inspiral, Gaussian-process merger approximant for the detection and characterization of eccentric binary black hole mergers. Phys. Rev. D, 97, 2, id. 024031 (2018). https://doi.org/10.1103/PhysRevD.97.02403 https://doi.org/10.1103/PhysRevD.97.024031
48. Huertas-Company M., Primack J.R., Dekel A., Deep Learning Identifies High-z Galaxies in a Central Blue Nugget Phase in a Characteristic Mass Range, Astrophys. J., 858, 2, 114 (2018). https://doi.org/10.3847/1538-4357/aabfed
49. Ivezič Z., Kahn S.M, Tyson J. A. LSST: From Science Drivers to Reference Design and Anticipated Data Products. Astrophys. J., 873, 2, 111 (2019). https://doi.org/10.3847/1538-4357/ab042c
50. Jacobs C., Collett T., Glazebrook K. Finding high-redshift strong lenses in DES using convolutional neural networks. Mon. Not. R. Astron. Soc., 484, 4, 5330-5349 (2019). https://doi.org/10.1093/mnras/stz272
51. Kang Shi-Ju, Fan Jun-Hui, Mao Weiming et al. Evaluating the Optical Classification of Fermi BCUs Using Machine Learning. Astrophys. J., 872, 2, 189 (2019). https://doi.org/10.3847/1538-4357/ab0383 https://doi.org/10.3847/1538-4357/ab0383
52. Karachentseva V.E., Vavilova I.B. Clustering of Low Surface Brightness Dwarf Galaxies in the Local Supercluster. European Southern Observatory Conference and Workshop Proceedings, 49, 91-100 (1994)
53. Khalifa Nour Eldeen M., Taha Mohamed Hamed N., Hassanien Aboul Ella. Deep Galaxy: Classification of Galaxies based on Deep Convolutional Neural Networks. Eprint arXiv:1709.02245 (2017).
54. Khramtsov V., Dobrycheva D. V., Vasylenko M. Yu. Deep Learning for Morphological Classification of Galaxies from SDSS. Odessa Astron. Publ., 32, 21 (2019). https://doi.org/10.18524/1810-4215.2019.32.182092 https://doi.org/10.18524/1810-4215.2019.32.182092
55. Khramtsov V. , Sergeyev A., Spiniello C. KiDS-SQuaD - II. Machine learning selection of bright extragalactic objects to search for new gravitationally lensed quasars. Astron. & Astrophys., 632, A56 (2019). https://doi.org/10.1051/0004-6361/201936006 https://doi.org/10.1051/0004-6361/201936006
56. Khramtsov V., Vavilova I.B., Vasylenko M.Yu., Dobrycheva D.V., Elyiv A.A., Akhmetov V.S., Dmytrenko A., Khlamov S. Machine learning technique for morphological classification of galaxies from SDSS. III. CNN Image-based inference of detailed morphology. Astronomy and Computing (2022) (submitted) https://doi.org/10.1051/0004-6361/202038981
57. Krause M., Pueschel E., Maier G. Improved gamma hadron separation for the detection of faint gamma-ray sources using boosted decision trees. Astroparticle Physics, 89, 1-9 (2017). https://doi.org/10.1016/j.astropartphys.2017.01.004
58. Kugler S. D. , Gianniotis, N. Modelling multimodal photometric redshift regression with noisy observation. eprint arXiv:1607.06059 (2016). https://arxiv.org/pdf/1607.06059.pdf
59. LeCun Yann, Chopra Sumit, Hadsell Raia et al. A tutorial on energy-based learning. In: Predicting Structured Data, MIT Press (2006) http://yann.lecun.com/exdb/publis/pdf/lecun-06.pdf
60. Leung E., Bekki K., While L. Automated Simulations of Galaxy Morphology Evolution using Deep Learning and Particle Swarm Optimisation. arXiv:1904.02906 (2019). https://arxiv.org/ftp/arxiv/papers/1904/1904.02906.pdf
61. Lintott C.J., Schawinski K., Slosar A. et al. Galaxy Zoo: morphologies derived from visual inspection of galaxies from the Sloan Digital Sky Survey. Mon. Not. R. Astron. Soc., 389, 3, 1179-1189 (2008) https://doi.org/10.1111/j.1365-2966.2008.13689.x
62. Lukic V., Bruggen, M., Banfield, J.K. Radio Galaxy Zoo: compact and extended radio source classification with deep learning. Mon. Not. R. Astron. Soc., 476, 1, 246-260 (2018). https://doi.org/10.1093/mnras/sty163
63. Lupton R., Blanton M.R., Fekete G. Preparing Red-Green-Blue Images from CCD Data. Publications of the Astronomical Society of the Pacific, 116, 816, p. 133-137 (2004). https://doi.org/10.1086/382245 https://doi.org/10.1086/382245
64. Ma Zhixian, Xu Haiguang, Zhu Jie et al. A Machine Learning Based Morphological Classification of 14,245 Radio AGNs Selected from the Best-Heckman Sample. Astrophys. J. Suppl. Ser., 240, 2, 34 (2019). https://doi.org/10.3847/1538-4365/aaf9a2
65. Mahabal A. A., Djorgovski S. G., Drake A.J., Discovery, classification, and scientific exploration of transient events from the Catalina Real-time Transient Survey. Bulletin of the Astronomical Society of India, 39, 3, 387-408 (2011)
66. Mahabal Ashish, Rebbapragada Umaa, Walters Richard. Machine Learning for the Zwicky Transient Facility. Publications of the Astronomical Society of the Pacific, 131, 997, id. 038002 (2019). https://doi.org/10.1088/1538-3873/aaf3fa
67. Melnyk O.V., Dobrycheva D.V., Vavilova I.B. Morphology and color indices of galaxies in Pairs: Criteria for the classification of galaxies. Astrophysics, 55, 3, 293-305 (2012). https://doi.org/10.1007/s10511-012-9236-7 https://doi.org/10.1007/s10511-012-9236-7
68. Mezcua M., Lobanov A.P., Mediavilla E., Karouzos M. Photometric Decomposition of Mergers in Disk Galaxies. Astrophys. J., 784, 1., 16 (2014) https://doi.org/10.1088/0004-637X/784/1/16
69. Mittal A., Soorya A., Nagrath P., Hemanth D.J. Data augmentation based morphological classification of galaxies using deep convolutional neural network. Earth Sci. Inform., 13, 601-617 (2020) https://doi.org/10.1007/s12145-019-00434-8
70. Morello G., Morris P.W., Van Dyk S.D. Applications of machine-learning algorithms for infrared colour selection of Galactic Wolf-Rayet stars. Mon. Not. R. Astron. Soc., 473, 2, 2565-2574 (2018). https://doi.org/10.1093/mnras/stx2474
71. Parikh T., Thomas D., Maraston C. et al. SDSS-IV MaNGA: local and global chemical abundance patterns in early-type galaxies. Mon. Not. R. Astron. Soc., 483, 3, 3420-3436 (2019) https://doi.org/10.1093/mnras/sty3339
72. Pasquet-Itam J., Pasquet J., Deep learning approach for classifying, detecting and predicting photometric redshifts of quasars in the Sloan Digital Sky Survey stripe 82. Astron. & Astrophys., 611, A97 (2018). https://doi.org/10.1051/0004-6361/201731106
73. Pearson W.J., Wang L., Trayford J.W. et al. Identifying galaxy mergers in observations and simulations with deep learning. Astron. Astrophys., 626, A49 (2019) https://doi.org/10.1051/0004-6361/201935355
74. Peebles P.E., Principles of Physical Cosmology. Princeton Univ. Press, Princeton, New Jersey, 718 p. (1993).
75. Peng Ying-jie, Lilly S.J., Kova K. et al. Mass and Environment as Drivers of Galaxy Evolution in SDSS and zCOSMOS and the Origin of the Schechter Function. Astrophys. J., 721, 1, 193-221 (2010). https://doi.org/10.1088/0004-637X/721/1/193
76. Rodriguez-Puebla A., Calette A.R., Avila-Reese V. et al. The bivariate gas-stellar mass distributions and the mass functions of early- and late-type galaxies at z~0. Publ. Astronomical Society of Australia, 37, article id. e024 (2020) https://doi.org/10.1017/pasa.2020.15
77. Pulatova N.G.; Vavilova I.B.; Sawangwit U.; Babyk Iu.; Klimanov S. The 2MIG isolated AGNs - I. General and multiwavelength properties of AGNs and host galaxies in the northern sky. Mon. Not. R. Astron. Soc., 447, 3, 2209-2223 (2015) https://doi.org/10.1093/mnras/stu2556
78. Reid B.A., Samushia L., White M. et al. The clustering of galaxies in the SDSS-III Baryon Oscillation Spectroscopic Survey: measurements of the growth of structure and expansion rate at z = 0.57 from anisotropic clustering. Mon. Not. R. Astron. Soc., 426, 4, 2719-2737 (2012). https://doi.org/10.1111/j.1365-2966.2012.21779.x
79. dos Reis S.N., Buitrago F., Papaderos P. et al. Structural analysis of massive galaxies using HST deep imaging at z < 0.5. Astron. Astrophys., 634, A11 (2020) https://doi.org/10.1051/0004-6361/201936276
80. Willi R., Luis P.C. Building Machine Learning Systems with Python (2013). http://gen.lib.rus.ec/book/index.php?md5=7a375749558682503761fa801a67d7ec
81. Ruhe Tim. Application of machine learning algorithms in imaging Cherenkov and neutrino astronomy. Intern. J. Modern Phys. A, 35, 33, 2043004 (2020) https://doi.org/10.1142/S0217751X20430046
82. Salvato M., Ilbert O. Hoyle Ben, the many flavours of photometric redshifts. Nature Astronomy, 3, 212-222 (2019). https://doi.org/10.1038/s41550-018-0478-0
83. Savanevych V.E., Khlamov S.V., Vavilova I.B. et al. A method of immediate detection of objects with a near-zero apparent motion in series of CCD-frames. Astron. & Astrophys., 609, id. A54, 11 pp. (2018) https://doi.org/10.1051/0004-6361/201630323
84. Scaife Anna M.M., Porter Fiona. Fanaroff-Riley classification of radio galaxies using group-equivariant convolutional neural networks. Mon. Not. R. Astron. Soc., 503, 2, 2369-2379 (2021). https://doi.org/10.1093/mnras/stab530
85. Schawinski Kevin, Zhang Ce, Zhang Hantian, Generative adversarial networks recover features in astrophysical images of galaxies beyond the deconvolution limit. Mon. Not. R. Astron. Soc. Letters, 467, 1, L110-L114, (2017). https://doi.org/10.1093/mnrasl/slx008
86. Schlegel D.J., Finkbeiner D.P., Davis M. Maps of Dust Infrared Emission for Use in Estimation of Reddening and Cosmic Microwave Background Radiation Foregrounds. Astrophys. J., 500, 2, 525-553 (1998) https://doi.org/10.1086/305772
87. Simmons B.D., Lintott C., Willett K.W. et al. Galaxy Zoo: quantitative visual morphological classifications for 48 000 galaxies from CANDELS. Mon. Not. R. Astron. Soc., 464, 4, 4420-4447 (2017) https://doi.org/10.1093/mnras/stw2587
88. Speagle J.S., Eisenstein D.J., Deriving photometric redshifts using fuzzy archetypes and self-organizing maps - II. Implementation. Mon. Not. R. Astron. Soc., 469, 1, p. 1205-1224, (2017). https://doi.org/10.1093/mnras/stx510
89. Sybilska A., Lisker T., Kuntschner H. et al. The hELENa project - I. Stellar populations of early-type galaxies linked with local environment and galaxy mass. Mon. Not. R. Astron. Soc., 470, 1, 815-838 (2017) https://doi.org/10.1093/mnras/stx1138
90. Tsizh M., Novosyadlyj B., Holovatch Yu. Large-scale structures in the Lambda-CDM Universe: network analysis and machine learning. Mon. Not. R. Astron. Soc., 495, 1, 1311-1320 (2020). https://doi.org/10.1093/mnras/staa1030
91. Tamburri S., Saracco P., Longhetti M. et al. The population of early-type galaxies: how it evolves with time and how it differs from passive and late-type galaxies. Astron. Astrophys., 570, A102 (2014) https://doi.org/10.1051/0004-6361/201424040
92. Vasylenko M. Yu. ,Dobrycheva D.V., Vavilova I.B., Verification of Machine Learning Methods for Binary Morphological Classification of Galaxies from SDSS, Odessa Astron. Publ., 32, 46 (2019). https://doi.org/10.18524/1810-4215.2019.32.182538
93. Vasylenko M., Dobrycheva, D., Khramtsov V. Deep Convolutional Neural Networks models for the binary morphological classification of SDSS-galaxies. Communication BAO, 67, 354 (2020). https://doi.org/10.52526/25792776-2020.67.2-354
94. Vavilova I.B., Karachentseva V.E.; Makarov D.I., Melnyk O.V. Triplets of Galaxies in the Local Supercluster. I. Kinematic and Virial Parameters. Kinematika i Fizika Nebesnykh Tel, 21, no. 1, p. 3-20 (2005)
95. Vavilova I.B.; Melnyk O.V.; Elyiv A.A. Morphological properties of isolated galaxies vs. isolation criteria. Astron. Nachr., 330, 1004 (2009) https://doi.org/10.1002/asna.200911281
96. Vavilova I.B., Pakuliak L.K., Protsyuk Yu.I. et al. UkrVO Joint digitized archive and scientific projects. Baltic Astronomy, 21, 356-365 (2012) https://doi.org/10.1515/astro-2017-0394
97. Vavilova I. B., Elyiv A. A., Vasylenko M. Yu., Behind the Zone of Avoidance of the Milky Way: what can we Restore by Direct and Indirect Methods? Radio Physics, Radio Astronomy, 23, 4, 244-257 (2018). https://doi.org/10.15407/rpra23.04.244
98. Vavilova I., Dobrycheva D., Vasylenko M. Multiwavelength Extragalactic Surveys: Examples of Data Mining. In: Knowledge Discovery in Big Data from Astronomy and Earth Observation, 1st Edition. Edited by Petr Skoda and Fathalrahman Adam. Elsevier, p.307-323 (2020). https://doi.org/10.1016/B978-0-12-819154-5.00028-X
99. Vavilova I., Pakuliak L., Babyk Iu., Surveys, Catalogues, Databases, and Archives of Astronomical Data. In: Knowledge Discovery in Big Data from Astronomy and Earth Observation, 1st Edition. Edited by Petr Skoda and Fathalrahman Adam. Elsevier, p. 57-102 (2020). https://doi.org/10.1016/B978-0-12-819154-5.00015-1
100. Vavilova I. ; Elyiv A.; Dobrycheva D.; Melnyk O. The Voronoi tessellation method in astronomy. In: Intelligent Astrophysics. Edited by I. Zelinka, M. Brescia and D. Baron. Emergence, Complexity and Computation, Vol 39. ISBN: 978-3-030-65867-0. Springer, Cham, 2021, p. 57-79 (2021). https://doi.org/10.1007/978-3-030-65867-0_3
101. Vavilova I. B., Dobrycheva D.V., Vasylenko M. Yu., Machine learning technique for morphological classification of galaxies from the SDSS. I. Photometry-based approach, 648, p. A122, (2021). https://doi.org/10.1051/0004-6361/202038981
102. Vavilova I. B., Dobrycheva D.V., Vasylenko M. Yu., VizieR Online Data Catalog: SDSS galaxies morphological classification (Vavilova+, J/A+A/648/A122, 2021), (2021). https://doi.org/10.26093/cds/vizier.36480122
103. Vega-Ferrero J., Dominguez Sanchez H., Bernardi M. et al. Pushing automated morphological classifications to their limits with the Dark Energy Survey. Mon. Roy. Astron. Soc., 506, 2, 1927-1943 (2021) https://doi.org/10.1093/mnras/stab594
104. Villarroel B.; Soodla J.; Comerón S. et al. The Vanishing and Appearing Sources during a Century of Observations Project. I. USNO Objects Missing in Modern Sky Surveys and Follow-up Observations of a "Missing Star". Astron. J., 159, 1, article id. 8, 19 pp. (2020). https://doi.org/10.3847/1538-3881/ab570f https://doi.org/10.3847/1538-3881/ab570f
105. Vol'Vach A.E.; Vol'Vach L N.; Kut'kin A.M. et al. Multi-frequency studies of the non-stationary radiation of the blazar 3C 454.3. Astronomy Reports, 55, 7, 608-615 (2011). https://doi.org/10.1134/S1063772911070092
106. Vulcani B., Poggianti B,M., Aragon-Salamanca A. et al. Galaxy stellar mass functions of different morphological types in clusters, and their evolution between z= 0.8 and 0. Mon. Not. R. Astron. Soc., 412, 1, 246-268 (2011) https://doi.org/10.1111/j.1365-2966.2010.17904.x
107. Walmsley M., Smith L., Lintott C. Galaxy Zoo: probabilistic morphology through Bayesian CNNs and active learning. Mon. Not. R. Astron. Soc., 491, 2, 1554-1574, (2020). https://doi.org/10.1093/mnras/stz2816
108. Walmsley M., Lintott C., Geron T. et al. Galaxy Zoo DECaLS: Detailed Visual Morphology Measurements from Volunteers and Deep Learning for 314,000 Galaxies. Mon. Not. R. Astron. Soc., 509, 3, 3966-3988 (2021) https://doi.org/10.1093/mnras/stab2093
109. Willett K.W., Lintott C.J., Bamford S.P. et al. Galaxy Zoo 2: detailed morphological classifications for 304 122 galaxies from the Sloan Digital Sky Survey. Mon. Not. R. Astron. Soc., 435, 4, 2835-2860, (2013). https://doi.org/10.1093/mnras/stt1458
110. Yang Xiaohu, Mo H. J. , van den Bosch Frank C., Constraining galaxy formation and cosmology with the conditional luminosity function of galaxies. Mon. Not. R. Astron. Soc., 339, 4, 1057-1080 (2003). https://doi.org/10.1046/j.1365-8711.2003.06254.x
111. Zevin M., Coughlin S., Bahaadini S. et al. Gravity Spy: integrating advanced LIGO detector characterization, machine learning, and citizen science. Classical and Quantum Gravity, 34, 6, id. 064003 (2017). https://doi.org/10.1088/1361-6382/aa5cea
112. Zhu Xiao-Pan, Dai Jia-Ming, BianChun-Jiang et al. Galaxy morphology classification with deep convolutional neural networks. Astrophys. & Space Sci., 364, 4, 55 (2019). https://doi.org/10.1007/s10509-019-3540-1 https://doi.org/10.1007/s10509-019-3540-1