The Data Reduction Pipeline for the SDSS-IV MaNGA IFU Galaxy Survey (1607.08619v3)

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 sky-subtracted, spectrophotometrically calibrated spectra and rectified 3-D data cubes that combine individual dithered observations. For the 1390 galaxy data cubes released in Summer 2016 as part of SDSS-IV Data Release 13 (DR13), we demonstrate that the MaNGA data have nearly Poisson-limited sky subtraction shortward of ~ 8500 Angstroms and reach a typical 10-sigma limiting continuum surface brightness mu = 23.5 AB/arcsec^2 in a five arcsec diameter aperture in the g band. The wavelength calibration of the MaNGA data is accurate to 5 km/s rms, with a median spatial resolution of 2.54 arcsec FWHM (1.8 kpc at the median redshift of 0.037) and a median spectral resolution of sigma = 72 km/s.

• The paper presents the design of the MaNGA DRP, processing IFU data through robust 2D and 3D stages to yield calibrated spectra for 10,000 galaxies.
• It employs techniques like cross-correlation, optimal extraction, and iterative sky subtraction to minimize fiber crosstalk and achieve near Poisson-limited accuracy.
• The pipeline’s modular structure enhances astrometric and flux calibration, enabling precise spatial analyses of galactic structures and evolution.

Overview of the MaNGA Data Reduction Pipeline

The paper authored by Law et al. elaborates on the sophisticated data reduction pipeline (DRP) developed for the Sloan Digital Sky Survey-IV's Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) integral-field unit (IFU) galaxy survey. The pipeline is a crucial component in processing the extensive dataset produced by MaNGA, which aims to collect spatially resolved spectroscopy of 10,000 galaxies, providing insights into galaxy formation, evolution, and structure at an unprecedented scale.

Key Features and Methodology

1. Pipeline Structure: The MaNGA Data Reduction Pipeline is structured into two primary stages:
   • 2D Stage: Handles preprocessing, optimal extraction, wavelength calibration, flat fielding, and produces sky-subtracted and flux-calibrated spectra for individual exposures.
   • 3D Stage: Combines multiple exposures to construct rectified 3-dimensional data cubes and row-stacked-spectra (RSS) for each target galaxy, incorporating astrometric calibration.
2. Preprocessing and Extraction:
   • Fiber Tracing and Extraction: The pipeline traces and extracts spectra from the detector using a cross-correlation technique to determine fiber locations more accurately. It employs optimal extraction techniques to mitigate cross-talk effects between fibers and account for fiber-to-fiber flatness variations.
   • Wavelength and LSF Calibration: The wavelength calibration is performed using arc lamp exposures, and the line spread function (LSF) is adjusted using observations of skylines to ensure consistent spectral resolution across exposures.
3. Sky Subtraction: A critical aspect in astronomy, the sky subtraction method employed by MaNGA utilizes a combination of all sky fibers to construct a super-sampled sky model. The process involves iterative modeling to minimize residuals from sky lines, achieving nearly Poisson-limited performance across most wavelengths.
4. Flux Calibration: Unlike traditional single-fiber systems, MaNGA uses the flux from standard star minibundles to separate the atmospheric/system response from aperture losses, achieving a calibration accuracy of about 5%.
5. Astrometric Calibration:
   • Basic Module: Accounts for differential atmospheric refraction, fiber positioning, and other systemic effects based on known metadata.
   • Extended Module: Refines the position and rotation of the IFUs by comparing against SDSS imaging, correcting for any inaccuracies due to cartridge re-plugging or other mechanical variations.
6. Data Cube Construction: The pipeline builds data cubes using a modified Shepard method, which reconstructs images from irregular data points to a regularly gridded format, maintaining flux conservation and accounting for covariance effects.

Implications and Applications

The pipeline's advanced design allows for a comprehensive analysis of the spectrophotometric data, uncovering the intricate details of galactic structures such as stellar populations, gas dynamics, and star formation patterns. This facilitates not only localized studies of individual galaxies but also broader statistical analyses across the sample. Potential future improvements aim to refine sky subtraction techniques, spectral resolution calibration, and astrometric precision further, enhancing the dataset's utility for upcoming astronomical investigations.

Future Directions

The research has implications for the next-generation astronomical surveys that will handle even larger datasets than MaNGA. The methodology developed here can serve as a template for future reduction pipelines, ensuring robust data handling in high-volume astronomical surveys. As new facilities like TMT/IRIS and JWST come online, the experience and techniques honed by MaNGA will be invaluable for processing and interpreting massive datasets, pushing the boundaries of astrophysical exploration further into the cosmos.