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SDSS-IV MaNGA IFS Galaxy Survey --- Survey Design, Execution, and Initial Data Quality

Renbin Yan, Kevin Bundy, David R. Law, Matthew A. Bershady, Brett Andrews, Brian Cherinka, Aleksandar M. Diamond-Stanic, Niv Drory, Nicholas MacDonald, José R. Sánchez-Gallego, Daniel Thomas, David A. Wake, Anne-Marie Weijmans, Kyle B. Westfall, Kai Zhang, Alfonso Aragón-Salamanca, Francesco Belfiore, Dmitry Bizyaev, Guillermo A. Blanc, Michael R. Blanton, Joel Brownstein, Michele Cappellari, Richard D'Souza, Eric Emsellem, Hai Fu, Patrick Gaulme, Mark T. Graham, Daniel Goddard, James E. Gunn, Paul Harding, Amy Jones, Karen Kinemuchi, Cheng Li, Hongyu Li, Roberto Maiolino, Shude Mao, Claudia Maraston, Karen Masters, Michael R. Merrifield, Daniel Oravetz, Kaike Pan, John K. Parejko, Sebastian F. Sanchez, David Schlegel, Audrey Simmons, Karun Thanjavur, Jeremy Tinker, Christy Tremonti, Remco van den Bosch, Zheng Zheng

arXiv:1607.08613v2astro-ph.GA

TL;DR

MaNGA addresses the need for a large, spatially resolved galaxy survey by designing and executing a 10K-galaxy IFS program around quantitative science requirements. The survey combines effective-radius-based sample coverage with wide wavelength observations and evaluates the resulting data quality, meeting most stated requirements while leaving some kinematic goals limited by modeling systematics.

  • Problem

    Large-scale IFS surveys require coordinated choices about science-driven depth, sample coverage, execution, and data quality for statistical studies of nearby galaxies.

  • Method

    The paper traces science requirements through hardware, sample selection, observing strategy, survey execution, and data-quality assessment.

  • Results

    69% of the Primary+ sample extends beyond 1.5Re and 66% of the Secondary sample beyond 2.5Re; about 85% of 402 galaxies have total enclosed-mass errors below 10%.

  • Takeaways & Limitations

    The first-year data meet most requirements for star formation, metallicity, and stellar-population measurements, while kinematic goals require further simulation-supported analysis.

  • Takeaways & Limitations

    Systematic errors from simplified JAM modeling dominate several kinematic measurements, including enclosed mass and dark matter fraction.

Abstract

from arXiv · show

The MaNGA Survey (Mapping Nearby Galaxies at Apache Point Observatory) is one of three core programs in the Sloan Digital Sky Survey IV. It is obtaining integral field spectroscopy (IFS) for 10K nearby galaxies at a spectral resolution of R~2000 from 3,622-10,354A. The design of the survey is driven by a set of science requirements on the precision of estimates of the following properties: star formation rate surface density, gas metallicity, stellar population age, metallicity, and abundance ratio, and their gradients; stellar and gas kinematics; and enclosed gravitational mass as a function of radius. We describe how these science requirements set the depth of the observations and dictate sample selection. The majority of targeted galaxies are selected to ensure uniform spatial coverage in units of effective radius (Re) while maximizing spatial resolution. About 2/3 of the sample is covered out to 1.5Re (Primary sample), and 1/3 of the sample is covered to 2.5Re (Secondary sample). We describe the survey execution with details that would be useful in the design of similar future surveys. We also present statistics on the achieved data quality, specifically, the point spread function, sampling uniformity, spectral resolution, sky subtraction, and flux calibration. For our Primary sample, the median r-band signal-to-noise ratio is ~73 per 1.4A pixel for spectra stacked between 1-1.5 Re. Measurements of various galaxy properties from the first year data show that we are meeting or exceeding the defined requirements for the majority of our science goals.

2.1. Requirements on the depth of the observation

MaNGA translates science goals into quantitative depth requirements for emission lines, stellar populations, kinematics, and dynamical masses. These requirements determine the signal-to-noise, spatial sampling, and exposure depth needed for the survey.

  • Gas metallicity gradients: 0.04 dex per Re precision is required for gas-phase metallicity gradients, measured across at least four elliptical annuli.Each annulus must achieve better than 0.05 dex accuracy, requiring continuum S/N greater than 10 per pixel near Hβ in stacked spectra.
  • Stellar populations: Mean stellar age in star-forming and newly quenched systems must be measured to better than 0.1 dex with median r-band S/N above 10 per pixel.The estimate uses the 4000Å break and Balmer indices.
  • Stellar populations: Quiescent-galaxy stellar age, metallicity, and abundance gradients require at least four annuli with precision better than 0.12 dex per annulus.The target gradient precision is better than 0.1 dex per decade in Re.
  • Kinematics: 17 S/N per pixel and approximately 20 independent spatial bins are required to measure a slow rotator’s λR to precision 0.05.The example assumes λR ∼0.1 and σ = 100km/s; this requirement is reachable for most of the 1.5Re sample.
  • Enclosed mass: 10% enclosed-mass precision requires Vrms measured to 11.3% with four bins at 1.5Re.The fractional mass error is twice the fractional Vrms error, with additional modeling systematics considered.
  • Dark matter fraction: Better than 10% dark-matter-fraction recovery within 1Re is possible for galaxies with second velocity moments above 60 km/s and bundle sizes larger than 19 fibers.This result combines simulations with MaNGA prototype data and uses anisotropic Jeans mass modelling.
  • Survey depth: A continuum S/N greater than 33 per r-band pixel, stacked across fibers from 1-1.5 Re and across exposures, sets the final exposure time.This is driven chiefly by stellar-population gradients and enclosed gravitating mass.

2.2. Requirements on the Sample

MaNGA’s sample requirements balance spatial coverage, resolution, statistical representativeness, environmental information, and ancillary-data overlap. The design targets about 10K galaxies while managing conflicts imposed by finite observing time and instrument capacity.

  • Representativeness: Selection should be simple and reproducible, representative across stellar masses from 10^9 to 10^12 M⊙, and weighted toward rare color-mass combinations.These criteria support statistical studies of both common and rare galaxy populations.
  • Spatial coverage and resolution: The sample seeks uniform coverage in Re while maximizing spatial resolution, with each galaxy resolved into at least three radial bins.The desired major-axis coverage is 1.5Re, which encloses 75% of exponential-disk light and 60% of de Vaucouleurs-profile light.
  • Spatial coverage and resolution: One-third of the sample must reach 2.5Re to probe outer metallicity, stellar populations, rotation curves, and dark-matter-dominated regions.Greater coverage sacrifices signal-to-noise and spatial resolution.
  • Sample size: The total sample should contain about 10K galaxies, split approximately 2-to-1 between Primary 1.5Re and Secondary 2.5Re samples.This size supports analyses across three independent variables with roughly 50 objects per bin under the stated assumptions.
  • Operational constraints: The combined subsamples require sufficient sky density for efficient IFU allocation, unbiased environmental coverage, and substantial overlap with H I and other ancillary observations.The design also considers deep optical and near-IR imaging and southern-hemisphere follow-up access.
  • Operational constraints: Fixed observing time and BOSS detector real estate force trade-offs between deeper observations and broader sample coverage.The survey jointly optimizes fiber-bundle sizes and sample selection to reach about 10K galaxies in six years at APO.

2.3. Requirements on Data Quality

MaNGA’s data-quality requirements connect measurement precision to flux calibration, reconstructed-image quality, and observing hardware. The survey’s sample design and fiber bundles are configured to support spatially resolved measurements across 1.5Re or 2.5Re.

  • Flux calibration must not dominate uncertainty in star-formation-rate and gas-metallicity measurements based on emission-line strengths and ratios.
  • The reconstructed PSF and effective exposure time are constrained across each bundle, including no more than 20% FWHM variation and 15% exposure-time variation.
  • 17 science IFU bundles per plate use five sizes, with 2″ fibers, 2.5″ spacing, and a 56% fill factor.
  • Primary and Color-Enhanced galaxies target 1.5Re, while Secondary galaxies target 2.5Re; one-third of bundle resources is allocated to the Secondary sample.
  • The combined MaNGA sample is not representative without inverse-selection-probability weighting, and Color-Enhanced selection is affected by uncorrected internal extinction trends.
  • 69% of the Primary+ sample extend beyond 1.5Re, while 66% of the Secondary sample extend beyond 2.5Re.

5.1. Observing Strategy

MaNGA uses dithered observations and constrained visibility windows to achieve uniform spatial sampling despite field-wide and chromatic atmospheric refraction. The observing strategy limits dither timing and exposure conditions to control these effects.

  • A three-point equilateral-triangle dither with 1.44″ sides is required because single-pointing exposures undersample the fiber-convolved PSF.
  • Large field-of-view and wide wavelength coverage create spatial and chromatic atmospheric-refraction complications for MaNGA observations.
  • The Uniformity Statistic Ω measures the maximum offset across plate locations and wavelengths during a three-point dither, and must remain below 0.4″.
  • Each dither set must be taken within one hour in hour angle, with seeing better than 2.5″ per exposure and set-averaged seeing better than 2.0″.
  • An atmospheric dispersion corrector would alleviate the observing problems caused by atmospheric refraction.

5.2. Plate Completeness Thresholds

Plate completeness thresholds translate the survey’s continuum signal-to-noise requirements into accumulated g- and i-band thresholds. The chosen exposure depth balances measurement precision against the 10K-galaxy sample size.

  • A typical 2.25-hour exposure per plate is needed for more than 75% of targets to reach S/N 33 per r-band pixel between 1 and 1.5Re.
  • Doubling S/N would reduce the sample size by a factor of 4, while shortening exposures to 45 minutes would triple the sample but reduce S/N by 40%.
  • The authors judge the current S/N threshold and 10K-galaxy sample size to be the appropriate balance for the available observing time.
  • Thresholds are set for a fixed reference fiber magnitude because galaxy sizes and surface brightnesses vary, making equal depth for every galaxy impractical.
  • The final criteria require accumulated (S/N)^2 above 20 pixel−1 fiber−1 in g at g=22 and above 36 pixel−1 fiber−1 in i at i=21.
  • Initial use of BOSS S/N relations set thresholds too high, causing overlong exposures until the issue was corrected in April 2015.

5.3. Field Planning

MaNGA plans fields through simulations that combine visibility, weather, observing efficiency, signal-to-noise predictions, and auxiliary-survey overlap. Daily scheduling also prioritizes completing and patching partially observed dither sets.

  • Tile selection prioritizes observable sky regions, overlap with imaging or other-waveband surveys, and scientific return from auxiliary data.
  • Field overlap is chosen to support gas measurements, stellar-mass constraints, structural studies, and independent halo-mass estimates.
  • The planning simulation selects tiles night by night and can model the full footprint or choose a plate for a specific drilling run.
  • Full-footprint simulations assume 42.75% clear nights and 75% observing efficiency while predicting exposure S/N from airmass and Galactic extinction.
  • Daily scheduling gives priority to incomplete plugged plates and boosts plates whose partial dither sets can be patched under similar observing conditions.

5.4. Plate Design

MaNGA’s plate design assigns targets and bundle sizes to tiles while prioritizing auxiliary-data overlap, reliable target photometry, practical fiber placement, and nearby sky sampling.

  • Target selection allocates galaxies to specific tiles and assigns each target a bundle of a specified size.
  • Visual inspection corrects inaccurate galaxy centers and rejects targets with unreliable photometry before plates are drilled.
  • Targets sharing a bundle size are assigned to physical bundles by proximity to anchoring points, reducing the chance of stretched fiber cables.
  • Selection of standard stars: Twelve late-F standard stars per plate support flux calibration, with selection emphasizing bright, common, smooth-spectrum stars.
  • Selection of standard stars: Standard-star selection uses a g-band PSFMAG5 range of 14.5–17.2, expanding to 17.7 or 18.2 when necessary.
  • Sky fibers are placed near each bundle, with 2, 2, 4, 6, and 8 sky fibers for 19-, 37-, 61-, 91-, and 127-fiber bundles.

5.5. Observing preparation and procedure

Observing preparation combines deterministic plugging, plate and cartridge verification, repeated spectrograph focusing, and calibration exposures to control instrumental performance.

  • MaNGA IFUs use deterministic plugging, with designated holes and ferrule IDs for science and standard-star bundles.
  • Laser illumination and video mapping verify bundle connections and map sky fibers after plugging.
  • Each plate’s curvature is measured at 40 locations, and plates outside the specified tolerance are not accepted.
  • Focus Optimization for MaNGA: Spectrographs are focused for each cartridge using arc-lamp exposures and adjustments to the collimator and camera focus rings.
  • Focus Optimization for MaNGA: Temperature changes require afternoon checkout to intentionally offset focus so the blue and red cameras remain within tolerance overnight.
  • Focus Optimization for MaNGA: The blue cameras have more curved focal planes than the red cameras, producing larger fiber-to-fiber resolution variations.

5.6. Dithered observations with guider offset

MaNGA uses guider-based offsets for dithering and quick-look signal-to-noise checks to maintain pointing and assess exposure completeness.

  • Dithered observations with guider offset: Sixteen coherent imaging fiber bundles monitor guide stars every 30 seconds, yielding 0.12″ RMS telescope pointing stability.
  • Dithered observations with guider offset: Dithering shifts the expected guide-star positions by 0.83″, but mechanical uncertainties in bundle orientation slightly worsen pointing stability.
  • Quicklook Verification of Data Quality: The quick-reduction pipeline fits fixed-slope relationships between (S/N)^2 and fiber magnitude in g=20.5–22.5 and i=19.5–21.5 ranges.
  • Quicklook Verification of Data Quality: Plate completeness requires accumulated blue (S/N)^2 above 20 pixel−1 fiber−1 and red (S/N)^2 above 36 pixel−1 fiber−1.

5.8. Data Reduction

The reduction pipeline produces calibrated data cubes and empirical signal-to-noise relations, while exposure performance depends on airmass, extinction, transparency, and sky brightness.

  • Data Reduction: The DRP converts raw frames into sky-subtracted, flux-calibrated spectra and coadded data cubes on common wavelength grids.
  • S/N as a function of fiber magnitude: The empirical S/N–flux relation fits g, r, and i-band data tightly because synthetic fiber fluxes avoid imaging astrometry, PSF, and photometric-calibration uncertainties.
  • S/N as a function of fiber magnitude: Equation 7 coefficients predict final-cube S/N from surface brightness after accounting for exposure number and fiber filling factor.
  • S/N as a function of fiber magnitude: An apparent r-band surface brightness of 22.5 mag arcsec−2 typically yields final-cube S/N of about 5.1 in a 2″ aperture.
  • Exposure S/N Dependence on Airmass and Galactic Extinction: MaNGA S/N depends weakly on airmass because differential atmospheric refraction shifts flux between bundle fibers rather than reducing total bundle S/N.
  • Exposure S/N Dependence on Airmass and Galactic Extinction: Transparency and sky-brightness variations, including moon and clouds, produce substantial scatter in the airmass relation.

6.3. S/N prediction and expected survey speed

MaNGA predicts stacked outer-annulus S/N from surface photometry and validates those predictions against stacked spectra. First-year performance exceeds the Primary+ requirement for most galaxies, while the survey remains slightly behind schedule but projects a final sample of about 10K galaxies.

  • S/N prediction: Photometry-based stacked S/N predictions agree closely with measurements from stacked RSS spectra.The comparison uses flux-weighted fibers within the defined outer elliptical annulus and median S/N in griz wavelength windows.
  • S/N performance: 89% of the first-year Primary+ sample exceeds r-band stacked S/N of 33 per pixel in the outer annulus.The requirement is S/N greater than 33 for more than 75% of the sample.
  • S/N performance: 78% of the first-year Secondary sample exceeds r-band stacked S/N of 33 per pixel between 1.7 and 2.5Re.The annulus is limited by the galaxy or bundle coverage, whichever is smaller.
  • S/N projection: 80% of the Primary+ sample is projected to exceed stacked r-band S/N of 33 per pixel in the outer tertile after six years.This projected fraction meets the stated science requirement.
  • Survey speed: Approximately 575–600 plates are expected to yield a final sample of approximately 10K galaxies by Summer 2020.The survey was slightly behind schedule after bad weather and first-season over-exposure, but later progress matched expectations.

7.1. Example Spectra from Data Cube

MaNGA data cubes provide usable spectra from galaxy centers to outer regions, while the survey characterizes PSF formation and recommends product-specific PSF handling. The reconstructed data-cube PSF has a median FWHM of 2.54′′.

  • Example spectra: Central and outer spaxels show r-band S/N of 200–250 and 20–30 per pixel, respectively, without smoothing.The outer example is 13′′ from the galaxy center.
  • PSF measurement: Guider images measure time-integrated seeing during each 15-minute science exposure, with median seeing of 1.50′′ and a 1–2.5′′ range.The guider frames are bias-subtracted, flat-fielded, and stacked.
  • PSF modeling: A double-Gaussian PSF model adequately describes the central profile but misses an extended tail containing about 3% of the flux.A Moffat model provides only a moderate improvement and also fails to fit the tail.
  • Reconstructed PSF: The reconstructed data-cube PSF has median FWHM 2.54′′, with a tail extending to 2.8′′.Its FWHM correlates well with the median seeing of the input exposures.
  • Product-specific PSF use: For RSS files, the per-exposure fiber-convolved PSF is derived from guider measurements, a 2′′ fiber aperture, and a 10% width reduction.The PSF varies across the focal plane and with wavelength.

7.3. Sampling Uniformity from the Actual Dithers

MaNGA evaluates spatial sampling, spectral resolution, and sky subtraction across first-year observations. Sampling is highly uniform at blue wavelengths, while spectral-resolution corrections remain important and sky residuals are close to Poisson expectations.

  • Sampling uniformity: At 3622Å, 98.6% of galaxies have dithering Uniformity Statistic Ω below 0.4′′.The largest offsets occur at the bluest wavelengths because of chromatic differential refraction.
  • Spectral resolution: Resampling and pixel-integration assumptions together cause roughly a 10% LSF increase, equivalent to a 10% decrease in spectral resolution.This correction was not included in the DR13 data release.
  • Spectral-resolution validation: Broadening the instrumental dispersion by 10% produces an intrinsic Hα line-width median near 26 km/s, with residual underestimation of about 3%.The resulting velocity-dispersion bias is at most 10% around 40 km/s.
  • Sky subtraction: Sky subtraction is very close to Poisson expectations in continuum regions and slightly above Poisson near strong sky-emission lines.Science-fiber Poisson ratios require wavelength-dependent scaling of 2%, 7%, 12%, and 15%.

7.6. Quality of Flux Calibration

Independent tests show MaNGA’s flux calibration is generally accurate, with relative color calibration near ±3% and absolute calibration better than 5% over most of the wavelength range.

  • ±3% relative calibration is achieved for g−r, r−i, and i−z colors.
  • Better than 5% absolute calibration is achieved across 89% of the wavelength range.
  • 1.7% relative calibration RMS is achieved between Hα and Hβ, compared with 4.7% between [N II] λλ6548,6583 and [O II] λ3727.
  • Repeated observations of the same galaxies provide an independent assessment using different plates and standard stars.
  • 5.4% Hα and 8.4% Hβ fractional uncertainties at threshold fluxes yield a 23.3% or 0.1 dex final SFR uncertainty.
  • The SFR surface-density science requirement is met, although uncertainty rises to 0.2 dex at 0.003 M⊙yr−1kpc−2 and 0.3 dex at 0.001 M⊙yr−1kpc−2.
  • Some high-flux repeated observations differ systematically by as much as 10%, and the cause remains under investigation.

8.2. Gas Metallicity Gradient

MaNGA measures gas-metallicity gradients after continuum subtraction, Voronoi binning, emission-line measurement, and star-forming-region classification, while calibration systematics remain a separate limitation.

  • Gas-metallicity gradients are measured with the R23 indicator after Voronoi binning based on Hα signal-to-noise.
  • The science requirement is a gradient precision better than 0.04 dex per Re.
  • Using O3N2 gives similar error distributions but different gradients.
  • Metallicity calibrations introduce systematic errors that deeper data cannot remove.
  • Estimating scatter among multiple Voronoi bins within an annulus is more reliable than treating the annulus as one stacked measurement.

8.4. Specific Angular Momentum

MaNGA’s first-year measurements of specific angular momentum meet the stated precision requirement near the fast–slow rotator divide, though beam-smearing systematics remain unassessed.

  • The requirement is to measure λRe within 1Re to better than 0.05 around λR = 0.1.
  • Uncertainty in λRe is estimated by repeatedly perturbing velocity and velocity-dispersion maps according to their measurement errors.
  • Nearly all galaxies near λRe = 0.1 have uncertainty below 0.05, meeting the requirement.
  • Beam-smearing systematics were not included and require future simulation-based assessment.

8.5. Enclosed Gravitating Mass and Dark Matter Fraction

MaNGA’s formal mass and dark-matter-fraction errors generally meet requirements, but dynamical-model systematics can dominate and leave broader kinematic goals uncertain.

  • Rotation-dominated galaxies: Enclosed mass in rotation-dominated disks is estimated from gas rotational velocity, with inclination uncertainty dominating the error budget.
  • Rotation-dominated galaxies: 62% of rotation-dominated galaxies are expected to have enclosed-mass fractional error below 10%.Systematic errors are below 10% in all inclination bins, while random errors are below 10% above 55° inclination.
  • Early-type galaxies: The early-type analysis rejects 160 of 562 elliptical galaxies because of data-quality, binning, foreground-star, merger, or close-pair criteria.
  • Early-type galaxies: About 85% of 402 accepted early-type galaxies have total enclosed-mass error below 10%, and 72% have dark-matter-fraction error below 10%.Errors are derived from one-dimensional marginalized MCMC distributions.
  • Systematic limitations: JAM systematic uncertainties exceed formal random errors, reaching 11–16% for total mass and approximately 33% for dark-matter mass in simulations.
  • Systematic limitations: The survey meets formal science requirements, but whether it can measure stellar mass-to-light ratios to better than 25% remains unclear.
  • Survey context: The survey design and first-year data-quality demonstration support a 10K-galaxy program with broad derived-property verification.
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