The SDSS-IV Mapping Nearby Galaxies at APO (MaNGA) program has been operating from 2014-2020, and has now observed a sample of 9,269 galaxies in the low redshift universe (z ~ 0.05) with integral-field spectroscopy. With rest-optical (lambdalambda 0.
36 - 1.0 um) spectral resolution R ~ 2000 the instrumental spectral line-spread function (LSF) typically has 1sigma width of about 70 km/s, which poses a challenge for the study of the typically 20-30 km/s velocity dispersion of the ionized gas in present-day disk galaxies. In this contribution, we present a major revision of the MaNGA data pipeline architecture, focusing particularly on a variety of factors impacting the effective LSF (e.g., undersampling, spectral rectification, and data cube construction). Through comparison with external assessments of the MaNGA data provided by substantially higher-resolution R ~ 10,000 instruments we demonstrate that the revised MPL-10 pipeline measures the instrumental line spread function sufficiently accurately (<= 0.6% systematic, 2% random around the wavelength of Halpha) that it enables reliable measurements of astrophysical velocity dispersions sigma_Halpha ~ 20 km/s for spaxels with emission lines detected at SNR > 50. Velocity dispersions derived from [O II], Hbeta, [O III], [N II], and [S II] are consistent with those derived from Halpha to within about 2% at sigma_Halpha > 30 km/s. Although the impact of these changes to the estimated LSF will be minimal at velocity dispersions greater than about 100 km/s, scientific results from previous data releases that are based on dispersions far below the instrumental resolution should be reevaulated.
The late assembly of massive galaxies is thought to be dominated by stellar accretion in their outskirts (beyond 2 effective radii Re) due to dry, minor galaxy mergers. We use observations of 1010 passive early-type galaxies (ETGs) within z<0.15 from
SDSS IV MaNGA to search for evidence of this accretion. The outputs from the stellar population fitting codes FIREFLY, pPXF, and Prospector are compared to control for systematic errors in stellar metallicity (Z) estimation. We find that the average radial logZ/Zsun profiles of ETGs in various stellar mass (M) bins are not linear. As a result, these profiles are poorly characterized by a single gradient value, explaining why weak trends reported in previous work can be difficult to interpret. Instead, we examine the full radial extent of stellar metallicity profiles and find them to flatten in the outskirts of M>10^{11}Msun ETGs. This is a signature of stellar accretion. Based on a toy model for stellar metallicity profiles, we infer the ex-situ stellar mass fraction in ETGs as a function of M and galactocentric radius. We find that ex-situ stars at 2Re make up 20% of the projected stellar mass of M<10^{10.5}Msun ETGs, rising up to 80% for M>10^{11.5}Msun ETGs.
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 reliabi
lity 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.
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 Surve
y (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.
We present a performance analysis of the DiskMass Survey. The survey uses collisionless tracers in the form of disk stars to measure the surface-density of spiral disks, to provide an absolute calibration of the stellar mass-to-light ratio, and to yi
eld robust estimates of the dark-matter halo density profile in the inner regions of galaxies. We find a disk inclination range of 25-35 degrees is optimal for our measurements, consistent with our survey design to select nearly face-on galaxies. Uncertainties in disk scale-heights are significant, but can be estimated from radial scale-lengths to 25% now, and more precisely in the future. We detail the spectroscopic analysis used to derive line-of-sight velocity dispersions, precise at low surface-brightness, and accurate in the presence of composite stellar populations. Our methods take full advantage of large-grasp integral-field spectroscopy and an extensive library of observed stars. We show that the baryon-to-total mass fraction (F_b) is not a well-defined observational quantity because it is coupled to the halo mass model. This remains true even when the disk mass is known and spatially-extended rotation curves are available. In contrast, the fraction of the rotation speed supplied by the disk at 2.2 scale lengths (disk maximality) is a robust observational indicator of the baryonic disk contribution to the potential. We construct the error-budget for the key quantities: dynamical disk mass surface-density, disk stellar mass-to-light ratio, and disk maximality (V_disk / V_circular). Random and systematic errors in these quantities for individual galaxies will be ~25%, while survey precision for sample quartiles are reduced to 10%, largely devoid of systematic errors outside of distance uncertainties.
We analyze integral-field ionized gas and stellar line-of-sight kinematics in the context of determining the stellar velocity ellipsoid for spiral galaxies observed by the Disk-Mass Survey. Our new methodology enables us to measure, for the first tim
e, a radial gradient in the ellipsoid ratio sigma_z / sigma_R. Random errors in this decomposition are 15% at two disk scale-lengths.
