Astronomy and Astrophysics – Astrophysics – Cosmology and Extragalactic Astrophysics
Scientific paper
2009-06-16
Astronomy and Astrophysics
Astrophysics
Cosmology and Extragalactic Astrophysics
10 pages, 3 figures and 4 tables, Accepted in MNRAS
Scientific paper
In this paper, we investigate the relevance of using the $^{12}$CO line emissions as indicators of star formation rates (SFR). For the first time, we present this study for a relatively large number of $^{12}$CO transitions (12) as well as over a large interval in redshift (from z$\sim$0 to z$\sim$6). For the nearby sources (D$\leq$10 Mpc), we have used homogeneous sample of $^{12}$CO data provided by Bayet et al. (2004, 2006), mixing observational and modelled line intensities. For higher-z sources (z $\geq$ 1), we have collected $^{12}$CO observations from various papers and have completed the data set of line intensities with model predictions which we also present in this paper. Finally, for increasing the statistics, we have included recent $^{12}$CO(1-0) and $^{12}$CO(3-2) observations of intermediate-z sources. Linear regressions have been calculated for identifying the tightest SFR-$^{12}$CO line luminosity relationships. We show that the \emph{total} $^{12}$CO, the $^{12}$CO(5-4), the $^{12}$CO(6-5) and the $^{12}$CO(7-6) luminosities are the best indicators of SFR (as measured by the far-infrared luminosity). Comparisons with theoretical approaches from Krumholz and Thompson (2007) and Narayanan et al. (2008) are also performed in this paper. Although in general agreement, the predictions made by these authors and the observational results we present here show small and interesting discrepancies. In particular, the slope of the linear regressions, for J$_{upper}\geq$ 4 $^{12}$CO lines are not similar between theoretical studies and observations. On one hand, a larger high-J $^{12}$CO data set of observations might help to better agree with models, increasing the statistics. On the other hand, theoretical studies extended to high redshift sources might also reduce such discrepancies.
Bayet Estelle
Contursi Alessandra
Gerin Maryvonne
Phillips Thomas G.
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