Abstract
This paper presents an artificial neural networks approach to the estimation of effective stellar temperatures by means of optical spectroscopy.
The present work is included in a global project, whose final objective is the development of an automatic system for the determination of the physical and chemical parameters of stars. In previous works, we designed a hybrid system that integrated neural networks, fuzzy logic and expert systems to obtain the stellar spectral type and luminosity in the MK standard system. Considering those results, we now propose the design of several neural networks for the calculation of stellar temperatures.
The proposed networks have been trained with synthetic spectra that were previously contrasted with statistical clustering techniques. The final networks obtained a success rate of 88% for public catalogue spectra.
Our final objective is to calibrate the MK classification system, obtaining thus a new relation between the temperature and the spectral type.
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Rodriguez, A., Carricajo, I., Dafonte, C., Arcay, B., Manteiga, M. (2004). An Artificial Neural Networks Approach to the Estimation of Physical Stellar Parameters. In: Bramer, M., Devedzic, V. (eds) Artificial Intelligence Applications and Innovations. AIAI 2004. IFIP International Federation for Information Processing, vol 154. Springer, Boston, MA. https://doi.org/10.1007/1-4020-8151-0_5
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DOI: https://doi.org/10.1007/1-4020-8151-0_5
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