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Mathematical Morphology: Star/Galaxy Differentiation & Galaxy Morphology Classification

Published online by Cambridge University Press:  05 March 2013

Jason A. Moore ,
Kevin A. Pimbblet  and
Michael J. Drinkwater
Jason A. Moore*
Affiliation:
Department of Physics, University of Queensland, Brisbane QLD 4072, Australia
Kevin A. Pimbblet
Affiliation:
Department of Physics, University of Queensland, Brisbane QLD 4072, Australia
Michael J. Drinkwater
Affiliation:
Department of Physics, University of Queensland, Brisbane QLD 4072, Australia
*
BCorresponding author. E-mail: jmoore@physics.uq.edu.au
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Abstract

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We present an application of Mathematical Morphology (MM) for the classification of astronomical objects, both for star/galaxy differentiation and galaxy morphology classification. We demonstrate that, for CCD images, 99.3 ± 3.8% of galaxies can be separated from stars using MM, with 19.4 ± 7.9% of the stars being misclassified. We demonstrate that, for photographic plate images, the number of galaxies correctly separated from the stars can be increased using our MM diffraction spike tool, which allows 51.0 ± 6.0% of the high-brightness galaxies that are inseparable in current techniques to be correctly classified, with only 1.4 ± 0.5% of the high-brightness stars contaminating the population. We demonstrate that elliptical (E) and late-type spiral (Sc-Sd) galaxies can be classified using MM with an accuracy of 91.4 ± 7.8%. It is a method involving fewer ‘free parameters’ than current techniques, especially automated machine learning algorithms. The limitation of MM galaxy morphology classification based on seeing and distance is also presented. We examine various star/galaxy differentiation and galaxy morphology classification techniques commonly used today, and show that our MM techniques compare very favourably.

Keywords

techniques: image processingmethods: data analysismethods: miscellaneous
Type
Research Article
Information
Publications of the Astronomical Society of Australia , Volume 23 , Issue 4 , 2006 , pp. 135 - 146
Copyright
Copyright © Astronomical Society of Australia 2006

References

Abraham, R. G., Valdes, F., Yee, H. K. C. & van den Bergh, S. 1994, ApJ, 432, 75 CrossRefGoogle Scholar
Andredakis, Y. C. & Sanders, R. H. 1994, MNRAS, 267, 283 CrossRefGoogle Scholar
Andreon, S., Gargiulo, G., Longo, G., Tangliaferri, R. & Capuano, N. 2000, MNRAS, 319, 700 CrossRefGoogle Scholar
Appleton, P. N., Siqueira, P. R. & Basart, J. P. 1993, AJ, 106, 1664 CrossRefGoogle Scholar
Bertin, E. & Arnouts, S. 1996, A&AS, 117, 393 Google Scholar
Candeas, A. J., Braga Neto, U. & Carvalho Filho, E. 1996, Anais do IX SIBGRAPI, 235 Google Scholar
Candeas, A. J., Braga Neto, U. & Carvalho Filho, E. 1997, J. Braz. Comp. Soc., 3 Google Scholar
Conselice, C. J. 2003, ApJS, 147, 1 Google Scholar
de Vaucouleurs, G. 1948, AnAp, 11, 247 Google Scholar
de Vaucouleurs, G. 1959, Handb. der Physik, 53, 275 Google Scholar
Doyle, M. T., et al. 2005, MNRAS, 361, 34 CrossRefGoogle Scholar
Drinkwater, M. J. & Schmidt, R. W. 1996, PASA, 13, 127 Google Scholar
Graham, A. W. & Driver, S. P. 2005, PASA, 22, 118 CrossRefGoogle Scholar
Hambly, N. C., et al. 2001a, MNRAS, 326, 1279 CrossRefGoogle Scholar
Hambly, N. C., Irwin, M. J. & MacGillivray, H. T. 2001b, MNRAS, 326, 1295 Google Scholar
Hambly, N. C., Davenhall, A. C., Irwin, M. J. & MacGillivray, H. T. 2001c, MNRAS, 326, 1315 Google Scholar
Harmon, R. & Mamon, G. 1993, In ASP Conf. Ser. 43: Sky Surveys: Protostars to Protogalaxies, Soifer, B. T., San Francisco, ASP, 15 Google Scholar
Haykin, S. 1998, Neural Networks – A Comprehensive Foundation, 2nd ed., Englewood Cliffs, Prentice-HallGoogle Scholar
He, L. X. 1996, Ph.D. Thesis, Iowa State University Google Scholar
Hubble, E. 1926, ApJ, 64, 321 Google Scholar
Hubble, E. 1936, The Realm of the Nebulae, New Haven, Yale University PressGoogle Scholar
Heijmans, H. J. A. M. 1992, Nieuw Archief voor Wiskunde, Vierde Serie, 10, 237 Google Scholar
Heijmans, H. J. A. M. 1994, in Shape in Picture: Mathematical Description of Shape in Grey-level Images, 147 Google Scholar
Heijmans, H. J. A. M. 1995, in SIAM Review, 37, 1 CrossRefGoogle Scholar
Jones, L. R., Fong, R., Shanks, T., Ellis, R. S. & Peterson, B. A. 1991, MNRAS, 249, 481 Google Scholar
Kibblewhite, E. J., Bridgeland, M. T., Bunclark, P. & Irwin, M. J. 1984, in NASA Conf. Publ. 2317: Astronomical Microdensitometry Conference, Klinglesmith, D. A., Washington, D.C., NASA Scientific and Technical Information Branch, 277 Google Scholar
Kron, R. G. 1980, ApJS, 43, 305 Google Scholar
Lea, S. M. & Kellar, L. A. 1989, AJ, 97, 1238 Google Scholar
Lefèvre, O., Bijaoui, A., Mathez, G., Picat, J. P. & Leli'evre, G. 1986, A&A, 154, 92 Google Scholar
MacArthur, L. A., Courteau, S. & Holtzman, J. A. 2003, ApJ, 582, 689 CrossRefGoogle Scholar
Maddox, S., Sutherland, W., Efstathiou, G. & Loveday, J. 1990, MNRAS, 243, 692 Google Scholar
Mähönen, P. & Frantti, T. 2000, ApJ, 541, 261 Google Scholar
Matheron, G. 1975, Random Sets and Integral Geometry, New York, John Wiley and SonsGoogle Scholar
Maragos, P. 1989, IEEE Trans. Pattern Anal. Mach. Intell., 11, 701 Google Scholar
Miller, A.S. & Coe, M. J. 1996, MNRAS, 279, 293 CrossRefGoogle Scholar
Odewahn, S., Stockwell, E., Pennington, R. M., Humphreys, R. & Zumach, W. 1992, AJ, 103, 318 Google Scholar
Odewahn, S., Humphreys, R. M., Aldering, G. & Thurmes, P. 1993, PASP, 105, 1354 Google Scholar
Philip, N. S., Wadadekar, Y., Kembhavi, A. & Joseph, K. B. 2002, A&A, 385, 1119 Google Scholar
Pimbblet, K. A., Smail, I., Edge, A. C., Couch, W. J., O'Hely, E. & Zabludoff, A. I. 2001, MNRAS, 327, 588 Google Scholar
Peng, C. Y., Ho, L. C., Impey, C. D. & Rix, H-W. 2002, AJ, 124, 266 Google Scholar
Reid, N. & Gilmore, G. 1982, MNRAS, 201, 73 Google Scholar
Reid, I. N., Yan, L., Majewski, S., Thompson, I. & Smail, I. 1996, AJ, 112, 1472 Google Scholar
Sandage, A. 1961, The Hubble Atlas of Galaxies, Washington, D.C., Carnegie Institution of WashingtonGoogle Scholar
Shaver, P. A. 1987, Nature, 326, 773 Google Scholar
Sebok, W. 1979, AJ, 84, 1526 CrossRefGoogle Scholar
Serra, J. 1982, Image Analysis and Mathematical Morphology, London, Academic PressGoogle Scholar
Sérsic, J. L. 1963, BAAA, 6, 41 Google Scholar
Sérsic, J. L. 1968, Atlas de Galaxias Australes, Cordoba, Observatorio AstronomicoGoogle Scholar
Smail, I., Dressler, A., Couch, W. J., Ellis, R. S., Oemler, A. (Jr), Butcher, H. & Sharples, R. M. 1997, ApJS, 110, 213 Google Scholar
Ueda, H. 1999, PASJ, 51, 435 CrossRefGoogle Scholar
van der Bergh, S. 1960, ApJ, 131, 215 Google Scholar
Weir, N., Fayyad, U. M. & Djorgovski, S. 1995, AJ, 109, 6 Google Scholar