Abstract
In this paper we present an efficient solution, based on active and instance-based machine learning, to the problem of analyzing galactic spectra, an important problem in modern cosmology. The input to the algorithm is the energy flux received from the galaxy; its expected output is the set of stellar populations and dust abundances that make up the galaxy. Our experiments show very accurate results using both noiseless and noisy spectra, and also that a further improvement in accuracy can be obtained when we incorporate prior knowledge obtained from human experts.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Szalay, A., Gray, J.: The World-Wide Telescope. Science 293, 2037–2040 (2001)
Owens, E.A., Griffiths, R.E., Ratnatunga, K.U.: Using oblique decision trees for the morphological classification of galaxies. Monthly Notices of the Royal Astronomical Society 281, 153–157 (1996)
Odewahn, S.C., Cohen, S.H., Windhorst, R.A., Philip, N.S.: Automated galaxy morphology: A Fourier approach. The Astrophysical Journal 568, 539–557 (2002)
Goderya, S.N., 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)
Bazell, D., Aha, D.A.: Ensembles of classifiers for morphological galaxy classi-fication. The Astrophysical Journal 548, 219–223 (2001)
de la Calleja, J., Fuentes, O.: Machine learning and image analysis for morphological galaxy classification. Monthly Notices of the Royal Astronomical Society 349, 87–93 (2004)
Bellman, R.E.: Dynamic Programming. Princeton University Press, Princeton (1957)
Dietterich, T.: Ensemble methods in machine learning. In: Haindl, M., Kittler, J., Roli, F. (eds.) MCS 2007. LNCS, vol. 4472, pp. 1–15. Springer, Heidelberg (2007)
Atkeson, C.G., Moore, A.W., Schaal, S.: Locally weighted learning. Artificial Intelligence Review 11, 11–73 (1997)
Faber, S.M.: Variations in Spectral-Energy Distributions and Absorption-Line Strengths among Elliptical Galaxies. ApJ 179, 731–754 (1973)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Fuentes, O., Solorio, T., Terlevich, R., Terlevich, E. (2004). Analysis of Galactic Spectra Using Active Instance-Based Learning and Domain Knowledge. In: Lemaître, C., Reyes, C.A., González, J.A. (eds) Advances in Artificial Intelligence – IBERAMIA 2004. IBERAMIA 2004. Lecture Notes in Computer Science(), vol 3315. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30498-2_22
Download citation
DOI: https://doi.org/10.1007/978-3-540-30498-2_22
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23806-5
Online ISBN: 978-3-540-30498-2
eBook Packages: Springer Book Archive