Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
143 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
46 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Metallicity Gradients in the Milky Way Disk as Observed by the SEGUE Survey (1110.5933v1)

Published 26 Oct 2011 in astro-ph.GA

Abstract: 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.

Summary

We haven't generated a summary for this paper yet.