CLASSIFICATION OF GALAXIES IN SHAPLEY CONCENTRATION REGION WITH MACHINE LEARNING

Yıl 2019, Cilt: 5 Sayı: 1, 119 - 126, 30.06.2019

Nazlı Deniz Ergüç Nida Gökçe Narin

Öz

The galaxies, are the systems consisting of
stars, gas, dust and dark matter combined with the gravitational force. There
are billions of galaxies in the universe. Since the cost of examining each
galaxy one by one is high, the classification of the galaxy is an important
part of the astronomical data analysis. Galaxies are classified according to
morphology and spectral properties. Machine learning methods aimed at revealing
the hidden pattern within the data set by analyzing the available data, it can
be used to estimate which group of galaxies whose natural groups have not yet
been identified. This will save time and cost for both researchers and
astronomers. This study has been classified five-variables (Right ascension,
Declination, Magnitude, Velocity, and Sigma of Velocity) 4215 galaxies.
Galaxies whose natural groups were determined with IDL were classified by using
machine learning algorithms with Weka program. Bayes classifier methods, Naive
Bayes and Bayes net, Decision tree methods J48, LMT and Random Forest
algorithms, Artificial Neural Networks Multilayer Perceptron and Support vector
classifier methods were used. The obtained classification results were compared
with the natural groups and the predictive performance of the methods were
evaluated. 

Anahtar Kelimeler

Galaxies Classification, Classification Algorithms, Machine Learning, Shapley Concentration Region

Kaynakça

• Hubble E., “Extra-galactic Nebulae”, Contributions from the Mount Wilson Observatory / Carnegie Institution of Washington, Vol. LXIV, No.324, pp.321-369,1926.
• Vaucouleurs, G. D., “Classification and Morphology of External Galaxies”, Handbuch der Physik, Vol.11 No.53, pp. 275-310, 1959.
• Sandage, A., “Hubble Atlas of Galaxies”, Carnegie Institution of Washington, Washington D.C,618,A.B.D, 1961.
• Lotz J.M., Primack J, and Madau P., “A New Nonparametrıc Approach to Galaxy Morphologıcal Classıfıcatıon”, the Astronomical Journal, Vol.128, No.1, pp. 163–182, 2004.
• Dressler, “A Catalog of Morphologıcal Types In 55 Rıch Clusters Of Galaxıes”, The Astrophysical Journal Supplement Series, 42, pp. 565-609, 1979.
• Kasivajhula S., Raghavan N. and Shah H., “Morphological Galaxy Classification Using Machine Learning” cs229.stanford.edu, 2007.
• Marin M., “A Hierarchical Model for Morphological Galaxy Classification”, Proceedings of the Twenty-Sixth International FLAIRS Conference, Florida, USA 2013.
• Miller A. S. and Coe M. J., ”Star/Galaxy Classification Using Kohonen Self-Organizing Maps”, Mon. Not. R. Astron. Soc. 279, pp. 293-300, 1996.
• Bailin J. ve Harris W.E., “Inclination-Independent Galaxy Classification”, The Astrophysical Journal, 681, pp. 225-231, 2008.
• Gauci A., Kristian Zarb Adami K.Z. and Abela J., “Machine Learning for Galaxy Morphology Classification”, Mon. Not. R. Astron. Soc., 000, 1–8, 2010.
• Gauthier A., Archa Jain A. and Emil Noordeh E., ”Galaxy Morphology Classification”, Stanford University, 2016.
• D.V. Dobrycheva, “Machine learning technique for morphological classification of galaxies at z < 0.1 from the SDSS”, Astronomy & Astrophysics manuscript no. Dobrycheva-EWASS-15 December 27, 2017.
• Remya G. and Mohan A.,”Deep Learning Approach for Classifying Galaxies”, Volume 6, Issue 4, April 2016. [14] Selim I.M., Keshk A.E. and El Shourbugy B.S., “Galaxy Image Classification using Non-Negative Matrix Factorization”, International Journal of Computer Applications · March 2016.
• Selim, I., Keshk, A. E., & El Shourbugy, B. M. Galaxy image classification using non-negative matrix factorization. International Journal of Computer Applications, 137(5), 4-8, 2016.
• Z.Frei and J.E.Gun,” A Catalog Of Digital Images Of 113 Nearby Galaxıes” Astrophysics and Space Science, Volume 269, Issue 0, pp 649–650, 1999.
• Goderya N.S. and Lolling S.M.,, “Morphological classification of Galaxies Using Computer Vision and Artificial Neural Networks: A Computational scheme”, Astrophysics and Space Science 279: 377–387, 2002.
• Abell, G. O., “The Distribution of rich clusters of galaxies”, The Astrophysical Journal Supplement Series, 3, pp.211-288, 1957.
• Driver S.P., Liske J,, Cross N. J. G., De Propris R. and Allen P. D., “The Millennium Galaxy Catalogue: The Space Density And Surface-Brightness Distribution(S) Of Galaxies”, Mon. Not. R. Astron. Soc. 360, 81–103, 2005.
• Abell, G. O., Corwın, H. G., Jr., Olowın, R. P., “A Catalog of Rich Clusters of Galaxies”, The Astrophysical Journal Supplement Series, 70, pp.1-138, 1988.
• http://astrostatistics.psu.edu/datasets/Shapley_galaxy.dat
• http://vizier.u-strasbg.fr/viz-bin/VizieR-2
• James, Gareth, et all. An introduction to statistical learning. Vol. 112. New York: springer, 2013.
• Haykin, Simon. Neural networks. Vol. 2. New York: Prentice hall, 1994.
• Ilin R., Kozma R. and Werbos P. J., "Beyond feedforward models trained by backpropagation: A practical training tool for a more efficient universal approximator." IEEE Transactions on Neural Networks 19.6 (2008): 929-937.
• Rumelhart, D. E., Hinton, G. E., & Williams, R. J., “Learning internal representations by error propagation” No. ICS-8506. California Univ San Diego La Jolla Inst for Cognitive Science, 1985.
• Cortes C., Vapnik V., 1995, “Support-Vector Networks”, Kluwer Academic Publishers Machine Learning, Vo. 20, No.3, pp. 273-297
• Quinlan, J. R., "Simplifying decision trees." International journal of man-machine studies 27.3 (1987): 221-234.
• Korting, T.S., "C4. 5 Algorithm And Multivariate Decision Trees." Image Processing Division, National Institute for Space Research–INPE Sao Jose dos Campos–SP, Brazil 2006.
• Shanon C.,”A Mathematical Theory Of Communication”, Bell System Tech. J. 27: 379-423, 623-656, 1948.
• Landwehr, N., Hall, M., & Frank, E., “Logistic Model Trees,” Machine Learning, 59, pp.161-205, 2005.
• Breiman L., "Random Forests", Machine Learning, Vol.45, No. 1, pp.5–32, 2001.
• Domingos, P., and Pazzani, M., “Beyond independence: Conditions for the optimality of the simple Bayesian classifier”, Machine Learning 29:103–130. 1997.
• Friedman, N., Geiger, D., & Goldszmidt, M., "Bayesian Network Classifiers." Machine learning 29,2-3 131-163, 1997.
• Wampold, B. E., “Kappa As A Measure Of Pattern İn Sequential Data”, Quality&Quantity, 23, 171-187, 1989,
• Godbole S., Sarawagi S., and Chakrabarti S. "Scaling multi-class support vector machines using inter-class confusion." Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2002.
• Friedman M., “The use of ranks to avoid the assumption of normality implicit in the analysis of variance.” J. Am. Stat. Assoc. 32:675-701, 1937.