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Register a new userThe Data Reduction Pipeline for the SDSS-IV MaNGA IFU Galaxy Survey
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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.
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Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) is acquiring integral-field spectroscopy for the largest sample of galaxies to date. By 2020, the MaNGA Survey --- one of three core programs in the fourth-generation Sloan Digital Sky Survey (SDSS-IV) --- will have observed a statistically representative sample of 10$^4$ galaxies in the local Universe ($zlesssim0.15$). In addition to a robust data-reduction pipeline (DRP), MaNGA has developed a data-analysis pipeline (DAP) that provides higher-level data products. To accompany the first public release of its code base and data products, we provide an overview of the MaNGA DAP, including its software design, workflow, measurement procedures and algorithms, performance, and output data model. In conjunction with our companion paper Belfiore et al., we also assess the DAP output provided for 4718 observations of 4648 unique galaxies in the recent SDSS Data Release 15 (DR15). These analysis products focus on measurements that are close to the data and require minimal model-based assumptions. Namely, we provide stellar kinematics (velocity and velocity dispersion), emission-line properties (kinematics, fluxes, and equivalent widths), and spectral indices (e.g., D4000 and the Lick indices). We find that the DAP provides robust measurements and errors for the vast majority ($>$99%) of analyzed spectra. We summarize assessments of the precision and accuracy of our measurements as a function of signal-to-noise, and provide specific guidance to users regarding the limitations of the data. The MaNGA DAP software is publicly available and we encourage community involvement in its development.
MaNGA (Mapping Nearby Galaxies at Apache Point Observatory) is an integral-field spectroscopic survey of 10,000 nearby galaxies that is one of three core programs in the fourth-generation Sloan Digital Sky Survey (SDSS-IV). MaNGAs 17 pluggable optical fiber-bundle integral field units (IFUs) are deployed across a 3 deg field, they yield spectral coverage 3600-10,300 Ang at a typical resolution R ~ 2000, and sample the sky with 2 diameter fiber apertures with a total bundle fill factor of 56%. Observing over such a large field and range of wavelengths is particularly challenging for obtaining uniform and integral spatial coverage and resolution at all wavelengths and across each entire fiber array. Data quality is affected by the IFU construction technique, chromatic and field differential refraction, the adopted dithering strategy, and many other effects. We use numerical simulations to constrain the hardware design and observing strategy for the survey with the aim of ensuring consistent data quality that meets the survey science requirements while permitting maximum observational flexibility. We find that MaNGA science goals are best achieved with IFUs composed of a regular hexagonal grid of optical fibers with rms displacement of 5 microns or less from their nominal packing position, this goal is met by the MaNGA hardware, which achieves 3 microns rms fiber placement. We further show that MaNGA observations are best obtained in sets of three 15-minute exposures dithered along the vertices of a 1.44 arcsec equilateral triangle, these sets form the minimum observational unit, and are repeated as needed to achieve a combined signal-to-noise ratio of 5 per Angstrom per fiber in the r-band continuum at a surface brightness of 23 AB/arcsec^2. (abbrev.)
SDSS-IV MaNGA (Mapping Nearby Galaxies at Apache Point Observatory) is the largest integral-field spectroscopy survey to date, aiming to observe a statistically representative sample of 10,000 low-redshift galaxies. In this paper we study the reliability of the emission-line fluxes and kinematic properties derived by the MaNGA Data Analysis Pipeline (DAP). We describe the algorithmic choices made in the DAP with regards to measuring emission-line properties, and the effect of our adopted strategy of simultaneously fitting the continuum and line emission. The effect of random errors are quantified by studying various fit-quality metrics, idealized recovery simulations and repeat observations. This analysis demonstrates that the emission lines are well-fit in the vast majority of the MaNGA dataset and the derived fluxes and errors are statistically robust. The systematic uncertainty on emission-line properties introduced by the choice of continuum templates is also discussed. In particular, we test the effect of using different stellar libraries and simple stellar-population models on the derived emission-line fluxes and the effect of introducing different tying prescriptions for the emission-line kinematics. We show that these effects can generate large ($>$ 0.2 dex) discrepancies at low signal-to-noise and for lines with low equivalent width (EW); however, the combined effect is noticeable even for H$alpha$ EW $>$ 6~AA. We provide suggestions for optimal use of the data provided by SDSS data release 15 and propose refinements on the DAP for future MaNGA data releases.
We present the data reduction procedures being used by the GALAH survey, carried out with the HERMES fibre-fed, multi-object spectrograph on the 3.9~m Anglo-Australian Telescope. GALAH is a unique survey, targeting 1 million stars brighter than magnitude V=14 at a resolution of 28,000 with a goal to measure the abundances of 29 elements. Such a large number of high resolution spectra necessitates the development of a reduction pipeline optimized for speed, accuracy, and consistency. We outline the design and structure of the Iraf-based reduction pipeline that we developed, specifically for GALAH, to produce fully calibrated spectra aimed for subsequent stellar atmospheric parameter estimation. The pipeline takes advantage of existing Iraf routines and other readily available software so as to be simple to maintain, testable and reliable. A radial velocity and stellar atmospheric parameter estimator code is also presented, which is used for further data analysis and yields a useful verification of the reduction quality. We have used this estimator to quantify the data quality of GALAH for fibre cross-talk level ($lesssim0.5$%) and scattered light ($sim5$ counts in a typical 20 minutes exposure), resolution across the field, sky spectrum properties, wavelength solution reliability (better than $1$ $mathrm{km s^{-1}}$ accuracy) and radial velocity precision.
We present the data reduction pipeline for the Hi-GAL survey. Hi-GAL is a key project of the Herschel satellite which is mapping the inner part of the Galactic plane (|l| <= 70cdot and |b| <= 1cdot), using 2 PACS and 3 SPIRE frequency bands, from 70{mu}m to 500{mu}m. Our pipeline relies only partially on the Herschel Interactive Standard Environment (HIPE) and features several newly developed routines to perform data reduction, including accurate data culling, noise estimation and minimum variance map-making, the latter performed with the ROMAGAL algorithm, a deep modification of the ROMA code already tested on cosmological surveys. We discuss in depth the properties of the Hi-GAL Science Demonstration Phase (SDP) data.
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