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Spectral fitting of SDSS passive galaxies with α-enhanced single stellar populations

Published online by Cambridge University Press:  17 August 2012

Jean Michel Gomes
Affiliation:
Centro de Astrofísica, Universidade do Porto, Rua das Estrelas, 4150-762 Porto, Portugal email: jean@astro.up.pt
Paula Coelho
Affiliation:
Núcleo de Astrofísica Teórica, Universidade Cruzeiro do Sul, R. Galvão Bueno 868, Liberdade, 01506-000, São Paulo, Brasil
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Abstract

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The power of population synthesis as a mean to estimate the star-formation and chemical histories of galaxies has been well established in the last decade. The major developments were due to a huge avalanche of methods, codes and high-quality galaxy data sets, such as the 2dF, 6dF and SDSS surveys. Semi-empirical spectral synthesis allows for the decomposition of a galaxy spectrum in terms of linear combinations of base elements, i.e. Single Stellar Populations (SSPs) of different ages and metallicities, which are computed from evolutionary synthesis codes (BPASS, GALEV, GALAXEV, MILES, PÉGASE, etc. . .), containing distinct ingredients like: stellar library, evolutionary tracks, metallicities and Initial Mass Function. In general, they have solar-scaled relative abundances, but this is about to change with the unfolding of new α-enhanced SSP models (Coelho et al. 2007). However, passive galaxies have some spectral features corresponding to “enhanced-ratios” ([E/Fe]), like O, Ne, Si, S, Mg, Na, C and N over Fe that are not well modeled using solar-scaled SSPs (Trager et al. 2000), leading to residuals between observed and modeled spectra, which also correlate with the velocity dispersion (σ*) and stellar mass (M*): Massive galaxies exhibit a larger [E/Fe] discrepancy than less massive ones. This result can be interpreted as a signature of distinct previous star-formation efficiencies in passive galaxies, leading to distinctive ratios of type Ia and II SNe.

We have applied the starlight spectral synthesis code (Cid Fernandes et al. 2005) to a sample of ~ 1000 passive galaxies from the SDSS DR7 with a S/N at the continuum ≥ 20 to investigate possible enhancements in the derived [E/Fe] ratios. Three sets of SSPs based on Coelho et al. (2007) theoretical models and Walcher et al. (2009) prescriptions were computed for [α/Fe]=0.0, [α/Fe]=0.2 and [α/Fe]=0.4. Our aim is to determine: (1) the quality of the fits, (2) the mean stellar age and metallicity distributions, and (3) the star-formation history of passive galaxies.

Using [α/Fe]=0.0 SSPs, we have identified the strongest residuals in the CN (4142.125-4177.125 Å), Na D (5876.875-5909.375 Å) and Mg (5069.125-5196.625 Å) bands. On the other hand, [α/Fe]=0.2 and [α/Fe]=0.4 SSP models tend to reproduce better the Mg band, as compared to solar-scaled SSPs ([α/Fe]=0.0). The residuals are decreased by 1.77 Å ([α/Fe]=0.2) and 2.92 Å ([α/Fe]=0.4). However, as expected, these α--enhanced models lead to worse fits for the CN and Na D bands. These residuals may even reach up to 2.08 Å (CN) and 4.20 Å (Na D), using [α/Fe]=0.2 SSPs and 2.28 Å (CN) and 7.94 Å (Na D), using [α/Fe]=0.4 SSPs.

In terms of mean stellar ages and metallicities, we obtain non-negligible biases in both quantities when we compare the solar-scaled SSPs with α-enhanced ones, which tend to have mean stellar ages by 0.12 dex ([α/Fe]=0.2) and 0.14 dex ([α/Fe]=0.4) higher and mean stellar metallicities by 0.1 dex ([α/Fe]=0.2) and 0.2 dex ([α/Fe]=0.4) lower.

Type
Contributed Papers
Copyright
Copyright © International Astronomical Union 2012

References

Cid Fernandes, R. et al. 2005, MNRAS 358 363CrossRefGoogle Scholar
Coelho, P. et al. 2007, MNRAS 382 498CrossRefGoogle Scholar
Lee, H.-C. et al. 2009, AJ 138 1442CrossRefGoogle Scholar
Trager, S. C. et al. 2000, AJ 120 165CrossRefGoogle Scholar
Walcher, C. J. et al. 2009, MNRAS 398 44CrossRefGoogle Scholar
Worthey, G., Faber, S. M., & Gonzalez, J. J. 1992, ApJ 398 69CrossRefGoogle Scholar
Worthey, G. 1994, ApJ 95 107Google Scholar