Automated Pipelines for Spectroscopic Analysis

TL;DR

This paper reviews the development and implementation of automated data processing pipelines for spectroscopic analysis in large-scale astronomical surveys, emphasizing their importance for handling extensive Gaia-related data.

Contribution

It provides an overview of recent and upcoming spectroscopic surveys and discusses strategies for automated analysis pipelines, highlighting their role in modern astronomy.

Findings

Automated pipelines are essential for processing large spectroscopic datasets.

Various strategies are adopted for efficient and accurate spectroscopic analysis.

Automated analysis enhances the scientific return of large astronomical surveys.

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.