Application of neural networks to the study of stellar model solutions

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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(circle dot) star CG Cyg B. Our ANN was trained using 60,000 different 0.8M(circle dot) 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 Delta Y/Delta Z = 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.
Original languageUnknown
Pages (from-to)629-633
JournalNew Astronomy
Volume17
Issue number7
DOIs
Publication statusPublished - 1 Jan 2012

Keywords

    Cite this

    @article{2a04b4f4273a40bda6d3d999d3cf55e6,
    title = "Application of neural networks to the study of stellar model solutions",
    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(circle dot) star CG Cyg B. Our ANN was trained using 60,000 different 0.8M(circle dot) 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 Delta Y/Delta Z = 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.",
    keywords = "Stars: evolution, Stars: fundamental parameters, Stars: interiors, Stars: individual (CG Cyg B)",
    author = "{DEE Group Author} and Ribeiro, {Maria Rita Sarmento de Almeida}",
    year = "2012",
    month = "1",
    day = "1",
    doi = "10.1016/j.newast.2012.03.007",
    language = "Unknown",
    volume = "17",
    pages = "629--633",
    journal = "New Astronomy",
    issn = "1384-1076",
    publisher = "Elsevier Science B.V., Amsterdam.",
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    Application of neural networks to the study of stellar model solutions. / DEE Group Author ; Ribeiro, Maria Rita Sarmento de Almeida.

    In: New Astronomy, Vol. 17, No. 7, 01.01.2012, p. 629-633.

    Research output: Contribution to journalArticle

    TY - JOUR

    T1 - Application of neural networks to the study of stellar model solutions

    AU - DEE Group Author

    AU - Ribeiro, Maria Rita Sarmento de Almeida

    PY - 2012/1/1

    Y1 - 2012/1/1

    N2 - 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(circle dot) star CG Cyg B. Our ANN was trained using 60,000 different 0.8M(circle dot) 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 Delta Y/Delta Z = 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.

    AB - 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(circle dot) star CG Cyg B. Our ANN was trained using 60,000 different 0.8M(circle dot) 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 Delta Y/Delta Z = 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.

    KW - Stars: evolution

    KW - Stars: fundamental parameters

    KW - Stars: interiors

    KW - Stars: individual (CG Cyg B)

    U2 - 10.1016/j.newast.2012.03.007

    DO - 10.1016/j.newast.2012.03.007

    M3 - Article

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    EP - 633

    JO - New Astronomy

    JF - New Astronomy

    SN - 1384-1076

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    ER -