Skip to main content
Log in

Determination of Stellar Parameters with GAIA

  • Published:
Astrophysics and Space Science Aims and scope Submit manuscript

Abstract

The GAIA Galactic survey satellite will obtain photometry in 15 filters of over 109 stars in our Galaxy across a very wide range of stellar types. No other planned survey will provide so much photometric information on so many stars. I examine the problem of how to determine fundamental physical parameters (T eff, log g, [Fe/H] etc.) from these data. Given the size, multidimensionality and diversity of this dataset, this is a challenging task beyond any encountered so far in large-scale stellar parametrization. I describe the problems faced (initial object identification, interstellar extinction, multiplicity, missing data etc.) and present a framework in which they can be addressed. A probabilistic approach is advocated on the grounds that it can take advantage of additional information (e.g. priors and data uncertainties) in a consistent and useful manner, as well as give meaningful results in the presence of poor or degenerate data. Furthermore, I suggest an approach to parametrization which can use the other information GAIA will acquire, in particular the parallax, which has not previously been available for large-scale multidimensional parametrization. Several of the problems identified and ideas suggested will be relevant to other large surveys, such as SDSS, DIVA,FAME, VISTA and LSST, as well as stellar parametrization in a virtual observatory.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  • Bailer-Jones, C.A.L.: 2002, Automated stellar classification for large surveys: a review of methods and results, in: R. Gupta, H.P. Singh and C.A.L. Bailer-Jones (eds.), Automated Data Analysis in Astronomy, Narosa Publishing House, New Delhi, India, pp. 83.

    Google Scholar 

  • Bailer-Jones, C.A.L., Bhadeshia, H.K.D.H. and MacKay, D.J.C.: 1999, Gaussian process modelling of austenite formation in steel, Materials Science and Technology 15, 287.

    Google Scholar 

  • Bailer-Jones, C.A.L., Gupta, R. and Singh, H.P.: 2002, An introduction to artificial neural networks, in: R. Gupta, H.P. Singh and C.A.L. Bailer-Jones (eds.), Automated Data Analysis in Astronomy, Narosa Publishing House, New Delhi, India, pp. 51.

    Google Scholar 

  • Bailer-Jones, C.A.L., Irwin, M., Gilmore, G. and von Hippel, T.: 1997, MNRAS 292, 157.

    ADS  Google Scholar 

  • ESA: 2000, GAIA: Composition, formation and evolution of the Galaxy, Technical Report, ESA-SCI(2000)4.

  • Lejeune, T., Cuisinier, F. and Buser, R.: 1997, A&AS 125, 229.

    ADS  Google Scholar 

  • Perryman, M.A.C. et al.: 2001, A&A 369, 339.

    Article  ADS  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

About this article

Cite this article

Bailer-Jones, C. Determination of Stellar Parameters with GAIA. Astrophysics and Space Science 280, 21–29 (2002). https://doi.org/10.1023/A:1015527705755

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1023/A:1015527705755

Keywords

Navigation