2003The Astrophysical JournalOpen access

Selection of Metal‐poor Giant Stars Using the Sloan Digital Sky Survey Photometric System

A. Helmi, Željko Ivezić, Francisco Prada, L. Pentericci, Constance M. Rockosi, Donald P. Schneider, E. K. Grebel, Daniel Harbeck, Robert H. Lupton, James E. Gunn, G. R. Knapp, Michael A. Strauss, J. Brinkmann

Open full text 54 citations

Abstract

We present a method for the photometric selection of metal-poor halo giants from the imaging data of the Sloan Digital Sky Survey (SDSS). These stars are offset from the stellar locus in the g-r versus u-g color-color diagram. Based on a sample of 29 candidates for which spectra were taken, we derive a selection efficiency of the order of 50% for stars brighter than r ~ 17 mag. The candidates selected in 400 deg 2 of sky from the SDSS Early Data Release trace the known halo structures (tidal streams from the Sagittarius dwarf galaxy and the Draco dwarf spheroidal galaxy), indicating that such a color-selected sample can be used to study the halo structure even without spectroscopic information. This method, and supplemental techniques for selecting halo stars, such as RR Lyrae stars and other blue horizontal-branch stars, can produce an unprecedented three-dimensional map of the Galactic halo based on the SDSS imaging survey.

Open-access reader

About this research paper

What this paper is about

We present a method for the photometric selection of metal-poor halo giants from the imaging data of the Sloan Digital Sky Survey (SDSS). These stars are offset from the stellar locus in the g-r versus u-g color-color diagram. Based on a sample of 29 candidates for which spectra were taken, we derive a selection efficiency of the order of 50% for stars brighter than r ~ 17 mag. The candidates selected in 400 deg 2 of sky from the SDSS Early Data Release trace the known halo structures (tidal streams from the Sagittarius dwarf galaxy and the Draco dwarf spheroidal galaxy), indicating that such a color-selected sample can be used to study the halo structure even without spectroscopic information. This method, and supplemental techniques for selecting halo stars, such as RR Lyrae stars and other blue horizontal-branch stars, can produce an unprecedented three-dimensional map of the Galactic halo based on the SDSS imaging survey.

Why it matters

OpenAlex reports 54 citations for this work. Citation counts describe recorded attention and do not establish research quality.

Key contribution

A contribution statement is not available in the OpenAlex record.

Method / approach

Method details are not available in the OpenAlex metadata.

Main findings

Findings are not separately available in the OpenAlex metadata.

Limitations

Limitations are not available in the OpenAlex metadata.

Applications

Application details are not available in the OpenAlex metadata.

Available abstract

We present a method for the photometric selection of metal-poor halo giants from the imaging data of the Sloan Digital Sky Survey (SDSS). These stars are offset from the stellar locus in the g-r versus u-g color-color diagram. Based on a sample of 29 candidates for which spectra were taken, we derive a selection efficiency of the order of 50% for stars brighter than r ~ 17 mag. The candidates selected in 400 deg 2 of sky from the SDSS Early Data Release trace the known halo structures (tidal streams from the Sagittarius dwarf galaxy and the Draco dwarf spheroidal galaxy), indicating that such a color-selected sample can be used to study the halo structure even without spectroscopic information. This method, and supplemental techniques for selecting halo stars, such as RR Lyrae stars and other blue horizontal-branch stars, can produce an unprecedented three-dimensional map of the Galactic halo based on the SDSS imaging survey.

Key concepts: Physics, Halo, Astrophysics, Sky, RR Lyrae variable, Stars, Galactic halo, Galaxy

Related papers

Back to paper searchBrowse research topicsOriginal source
Selection of Metal‐poor Giant Stars Using the Sloan Digital Sky Survey Photometric System — Research Paper | ScholarLens