Abstract
Astrophysics is entering in a new epoch characterized by a huge increment of the volume of data accessible. Consequently, scientists are modifying their traditional data analysis and simulation procedures to adapt them to this circumstance. As part of this increment, it should be underlined the number of high-quality galaxy spectra produced by the new instruments. Galaxy spectra are important in Astrophysics since they encode essential information, as the age and the metallicity, of the constituent stellar populations. These galaxy spectra can be modelled based on Simple Stellar Population. This procedure allows understanding the present state of the galaxy, and also inferring the evolution of the whole stellar system. However, this modelling requires to combine adequately more than one Simple Stellar Population to reproduce the galaxy spectral energy distribution. In order to deal with this modelling process, metaheuristics techniques are suitable, and for this reason, in this work diverse metaheuristics are implemented and tested to model the low resolution Spectral Energy Distribution of the M110 galaxy. The final purpose of this work is to pave the way to create and deliver a code based on metaheuristics techniques able to fit the Spectral Energy Distribution of a galaxy as a combination of predefined Simple Stellar Populations Spectra.
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References
Cárdenas-Montes, M., Mollá, M., Vega-Rodríguez, M.A., Rodríguez-Vázquez, J.J., Gómez-Iglesias, A.: Adjustment of observational data to specific functional forms using a particle swarm algorithm and differential evolution: Rotational curves of a spiral galaxy as case study. In: Sarro, L.M., Eyer, L., O’Mullane, W., De Ridder, J. (eds.) Astrostatistics and Data Mining. Springer Series in Astrostatistics, vol. 2, pp. 81–88. Springer, New York (2012)
Charbonneau, P.: Genetic algorithms in astronomy and astrophysics. The Astrophysical Journal Supplement Series 101, 309–334 (1995)
Conroy, C.: Modeling the panchromatic spectral energy distributions of galaxies. To appear in Annual Review of Astronomy and Astrophysics (ARAA) 51, 66 pages, 14 figures (2013), cite arxiv:1301.7095
Walcher, C.J., Groves, B., Budavari, T., Dale, D.: Fitting the integrated Spectral Energy Distributions of Galaxies. Astrophysics and Space Science 331(1), 1–51 (2011)
Mateus, A., Sodré, L., Fernandes, R.C., Stasińska, G., Schoenell, W., Gomes, J.M.: Semi-empirical analysis of sloan digital sky survey galaxies – ii. the bimodality of the galaxy population revisited. Monthly Notices of the Royal Astronomical Society 370(2), 721–737 (2006)
Fernandes, R.C., Perez, E., Benito, R.G., Delgado, R.M.G., de Amorim, A.L., Sanchez, S.F., Husemann, B., Barroso, J.F., Sanchez-Blazquez, P., Walcher, C.J., Mast, D.: Resolving galaxies in time and space: I: Applying starlight to califa data cubes. Technical Report arXiv:1304.5788 (April 2013)
Mollá, M., García-Vargas, M.L., Bressan, A.: PopStar I: evolutionary synthesis model description. Monthly Notices of the Royal Astronomical Society 398, 451–470 (2009)
Cid Fernandes, R., Mateus, A., Sodré, L., Stasińska, G., Gomes, J.M.: Semi-empirical analysis of Sloan Digital Sky Survey galaxies - I. Spectral synthesis method. Monthly Notices of the Royal Astronomical Society 358, 363–378 (2005)
Corchado, E., Wozniak, M., Abraham, A., de Carvalho, A.C.P.L.F., Snásel, V.: Recent trends in intelligent data analysis. Neurocomputing 126, 1–2 (2014)
Abraham, A.: Special issue: Hybrid approaches for approximate reasoning. Journal of Intelligent and Fuzzy Systems 23(2-3), 41–42 (2012)
García, S., Molina, D., Lozano, M., Herrera, F.: A study on the use of non-parametric tests for analyzing the evolutionary algorithms’ behaviour: a case study on the cec’2005 special session on real parameter optimization. J. Heuristics 15(6), 617–644 (2009)
García, S., Fernández, A., Luengo, J., Herrera, F.: A study of statistical techniques and performance measures for genetics-based machine learning: accuracy and interpretability. Soft Comput. 13(10), 959–977 (2009)
Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Programs. Springer-Verlag New York, Inc. (1994)
Mitchell, M.: An Introduction to Genetic Algorithms. MIT Press, Cambridge (1998)
Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: Proceedings of the IEEE International Conference on Neural Networks, vol. IV, pp. 1942–1948 (1995)
Eberhart, R.C., Shi, Y., Kennedy, J.: Swarm Intelligence (The Morgan Kaufmann Series in Artificial Intelligence), 1st edn. Morgan Kaufmann (2001)
Eberhart, R.C.: Computational Intelligence: Concepts to Implementations. Morgan Kaufmann Publishers Inc., San Francisco (2007)
Deep, K., Bansal, J.C.: Mean particle swarm optimisation for function optimisation. Int. J. Comput. Intell. Stud. 1(1), 72–92 (2009)
Price, K.V., Storn, R.M., Lampinen, J.A.: Differential Evolution A Practical Approach to Global Optimization. Natural Computing Series. Springer, Berlin (2005)
Storn, R., Price, K.: Differential evolution – a simple and efficient heuristic for global optimization over continuous spaces. J. of Global Optimization 11(4), 341–359 (1997)
Cárdenas-Montes, M., Vega-Rodríguez, M.A., Gómez-Iglesias, A., Morales-Ramos, E.: Empirical study of performance of particle swarm optimization algorithms using grid computing. In: González, J.R., Pelta, D.A., Cruz, C., Terrazas, G., Krasnogor, N. (eds.) NICSO 2010. SCI, vol. 284, pp. 345–357. Springer, Heidelberg (2010)
Mezura-Montes, E., Velázquez-Reyes, J., Coello, C.A.C.: A comparative study of differential evolution variants for global optimization. In: GECCO, pp. 485–492 (2006)
Alba, E., Tomassini, M.: Parallelism and evolutionary algorithms. IEEE Trans. Evolutionary Computation 6(5), 443–462 (2002)
Matsumoto, M., Nishimura, T.: Mersenne twister: A 623-dimensionally equidistributed uniform pseudorandom number generator. ACM Transactions on Modeling and Computer Simulation 8(1), 3–30 (1999)
Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Morgan Kaufmann (2000)
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Cárdenas-Montes, M., Vega-Rodríguez, M.A., Molla, M. (2014). Metaheuristics for Modelling Low-Resolution Galaxy Spectral Energy Distribution. In: Polycarpou, M., de Carvalho, A.C.P.L.F., Pan, JS., Woźniak, M., Quintian, H., Corchado, E. (eds) Hybrid Artificial Intelligence Systems. HAIS 2014. Lecture Notes in Computer Science(), vol 8480. Springer, Cham. https://doi.org/10.1007/978-3-319-07617-1_43
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DOI: https://doi.org/10.1007/978-3-319-07617-1_43
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