Automated Pipelines for Spectroscopic Analysis

Abstract

The Gaia mission will have a profound impact on our understanding of the structure and dynamics of the Milky Way. Gaia is providing an exhaustive census of stellar parallaxes, proper motions, positions, colors and radial velocities, but also leaves some flaring holes in an otherwise complete data set. The radial velocities measured with the on-board high-resolution spectrograph will only reach some 10% of the full sample of stars with astrometry and photometry from the mission, and detailed chemical information will be obtained for less than 1%. Teams all over the world are organizing large-scale projects to provide complementary radial velocities and chemistry, since this can now be done very efficiently from the ground thanks to large and mid-size telescopes with a wide field-of-view and multi-object spectrographs. As a result, automated data processing is taking an ever increasing relevance, and the concept is applying to many more areas, from targeting to analysis. In this paper, I provide a quick overview of recent, ongoing, and upcoming spectroscopic surveys, and the strategies adopted in their automated analysis pipelines.

7 pages, 1 figure. To appear in Astronomische Nachrichten, special issue "Reconstruction the Milky Way's History: Spectroscopic surveys, Asteroseismology and Chemo-dynamical models", Guest Editors C. Chiappini, J. Montalban, and M. Steffen, AN 2016 (in press)

References in corpus (18)

• The Eleventh and Twelfth Data Releases of the Sloan Digital Sky Survey: Final Data from SDSS-III
• The 2.5 m Telescope of the Sloan Digital Sky Survey
• The Radial Velocity Experiment (RAVE): first data release
• The First Data Release (DR1) of the LAMOST general survey
• ASPCAP: The Apogee Stellar Parameter and Chemical Abundances Pipeline
• The SEGUE Stellar Parameter Pipeline. I. Description and Initial Validation Tests
• Abundances, Stellar Parameters, and Spectra From the SDSS-III/APOGEE Survey
• The Cannon: A data-driven approach to stellar label determination
• Gaia FGK Benchmark Stars: Effective temperatures and surface gravities
• The Gaia-ESO Survey: the Galactic Thick to Thin Disc transition
• Gaia-ESO Survey: The analysis of high-resolution UVES spectra of FGK-type stars
• Gaia FGK benchmark stars: abundances of alpha and iron-peak elements
• Automated derivation of stellar atmospheric parameters and chemical abundances: the MATISSE algorithm
• The extended Baryon Oscillation Spectroscopic Survey (eBOSS): a cosmological forecast
• Project overview and update on WEAVE: the next generation wide-field spectroscopy facility for the William Herschel Telescope
• Deep SDSS optical spectroscopy of distant halo stars I. Atmospheric parameters and stellar metallicity distribution
• Deep SDSS optical spectroscopy of distant halo stars II. Iron, calcium, and magnesium abundances
• Solar and Stellar Photospheric Abundances

Cited by in corpus (5)

• Gaia Data Release 3: Analysis of RVS spectra using the General Stellar Parametriser from spectroscopy
• Accuracy and precision of industrial stellar abundances
• Gaia FGK Benchmark stars: Opening the black box of stellar element abundance determination
• Atmospheric Stellar Parameters from Cross-Correlation Functions
• SpectroTranslator: a deep-neural network algorithm to homogenize spectroscopic parameters