The Fifteenth Data Release of the Sloan Digital Sky Surveys: First Release of MaNGA-derived Quantities, Data Visualization Tools, and Stellar Library

The MaNGA DRP is the IDL-based software suite that produces final flux-calibrated data cubes from the raw dispersed fiber spectra obtained at APO. The DRP is described in detail by Law et al. (2016) and consists of two stages. The "2d" DRP processes individual exposures, applying bias and overscan corrections, extracting the one-dimensional fiber spectra, sky-subtracting and flux-calibrating the spectra, and combining information from the four individual cameras in the BOSS spectrographs into a single set of RSS (mgCFrame files) on a common wavelength grid. The "3d" DRP uses astrometric information to combine the mgCFrame fiber spectra from individual exposures into a composite data cube on a regularized 05 grid, along with information about the inverse variance, spaxel mask, instrumental resolution, and other key parameters. The mgCFrame per-exposure files are produced on both linear and logarithmic wavelength grids directly from the raw detector pixel sampling, and used to construct the corresponding logarithmic and linearly sampled data cubes.

The DRP data products released in DR15 are largely similar to those released in DR13 and DR14 (and identical to the internal collaboration release MPL-7) and consist of multiextension FITS files giving the flux, inverse variance, mask, and other information for each object. The metadata from all of our observations are summarized in a FITS binary table, "drpall-v2_4_3.fits," detailing the coordinates, targeting information, redshift, data quality, etc. The version of the MaNGA DRP used for DR15 (v2_4_3)¹¹¹ incorporates some significant changes compared to the DR14 version of the pipeline (v2_1_2). These changes include the following:

• 1.  
  The MaNGA DRP has been extended to produced one-dimensional reduced spectra for each of the MaStar targets observed during bright time; details of these modifications are described in Section 4.3.
• 2.  
  DR15 introduces some significant changes in the overall flux calibration relative to DR13/DR14 (and relative to the description by Yan et al. 2016b). Foremost among these is the use of BOSZ stellar spectral models (Bohlin et al. 2017) instead of the original Kurucz templates built in 2003 to derive the spectrophotometric calibration based on contemporaneous observations of standard stars with the MaNGA seven-fiber mini bundles. Since the BOSZ templates picked by the pipeline are bluer by 0.03 mag in SDSS u − r color than the version of the Kurucz models produced in 2003, this change slightly increases the overall flux blueward of 4000 Å in the MaNGA data cubes. Test observations of hot white dwarfs compared to ideal blackbody models generally show better performance using the new BOSZ calibration (as illustrated in Figure 3). Additionally, the throughput loss vector applied to the observational data is now smoother at many wavelengths; high-frequency basis spline fits are still used in telluric regions, but the spline has a much lower frequency outside the telluric regions to avoid introducing artifacts due to slight template mismatches. This significantly reduces the amount of artificially high-frequency, low-level (∼few percent) variations seen in the resulting spectra from earlier versions. The list of telluric regions is also updated.
• 3.  
  Many aspects of the spectral line-spread function (LSF) estimation in the DRP have changed in DR15 in order to improve the level of agreement with independent estimates (observations of bright stars and galaxies previously observed at higher spectral resolution, observations of the solar spectrum, etc.). These changes include the use of a Gaussian comb method to propagate LSF estimates through the wavelength rectification step, computation of both pre-pixelized and post-pixelized LSF estimates,¹¹² improved interpolation over masked regions, and a modified arc lamp reference line list to improve LSF estimation and wavelength calibration in the far blue by rejecting poor-quality lines. The DRP data products contain additional extensions to describe this new information, including a 3D cube describing the effective LSF at each spaxel within the MaNGA data cube as a function of wavelength; this combines the information known about the LSF in each individual fiber spectrum to describe the net effect of stacking spectra with slightly different resolutions. The LSF changes and assessment against various observational calibrators will be described in greater detail by D. Law et al. (2019, in preparation).
• 4.  
  The DRP data cubes now contain extensions describing the spatial covariance introduced in the data cubes by the cube-building algorithm. This information is provided in the form of sparse correlation matrices at the characteristic wavelengths of the SDSS griz filters and can be interpolated to estimate the correlation matrix at any other wavelength in the MaNGA data cubes. Note that the DR14 paper incorrectly stated that those data included these extensions. They did not (the team-internal MPL-5, which is the most similar MPL to DR14, did, but DR14 itself did not), so this is the first release of these extensions.
• 5.  
  The DRPall summary file for DR15 contains 10 additional columns with respect to DR14. These columns include an estimate of the targeting redshift z that is used as the starting guess by the DAP when analyzing the MaNGA data cubes. z is generally identical to the NASA-Sloan Atlas (NSA) (Blanton et al. 2011) catalog redshift for the majority of MaNGA galaxies, but the origin of the redshift can vary for galaxies in the ∼25 MaNGA ancillary programs. Additional columns include a variety of estimates of the volume weights for the MaNGA primary and secondary galaxy samples.
• 6.  
  Additional under-the-hood modifications to the DRP have been made for DR15 that provide minor bug fixes and performance improvements. These include modifications to the reference pixel flat fields for certain MJDs, updates to the reference bias and bad pixel masks, better rejection of saturated pixels, updates to the algorithms governing weighting of the wavelength rectification algorithm near ultrabright emission lines, etc. A detailed change log can be found in the DRP online repository.¹¹³

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When working with the MaNGA data, note that there are several quality-control features that should be used to ensure the best scientific quality output. First, each MaNGA data cube has a FITS header keyword DRP3QUAL that describes the overall quality of the cube (identifying issues such as focus problems, flux-calibration problems, large numbers of dead fibers, etc.). About 1% of the data cubes are flagged as significantly problematic (i.e., have the CRITICAL quality bit set) and should be treated with extreme caution. Additionally, there is a 3d mask extension to each data cube that contains spaxel-by-spaxel information about problematic regions within the cube. This mask identifies issues such as dead fibers (which can cause local glitches and holes within the cube), foreground stars that should be masked by analysis packages such as the DAP, etc. Although the vast majority of cosmic rays and other transient features are detected by the DRP and flagged (either for removal or masking), lower intensity glitches (e.g., where the edge of a cosmic-ray track intersects with a bright emission line) can sometimes be missed and propagate into the final data cubes where they show up as unmasked hot pixels. Future improvements to the DRP may further address this issue, but caution is thus always advised when searching for isolated emission features in the data cubes.

For information on downloading MaNGA data in DR15, please see Section 3; new for DR15 is the Marvin interface to MaNGA data (see Section 4.2 below).

The MaNGA DAP is the SDSS-IV software package that analyzes the data produced by the MaNGA DRP. The DAP currently focuses on "model-independent" properties, i.e., those relatively basic spectral properties that require minimal assumptions to derive. For DR15, these products include stellar and ionized-gas kinematics, nebular emission-line fluxes and equivalent widths, and spectral indices for numerous absorption features, such as the Lick indices (Worthey & Ottaviani 1997; Trager et al. 1998) and D4000 (Bruzual 1983). Examples of the DAP-provided measurements and model fits are shown in Figure 4, discussed through the rest of this section.

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An overview of the DAP is provided by Westfall et al. (2019). There, we describe the general workflow of the pipeline, explain the detailed algorithm used for each of its primary products, provide high-level assessments of its performance, and describe the delivered data products in detail. In-depth assessments of the stellar kinematics, ionized-gas kinematics, emission-line fluxes, and emission-line equivalent widths are provided by Westfall et al. (2019) and Belfiore et al. (2019). All survey-provided properties are currently derived from the data cubes sampled in constant steps of the logarithm of the wavelength (i.e., the LOGCUBE files). However, the core functions are developed to consider each spectrum largely independently.

The DAP allows for a number of different options when analyzing the data, which we refer to as the analysis mode or DAPTYPE. In DR15, the DAPTYPE joins the keywords identifying the type of spatial binning applied (e.g., Voronoi-binned to signal-to-noise ratio (S/N) ≳ 10, VOR10), the parametric form of the line-of-sight velocity distribution (LOSVD) used for the stellar kinematics (a Gaussian function, GAU), and the template set used to model the stellar continuum (a hierarchically clustered distillation of the MILES stellar library, MILESHC). For DR15, two DAPTYPEs have been made available, VOR10-GAU-MILESHC and HYB10-GAU-MILESHC. That is, only the binning approach differs between the two available DAPTYPEs, primarily distinguishing whether or not the main analysis steps are performed on binned spectra or individual spaxels. The stellar LOSVD is always assumed to be Gaussian, and the 42 templates resulting from a hierarchical clustering analysis of the MILES stellar library (Sánchez-Blázquez et al. 2006; Falcón-Barroso et al. 2011) are always used for the continuum templates; details regarding the latter are discussed in Westfall et al. (2019).

In the first mode (VOR10-GAU-MILESHC), the spaxels are binned using the Voronoi-binning scheme from Cappellari & Copin (2003) to a minimum g-band S/N of 10 per spectral pixel. The first mode then performs all subsequent analysis on those binned spectra. Alternatively, the second mode (HYB10-GAU-MILESHC) only performs the stellar kinematics on the binned spectra; the subsequent emission-line and spectral-index measurements are all performed on individual spaxels. This "hybrid" binning approach is likely the approach that most users will want to use in their analysis. The main exception to this is if any subsequent analyses depend on, e.g., the availability of emission-line models for the binned spectra, as is the case for the FIREFLY VAC (Wilkinson et al. 2017; see Section 4.5.1). The example data shown in Figure 4 are for observation 8138-12704 following from the hybrid-binning approach. Close inspection of the stellar velocity field will show that outermost regions have been binned together, all showing the same stellar velocity measurement. However, the Hα flux and D4000 maps have measurements for each spaxel.

The DAP is executed for all observations obtained by the MaNGA survey; however, some observations, primarily those obtained for our ancillary science programs, do not have all the required parameters currently needed as input by the DAP. Additionally, a few observations (<0.3%) trip corner failure modes of the DAP leading to errors in the construction of its main output files. These issues mean that not all LOGCUBE files provided by the DRP have associated DAP products. For those observations that are successfully analyzed (4718 in total), the DAP provides two main output files for each DAPTYPE, the MAPS file and the model LOGCUBE file. Examples of how to access and plot the data in these files are provided in a set of tutorials on the data-release website at https://www.sdss.org/dr15/manga/manga-tutorials/dap-tutorial/.

The MAPS file contains all of the derived properties organized as a series of maps, or images, that have the same on-sky projection as a single wavelength channel in the analyzed DRP LOGCUBE file. The images in the left panels of Figure 4 are example maps taken from the DAP MAPS file for observation 8138-12704. The maps are organized in a series of extensions grouped by the measurement they provide. Some extensions contain a single image with all of the relevant data, whereas other extensions have multiple images, one for, e.g., each of the measured emission lines. For example, the STELLAR_VEL extension has a single image with the measured single-component stellar velocity measured for each spatial bin (like that shown in Figure 4), while the SPECINDEX extension has 46 images, organized similarly to the wavelength channels in the DRP data cubes (the D4000 map shown in Figure 4 is in the 44th channel of the SPECINDEX extension).

The DAP-output model LOGCUBE file provides both the S/N-binned spectra and the best-fitting model spectra. From these files, users can plot the best-fitting model spectra against the data, as demonstrated in Figure 4, as an assessment of the success of the DAP. This is particularly useful when a result of the fit, e.g., the Hα flux, seems questionable. Indeed, Westfall et al. (2019) note regimes where the DAP has not been appropriately tailored to provide a successful fit; this is particularly true for spectra with very broad emission lines, such as the broad-line regions of AGNs. Users are encouraged to make sure they are well aware of these limitations in the context of their science goals. Finally, in combination with the DRP LOGCUBE file, users can use the model LOGCUBE data to construct emission-line-only or stellar-continuum-only data cubes by subtracting the relevant model data.

Although we have endeavored to make the output data user-friendly, there are a few usage quirks of which users should be aware:

• 1.  
  As with all SDSS data products, users are strongly encouraged to understand and use the provided quality flags, for those data provided as masks. The mask bits provide important information as to whether or not users should trust the provided measurements in their particular use case. The conservative approach of ignoring any measurement where the mask bit is nonzero is safe, at least in the sense of not including any measurements we know to be dubious. However, the DAP makes use of an UNRELIABLE flag that is intended to be more of a warning that users should consider how the measurements affect their science as opposed to an outright rejection of the value. The UNRELIABLE flag is put to limited use in DR15, only flagging measurements that hinge on bandpass integrals (emission-line moments and non-parametric equivalent widths, and spectral indices) where any pixels are masked within the bandpass. However, this bit may become more extensively used in future releases as we continue to vet the results of the analysis. A more extensive discussion of the mask bits and their usage is provided by Westfall et al. (2019).
• 2.  
  To keep the format of the output files consistent with the DRP LOGCUBE files, the binned-spectra and binned-spectra measurements are repeated for each spaxel within a given bin. This means that, e.g., the stellar velocity dispersion measured for a given binned spectrum is provided in the output DAP map at the location of each spaxel in that bin. Of course, when analyzing the output, one should most often only be concerned with the unique measurements for each observation. To this end, we provide an extension in the MAPS file that provides a "bin ID" for each spaxel. Spaxels excluded from any analysis (as in the buffer region during the data cube construction) are given a bin ID of −1. This allows the user to select all of the unique measurements by finding the locations of all unique bin ID values, ignoring anything with a bin ID of −1. Tutorials for selecting the unique measurements in the DAP-output maps are provided via the data-release website at https://www.sdss.org/dr15/manga/manga-tutorials/dap-tutorial/.
• 3.  
  Corrections that have not been applied to the data in the output files are provided for a few quantities in the MAPS file. The stellar velocity dispersion and ionized-gas velocity dispersions are provided as measured from the core pPXF software (Cappellari & Emsellem 2004; Cappellari 2017) used by the DAP. This means that any instrumental effects present during the fitting process are also present in the output data. For both the stellar and ionized-gas dispersions, we have estimated the instrumental corrections for each measurement and provided the result in extensions in the MAPS file. These corrections should be applied when using the data for science. For the velocity dispersion measurements, our purpose in not applying the corrections ourselves is to allow the user freedom in how they deal with measurements of the dispersion that are below our measurement of the instrumental resolution. Such issues can be pernicious at low velocity dispersion, and the treatment of these data can have significant effects on, e.g., the construction of a radially averaged velocity dispersion profile (see Westfall et al. 2019, who discuss this at length, and also Penny et al. 2016, who discuss this issue for dwarf galaxies). Corrections are also provided (but not applied) for the spectral indices to convert the measurement to zero velocity dispersion at the spectral resolution of the MILES stellar templates (Beifiori et al. 2011) used during the stellar-continuum fit. Additional details regarding these corrections are provided in Westfall et al. (2019), and tutorials demonstrating how to apply them to the data are provided via the data-release website.
• 4.  
  In the hybrid-binning scheme, the stellar kinematics are performed on the binned spectra, but the emission-line fits are performed on the individual spaxels. When comparing the model to the data, the user must compare the emission-line modeling results to the DRP LOGCUBE spectra, not to the binned spectra provided in the DAP model LOGCUBE file, unless the "binned" spectrum is actually from a single spaxel. Tutorials for how to overplot the correct stellar-continuum and emission-line models are provided via the data-release website.

Finally, similar to the DRPall file provided by the MaNGA DRP, the DAP constructs a summary table called the DAPall file. This summary file collates useful data from the output DAP files, as well as providing some global quantities drawn from basic assessments of the output maps, that may be useful for sample selection. For example, the DAPall file provides the luminosity-weighted stellar velocity dispersion and integrated star formation rate within 1 R_e. The sophistication of these measurements is limited in some cases. For example, the star formation rate provided is simply based on the measured Hα luminosity and does not account for internal attenuation or sources of Hα emission that are unrelated to star formation; as such, we caution users to make use of this for science only after understanding the implications of this caveat. Development and refinement of DAPall output will continue based on internal and community input. Additional discussion of how these properties are derived is provided by Westfall et al. (2019).

Marvin Web provides quick visual access to the set of MaNGA galaxies. It provides a dynamic, interactive, point-and-click view of individual galaxies to explore the output from the MaNGA DRP and DAP (Sections 4.1.1 and 4.1.2, respectively), along with galaxy information from the NSA catalog (Blanton et al. 2011).¹¹⁷

We show a screenshot of the View-Spectra page of Marvin Web in Figure 5. By clicking anywhere within the galaxy IFU bundle on the SDSS three-color image, or any Data Analysis 2D Map, the user can explore the spectrum at that location for quick inspection. The visualized spectrum is interactive as well, allowing panning and zooming.

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Additional pages that Marvin Web provides are:

• 1.  
  a Query page, for searching the MaNGA data set through an SQL-like interface;
• 2.  
  a Plate page, containing all MaNGA galaxies observed on a given SDSS plate;
• 3.  
  and an Image Roulette page, for randomly sampling images of MaNGA galaxies.

Tutorials for navigating Marvin Web can be found at https://www.sdss.org/dr15/manga/manga-tutorials/marvin-tutorial/marvin-web/.

Marvin Web is designed as a gateway to entry into real MaNGA data, providing commonly desired functionalities all in one location, as well as code snippets to help transition users into a more programmatic environment using the Marvin Tools.

Marvin Tools provide a programmatic interaction with the MaNGA data, enabling rigorous and repeatable science-grade analyses. Marvin Tools come in the form of a Python package that provides convenience classes and functions that simplify the processes of searching, accessing, downloading, and interacting with MaNGA data; selecting a sample; running a user-defined analysis code; and producing publication-quality figures.

Marvin Tools are a pip-installable product, packaged under sdss-marvin, with full installation instructions at the Marvin documentation website¹¹⁸ and the source code on Github.¹¹⁹

Overall, Marvin Tools allow for easier access to the data without knowing much about the data model, by seamlessly connecting all of the MaNGA data products, eliminating the need to micromanage a multitude of files. The user can do all of their analysis from one interface.

Both Marvin Web and Tools provide interfaces for searching the MaNGA data set through a Structured Query Language (SQL)-like interface, either via a web form or a Python class. The Marvin Query system uses a simplified SQL syntax that focuses only on a filter condition using Boolean logic operators and a list of parameters to return. This eliminates the need to learn the full SQL language and the detailed MaNGA database layout. With this query system, users can make queries across the entire MaNGA sample using traditional global galaxy properties (functionality to perform intragalaxy queries using individual spaxel measurements is planned for a future release). Tutorials for querying with Marvin can be found for the web¹²⁰ and for the tools.¹²¹

Good target selection is essential to achieve wide sampling of the stellar parameter space. We aim to cover the stellar parameter space as completely as possible and sample it roughly evenly. We base our selection primarily on existing stellar parameter catalogs, including APOGEE-1 and -2 (Majewski et al. 2017), SEGUE (Yanny et al. 2009), and LAMOST (Luo et al. 2015). Given the field plan of APOGEE-2, we select all of the stars available in these catalogs in the planned APOGEE-2 footprint. For each star, we count its neighboring stars in stellar parameter space (${T}_{\mathrm{eff}}$, $\mathrm{log}\,g$, [Fe/H]) and assign it a selection weight that is inversely proportional to its number of neighbors. The number of APOGEE-2 targeting designs for each field is also taken into account. We then draw our targets randomly in proportion to the normalized selection weight. This method flattens the stellar parameter space distribution and picks rare stars in those fields where they are available.

In fields without stars with known stellar parameters, we use spectral energy distribution (SED) fitting to search for hot and cool stars to patch the stellar parameter distribution at the hot and cool ends.

The targets are required to have g- or i-band magnitude brighter than 17.5 in order to achieve an S/N greater than 50 per pixel in 3 hr of integration, although not all fields have the same integration time or the same number of visits. They are also required to be fainter than 12.7 mag in both g- and i-bands in order to stay below the saturation limit of the detector for 15 minute exposures. We later lowered the saturation limit to 11.7 to include more luminous stars, with a slight offset in fiber placement for stars with magnitudes between 11.7 and 12.7. This slight offset does not affect our flux calibration due to our unique calibration procedure.

These magnitude limits yield relatively few OB stars and blue supergiants, as they have to be very distant or very extincted to fall within this magnitude range. Therefore, we are currently adjusting our exposure time in certain fields to expand our parameter space distribution in the blue and luminous end. The first version of the library does not have many such stars, but we will improve on this for the final version, which we expect to come out in the final SDSS-IV Data Release.

Observations for MaStar are obtained in a similar fashion to the MaNGA observations except that they are conducted under bright time and without dithering. Since we are piggybacking on APOGEE-2, if APOGEE-2 visits a field multiple times, we would obtain multiple visits for the stars on that plate as well. Therefore, some stars have many visits and some stars have only one visit. Each visit of APOGEE-2 is typically 67 minutes long, which would allow us to take four 15 minute exposures, unless interrupted by weather or other reasons. Each plate has 17 science targets and 12 standard stars, same as MaNGA. We take flat and arc frames before each visit.

The reduction of the MaStar data is handled by the MaNGA DRP (see Section 4.1.1, Law et al. 2016). It has two stages. The first stage processes the raw calibration frames and science frames to produce the sky-subtracted, flux-calibrated, camera-combined spectra for each fiber in each exposure. The second stage differs between MaNGA galaxy data and MaStar stellar data. For MaStar stellar data, we evaluate the flux ratios among fibers in a bundle as a function of wavelength and constrain the exact location of the star relative to the fiber positions. This procedure helps us derive the light loss due to the finite fiber aperture as a function of wavelength. This procedure takes into account the profile of the PSF and the differential atmosphere refraction. It is similar to how we handle flux calibration in MaNGA data (Yan et al. 2016b). We then correct the spectra for this aperture-induced light loss and arrive at the final flux-calibrated stellar spectra. Comparison with photometry shows that our relative flux calibration are accurate to 5% between the g- and r-bands, and to 3% between r and i, and between the i and z bands.

For each star, we combine the spectra from multiple exposures on the same night and refer to these combined spectra as "visit spectra." We do not combine spectra from different nights together for the same star, because they can have different instrumental resolution vectors and some stars could be variable stars. By summer 2017, we have obtained 17,309 visit spectra for 6042 unique stars. Because not all visit spectra are of high quality, as we will discuss below, we selected only those with high quality and present this subset as the primary set to be released. The primary set contains 8646 visit spectra for 3321 unique stars.

The final spectra are not corrected for foreground dust extinction. Users should make these corrections before using them.

A stellar library requires strict quality control. We have a number of quality assessments carried out in the pipeline to flag poor-quality spectra. We identify cases having low S/N, bad sky subtraction, high scattered light, low PSF-covering fraction, uncertain radial velocity (RV) measurement, and/or those with problematic flux calibration. Each spectrum we release has an associated quality bitmask (MJDQUAL for each visit spectrum) giving these quality information. We provide a summary of these in Table 3 and describe them in more detail here.

A large fraction of the observed stars have problematic flux calibration due to the fact that the standard stars on those plates have much less extinction than given by the Schlegel et al. (1998, SFD) dust map. In the flux-calibration step of the MaNGA pipeline, we assume that the standard stars are behind the Galactic dust, and we compare the observed spectra of the standard stars with the dust-extincted theoretical models to derive the instrument throughput curve. This assumption is valid for all galaxy plates that are at high Galactic latitude and have relatively faint standard stars, placing them at a safe distance behind most of the dust. However, this assumption fails for many fields targeted by MaStar, which are at low Galactic latitudes. Stars at low Galactic latitude are quite likely to be found in front of some fraction of the dust in that direction. Thus, when applying the extinction given by SFD, we overly redden the theoretical models and arrive at an incorrect flux calibration for these fields. We have a solution to this problem, which will be incorporated into the MaStar pipeline in the future. In the current release, the spectra for these stars are just flagged as having poor flux calibration (bit 5 of MJDQUAL).

A significant fraction of the spectra also have unreliable RV estimates. We used a rather limited set of templates in our derivation of the RVs. Thus, stars with very hot or very cool effective temperature, and those with very high and low surface gravity, are more likely to be affected by this issue. These can be identified by checking bit 6 of the MJDQUAL bitmask. All spectra are shifted to the rest frame according to the reported heliocentric RV, regardless of whether the measurement is robust or not.

In addition to these automated checks, we carried out a visual inspection campaign to ensure the quality of each spectrum. Using the Zooniverse Project Builder interface,¹²² we started a private project for visually inspecting the spectra. A total of 28 volunteers from within the collaboration participated in the campaign; 10,797 visit spectra were inspected, each by at least three volunteers, to check for issues in flux calibration, sky subtraction, telluric subtraction, emission lines, etc. The results are input to the DRP to assign the final quality flags.

The primary set of spectra we are releasing contains only those spectra that are deemed to have sufficient quality to be useful. We have excluded from the primary set those spectra with problematic sky subtraction (bit 1 of MJDQUAL), low PSF-covering fraction (bit 4), poor flux calibration (bit 5), or low S/N (bit 9), and those identified as problematic by visual inspection (bit 7). The primary set still contains spectra with unreliable heliocentric velocity measurements (bit 6), whose spectra would still be in the observed frame. It also contains spectra with strong emission lines (bit 8), some of which are intrinsic to the star. In addition, stars flagged to have high scattered light in the raw frame (bit 2) are also included as they may not be affected significantly. Other bits that are not mentioned above were never set in the current data release. The users are strongly advised to check the quality flags when using the spectra. Detailed information about the quality flags can be found in Yan et al. (2018).

In addition to these basic quality checks, we are testing the spectra by running them through a population synthesis code (Maraston 2005). This procedure, which will be described in C. Maraston et al. (2019, in preparation), allows us to test the total effect of the goodness of the spectra plus assigned stellar parameters and will be crucial for the joint calculation of stellar parameters and stellar population models. Relevant to this description, this method allows us to spot bad or highly extincted spectra.

Robust assignment of stellar parameters to the stars are also critical for the stellar library. Our targets are selected from heterogeneous sources. Those selected from APOGEE, SEGUE, and LAMOST have parameters available from their respective catalogs. However, they are measured with different methods and may not be consistent with each other. They also have different boundaries applied in the determination of the parameters. For our target selection purposes, we have made small constant corrections to the parameters to remove the overall systematic difference. These slightly adjusted parameters are included in the catalog we release. However, the corrections are done independent of detailed stellar types. As a result, the parameters from different catalogs can still have subtle stellar-type-dependent systematic differences. For individual stars, they could be used to determine the rough stellar type. But for the library as a whole, we caution against using these input parameters to compare the stars or to construct stellar population models with them.

We are still in the process of determining stellar parameters for all stars in the MaStar library in a way that is as homogeneous as possible. This is not an easy task, because for stars with different stellar types, we need to rely on different spectral features and different methodologies. Although these are not yet available in this version of the library, we present here the extinction-corrected Hertzsprung–Russell (HR) diagram for our stars using photometry and parallax from Gaia DR2 (Evans et al. 2018; Gaia Collaboration et al. 2018) and Gaia-parallax-based distance estimates from Bailer-Jones et al. (2018). This provides a rough idea of our stellar parameter coverage. Here we only plot stars that are either in directions with a total $E(B-V)$ less than 0.1 mag or more than 300 pc above or below the Milky Way midplane so that we can use the total amount of dust measured by Schlegel et al. (1998) for the extinction correction reliably. The photometry of our targets also come from various sources, including PanSTARRS1 (Chambers et al. 2016), APASS,¹²³ SDSS, Gaia DR1 (Gaia Collaboration et al. 2016), and Tycho-2 (Høg et al. 2000) for a few stars. For stars with PanSTARRS1 photometry, we converted them to SDSS using the formula provided by Finkbeiner et al. (2016). For APASS, we assumed that they are in SDSS filters already. For all of the other non-SDSS stars, we use Gaia DR2 photometry to derive the magnitudes in SDSS gri bands according to the conversion given by Evans et al. (2018). In Figure 6, we show the r-band absolute magnitude (M_r) versus g − i for these stars. The color-coding is based on our preliminary measurement of metallicity using the ULySS pipeline (Koleva et al. 2009, 2011) with MILES (Sánchez-Blázquez et al. 2006) as the training set.

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From Figure 6, we can see that the current released subset of the MaStar library already has a very good coverage across the HR diagram for a wide range of metallicities. But there is significant room for improvement. We need to cover more stars at the luminous end of the red giant branch (RGB) and at the blue end of the main sequence. These will be added in future versions of the library.

The MaStar data for this release can be found in two main files: the "mastarall" and the "mastar-goodspec" files. Both files can be found on the SAS (https://dr15.sdss.org/sas/dr15/manga/spectro/mastar/v2_4_3/v1_0_2/, with data models at https://internal.sdss.org/dr15/datamodel/files/MANGA_SPECTRO_MASTAR/DRPVER/MPROCVER/.

The "mastarall" file contains four summary tables of the basic information about the stars and of the information about one or more of their visits. They include the identifier (MaNGAID), astrometry, photometry, targeting bitmask indicating source of photometry, input stellar parameters, plate, IFU, modified Julian date of the visit, derived heliocentric velocity, and spectral quality information. MaNGAID is the identifier to identify unique stars, except in a few cases where the same star was assigned two different MaNGAIDs. These are documented in detail online and in Yan et al. (2018).

The "mastar-goodspec" file contains the primary set of high-quality visit spectra. In addition to identification information, we provide the wavelength, flux, inverse variance, mask, spectral resolution vector, and the spectral quality bitmask.

MaStar spectra can also be visualized in SAW (see Section 3 for details).

In DR15, there are new releases for both the FIREFLY (Goddard et al. 2017a) and Pipe3D (Sánchez et al. 2016) stellar population modeling and emission-line analysis VACs. Full details of both can be found in the DR14 paper (and references therein), so we give only updates specific to this DR15 release version below.

For DR15, Pipe3D version 2.4.3 was run over the MaNGA DR15 data set. The main difference with respect to version 2.1.2 (used in DR14), besides the number of analyzed galaxies, was to solve a bug in the derivation of the equivalent widths of the analyzed emission lines. The current Pipe3D VAC provides two different types of data products: (1) a catalog comprising 94 different parameters measured for each of the 4660 galaxies (all galaxies in MaNGA cubes for which Pipe3D was able to derive the main stellar population, emission-line, and kinematics properties), and (2) a set of 4660 data cubes manga.Pipe3D.cube.fits presenting a set of spatially resolved parameters. The parameters are the same as they were in the DR14 version (Sánchez et al. 2018). More details are available on the data release website, https://www.sdss.org/dr15/manga/manga-data/manga-pipe3d-value-added-catalog/.

The major update to FIREFLY with respect to DR14 is the extension of the stellar population modeling grid based on the models of Maraston & Strömbäck (2011). The new catalog uses a finer metallicity grid with the following grid values: [Z/H] = −2.3, −1.9, −1.6, −1.2, −0.9, −0.6, −0.3, 0.0, and 0.3. The new version of the VAC also provides geometrical information so that maps can be produced directly from the VAC (a Python plotting routine is available from the data release website). The entire VAC is available as either a single FITS file containing all measurements or smaller FITS files with selected subsets of the derived parameters. More detail on the catalog is provided on the data release website, https://www.sdss.org/dr15/manga/manga-data/manga-firefly-value-added-catalog/, and in Goddard et al. (2017a) and Parikh et al. (2018).

As part of DR15, we release one photometry VAC and two morphology VACs.

The PyMorph catalog provides photometric parameters obtained from Sérsic and Sérsic+Exponential fits to the 2D surface brightness profiles of the MaNGA DR15 galaxy sample. It uses the PyMorph algorithm, which has been extensively tested, to determine the fits (Meert et al. 2013; Bernardi et al. 2017; Fischer et al. 2017), and PyMorph reductions of SDSS DR7 galaxies (Abazajian et al. 2009) are available (the UPenn SDSS PhotDec Catalog; Meert et al. 2015, 2016). We have re-run PyMorph for all galaxies in the MaNGA DR15 sample. These re-runs incorporate three improvements: they use the SDSS DR14 images, improved bulge-to-disk decomposition by slightly modifying our criteria when using PyMorph (see Fischer et al. 2018. for details), and all of the fits in this catalog have been visually inspected for additional reliability (we recommend using "flag_fit"). The catalog contains these fits for the g-, r-, and i-bands. One important caveat to note is that position angles (PAs) are reported relative to the SDSS imaging camera columns, which are not aligned with north, so a correction is needed to convert to true PAs. To convert to the usual convention, where north is up, east is left (note that the MaNGA data cubes have north up, east right) set ${\mathrm{PA}}_{\mathrm{MaNGA}}=90^\circ -{\mathrm{PA}}_{\mathrm{PyMorph}}-{\mathrm{PA}}_{\mathrm{SDSS}}$, where ${\mathrm{PA}}_{\mathrm{PyMorph}}$ is the value given in this catalog, and ${\mathrm{PA}}_{\mathrm{SDSS}}$ is the SDSS camera column position angle with respect to north.

A curated version of the Galaxy Zoo crowdsourced classifications containing an entry for all MaNGA target galaxies is released in DR15. This catalog contains galaxy classifications previously released in Willett et al. (20013, which was selected from the SDSS DR7 galaxy catalog), as well as new unpublished classifications for MaNGA targets missing from that list. All morphological identifications are provided based on the citizen scientist input using the improved technique for aggregation and debiasing described in Hart et al. (2016). This accounts better for redshift bias in the detailed classifications of spiral arms, bars than the version used in Willett et al. (2013). For a simple conversion between Galaxy Zoo classifications and modern (bulge-sized based) T-types, see details in Willett et al. (2013), which also includes general advice on how to best use Galaxy Zoo classifications for science.

A second morphology catalog that has been obtained with the help of "Deep Learning" models is provided. The models were trained (making use of Galaxy Zoo morphologies, as well as morphologies from Nair & Abraham 2010) and tested on SDSS DR7 images (Domínguez Sánchez et al. 2018). The morphological catalog contains a series of Galaxy-Zoo-like attributes (edge-on, barred, bulge prominence and roundness), as well as a T-Type and a finer separation between pure elliptical and S0 galaxies.

The first data release of "H i-MaNGA," the H i follow-up project for MaNGA, is provided as a VAC in DR15. This follow-up program is presented in Masters et al. (2018) and is the result of single-dish radio 21 cm H i observations of MaNGA galaxies using the Robert C. Byrd Green Bank Telescope (GBT). The depth of this observing is aimed to be similar to the Arecibo Legacy Fast Arecibo L-band Feed Array (ALFALFA) blind H i survey (Haynes et al. 2018), which covers some of the MaNGA footprint (see Figure 2), with a goal of enabling studies to use H i data from both surveys. In this first release, data are provided for 331 MaNGA galaxies observed in the 2016 GBT observing seasons under project code AGBT16A_95. Total H i masses and line widths (measured with five different common techniques) are provided for all detections, while H i mass upper limits (assuming a line width of 200 km s⁻¹) are provided for non-detections. H i-MaNGA has observed an additional ∼2000 MaNGA galaxies in the 2017 observing season (under project code AGBT17A_12); these data will be released in a future VAC. The sky distribution of all MaNGA galaxies observed by this program is shown in Figure 2.

The environment in which a galaxy resides plays an important role in its formation and evolution. Galaxies evolve as a result of intrinsic processes (i.e., their nature—this includes processes such as internal secular evolution, feedback of various kinds etc.), but they are also exposed to the influences of their local and large-scale environments (i.e., how they are "nurtured"). We present the Galaxy Environment for MaNGA Value Added Catalog (GEMA-VAC), which provides a quantification of the local and large-scale environments of all MaNGA galaxies in DR15. There are many different definitions of environment, and there are also several ongoing projects within the MaNGA team exploring the influence the environment has on galaxy properties. With this VAC, we aim to join and coordinate efforts so that the entire astronomical community can benefit from the products. The GEMA-VAC catalog will be described in more detail in M. Argudo-Fernández et al. (2019, in preparation). We describe the contents of the VAC briefly below.

We estimate the tidal strength parameter for MaNGA galaxies in pairs/mergers (B. Hsieh et al. 2019, in preparation), the tidal strengths exerted by galaxies in the catalog of galaxy groups in Yang et al. (2007), and tidal forces exerted by nearby galaxies in two different fixed-aperture volume-limited samples (namely 1 and 5 Mpc projected distances within a line-of-sight velocity difference of ${\rm{\Delta }}v\,\leqslant 500$ km s⁻¹; Argudo-Fernández et al. 2015). Estimations of the local densities with the distance to the fifth nearest neighbor are also provided. The local density within the N nearest neighbors includes the corrections explained in Goddard et al. (2017b) following the methodology described in Etherington & Thomas (2015). To have a more general picture of the environment, we also provide a characterization of the cosmic web environment, which can be used to identify galaxies in clusters, filaments, sheets, or voids, as explained in Zheng et al. (2017). The full details of the reconstruction of these density and tidal fields are described in Wang et al. (2009, 2012).

We present a VAC that contains the best-fit spectroscopic redshift and corresponding model flux for each MaNGA spectrum that has sufficient S/N. We provide the mean of the spectroscopic redshifts sampled within the inner high-S/N region of the MaNGA galaxies, which can be compared to the single-valued NSA catalog redshift. Since the MaNGA instrument uses the BOSS spectrograph, our results are derived by iterative application of the BOSS pipeline's spec1d software (Bolton et al. 2012) to precisely measure the redshift using the higher S/N measurements as a prior to the algorithm, which allows us to determine good redshifts on spectra that would not otherwise have sufficient S/N to result in a good redshift. Since the MaNGA survey uses an IFU, the RV profile of the galaxy can significantly impact the redshift of each spectra. Thus, the spectroscopic redshifts can both be a benefit to galaxy kinematic measurements and improve the accuracy of spectra modeling and analysis. The authors of this VAC have used the spectroscopic redshifts to search for background emission lines to discover strong gravitational lenses in MaNGA (Talbot et al. 2018).

Two new APOGEE-related technical papers are highlighted below: the instrument paper from Wilson et al. (2018), which relays an extensive description of the APOGEE spectrographs, and the APOKASC paper from Pinsonneault et al. (2018), which details the APOGEE spectroscopic follow-up of Kepler stars.

APOGEE Instrument Paper. The publication from Wilson et al. (2018) describes the design and performance of the near-infrared, fiber-fed, multiobject, high-resolution APOGEE spectrographs. The first APOGEE instrument has been in operation on the 2.5 m Sloan Telescope at the Apache Point Observatory in New Mexico, USA, since 2011 (a northern hemisphere site). Several key innovations were made during the development of the APOGEE instrument, which include a multifiber connection system known as a "gang connector," which allows for the simultaneous disconnection and reconnection of 300 fibers; hermetically sealed feedthroughs to permit fibers to pass through the cryostat wall continuously; the first cryogenically deployed mosaic volume phase holographic grating; and a massive refractive camera that comprising large-diameter monocrystalline silicon and fused silica elements. Specifically for the northern spectrograph, Wilson et al. (2018) report on the following: the performance of the 2.5 m Sloan Foundation Telescope in the near-infrared wavelength regime, the cartridge and fiber systems, the optical and optomechanical systems, the detector arrays and electronic controls, the cryostat, the instrument control system, calibration procedures, instrument optical performance and stability, and lessons learned. The final sections of Wilson et al. (2018) provide similar details on the second APOGEE spectrograph located at the 2.5 m du Pont Telescope at LCO in Chile. This second (southern hemisphere-based) instrument, a close copy of the first, has been operating since 2017 April. Wilson et al. also contains multiple appendices for the interested user.

The Second APOKASC Catalog. Over both the APOGEE-1 and APOGEE-2 Surveys, a joint effort known as the APOGEE Kepler Asteroseismic Science Consortium (APOKASC), APOGEE has engaged in a spectroscopic follow-up of stars in the Kepler field. Pinsonneault et al. (submitted) present the second APOKASC Catalog of stellar properties for a sample of 6681 evolved stars with APOGEE spectroscopic parameters and Kepler asteroseismic data analyzed using five independent techniques. The APOKASC data include the evolutionary state, surface gravity, mean density, mass, radius, and age, and the spectroscopic and asteroseismic measurements used to derive them. As shown in Figure 7, the APOKASC catalog asteroseismic log g values and evolutionary state classifications allow for a clear distinction between RGB and red clump (RC) members. Pinsonneault et al. (2018) employ a new empirical approach for combining asteroseismic measurements from different methods, calibrating the inferred stellar parameters, and estimating uncertainties. With high statistical significance, they find that asteroseismic parameters inferred from the different pipelines have systematic offsets that are not removed by accounting for the differences in their solar reference values. Pinsonneault el al. include theoretically motivated corrections to the large frequency spacing (${\rm{\Delta }}\nu$) scaling relation as well as calibrates the zero point of the frequency of maximum power (${\nu }_{\max }$) relation to be consistent with masses and radii for members of star clusters. For most targets, the parameters returned by different pipelines are in much better agreement than would be expected from the pipeline-predicted random errors, but 22% of them had at least one method not return a result and a much larger measurement dispersion. This supports the usage of multiple analysis techniques for asteroseismic stellar population studies.

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In the SDSS DR14 data release paper (Abolfathi et al. 2018), brief references were made to the Holtzman et al. (2018) and Jönsson et al. (2018) publications. For the benefit of users, concise descriptions of each are now provided. Please note that in addition to the DR15 documentation, users should refer to these publications for detailed information regarding the Data Reduction Pipeline (DRP) and the APOGEE Stellar Parameter and Chemical Abundance Pipeline (ASPCAP), as well as to understand data quality and performance.

SDSS/APOGEE DR13 and DR14 Pipeline Processing and Data Description. Holtzman et al. (2018) describe the data and analysis methodology used for the SDSS/APOGEE Data Releases 13 and 14, as well as highlight differences from the DR12 analysis presented in Holtzman et al. (2015). For example, the work demonstrates some improvement in the handling of telluric absorption and persistence in the DR13/DR14 versions of APOGEE-2 data as opposed to DR12. Holtzman et al. (2018) detail the derivation and calibration of stellar parameters, chemical abundances, and respective uncertainties, along with the ranges over which calibration was performed. The work reports some known issues with the public data related to the calibration of the effective temperatures (DR13), surface gravity (DR13 and DR14), and C and N abundances for dwarfs (DR13 and DR14). Holtzman et al. (2018) also discuss how results from The Cannon (Ness et al. 2015) are included in DR14 and compare those with the values from ASPCAP.

Comparison of SDSS/APOGEE DR13 and DR14 Values to Optical Results. Jönsson et al. (2018) evaluate the ASPCAP performance for both the DR13 and DR14 APOGEE data sets with 160,000 and 270,000 stars, respectively. A comparison of the ASPCAP-derived stellar parameters and abundances to analogous values inferred from optical spectra and analysis with a subset of several hundred stars is done. For most elements, Jönsson et al. (2018) find that the DR14 ASPCAP results have systematic differences with the comparison samples of less than 0.05 dex (median) and random differences of less than 0.15 dex (standard deviation). These departures are attributed to a combination of uncertainties in both the comparison samples as well as the ASPCAP analysis. Specifically, in comparison to the optical data, Jönsson et al. (2018) find that magnesium is the most accurate alpha-element derived by ASPCAP while nickel is the most accurate Fe-peak element (excluding iron).

Additionally, in Abolfathi et al. (2018), detailed information was provided regarding the recently published APOGEE-2 Targeting Paper from Zasowski et al. (2017). Users are encouraged to consult Zasowski et al. for specific details and insight regarding APOGEE-2 targeting.

The Open Cluster Chemical Analysis and Mapping (OCCAM) Survey generates a VAC of open cluster members as targeted in both APOGEE-1 and APOGEE-2 fields. To establish membership probabilities, the catalog combines APOGEE DR14 DRP-derived RVs and ASPCAP-derived metallicities with proper motion (PM) data from Gaia DR2. This first VAC from the OCCAM Survey includes 19 open clusters, each with four or more APOGEE members. The OCCAM VAC consists of two components: a set of bulk cluster properties, which include motions (RV, PM) as well as robust average element abundance ratios, and a set of membership probabilities for all stars considered in the analysis of the 19 open clusters. For further information on the OCCAM Survey, please consult Donor et al. (2018).

SkyServer has been the primary online web portal to the CAS since the beginning of SDSS, and although it underwent a significant facelift in 2007, it is now woefully outdated in terms of its layout and user experience, and generally in terms of its usability and accessibility. SkyServer has been due for a rewrite with modern web technology for several years now, and we are finally undertaking this daunting task as we wind down SDSS-IV and look forward to SDSS-V. One of the biggest constraints that makes this a difficult enterprise is the large user base that SkyServer has built over the past 15+ years. We do not want to completely rearrange the site in such a way that users no longer recognize it, and more importantly, we do not want to break all of the functionality that works very well currently in spite of the outdated interface. In short, we want to adopt a philosophy of going from "working to working" versions as we modernize the site. We list below the specific changes we are currently working on.

• 1.  
  Upgrade Technology: First and foremost, we are upgrading the web technology underneath SkyServer. This includes everything from the version of HTML and CSS that it was originally written in, to the way that the SkyServer website code is logically organized. We are going toward the MVC (model–view–controller) paradigm that modern websites use to produce modular, reusable, and robust web applications.
• 2.  
  Portability: SkyServer has been a Microsoft Windows application developed with the .NET framework all along. This has made it very difficult to port even the front end to any other platform. Now, with the availability of .NET Core, we have the opportunity to migrate the website to a portable platform that can not only run in Linux, but can also be Dockerized.
• 3.  
  Usability: Usability standards have changed considerably since the time when SkyServer originally came online. In spite of significant upgrades to different parts of SkyServer over the years, there has not been a comprehensive reexamination of the usability aspects. We aim to rectify this as we redesign the user interface. Usability changes will include the effective presentation of information, compatibility with all browsers, responsiveness of page loads, and consistency of display modes (e.g., opening a new tab for results from a query and/or bringing the results pane to the front).
• 4.  
  Accessibility: Accessibility pertains to the versatility of the website and how responsive and easy it is to use and work with for users that are restricted in various ways. This ranges from users on mobile devices to users with restricted access to the internet as well as users with impaired vision or other handicaps. Incorporating modern web design standards and technologies will mostly take care of these aspects, but we will pay special attention to make sure that SkyServer can be used by as many people as possible anywhere in the world there is internet access.
• 5.  
  Integrate SkyServer and Voyages: SkyServer has an extensive educational section that contains several levels of classroom exercise based on SDSS data. These are known collectively as the SkyServer Projects. Voyages is a SkyServer "spinoff" website that has become quite popular and presents several virtual "voyages" through the SDSS data for non-scientist audiences. The Voyages website is a much more modern web application that is based on a content management system (CMS)—WordPress. This allows new pages and functionality to be added to Voyages much more easily than to SkyServer. As part of the SkyServer modernization, we are migrating all of the SkyServer student projects to Voyages and using the same CMS (WordPress) for SkyServer too. We are also integrating Voyages further with SkyServer so that it uses the SkyServer API to run queries on the SDSS data.
• 6.  
  Streamline CAS $\iff$ SAS Interface: There are hooks currently between SkyServer/Voyages and the SAS, but they are awkward at best. The SAS API has recently been upgraded, and the points of access to SAS data that currently exist in SkyServer and Voyages will be updated to use the proper SAS API calls.

These steps will help ensure the availability of SDSS data to astronomers for years to come, and long beyond the current funded plans for SDSS-IV and SDSS-V. The CAS data and access tools will at that point be well positioned to be readily integrated into existing data centers for a minimal incremental cost.