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Automated stellar abundance analysis

Published online by Cambridge University Press:  06 January 2014

Alejandra Recio-Blanco*
Affiliation:
Laboratoire Lagrange (UMR7293), Université de Nice Sophia Antipolis, CNRS, Observatoire de la Côte d'Azur, BP 4229, F-06304 Nice cedex 4, France email: [email protected]
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Abstract

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The advent of Milky Way high-resolution spectroscopic surveys has brought our attention to the importance of precise chemical abundance measurements to disentangle the stellar population puzzle of the Galaxy. Moreover, automated stellar parameters are the bedrock of Galactic spectroscopic surveys science. They allow a rapid and homogeneous processing of extensive data sets, necessary for an efficient scientific return. In this review, I discuss the different parametrization techniques, focusing on the automated determination of individual element abundances. Each of them has its optimal application conditions that mainly depend on the computation time constraints, the spectral resolution, the wavelength domain, the data signal-to-noise ratio and parameter degeneracy problems. The main algorithms in the literature are also reviewed.

Type
Contributed Papers
Copyright
Copyright © International Astronomical Union 2014 

References

Allende-Prieto, C., Beers, T. C., Wilhelm, 2006, ApJ, 636, 804CrossRefGoogle Scholar
Bijaoui, A., Recio-Blanco, A., de Laverny, P.et al. 2012, Statistical Methodology, 9, 55CrossRefGoogle Scholar
Boeche, C., Siebert, A., Williams, M.et al. 2011, AJ, 142, 193Google Scholar
Bonifacio, P., Caffau, E. 2003, A&A, 399, 1183Google Scholar
Bovy, J., Rix, H.-W., Liu, C.et al. 2012, ApJ, 753, 148Google Scholar
de Laverny, P., Recio-Blanco, A., Worley, C. C.et al. 2012, A&A, 544, A126Google Scholar
Freeman, K., Bland-Hawthorn, J. 2002, ARA&A, 40, 487Google Scholar
Gazzano, J.-C., de Laverny, P., Deleuil, M.et al. 2010, A&A, 523, A91Google Scholar
Gazzano, J.-C., Kordopatis, G., Deleuil, M.et al. 2013, A&A, 550, A125Google Scholar
Gilmore, G., Randich, S., Asplund, M.et al. 2012, The Messenger, 147, 25Google Scholar
Koleva, M., Prugniel, P., Bouchard, A.et al. 2009, A&A, 501, 1269Google Scholar
Kordopatis, G., Recio-Blanco, A., de Laverny, P.et al. 2011, A&A, 535, 106Google Scholar
Kordopatis, G., Recio-Blanco, A., de Laverny, P.et al. 2011b, A&A, 535, A107Google Scholar
Lee, Y. S., Beers, T. C., An, D.et al. 2011, ApJ, 738, 187Google Scholar
Mucciarelli, A., Pancino, A., Lovisi, L.et al. 2013, ApJ, 766, 78Google Scholar
Nordström, B., Mayor, M., Andersen, J.et al. 2004, A&A, 418, 989Google Scholar
Nelder, J. A. & Mead, R. 1965, Computer Journal, 7, 308Google Scholar
Posbic, H., Katz, D., Caffau, E.et al. 2012, A&A, 544, 154Google Scholar
Re Fiorentin, P., Bailer-Jones, C. A. L., Lee, Y. S et al. 2007, A&A, 467, 1373Google Scholar
Recio-Blanco, A., Bijaoui, A., de Laverny, P. 2006, MNRAS, 370, 141Google Scholar
Rix, H.-W., Bovy, J. 2013, A&A Rev., 21, 61Google Scholar
Sousa, S. G., Santos, N. C., Israelian, G.et al. 2007, A&A, 469, 783Google Scholar
Steinmetz, M., Zwitter, T., Siebert, A.et al. 2006, AJ, 132, 1645Google Scholar
Valenti, J. A., Piskunov, N. 1996, A&A Supp. Ser., 118, 595Google Scholar
Van der Swaelmen, M., Hill, V., Primas, F.et al. 2013, ArXiv, 1306.4224Google Scholar
Worley, C., de Laverny, P., Recio-Blanco, A.et al. 2012, A&A, 542, A48Google Scholar
Yanni, B., Rockosi, C., Newberg, H. J.et al. 2009, AJ, 137, 4377CrossRefGoogle Scholar
Zwitter, T., Siebert, A., Munari, U.et al. 2008, AJ, 136, 421Google Scholar