Papers
Topics
Authors
Recent
Assistant
AI Research Assistant
Well-researched responses based on relevant abstracts and paper content.
Custom Instructions Pro
Preferences or requirements that you'd like Emergent Mind to consider when generating responses.
Gemini 2.5 Flash
Gemini 2.5 Flash 79 tok/s
Gemini 2.5 Pro 41 tok/s Pro
GPT-5 Medium 25 tok/s Pro
GPT-5 High 23 tok/s Pro
GPT-4o 99 tok/s Pro
Kimi K2 199 tok/s Pro
GPT OSS 120B 444 tok/s Pro
Claude Sonnet 4 36 tok/s Pro
2000 character limit reached

SEGUE: A Spectroscopic Survey of 240,000 stars with g=14-20 (0902.1781v2)

Published 11 Feb 2009 in astro-ph.GA

Abstract: The SEGUE survey obtained 240,000 moderate resolution (R = 1800) spectra from 3900 - 9000 Angstroms of fainter Milky Way stars (14.0 < g < 20.3) of a wide variety of spectral types, both main sequence and evolved objects, with the goal of studying the kinematics and populations of our Galaxy and its halo. The spectra are clustered in 212 regions spaced over three-quarters of the sky. Radial velocity accuracies for stars are 4 km/s at g < 18, degrading to 15 km/s at g = 20. For stars with S/N > 10 per resolution element, stellar atmospheric parameters are estimated, including metallicity, surface gravity, and effective temperature. SEGUE obtained 3500 square degrees of additional ugriz imaging (primarily at low Galactic latitudes) providing precise multi-color photometry (g,r,i = 2%), (u,z = 3%) and astrometry (0.1 arcsec) for spectroscopic target selection. The stellar spectra, imaging data, and derived parameter catalogs for this survey are publicly available as part of SDSS Data Release 7 (DR7).

Citations (862)

Summary

  • The paper presents SEGUE, a survey that collected 240,000 moderate-resolution spectra of stars with magnitudes g=14-20 to explore the Milky Way’s structure.
  • It provides precise radial velocity measurements (σ ~ 4-15 km/s) that enable differentiation between Galactic components such as the thin disk, thick disk, and halo.
  • The comprehensive dataset, complemented by high-quality imaging, supports robust statistical analysis and advances models of Galactic formation and evolution.

SEGUE: A Spectroscopic Survey of 240,000 Stars with g=g=14-20

The paper "SEGUE: A Spectroscopic Survey of 240,000 Stars with g=g=14-20" details the Sloan Extension for Galactic Understanding and Exploration (SEGUE), a significant spectroscopic survey aimed at exploring the stellar kinematics and populations of the Milky Way and its halo. The survey encompasses approximately 240,000 moderate-resolution spectra of stars with magnitudes ranging from 14 to 20, covering a vast array of spectral types, from main-sequence to evolved stars, across a substantial portion of the sky. The paper aims to derive insights into the Milky Way’s structure and formation through comprehensive spectral data.

Overview of the SEGUE Survey

SEGUE represents an extension of the Sloan Digital Sky Survey (SDSS) and is focused primarily on the collection of stellar spectra to paper the Galaxy's structure and formation history. The survey collects data in 212 regions over three-quarters of the sky, providing radial velocity measurements, stellar atmospheric parameters including metallicity, surface gravity, and effective temperature. This information is crucial for understanding the distribution and dynamics of stars in different Galactic components, such as the thin and thick disks, and the halo.

Radial velocity measurements from SEGUE spectra achieve an accuracy of approximately σ(RV)4km s1\sigma(\rm RV) \sim 4 \>\rm km~s^{-1} for stars with g<18g < 18, which decreases to approximately σ(RV)15km s1\sigma(\rm RV) \sim 15\rm \>km~s^{-1} at g20g\sim 20. This accuracy level enables the separation of coherence structures in the Galactic halo from the field stars, assisting in mapping the kinematic landscape of the Milky Way.

Data Collection and Methodology

SEGUE obtained spectra using the SDSS telescope's efficient twin CCD camera spectrographs, yielding data with a resolving power R1800R \sim 1800. It focuses on various stellar targets including rare objects like low-metallicity stars, high proper motion subdwarfs, and distant halo stars, over a large range of distances. A significant component of SEGUE is the spectroscopic characterization of G dwarfs, which are used to paper the Milky Way’s thin-disk, thick-disk, and halo structures.

Additionally, SEGUE supplements these spectroscopic endeavors with 3500deg23500 \rm deg^2 of ugrizugriz imaging, achieving high photometric precision and astrometric accuracy. The SEGUE imaging data facilitates target selection across the diverse Galaxy components.

Implications and Future Directions

The datasets from SEGUE are publicly available, providing a robust resource for ongoing and future investigations into Galactic astronomy. The data aids in refining models of Galactic chemical evolution, understanding stellar populations, and dissecting the kinematic properties of the Galaxy. SEGUE’s large sample size enables statistically robust analyses, helping to constrain models of galaxy formation and evolution.

Looking forward, the SEGUE-2 project extends these efforts by further enhancing spectroscopic sampling of the Milky Way's diverse stellar populations, promising to deepen understanding of Galactic dynamics and stellar evolution.

The SEGUE survey has provided a wealth of data essential for improving the comprehension of the Milky Way’s structure. Its combination of spectroscopic fidelity, large survey footprint, and comprehensive targeting strategies make it a cornerstone in modern Galactic studies. The survey’s contributions to the development of Galactic models and its integration with the broader SDSS datasets underscore its enduring scientific value.

Lightbulb Streamline Icon: https://streamlinehq.com

Continue Learning

We haven't generated follow-up questions for this paper yet.

List To Do Tasks Checklist Streamline Icon: https://streamlinehq.com

Collections

Sign up for free to add this paper to one or more collections.

Don't miss out on important new AI/ML research

See which papers are being discussed right now on X, Reddit, and more:

“Emergent Mind helps me see which AI papers have caught fire online.”

Philip

Philip

Creator, AI Explained on YouTube