The Data Reduction Pipeline for the SDSS-IV MaNGA IFU Galaxy Survey
David R. Law, Brian Cherinka, Renbin Yan, Brett H. Andrews, Matthew A., Bershady, Dmitry Bizyaev, Guillermo A. Blanc, Michael R. Blanton, Adam S., Bolton, Joel R. Brownstein, Kevin Bundy, Yanmei Chen, Niv Drory, Richard, D'Souza, Hai Fu, Amy Jones, Guinevere Kauffmann

TL;DR
The paper describes the MaNGA Data Reduction Pipeline, which processes large-scale galaxy spectroscopic data to produce calibrated, high-quality 3D data cubes enabling detailed internal galaxy studies.
Contribution
It introduces the algorithms and metadata framework of the MaNGA Data Reduction Pipeline, improving data quality and calibration for the SDSS-IV galaxy survey.
Findings
Sky subtraction is nearly Poisson-limited below 8500 Angstroms.
Achieves typical 10-sigma surface brightness limit of 23.5 AB/arcsec^2.
Wavelength calibration accuracy within 5 km/s rms.
Abstract
Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) is an optical fiber-bundle integral-field unit (IFU) spectroscopic survey that is one of three core programs in the fourth-generation Sloan Digital Sky Survey (SDSS-IV). With a spectral coverage of 3622 - 10,354 Angstroms and an average footprint of ~ 500 arcsec^2 per IFU the scientific data products derived from MaNGA will permit exploration of the internal structure of a statistically large sample of 10,000 low redshift galaxies in unprecedented detail. Comprising 174 individually pluggable science and calibration IFUs with a near-constant data stream, MaNGA is expected to obtain ~ 100 million raw-frame spectra and ~ 10 million reduced galaxy spectra over the six-year lifetime of the survey. In this contribution, we describe the MaNGA Data Reduction Pipeline (DRP) algorithms and centralized metadata framework that produces…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
