Application of neural networks to the study of stellar model solutions |
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Authors: | FJG Pinheiro T Simas J Fernandes R Ribeiro |
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Institution: | 1. Observatório Astronómico da Universidade de Coimbra, Santa Clara, 3040-004 Coimbra, Portugal;2. Lesia, Observatoire de Paris, 5, place Jules Janssen, 92195 Meudon, France;3. UNINOVA/CA3, Universidade Nova de Lisboa, Quinta da Torre, 2829-516 Monte de Caparica, Portugal;4. Departamento de Matemática da Universidade de Coimbra, 3001-454 Coimbra, Portugal |
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Abstract: | Artificial neural networks (ANN) have different applications in Astronomy, including data reduction and data mining. In this work we propose the use ANNs in the identification of stellar model solutions. We illustrate this method, by applying an ANN to the 0.8M⊙ star CG Cyg B. Our ANN was trained using 60,000 different 0.8M⊙ stellar models. With this approach we identify the models which reproduce CG Cyg B’s position in the HR diagram. We observe a correlation between the model’s initial metal and helium abundance which, in most cases, does not agree with a helium to metal enrichment ratio ΔY/ΔZ = 2. Moreover, we identify a correlation between the model’s initial helium/metal abundance and both its age and mixing-length parameter. Additionally, every model found has a mixing-length parameter below 1.3. This means that CG Cyg B’s mixing-length parameter is clearly smaller than the solar one. From this study we conclude that ANNs are well suited to deal with the degeneracy of model solutions of solar type stars. |
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