Targeting Bright Metal-poor Stars in the Disk and Halo Systems of the Galaxy

Authors: Guilherme Limberg, Rafael M. Santucci, Silvia Rossi, Derek Shank, Vinicius M. Placco, Timothy C. Beers, Kevin C. Schlaufman, Andrew R. Casey, Hélio D. Perottoni, Young Sun Lee

arXiv: 2103.07621v1 - DOI (astro-ph.GA)
Accepted for ApJ. Long tables at the end of the manuscript
License: CC BY 4.0

Abstract: We present the results of spectroscopic follow-up for 1897 low-metallicity star candidates, selected from the Best & Brightest (B&B) Survey, carried out with the GMOS-N/S (Gemini North/South telescopes) and Goodman (SOAR Telescope) spectrographs. From these low-resolution ($R \sim 2000$) spectra, we estimate stellar atmospheric parameters, as well as carbon and magnesium (representative of $\alpha$ elements) abundance ratios. We confirm that $56\%$ of our program stars are metal-poor ([Fe/H] $< -1.0$), $30\%$ are very metal-poor (VMP; [Fe/H] $< -2.0$) and $2\%$ are extremely metal-poor (EMP; [Fe/H] $< -3.0$). There are 191 carbon-enhanced metal-poor (CEMP) stars, resulting in CEMP fractions of $19\%$ and $43\%$ for the VMP and EMP regimes, respectively. A total of 94 confirmed CEMP stars belong to Group I ($A({\rm C}) \gtrsim 7.25$) and 97 to Group II ($A({\rm C}) \lesssim 7.25$) in the Yoon-Beers $A$(C)$-$[Fe/H] diagram. Moreover, we combine these data with Gaia EDR3 astrometric information to delineate new target-selection criteria, which have been applied to the Goodman/SOAR candidates, to more than double the efficiency for identification of bona-fide VMP and EMP stars in comparison to random draws from the B&B catalog. We demonstrate that this target-selection approach can achieve success rates of $96\%$, $76\%$, $28\%$ and $4\%$ for [Fe/H] $\leq -1.5$, $\leq -2.0$, $\leq -2.5$ and $\leq -3.0$, respectively. Finally, we investigate the presence of dynamically interesting stars in our sample. We find that several VMP/EMP ([Fe/H] $\leq -2.5$) stars can be associated with either the disk system or halo substructures like Gaia-Sausage/Enceladus and Sequoia.

Submitted to arXiv on 13 Mar. 2021

Explore the paper tree

Click on the tree nodes to be redirected to a given paper and access their summaries and virtual assistant

Also access our AI generated Summaries, or ask questions about this paper to our AI assistant.

Look for similar papers (in beta version)

By clicking on the button above, our algorithm will scan all papers in our database to find the closest based on the contents of the full papers and not just on metadata. Please note that it only works for papers that we have generated summaries for and you can rerun it from time to time to get a more accurate result while our database grows.