Metallicity Gradients in the Milky Way Disk as Observed by the SEGUE Survey

Details Metallicity Gradients in the Milky Way Disk as Observed by the SEGUE Survey Metallicity Gradients in the Milky Way Disk as Observed by the SEGUE Survey

2011-10-26

arxiv.org/abs/1110.5933v1

2012, ApJ, 746, 149

Astronomy and Astrophysics

Astrophysics

Galaxy Astrophysics

Submitted to ApJ; 22 pages, 14 figures in emulateapj format; Full resolution version available at http://www.ucolick.org/~jy

Scientific paper

The observed radial and vertical metallicity distribution of old stars in the Milky Way disk provides a powerful constraint on the chemical enrichment and dynamical history of the disk. We present the radial metallicity gradient, \Delta[Fe/H]/\Delta R, as a function of height above the plane, |Z|, using 7010 main sequence turnoff stars observed by the Sloan Extension for Galactic Understanding and Exploration (SEGUE) survey. The sample consists of mostly old thin and thick disk stars, with a minimal contribution from the stellar halo, in the region 6 < R < 16 kpc, 0.15 < |Z| < 1.5 kpc. The data reveal that the radial metallicity gradient becomes flat at heights |Z| > 1 kpc. The median metallicity at large |Z| is consistent with the metallicities seen in outer disk open clusters, which exhibit a flat radial gradient at [Fe/H] ~ -0.5. We note that the outer disk clusters are also located at large |Z|; because the flat gradient extends to small R for our sample, there is some ambiguity in whether the observed trends for clusters are due to a change in R or |Z|. We therefore stress the importance of considering both the radial and vertical directions when measuring spatial abundance trends in the disk. The flattening of the gradient at high |Z| also has implications on thick disk formation scenarios, which predict different metallicity patterns in the thick disk. A flat gradient, such as we observe, is predicted by a turbulent disk at high redshift, but may also be consistent with radial migration, as long as mixing is strong. We test our analysis methods using a mock catalog based on the model of Sch\"onrich & Binney, and we estimate our distance errors to be ~25%. We also show that we can properly correct for selection biases by assigning weights to our targets.

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