build the MASTARHC2 library is inspired by, but different in many details,
from the hierarchical clustering method used to build the MILESHC library
(cf., Westfall et al., 2019, Section 5), as described below.

The principles of the hierarchical clustering approach used by Westfall et al.
(2019) to construct the MILESHC library are maintained, except we perform the
clustering for the MASTARHC2 library in two steps. The first step clusters
spectra based on their low-order continuum differences, leading to a set of
“base clusters.” We use pPXF (Cappellari & Emsellem, 2004; Cappellari, 2017)
to perform a least-squares fit of each spectrum using every other spectrum;
however, we do not include Gaussian kernel terms or polynomial continuum
optimization, meaning the least-squares fit simply optimizes the scaling
between the two spectra. We use the $rms$ difference between the best-fit
spectra as the clustering “distance,” and the distance matrix is used to
construct eight base clusters. The choice of eight clusters was based on an a
qualitative assessment of the appropriate number which separated MaStar
spectra into distinct types. The second step uses pPXF to fit each spectrum
using every other spectrum within its base cluster. In this step, we modestly
degrade the resolution of the template being fit with $\sigma=1$ pixel, and
then our pPXF fit includes a freely fit Gaussian kernel with bounds of a $\pm
1$ pixel shift and a $0.1-2$ pixel broadening. This was done in the same way
across all parts of the spectra. We also include a multiplicative Legendre
polynomial of order 100 to optimize the continuum match between the two
templates. The very high-order fit (the choice of the exact number of 100 was
arbitrary) acts like a high-pass filter on the differences between the two
spectra, ensuring that the optimized $rms$ difference between the two spectra
is driven by the high-order (line) structure differences. The spectra within
each base cluster are organized into “template clusters” and visually
inspected. The visual inspection leads to iterations on the number of template
clusters in each base cluster, as well as removing some of the spectra from
the analysis. The number of template clusters per base cluster ranged from 6
to 16, depending on a by-eye assessment of the spectra in each template
cluster. The final assignment of each MaStar spectrum (identified by its
MANGAID) to a template cluster is provided in the DAP code
repository.171717https://github.com/sdss/mangadap/blob/master/mangadap/data/spectral_templates/mastarhc_v2/README
Note that 34 of 99 clusters were not included in the MASTARHC2 library because
they were either composed of single stars, resulted in noisy spectral stacks,
contained isolated specific data-reduction artifacts, or contained a set of
spectra that were considered too disparate for a single cluster. For the
vetted set of 65 template clusters, the median number of spectra per cluster
is 14, but the range is from 2 to more than 300.

With the assignments in hand, we combine spectra in each template cluster as
follows. We first scale each spectrum by their median flux and create an
initial stack, weighting each spectrum by its median SNR. We then calculate
the ratio of each spectrum to the stacked spectrum and fit this with an
order-14 Legendre polynomial, which provides a low-order correction function
to the continuum shape of each spectrum. The specific choice of order 14 was
driven by a desire to match the choice made in the DAP fitting of galaxy
spectra, which was justified in Westfall et al. (2019) We constrain the
correction function to be no more than a factor of 2, which is particularly
important to the stacks of late-type stars with very little flux toward the
blue end of MaStar’s spectral range. The low-order correction function is then
applied to each spectrum in the template cluster before the final S/N weighted
stack. The error vector for each stack is the quadrature sum of the propagated
error from the stacking operations and the, typically much more significant,
standard deviation measured for the spectra in the stack. The final spectra in
the MASTARHC2 library are shown in Figure 6.

Figure 6.— Spectra in the MASTARHC2 template library. Spectra are arranged and
colored according to the membership in one of eight base clusters (the first
clustering step used in the process to generate the template library from
individual MaStar spectra).

####  5.2.2 Spectral Index Measurements

In DR17, we have added spectral index measurements that are more suited to
stacking analyzes and coaddition of spectra among spaxels, such as those based
on definitions of Burstein et al. (1984) and Faber et al. (1985). These
measurements are particularly useful for low SNR spaxels.

The motivation for this change emerges from the fact that in DAP’s hybrid
binning scheme, the spectral index measurements are performed on individual
spaxels, which can have very low S/N (cf. Westfall et al., 2019, Section 9).
Westfall et al. (2019) recommend improving the precision of the spectral index
measurements using specific aggregation calculations that closely match the
results obtained by performing the measurements on stacked spectra over the
same spatial regions (specifically, see their Section 10.3.3). However, the
comparison between an aggregated index and an index measured using a stacked
spectrum is not mathematically identical for the index definitions used by
Westfall et al. (2019). Motivated by the analysis of Molina et al. (2020), the
DAP calculates the spectral indices (specifically the absorption line indices)
using two definitions for DR17: (1) those definitions provided by Worthey
(1994) and Trager et al. (1998) and (2) earlier definitions provided by
Burstein et al. (1984) and Faber et al. (1985). The advantage of the
definitions provided by Burstein et al. (1984) and Faber et al. (1985) is that
they allow for a mathematically rigorous aggregation of spectral indices, as
we derive below.

Following the derivation by Westfall et al. (2019), we define a utility
function, which is a sum of pixel values, multiplied by pixel width,

$S(y)\equiv\int_{\lambda_{1}}^{\lambda_{2}}y\ d\lambda\approx\sum_{i}y_{i}\
{\rm d}p_{i}\ {\rm d}\lambda_{i},$ (1)

where $y$ is usually, but not always a function describing the flux in the
spectrum, $f(\lambda)$, ${\rm d}p_{i}$ is the fraction of spectral pixel $i$
(with width ${\rm d}\lambda_{i}$) in the passband defined by
$\lambda_{1}<\lambda<\lambda_{2}$. Note that masked pixels in the passband are
excluded by setting ${\rm d}p_{i}=0$, and
$S(1)=\Delta\lambda\equiv\lambda_{2}-\lambda_{1}$ if no pixels are masked. We
can then define a linear continuum between two sidebands, referred to as the
blue and red sidebands, as

$C(\lambda)=(\langle f\rangle_{\rm red}-\langle f\rangle_{\rm blue})\
\frac{\lambda-\lambda_{\rm blue}}{\lambda_{\rm red}-\lambda_{\rm
blue}}+\langle f\rangle_{\rm blue},$ (2)

where $f$ is the spectrum flux density, $\lambda_{\rm blue}$ and $\lambda_{\rm
red}$ are the wavelengths at the center of the two sidebands, and $\langle
f\rangle=S(f)/S(1)$.

The absorption-line index definitions used by Worthey (1994) and Trager et al.
(1998) are:

${\mathcal{I}}_{\rm WT}=\left\\{\begin{array}[]{ll}S(1-f/C),&\mbox{for \AA\
units}\\\\[3.0pt] -2.5\log\left[\langle f/C\rangle\right],&\mbox{for magnitude
units}\end{array}\right.,$ (3)

where the measurements are made on a rest-wavelength spectrum.181818Note the
subtle difference between Equation 3 and Equation 22 from Westfall et al.
(2019); the latter has an error in the expression for the index in magnitudes
units. Under this definition, the integration is performed over the ratio of
the flux to a linear continuum, which means that the sum of, say, two index
measurements is not identical to a single index measurement made using the sum
of two spectra. In contrast, Burstein et al. (1984) and Faber et al. (1985)
define:

${\mathcal{I}}_{\rm BF}=\left\\{\begin{array}[]{ll}S(1)-S(f)/C_{0},&\mbox{for
\AA\ units}\\\\[3.0pt] -2.5\log\left[\langle f\rangle/C_{0}\right],&\mbox{for
magnitude units}\end{array}\right.,$ (4)

where $C_{0}$ is the value of the continuum, $C(\lambda)$, at the center of
the main passband. Note that, given that $C(\lambda)$ is linear and assuming
no pixels are masked,
$S(C)=C_{0}\ \Delta\lambda$. Using the definition in Equation 4, we can
calculate a weighted sum of indices using the value of the continuum, $C_{0}$,
for each index as the weight to obtain

$\frac{\sum_{i}C_{0,i}{\mathcal{I}}_{\rm
BF}}{\sum_{i}C_{0,i}}=\Delta\lambda-\frac{\sum_{i}S(f)_{i}}{\sum_{i}C_{0,i}},$
(5)

assuming no pixels are masked such that $S(1)=\Delta\lambda$. That is, the
weighted sum of the individual indices is mathematically identical (to within
the limits of how error affects the construction of the linear continuum) to
the index measured for the sum (or mean) of the individual spectra. Similarly,
for the indices in magnitude units, we find:

$-2.5\log\left[\frac{\sum_{i}C_{0,i}10^{-0.4{\mathcal{I}}_{\rm
BF}}}{\sum_{i}C_{0,i}}\right]=-2.5\log\left[\frac{\sum_{i}\langle
f\rangle_{i}}{\sum_{i}C_{0,i}}\right].$ (6)

Given the ease with which one can combine indices in the latter definition, we
provide both ${\mathcal{I}}_{\rm BF}$ (in the SPECINDEX_BF extension of the
DAP MAPS file) and $C_{0}$ (in SPECINDEX_WGT) for all absorption-line indices
in DR17, along with the original definitions (${\mathcal{I}}_{\rm WT}$;
SPECINDEX) provided in DR15/DR16.

###  5.3. Marvin Visualization and Analysis Tools

Marvin (Cherinka et al., 2019) was developed as the tool for streamlined
access to the MaNGA data, optimized for overcoming the challenges of
searching, accessing, and visualizing the complexity of the MaNGA dataset.
Besides patches and internal optimizations, the DR17 updates to Marvin include
several enhancements such as querying targets by MaNGA quality and target
bitmasks and values, full support for installation on Windows machines, as
well as updates to the web interface. The Marvin Web Galaxy Page (Figure 7)
now includes data quality indicators for the DAP Maps, as well as toggle-able
features for the spectrum display. For how to use the web or Python tools, see
the Marvin documentation191919https://sdss-
marvin.rtfd.io/en/latest/index.html. See the Marvin Changelog for a complete
list of what has changed since the last released version. Contributions to
Marvin are welcome and encouraged. Please see the contribution
guidelines202020https://sdss-
marvin.rtfd.io/en/latest/contributing/contributing.html for more details.

Figure 7.— A screenshot of the galaxy maps view of the Marvin Web for the
MaNGA galaxy 12-193481 (Mrk 848). The SDSS three-color image of the galaxy is
shown in the top left part of the figure. The upper right panel shows the
spectrum of the spaxel at the position (37,37), which corresponds to the
center of the bundle. The maps show: (lower left) stellar kinematics; (lower
middle) H$\alpha$ emission line flux; and (lower right) D4000 spectral index
for this galaxy based on its “hybrid-binned” spectral data cube from the MaNGA
DAP.

Marvin now includes access for many of the MaNGA VACs212121https://sdss-
marvin.rtfd.io/en/latest/tools/vacs.html, that have been integrated into the
Marvin ecosystem. Each integrated VAC is accessible either as the full catalog
through the new Marvin VACs Tool, or on a per-target basis through the
existing Marvin Tools, e.g. Cube or Maps, via a new “vacs” attribute attached
to each Marvin Tool instance. Check the VACs section of the DR17 Datamodel in
the Marvin documentation to see which VACs are available in this release.

###  5.4. Ancillary Programs

As described in detail in Wake et al. (2017), the MaNGA galaxy sample is
comprised of a Primary sample covering galaxies to 1.5 $r_{e}$ (where $r_{e}$
is the effective radius; the radius containing 50% of the light), a Secondary
sample covering galaxies to 2.5 $r_{e}$, and a color-enhanced sample designed
to fill in underrepresented locations in the galaxy color-magnitude plane.
However the number density of these main sample targets was not uniform on the
sky, and in regions of lower-than-average target density, not all of the IFUs
on a plate could be assigned to core target categories. In order to fill the
remaining $\sim$5% of MaNGA bundles, MaNGA held two competitive calls for
ancillary targets (in July 2014 and January 2017), and a variety of ancillary
programs targeting assorted kinds of galaxies or other targets were selected.

We document here the final set of ancillary targets, with detail on how to
identify them for use, or to exclude them from studies of the primary,
secondary and/or color-enhanced samples. We provide in Table 4 an updated list
of the number of bundles available in each documented sample, along with the
binary bit mask digit stored in MANGA_TARGET3 (sometimes MNGTARG3) which can
be used to identify the sample.222222For advice on using bitmasks see
https://www.sdss.org/dr17/algorithms/bitmasks

Ancillary programs in general were designed to increase the numbers of
specific types of galaxies in the MaNGA sample. We provide a short summary of
all programs here (also see http://www.sdss.org/dr17/manga/manga-target-
selection/ancillary-targets):

  1. 1.

Luminous AGN: various luminous active galactic nuclear (AGN) samples were
targeted, either selected from Swift BAT (AGN_BAT), [OIII] emission selected
(Mullaney et al., 2013, AGN_OIII), Wide-field Infrared Survey Explorer (WISE;
Wright et al., 2010) colors (AGN_WISE), or other AGN from the Palomar survey
(AGN_PALOMAR, Ho, 1995). The goal of this program was to increase the range of
luminosities of AGN observed by MaNGA.

  2. 2.

Void Galaxies: (VOID) this program targeted rare void galaxies located in low-
density large scale environments. Targets were selected from the Void Galaxy
Survey (VGS; Kreckel et al. 2011).

  3. 3.

Edge-On SF Galaxies: a set of edge-on star forming (SF) galaxies were
selected, using WISE data to estimate star-formation rates (SFRs), and Sérsic
axial ratios ($b/a$) from the NASA Sloan Atlas (NSA; Blanton et al. 2011) to
estimate inclinations. The BITNAME is EDGE_ON_WINDS.

  4. 4.

Close Pairs and Mergers:  a set of close pairs and/or mergers were observed.
These were either selected to be in larger bundles than typical
(PAIR_ENLARGE), re-centered bundles (i.e. not centered on one of the pair, but
somewhere in the middle, (PAIR_RECENTER)), or in overlapping tiles, sometimes
two bundles are assigned - one to each of the pair (PAIR_2IFU). In addition
one bundle was assigned to a merger simulated by the Galaxy Zoo: Mergers
program (PAIR_SIM, Holincheck et al., 2016).

  5. 5.

Writing MaNGA: two bundles were assigned to an education/public outreach (EPO)
program to obtain MaNGA data for galaxies in the shape of letters in the word
MaNGA (selected from the Galaxy Zoo
“Alphabet”232323https://writing.galaxyzoo.org/). These are an “M” (plate-ifu =
8721-6102) and a “g” (9499-9102). See LETTERS.

  6. 6.

Massive Nearby Galaxies: very massive nearby galaxies are underrepresented in
MaNGA as they are too large in angular size to fit in the bundles. This
program targeted bundles at the central regions of very massive nearby
galaxies (MASSIVE).

  7. 7.

Milky Way Analogs: two different sets of Milky Way Analog galaxies are
included. These galaxies are identified using the method described in Licquia
& Newman (2015): one set matched on stellar mass and SFR (MWA), the other
matched on stellar mass and bulge-to-total ratio (MW_ANALOG).

  8. 8.

Dwarf Galaxies: a sample of dwarf galaxies selected from the Geha et al.
(2012) catalog (DWARF).

  9. 9.

ETGs with Radio Jets: a sample of early-type galaxies (ETGs) with radio jets
and evidence of suppressed SF (RADIO_JETS Lin et al., 2010).

  10. 10.

DiskMass Sample: a sample of face-on disc galaxies which had previously been
observed in the DiskMass survey (Bershady et al., 2011) with the goal of
providing a cross-calibration set (DISKMASS).

  11. 11.

Brightest Cluster Galaxies: a sample of Brightest Cluster Galaxies (BCGs; BCG)
from the Yang et al. (2007) catalog. This type of galaxy is otherwise
underrepresented in MaNGA.

  12. 12.

Resolved Stellar Populations: observations of very nearby galaxies in the ACS
Nearby Galaxy Survey Treasury (ANGST, ANGST; Dalcanton et al., 2009) survey,
as well as in M31 (M31), to facilitate detailed studies of the resolved
stellar populations.

  13. 13.

Coma Plates: a set of very deep observations of the Coma cluster (DEEP_COMA).
Each dedicated plate used for this program observes the central massive cD
galaxies (varying placement between the central regions and the galaxy
outskirts), a selection of ordinary galaxies, 3 ultrafaint galaxies, and 3
regions of intracluster light (ICL). The goal was to provide very high quality
spectra to enable detailed stellar population analysis.

  14. 14.

IC 342: a mosaic of 49 MaNGA plates, covering $5\arcmin\times 5\arcmin$ (5 kpc
$\times$ 5 kpc) across the disk of the nearby galaxy IC342 with $\sim$30 pc
spatial resolution (IC342). This project provides test data for the Local
Volume Mapper (LVM) in SDSS-V (see §8).

  15. 15.

SN Hosts: observations of the host galaxies of known Supernova, both SN Type
1a under SN1A_HOST and other types of SN under SN_ENV.

  16. 16.

Giant LSB galaxies: a set of giant low surface brightness (GLSB, GLSB)
galaxies identified in the NSA.

  17. 17.

Globular clusters: a set of dithered observations of the cores of eight
globular clusters (GCs) and 19 bulge/background fields around 3 GCs (NGC6316,
NGC6522, and NGC6528) taken to help with MaStar (GLOBULAR_CLUSTER).

Table 4Summary of MaNGA Ancillary Programs and Targeting Bits. See §5.4 for an explanation of each program and the BITNAMES Ancillary Program | Number of bundles observed | BITNAME | Binary digit
---|---|---|---
Luminous AGN | 6 | AGN_BAT | 1
| 37 | AGN_OIII | 2
| 23 | AGN_WISE | 3
| 5 | AGN_PALOMAR | 4
Void Galaxies | 4 | VOID | 5
Edge-On SF Galaxies | 58 | EDGE_ON_WINDS | 6
Close Pairs and Mergers | 56 | PAIR_ENLARGE | 7
| 38 | PAIR_RECENTER | 8
| 1 | PAIR_SIM | 9
| 22 | PAIR_2IFU | 10
Writing MaNGA | 2 | LETTERS | 11
Massive Nearby Galaxies | 70 | MASSIVE | 12
Milky Way Analogs | 38 | MWA | 13
| 40 | MW_ANALOG | 23
Dwarf Galaxies | 31 | DWARF | 14
ETGs with Radio Jets | 10 | RADIO_JETS | 15
DiskMass Sample | 7 | DISKMASS | 16
Brightest Cluster Galaxies | 55 | BCG | 17
Resolved Stellar Pops. | 3 | ANGST | 18
| 18 | M31 | 21
Coma | 85 | DEEP_COMA | 19
IC 342 (LVM like observations) | 810 | IC342 | 20
SN Hosts | 19 | SN1A_HOST | 26
| 30 | SN_ENV | 22
Post-starburst galaxies | 24 | POST-STARBURST | 24
Giant LSB galaxies | 3 | GLSB | 25
Globular clusters | 27118 GC targets, plus 19 bulge background fields | GLOBULAR_CLUSTER | 27
|  |  |

###  5.5. MaNGA Related VACs

A large number of MaNGA related VACs are presented in DR17, and will be
summarized in brief below.

####  5.5.1 DR16+ VACs

Two MaNGA related VACs were released in DR16+ (a mini-data release which
happened in July 2020). In addition a version of the “Visual Morphology from
DECaLS Images” VAC, which is updated for DR17, was also released in DR16+. We
document those first.

  1. 1.

NASA Sloan Atlas Images and Image Analysis: This VAC contains the underlying
image and image analysis for the NASA Sloan Atlas (NSA). The methods used are
described in Blanton et al. (2011) and Wake et al. (2017). Briefly, for a set
of nearby galaxies of known redshift ($z<0.15$) within the SDSS imaging area,
we have created and analyzed GALEX (Morrissey et al., 2007) and SDSS images.
This analysis forms the basis for the MaNGA targeting, and resulted in the
v1_0_1 NSA catalog released originally with DR14. We are now releasing the
images which were analyzed to create those parameters. The data set includes
the original catalogs from which the NSA sample was drawn, the mosaic images
and inverse variance images that were analyzed, the deblending results for
each object, the curve-of-growth and aperture corrections for each object, and
other intermediate outputs. We expect that this data set may be useful for
reanalysis of the GALEX or SDSS imaging. The full data set is large (15
terabytes) and therefore any users interested in using a large fraction of it
should transfer the data through Globus (see §3 for details on how to use
Globus242424and https://www.sdss.org/dr16/data_access/bulk/#GlobusOnline).

  2. 2.

MaNGA SWIFT VAC: The Swift+MaNGA (SwiM) value added catalog comprises 150
galaxies with both SDSS-IV/MaNGA IFU spectroscopy and archival Swift
Ultraviolet Optical Telescope (UVOT) near-ultraviolet (NUV) images, and is
presented in Molina et al. (2020). The similar angular resolution ($\sim
3\arcsec$) between the Swift/UVOT three NUV filters and the MaNGA IFU maps
allows for spatially-resolved comparisons of optical and NUV star formation
indicators, which is crucial for constraining attenuation and star formation
quenching in the local universe. The UVOT NUV images, SDSS optical images, and
MaNGA emission line and spectral index maps have been spatially matched and
re-projected so that all of the data match the pixel sampling, resolution and
coordinate system of the UVOT uvw2 image for each galaxy. The spectral index
maps utilize the definition given in Burstein et al. (1984), which allows
users to more easily compute spectral indices when binning the maps. Spatial
covariance is properly accounted for in the propagated uncertainties. In
addition to the spatially-matched maps, Molina et al. (2020) also provides a
catalog with PSF-matched aperture photometry for the SDSS optical and
Swift/UVOT NUV bands.

####  5.5.2 Galaxy Morphology VACs

A variety of galaxy morphology catalogs are provided as VACs, with analysis
done in a variety of ways, using a variety of images. We provide a short
summary of each here - for more details please see the appropriate paper.

  1. 1.

Galaxy Zoo: 3D: (GZ:3D; Masters et al. 2021) provides crowdsourced spaxel
masks locating galaxy centers, foreground stars, bars and spirals in the SDSS
images of MaNGA target galaxies. Available for use within Marvin, these masks
can be used to pick out spectra, or map quantities likely associated with the
different structures (see Peterken et al., 2019b, a; Fraser-McKelvie et al.,
2019, 2020; Greener et al., 2020; Krishnarao et al., 2020, for example use
cases).

  2. 2.

Galaxy Zoo Morphologies from SDSS, DECaLS and UKIDSS: The Galaxy Zoo method,
which involves combining classifications from a large number of classifiers
collected via an online interface, has been applied to a variety of images,
including the original SDSS images (Willett et al., 2013; Hart et al., 2016),
the UK Infrared Telescope Infrared Deep Sky Survey (UKIDSS; Lawrence et al.,
2007; Galloway, 2018) and most recently the Dark Energy Camera Legacy Survey
(DECaLS; Dey et al., 2019; Walmsley et al., 2022). This latter analysis
combines Machine Learning (ML) methods with crowdsourcing in an active loop
(for details see Walmsley et al. 2022). We collect together all these
crowdsourced morphologies for as many MaNGA galaxies as possible in this VAC.

  3. 3.

Visual Morphology from DECaLS Images: This VAC contains a direct visual
morphological classification, based on the inspection of image mosaics
generated from a combination of SDSS and DECaLs (Dey et al., 2019) images, for
the MaNGA galaxies. The DR16+ version contains the classification for the
first half of MaNGA galaxies (4600, MaNGA DR15) while the DR17 version
contains the classification for the full MaNGA DR17 with unique MaNGAID.
Through a digital image post-processing, we exploit the advantages of this
combination of images to identify inner structures, as well as external low
surface brightness features for an homogeneous classification, following an
empirical implementation of the methods in Hernández-Toledo et al. (2010) and
Cheng et al. (2011). The visual morphological classification is carried out by
two classifiers inspecting three-panel image mosaics, containing a gray
logarithmic-scaled $r-$band image, a filter-enhanced $r-$band image and the
corresponding RGB color composite image from SDSS and a similar mosaic using
DECaLS images incorporating the residual image after subtraction of a best
surface brightness model from the DESI _legacypipeline_
252525https://github.com/legacysurvey/legacypipe. The catalog contains the
T-Type morphology, a variety of visual morphological attributes (bars, bar
families, tidal debris, etc.) and our estimate of the non-parametric
structural, Concentration, Asymmetry and Clumpiness (CAS; Conselice, 2003)
parameters from the DECaLS images. For more detail in see Vázquez-Mata et al.
(2021). An updated version including morphologies for all DR17 MaNGA galaxies
in being prepared, see Vázquez-Mata et al. (in prep.).

  4. 4.

MaNGA PyMorph DR17 photometric catalog: (MPP-VAC, see Fischer et al. 2019;
Domínguez Sánchez et al. 2022 for details) provides photometric parameters
obtained from Sèrsic and Sèrsic+Exponential fits to the 2D surface brightness
profiles of the final MaNGA DR17 galaxy sample (e.g. total fluxes, half light
radii, bulge-disk fractions, ellipticities, position angles, etc.). It extends
the MaNGA PyMorph DR15 photometric VAC to now include all MaNGA galaxies in
DR17.

  5. 5.

MaNGA Morphology Deep Learning DR17 catalog: (MDLM-VAC, see Domínguez Sánchez
et al. 2022 for details) provides morphological classifications for the final
MaNGA DR17 galaxy sample using Convolutional Neural Networks (CNN). The
catalog provides a T-Type value (trained in regression mode) plus four binary
classifications: PLTG (separates early type galaxies, or ETGs, from late
types, or LTGs), PS0 (separates ellipticals from S0) , Pedge-on (identifies
edge-on galaxies) , Pbar (identifies barred galaxies). It extends the “MaNGA
Deep Learning Morphology DR15 VAC” (Fischer et al., 2019) to now include
galaxies which were added to make the final DR17. There are some differences
with respect to the previous version - namely, the low-end of the T-Types are
better recovered in this new version. In addition, the PLTG classification
separates ETGs from LTGs in a cleaner way, especially at the intermediate
types ($-1<$T-Type $<2$), where the T-Type values show a large scatter.
Moreover, the value provided in the catalog is the average of 10 models
trained with k-folding for each classification task (15 for the T-Type
classification). The standard deviation, which can be used as a proxy for the
uncertainty in the classification, is also reported.

####  5.5.3 Stellar Population Modeling VACs

There are a variety of stellar population modeling based VACs released.

  1. 1.

Principle Component Analysis (PCA) VAC (DR17): this VAC includes measurements
of resolved and integrated galaxy stellar masses, obtained using a low-
dimensional, PCA-derived fit to the stellar continuum and subsequent matches
to simulated star-formation histories (SFHs). The general methodology for
obtaining the principal component basis set, the stellar continuum fitting
routine, and the process of inferring stellar population properties such as
mass-to-light ratio are discussed in Pace et al. (2019a). The aggregation of
pixel-based mass estimates and adopted aperture-correction procedure are
described in Pace et al. (2019b). This procedure yields estimates of galaxy-
wide, integrated stellar masses also provided as part of this VAC. Key VAC
characteristics remain unchanged in comparison to DR16 (Ahumada et al., 2020),
where a holistic description of the VAC can be found. The principal
enhancement in this release is in the sample size: the number of galaxies has
been expanded to include all MaNGA galaxies to which the analysis could be
readily applied, a total of 10223 unique plate-ifu designations (this number
differs from unique galaxy counts, as some MaNGA galaxies were observed
multiple times, so have multiple plate-ifus, each of which are analyzed
separately in this VAC).

  2. 2.

Firefly Stellar Populations: This VAC provides measurements of spatially
resolved stellar population properties of MaNGA galaxies employing the
Firefly262626https://www.icg.port.ac.uk/firefly/ (Wilkinson et al., 2017) full
spectral fitting code. For DR17, Firefly v1.0.1 was run over all 10735
datacubes that had been processed by both the DRP and the DAP272727This number
differs from the total number of cubes listed in Table 1, as 538 data cubes
did not run through the DAP for various reasons. The major difference to the
DR15 VAC is that we now provide the catalog in two versions. The first employs
the stellar population models of Maraston & Strömbäck (2011) based on the
MILES stellar library (Sánchez-Blázquez et al., 2006). The second version uses
new MaStar models described in Maraston et al. (2020). Both model libraries
assume a Kroupa (2001) IMF. Compared to the Firefly VAC in DR15, the radius
(stored in HDU4 in the file) is now given in elliptical coordinates and the
azimuth is added. Masses (HDU11 and HDU12) are given per spaxel and per
Voronoi cell. We do not provide absorption index measurements anymore. Each
version of the VAC is offered as a single FITS file ($\sim$ 6 GB) comprising
the whole catalog of global and spatially resolved parameters and,
additionally, as a small version ($\sim$ 3 MB) that contains only global
galaxy stellar population parameters. A detailed description can be found in
J. Neumann et al. (in prep.) and Goddard et al. (2017).

  3. 3.

Pipe3D: This VAC containes the Pipe3D (Sánchez et al., 2016) analysis of the
full MaNGA dataset comprises the main properties of the stellar populations
and emission lines for more than 10,000 galaxies, both spatial resolved and
integrated across the entire field-of-view (FoV) of the IFUs. The content of
the released distribution was originally described in Sánchez et al. (2018),
and updated in S. F. Sánchez et al in prep. The new releases include
considerable modifications from the previous ones, the most important ones
being (i) the use of an updated version of the code fully transcribed to
python (E. Lacerda et al. in prep.), (ii) the use of a new stellar population
library based on the MaStar stellar library (A. Mejia-Narvaez et al. in
prep.), and (iii) an update on the list of analyzed emission lines.

####  5.5.4 HI-MaNGA DR3

HI-MaNGA is a HI 21cm line followup program, to provide estimates of total
atomic hydrogen content for galaxies in the MaNGA survey (Masters et al.,
2019). It makes use of both previously published HI data (primarily from the
ALFALFA survey; Haynes et al. 2018) and new observations using the Robert C.
Bryd Green Bank Telescope (GBT; to date under observing codes GBT16A_095,
GBT17A_012, GBT19A_127, GBT20B_033 and GBT21B_130). This VAC comprises the
third data release (DR3) of HI 21cm detections or upper limits for 6358
galaxies in the MaNGA sample. In some cases both GBT and ALFALFA data exist
for a single galaxy, and we provide both observations separately, so the total
number of rows is 6632, with 3358 coming from our GBT observations. The
observation and reduction strategy are documented in Masters et al. (2019);
Stark et al. (2021). As part of this program a 20% offset between actual, and
estimated L-band calibration at GBT was noticed (see Goddy et al. 2020). Stark
et al. (2021) provide guidance on dealing with confusion, and including upper
limits into statistical analysis. Observations are ongoing (under proposal
code GBT21B_130), with the program on track to observe, or homogenise HI data
for all MaNGA galaxies at $z<0.05$, with no pre-selection on color or
morphology. This is expected to result in a final HI-MaNGA sample size of
around 7000 MaNGA galaxies, with over 6800 already having at least some data
in hand.

####  5.5.5 The MaNGA AGN Catalog

The MaNGA AGN Catalog presents AGN in the DR15 sample of MaNGA that are
identified via mid-infrared WISE colors, Swift/BAT ultrahard X-ray detections,
NRAO VLA Sky Survey (NVSS) and Faint Images of the Radio Sky at Twenty-cm
(FIRST) radio observations, and broad emission lines in SDSS spectra. The
catalog further divides the radio AGN into quasar-mode and radio-mode
subpopulations, and provides estimates of the AGN bolometric luminosities.
Full details of the AGN selection and luminosity measurements are described in
Comerford et al. (2020). It is intended that this will be updated to include
all MaNGA galaxies in the future.

####  5.5.6 GEMA-VAC: Galaxy Environment for MaNGA Value Added Catalog

The Galaxy Environment for MaNGA (GEMA) VAC (M. Argudo-Fernádez et al. in
prep.) provides a variety of different measures of environment for galaxies in
MaNGA. The combination of mass-dependent and mass-independent parameters
provided in the catalog can be used to explore the effects of the local and
large-scale environments on the spatial distribution of star formation
enhancement/quenching, in the interaction of AGN with galaxies, or in the
connection with kinematics or galaxy morphology, for instance, in an
homogenous way allowing comparisons between different studies. In DR17, we
present the final and updated version of GEMA-VAC for the final MaNGA sample.
The quantifications of the environments are based on the methods described in
Argudo-Fernández et al. (2015) to estimate tidal strengths and projected
number densities, as well as that in Etherington & Thomas (2015) to estimate
overdensity-corrected local densities (MaNGA galaxies in the SDSS-DR15 only),
and Wang et al. (2016) for an estimation of the cosmic web environment. To
better explore the environment of galaxies located in dense local
environments, for instance, galaxies in compact groups or with strong
interactions (close paris/mergers), but not necessarily a high density
environment at larger scale, we also provide these same quantifications
considering MaNGA galaxies in groups, according to an updated version of the
catalog of groups compiled by Yang et al. (2007); and MaNGA galaxies in close
pairs, according to the sample used in Pan et al. (2019).

####  5.5.7 MaNGA Spectroscopic Redshifts for DR17

We present a catalog of precise spectroscopic redshifts M. Talbot et al. (in
prep.) for the RSS and spaxels in MaNGA, updating the previous version of this
catalog (Talbot et al., 2018) to include the completed sample of MaNGA
galaxies. These spectroscopic redshifts are computed using the spec1d – zfind
code from the publicly available BOSS pipeline (Bolton et al., 2012), in which
the NSA catalog provides the initial redshift. Once spectroscopic redshifts
were determined for the high signal-to-noise region within the galaxy half-
light radius, a second pass attempted to determine the remaining redshifts in
the low signal-to-noise spectra using the mean spectroscopic redshift as a
prior. The spectroscopic redshifts and a foreground model are presented for
each spectrum with sufficient SNR to model, in this VAC.

####  5.5.8 MaNGA Strong Gravitational Lens Candidate catalog

We present six likely, 12 probable, and 74 possible candidate strong galaxy-
galaxy scale gravitational lenses found within the completed MaNGA survey. The
lens candidates are found by the Spectroscopic Identification of Lensing
Object program (SILO; Talbot et al. 2018, M. Talbot et al. in prep) , which
was adapted from the BELLS (Brownstein et al., 2012) spectroscopic detection
method to find background emission-lines within co-added foreground-subtracted
row-stacked-spectra of the MaNGA IFU, in which the co-added residuals are
stacked across exposures from the same fiber at the same dither position.
Visual inspection of any background emission-line detected was performed,
including the position of detections in proximity to an estimated Einstein
radius. Narrowband images were constructed from the co-added residuals to
search for any lensing features.

##  6\. MaStar: The MaNGA Stellar Library

The MaNGA Stellar Library (MaStar) is a project in SDSS-IV to build a large
library of well-calibrated empirical stellar spectra, covering a wide range in
stellar parameter space, roughly from 2,500K to 35,000K in effective
temperature ($T_{\rm eff}$), from -1 to 5.5 in surface gravity ($\log g$), and
from -2.5 to 0.5 in metallicity ([Fe/H]). It is conducted using the same
instrument as MaNGA but during bright time (Yan et al., 2019). Most of the
observations were done by piggybacking on APOGEE-2N, in the sense that the
field centers of those plates, the time spent on the field, and the number of
visits were determined according to the science need of APOGEE-2N. Only in a
small number of fields were observational parameters determined by the science
needs of MaStar. DR17 presents data for all of the stars observed in the
MaStar program, along with complementary analysis of all the standard stars
targeted on MaNGA-led plates.

The MaStar targets were selected to cover a wide range in the 4-dimensional
stellar parameter space ($T_{\rm eff}$, $\log g$, [Fe/H], [$\alpha$/Fe].) In
the part of parameter space covered by APOGEE (Majewski et al., 2017),
APOGEE-2 (§4), SEGUE (Yanny et al., 2009), and the Large-sky Area Multi-Object
fiber Spectroscopic Telescope (LAMOST Luo et al. 2015), we make use of the
stellar parameters derived from these surveys to select targets, aiming to
evenly sample the parameter space. However, due to availability of stars of
certain parameters, the constraints of the fields selected by APOGEE-2N and
the evolving field choices, the stellar parameter space coverage cannot be
completely even. In addition to these selections, we further use photometry
data from the Panoramic Survey Telescope and Rapid Response System
(PanSTARRS-1; Kaiser et al. 2010) and the American Association of Variable
Star Observers Photometric All-Sky Survey (APASS; Munari et al. 2014) to
select stars that are more likely to have extreme temperatures, either very
hot or very cool. Further details of the MaStar target selection have been
described in Yan et al. (2019). Once Gaia Data Release 2 (Gaia Collaboration
et al., 2018, 2016) was available, we made use of Gaia color and absolute
magnitudes (derived using distances from Bailer-Jones et al. 2018) to select
stars to fill up the parts of the color-magnitude space that were not
sufficiently sampled, including hot main sequence stars, blue supergiants,
yellow supergiants, stars at the tip of red giant branch, and red supergiants,
Carbon stars and other asymptotic giant branch (AGB) stars, white dwarfs (WD),
extreme horizontal branch stars, metal poor dwarfs, and late M-dwarfs. These
recent changes to the target selection involving Gaia photometry will be
described by R. Yan et al. (in prep.).

Within the APOGEE-2 bright time extension ancillary call, the MaStar project
was given a small number of hours to observe stars that could not be targeted
by piggybacking on APOGEE-2N. During these times, we targeted a number of
star-forming fields with a large number of hot main sequence stars, blue and
red supergiants, a number of fields with known metal-poor late M dwarfs, and a
number of open cluster and globular cluster fields. For the dedicated globular
cluster fields, we conducted dithered observations to obtain integrated
spectra for the core regions of the globular clusters with some fibers
targeting relatively isolated stars in the outskirts of globular clusters.

###  6.1. MaStar-specific Changes to the MaNGA DRP

The MaStar data are obtained using the same MaNGA fiber feed system and the
BOSS spectrographs as the main MaNGA survey. The data reductions for MaStar
are done with the MaNGA DRP through its 2D phase. The details of this were
described by Law et al. (2016) with DR17 updates described in §5.1. In the 2D
phase, the only difference is in how we correct for the extinction of the
standard stars in the flux calibration module, which we describe below. The
reduction for the 3D phase is done differently from MaNGA. The basics of the
data reduction were described by Yan et al. (2019). We briefly describe the
updates since DR15/16 below. More details of these will be presented in R. Yan
et al. (in prep.).

  * •

Flux calibration for both MaNGA and MaStar plates are done using a set of 12
standard stars observed simultaneously with the science targets. By comparing
the spectra of these 12 stars with the theoretical spectra we determine the
throughput ratio between the observed spectra and the expected spectra above
the atmosphere. This ratio is then applied to all the spectra from all the
fibers to determine the per-fiber spectra. The theoretical spectra used in the
comparison need to have galactic extinction applied. For MaNGA-led plates, we
use the values from Schlegel et al. (1998) dust map as the standards in MaNGA-
led fields are at high galactic latitude and at a far enough distance to be
beyond most of the dust in the Milky Way in those line of sights. But this is
not always the case for MaStar plates. On MaStar plates, we use the spectra
themselves (relative to their respective models) to estimate the relative
extinction difference between different standard stars. Then we use the
broadband colors to estimate the absolute extinction level for all the stars.
With that, we can then determine the combined throughput curve of the
atmosphere, the telescope, and the instrument. This throughput curve is then
applied to calibrate spectra from all science fibers.

  * •

Standard stars targeted with mini-bundles on MaNGA-led plates are also treated
like other MaStar targets. This adds a significant number of F stars to the
library.

  * •

We have updated the template set used in the determination of stellar radial
velocity search, which is a selected subset from the BOSZ templates (Bohlin et
al., 2017). We expanded the subset to include templates with temperatures
between 3500K and 35000K, with surface gravity between 1 and 5 in log (g cm-1
s2), and with two different [$\alpha$/Fe] settings (0 and 0.5). We also
included the Koester white dwarf templates for DA-type white dwarfs (Koester,
2010).

  * •

We changed the method used to select the fiber on which the final spectrum is
based, for stars that saturate the central fiber in a bundle. We also changed
the criteria of determining whether to combine spectra of multiple fibers
together in each exposure. When combining spectra, the risk of “red upturn” is
evaluated and taken into consideration. The “red upturn” refers to the
artificial extra flux at the extreme red wavelengths in some of the spectra,
which is likely introduced by crosstalk between adjacent spectral traces and
imperfect 1D extraction at the extreme wavelengths. This will be discussed in
more detail in R. Yan et al. (in prep.).

  * •

For some MaStar plates (usually those done in APOGEE-2 time and therefore led
by APOGEE-2), we adopted exposure times much shorter than 900s in order to
target bright stars. Three exposures time settings were adopted: 28s, 83s, and
250s. For those exposures shorter than 180s, the flexure-compensation
algorithm adopted by DRP for MaNGA-length exposures no longer works due to the
faintness of sky emission lines. In these cases, we measure the radial
velocities for the standard stars, separately for the blue and red cameras, to
figure out the median relative shifts between the two cameras. This is then
used to adjust the blue cameras’ wavelength solutions to match those of the
red cameras’. The flexure could also cause the wavelength solution to differ
from exposure to exposure. In this case, we use the radial velocities derived
for all stars (both science targets and standard stars) to shift the
wavelength solutions of all exposures to be consistent with the first exposure
on a given visit, which is closest in time to the arc calibration frame.

We also added many quality checks and quality flagging in this data release.

  * •

We added checks in the DRP to indicate the risk of red upturns. A subset of
per-exposure spectra with significant upturn or downturn risks were visually
inspected. The results were stored in a metadata file and read in by the
pipeline to flag those per-exposure spectra. We exclude those per-exposure
spectra with UPTURNRISK, REDUPTURN, or REDDOWNTURN set in their quality
bitmask, if possible, when producing the per-visit spectra.

  * •

We run all MaStar spectra through an emission-line measurement code. The
results were used to select a subset for visual inspection. The visual
inspection results were combined with the automated measurements to decide
which spectra to be flagged as having emission lines. Spectra with H$\alpha$
equivalent widths (EW) greater than 0.6 Angstrom are flagged with the EMLINE
bit in EXPQUAL and MJDQUAL. We note that usual convention has positive EW for
absorption not emission, but for this context (MaStar emission-line
identification), we define emission lines to be positive in EW.

  * •

We evaluated the quality of flux calibration for both the spectra from
individual exposures and the combined spectra per visit. For individual
exposure spectra, we flag them according to the chi-square produced when
fitting for the flux ratios between the central fiber and the surrounding
fibers. When the chi-square is greater than 50, we flag the BADFLUX bit of the
quality bitmask of this exposure (EXPQUAL). This threshold corresponds to an
uncertainty larger than 0.15 mag ($\sim 14\%$) uncertainty in the relative
flux calibration between the BP and RP bands (evaluated by comparing the
synthetic color with Gaia photometry). This only flags the 1% worst cases on a
per-exposure per-star basis..

  * •

When we combine spectra from multiple exposures together to construct the
combined exposure per visit, the quality of flux calibration is going to
significantly improve due to averaging and due to the dominance by spectra
with higher signal-to-noise ratio which tend to have better calibration. The
evaluation of flux calibration quality for the combined spectra per-visit is
based on uncertainty determined through Jackknife resampling technique. If the
uncertainty on the synthetic BP-RP color is more than 0.05 mag, which
corresponds to 5% relative calibration error, we flag the BADFLUX bit of the
quality bitmask for this visit (MJDQUAL).

  * •

We exclude those per-exposure spectra with BADSKYSUB, POORCAL, or SEVERBT set,
if possible, when producing the per-visit spectra.

All of these changes and updates will be discussed in more detail by R. Yan et
al. (in prep.).

###  6.2. Changes to the MaStar Post-processing Pipeline

The MaStar post-processing pipeline (mastarproc) is updated for several
purposes. It processes the result from a preliminary DRP run to identify
candidates with emission line risks and red upturn/downturn risks for visual
inspection. It is also updated to give more information in the MaStar summary
files. We evaluate the variations of heliocentric radial velocities among all
visits of a star (same MaNGAID) and provide both the median velocity per visit
and that across all visits. The significance of the variation is provided and
if it is more than 3$\sigma$, we flag the VELVARFLAG column in the summary
file. We also added several useful columns to the summary files to indicate
the SNR, bad pixel fraction, etc. See more details in R. Yan et al. (in
prep.).

###  6.3. MaStar Summary files

In this data release, we provide several summary files. The mastarall file
contains only metadata information about the stars and visits, but no spectra.
It has four extensions containing four tables: GOODSTARS, GOODVISITS,
ALLSTARS, and ALLVISITS. The GOODSTARS table contains the summary information
for all stars with at least one good quality visit spectrum, which we define
as good stars. It has one entry per unique MaNGAID. The GOODVISITS table lists
out all of the good quality visits for the good stars. It has one entry per
unique visit. The ALLSTARS table contains the summary information for all of
the stars observed in MaStar, regardless of the quality of the visits, with
one entry per unique MaNGAID. The ALLVISITS table contains the information for
all of the visits of all of the stars.

The mastar-goodspec file contains all of the good quality visit-spectra. It
matches row-to-row to the GOODVISITS table in the mastarall file. The file
mastar-badspec-v3_1_1-v1_7_7.fits.gz contains all the other visit-spectra,
that are excluded from the GOODVISITS table.

In addition, we also provide two sets of files that contain spectra with
unified spectral resolution curves. Because each spectrum in mastar-goodspec
files can have different spectral resolution curves or line spread function
curves, it could be cumbersome for the users. We thus defined four resolution
curves based on the distribution of the spectral resolution at each wavelength
among all good visit-spectra. For each resolution curve, we select visit-
spectra that have higher resolution at all wavelengths and broadened their
line spread function by convolution to match the common resolution curve. We
provide four files containing four subsets of visit-spectra convolved to these
four uniform resolution curves, respectively.

With resolution curves unified, we could easily combine multiple visit-spectra
for the same star to improve signal-to-noise. These combined spectra are also
provided corresponding to the four subsets defined by the four resolution
curves.

The detailed data models for these files can be found in R. Yan et al. (in
prep.) or on the SDSS data release website (see §3).

###  6.4. Photometry crossmatch

With this data release, we also provide a value-added catalog giving
crossmatch information between MaStar and a few other catalogs. We crossmatch
it with Gaia DR2 (Gaia Collaboration et al., 2018, 2016), Gaia EDR3 (Gaia
Collaboration et al., 2021), PanSTARRS-1 (Flewelling et al., 2020), 2MASS
(Skrutskie et al., 2006), and Simbad (Wenger et al., 2000).

The crossmatches between MaStar and Gaia (DR2 or EDR3) are performed in a few
steps. For each MaStar target, given its coordinates and epoch of the
coordinates, we select all Gaia targets within 40″ of the star, apply proper
motion correction to shift them to the epoch for which the MaStar coordinates
were given. Then we search for the corresponding match within 3″. If there is
only one match, the match is considered to be the correct one. If there is
more than one candidate within the search radius, we compute a positional
matching probability and a photometry-matching probability for all candidates.
The photometry-matching probability is the average among probabilities in
multiple bands available for the MaStar targets, computed using empirical
relationships we established between Gaia photometry bands and the photometry
bands of the MaStar targets. Both probabilities take into account their
respective uncertainties. The product of the two probabilities are used to
determine the best match among the multiple matches within 3″. Nearly all
MaStar good stars have a match with Gaia. Among 24,290 good stars with unique
MaNGAID, all but one have a match in Gaia EDR3; all but 14 have a match in
Gaia DR2.

Sometimes, a single MaStar target based on ground-based photometry is resolved
by Gaia into multiple sources. Our algorithm tends to choose the brighter and
more dominant source as the match. In such cases, the MaStar spectra could
also be affected in two ways. First, the spectrum would contain light from
both stars. Second, if the separation between the two stars is large enough to
make the combined image non-circular, then the fiber-aperture correction could
be significantly affected resulting in a poor flux calibration for the final
spectrum. These can be identified or excluded by checking the gaia_cleanmatch
column in the two tables. Sources with gaia_cleanmatch=1282828This corresponds
to a value lower than 0.0084 in the “contamination” column, whose meaning is
defined in Appendix D.8 of Yan et al. (2019). The threshold adopted here is 3
times larger than that in Yan et al. (2019). The threshold adopted by Yan et
al. (2019) is more conservative for targeting purposes. are considered cleanly
isolated, for which the flux correction should be sufficiently accurate,
according to Gaia astrometry and photometry.

With most stars crossmatched with Gaia , the crossmatch with PanSTARRS-1 is
done through Gaia astrometry since it has more accurate coordinates and epoch
information. Applying Gaia proper motion, we shift the Gaia-provided
coordinates for MaStar targets to Epoch 2012.3 which is the approximate
average epoch of the PanSTARRS-1 photometry catalog we used. Then we search
for crossmatches in the PanSTARRS-1 catalog using a search radius of 2″. All
sources with a unique match within 1″ are considered a secure match. For those
with multiple candidates within 2″, we choose the candidate with the largest
product of the positional-matching probability and the photometry-matching
probability.

The Simbad catalog (Wenger et al., 2000) contains useful spectral type and
object type information for a small fraction of our targets. For crossmatching
to Simbad, we shift the coordinates of all our targets to epoch 2000.0, then
use a search radius of 3″. Sometimes, the same source appears as multiple
entries in Simbad with different object types. In these cases, we choose the
one that is most relevant or more informative for the star. About 13.3% of all
the good stars and 23.4% of the science targets have a Simbad crossmatch with
object type information. About 6.1% of all good stars and 9.9% of the science
targets have a Simbad match with spectral type information.

For 2MASS, the crossmatch is done through the crossmatch table provided by
Gaia (Marrese et al., 2019), using the astrometry solution provided by Gaia
Data Processing and Analysis Consortium (DPAC Lindegren et al., 2018, 2021).
We also derive extinction-corrected absolute magnitude and colors based on the
Gaia photometry using a 3D dust map (Green et al., 2019) and Bailer-Jones
distances (Bailer-Jones et al. 2018 for DR2 and Bailer-Jones et al. 2021 for
EDR3). We also include spectral type and object type information available
from Simbad. We provide two files. Both contain information from MaStar,
PanSTARRS-1, 2MASS, and Simbad. The only difference is that one file is based
on Gaia DR2, while the other file is based on Gaia EDR3.

###  6.5. MaStar Stellar Parameters VAC

Accurate stellar parameter labeling of the stars is essential for a stellar
library. Although a significant fraction of the stars targeted in our library
have been observed by other surveys with parameters derived, a large fraction
still lack such information. Furthermore, the parameter derivations from
previous surveys were inhomogeneous and some were based on data with poorer
quality than we have. Thus, we initiated multiple parallel efforts to
determine the stellar parameters for MaStar based on MaStar spectra
themselves. Within DR17, we include a VAC giving four sets of stellar
parameter measurements based on different methods along with the median values
of them when available and deemed robust.

The four sets of parameters are described below. More details can be found in
the respective papers. A comparison between the parameters will be presented
in R. Yan et al. (in prep.).

DL

This parameter set (D. Lazarz et al. in prep.) is derived using full-spectrum
fitting with an Markov Chain Monte Carlo (MCMC) sampler using interpolated
BOSZ model spectra with continuum shape information included in the chi-square
calculation. Extinction is fitted as a by-product. No photometry prior is
used.

JI

This parameter set (Imig et al., 2021) is derived using a neural network which
models flux as a function of labels and is trained on a combination of
empirical MaStar spectra with parameters from the APOGEE Stellar Parameters
and Chemical Abundance Pipeline (ASPCAP, see §4.2.2 below) and the model
spectra produced by Allende Prieto et al. (2018).

LH

This parameter set (Hill et al., 2022) is derived using full-spectrum, single-
template, pPXF fitting with an MCMC sampler, using interpolated BOSZ and MARCS
model spectra, with a flat prior based on Gaia color-magnitude diagram. The
continuum is modeled with a multiplicative polynomial.

YC

This parameter set (Y. Chen et al in prep.) is derived using full-spectrum
fitting using both the BOSZ and MARCS model spectra without interpolation,
with the result produced by Bayesian average and a flat prior based on Gaia
color-magnitude diagram. The continuum is modeled with a multiplicative
polynomial.

All four methods provide $T_{\rm eff}$, $\log\leavevmode\nobreak\ g$, and
[Fe/H]. On top of that, the methods by DL, LH and JI also provide
[$\alpha$/Fe], and the method by JI additionally provides micro turbulence
velocity ($v_{\rm micro}$). All methods have been applied to all spectra with
quality control applied differently for different methods. Each method flags
the spectra for which the parameters are considered invalid due to poor
fitting quality. When we take the median, we only take the median among those
methods that provide a valid measurement for a given spectrum. The
uncertainties of the median values are also computed accordingly. Which
methods are used in the median calculation are indicated by the INPUT_GROUPS
and INPUT_GROUPS_NAME columns for $T_{\rm eff}$, $\log\leavevmode\nobreak\ g$,
and [Fe/H]. The quality control is more strict for [$\alpha$/Fe], for which
the contributing methods are indicated by the INPUT_ALPHA_GROUPS and
INPUT_ALPHA_GROUPS_NAME columns in the VAC. The details of these will be
provided in R. Yan et al. in prep.

Metallicity measurements are crucial for assigning the right library spectra
to the right metallicity bin when building stellar population models. We found
some of the parameter sets could have slight systematic bias in metallicity
measurements. Therefore, we calibrated the metallicity measurements for three
of the four sets against APOGEE ASPCAP [Fe/H] measurements. In the VAC, in
addition to the straight median among the four sets, we also provide a
calibrated metallicity for each set and the median calibrated one among the
four. We consider this to be a more accurate representation of the true
metallicities of the stars. The stellar parameters are more reliable when at
least two of the four groups have valid measurements for the given star. The
users could choose to apply similar cuts to select a set of stars with more
reliable parameters.

In the right panel of Figure 8, we show the median effective temperature vs.
median surface gravity for the subset of good science stars in MaStar with at
least two of four groups providing valid measurements. This includes 91% of
all good science stars.

###  6.6. MaStar Sample Statistics

In total, the MaStar library includes 24,130 unique good quality stars with
59,266 good quality visits. Among these, 11,817 unique stars were targeted as
science targets and 12,345 unique stars were targeted as flux standards, with
some overlap between the two categories. The 24,130 unique stars correspond to
24,290 unique MaNGAIDs as some stars correspond to more than one MaNGAIDs when
taken from different source catalogs.

Figure 8.— Left: Extinction-corrected G-band absolute magnitude vs. BP-RP
color for all good science targets in the MaStar library. The color coding
indicates the median calibrated metallicity. Stars without valid metallicity
measurements are marked as red crosses. Right: Median effective temperature
vs. median surface gravity for MaStar science targets with valid measurements
from at least two of the four groups. Standard stars are also included in the
library but are not shown in these plots.

In Figure 8, we show a Hertzsprung-Russell diagram for all good stars in
MaStar, based on Gaia EDR3 photometry after correcting for dust using the 3D
dust map. The points are color-coded according to the median calibrated
metallicity. This illustrates the comprehensive stellar parameter coverage of
our library.

##  7\. eBOSS Like Data

While both the main eBOSS as well as the co-observed TDSS made their final,
full catalog release in DR16, new eBOSS like data is released for both the
SPIDERS sub-survey, and the eBOSS-RM program. A number of eBOSS related VACs
are also released.

###  7.1. eBOSS VACs

eBOSS (Dawson et al., 2016) concluded its observations of galaxies and quasars
as tracers of large-scale structure on March 1, 2019. The goal of these
measurements was to measure the distance-redshift relation with the baryon
acoustic oscillation (BAO) feature that appears at a scale of roughly 150 Mpc.
These data were also used to measure the growth of structure through redshift
space distortions (RSD; Kaiser, 1987).

The final eBOSS cosmology measurements were presented in a series of papers
submitted in July, 2020. These results included consensus measurements of BAO
and RSD for luminous red galaxies (LRG; Bautista et al., 2021; Gil-Marín et
al., 2020) over $0.6<z<1.0$, emission line galaxies (ELG; Tamone et al., 2020;
de Mattia et al., 2021) over $0.6<z<1.1$, and quasars (Hou et al., 2021;
Neveux et al., 2020) over $0.8<z<2.2$. In addition, measurements of BAO were
performed at $z>2.1$ using clustering in the Lyman-$\alpha$ forest and cross-
correlations between quasars and the forest (du Mas des Bourboux et al.,
2020). These measurements were combined with the final SDSS and BOSS (Dawson
et al., 2013) BAO and RSD measurements spanning redshifts $0.07<z<0.6$ (Ross
et al., 2015; Howlett et al., 2015; Alam et al., 2017) to form a final sample
of distinct clustering measurements over roughly ten billion years. The
aggregate precision of the expansion history measurements is 0.70% at
redshifts $z<1$ and 1.19% at redshifts $z>1$, while the aggregate precision of
the growth measurements is 4.78% over the redshift interval $0<z<1.5$. Using
the BAO technique by itself, with no other constraints, SDSS has built up a
clear picture of the distance-redshift relationship revealing a clear need for
dark energy with a detection significance of $8\sigma$ (Alam et al., 2021).

The full cosmological interpretation of these data are described in Alam et
al. (2021) and demonstrate the power of BAO for constraining curvature and
providing robust estimates of $H_{0}$ The analysis also demonstrates the
ability of RSD data to complement weak lensing and cosmic microwave background
measurements in providing independent evidence for a flat cosmological model
with dark energy described by a cosmological constant. The combined BAO and
RSD measurements indicate $\sigma_{8}=0.85\pm 0.03$, implying a growth rate
that is consistent with predictions from $\it Planck$ temperature and
polarization data (Planck Collaboration et al., 2020) and with General
Relativity. Combining these results with $\it Planck$, Pantheon Type Ia
supernovae (SNe Ia; Scolnic et al., 2018), and weak lensing and clustering
measurements (Troxel et al., 2018) from the Dark Energy Survey (DES) leads to
significant advances in cosmological constraints relative to the prior
generation of experiments. Each of the three parameters, $\Omega_{\Lambda}$,
$H_{0}$, and $\sigma_{8}$ is constrained at roughly 1% precision, even in a
model that allows free curvature and a time-evolving equation of state for
dark energy. In total, the data are best described by a flat $\Lambda CDM$
model with $H_{0}=68.18\pm 0.79\,{\rm km\,s^{-1}Mpc^{-1}}$ (for full details
of this analysis see Alam et al. 2021). The Dark Energy Task Force Figure of
Merit (Albrecht et al., 2006) of these data sets together is 94292929The
measurements and cosmological impact are summarised in two web pages
https://www.sdss.org/science/final-bao-and-rsd-measurements/ and
https://www.sdss.org/science/cosmology-results-from-eboss/.

New value-added catalogs derived from eBOSS data were released publicly at the
same time as the cosmology results. These catalogs contain the redshifts and
weights for each of the LRG, ELG, quasar, and Ly-$\alpha$ forest samples, as
well as the properties of all quasars observed during the four generations of
SDSS. In addition, the mock catalogs used to characterize covariance in the
clustering measurements are being released in coordination with DR17. A
description of each of these cosmology value-added catalogs is as follows:

####  7.1.1 eBOSS Large Scale Structure Catalogs

DR16 included full reductions of the completed set of observed eBOSS spectra.
An additional series of redshift estimates for the eBOSS galaxy samples was
produced by an algorithm known as
redrock303030https://github.com/desihub/redrock. The galaxy redshifts derived
from redrock are described in Section 4 of Ross et al. (2020). An additional
series of classifications and redshift estimates was also performed for the
BOSS and eBOSS quasar samples. The origin of quasar redshift estimates is
described in Lyke et al. (2020), while a summary of how those redshifts were
used in the clustering measurements is also found in Section 4 of Ross et al.
(2020). From these updated redshift estimates large-scale structure (LSS) VACs
are created, which, together map the three-dimensional structure of the
Universe using galaxies and quasars over redshifts $0.6<z<2.2$. These maps are
carefully constructed with corrections for observational systematic errors and
random positions that sample the survey selection function to allow unbiased
cosmological inference. Three distinct samples were observed by SDSS-IV and
used to produce LSS catalogs: LRG (Prakash et al., 2016); ELG (Raichoor et
al., 2017); and quasars (Myers et al., 2015). A value-added catalog for each
of these samples was released in July 2020. The LSS catalogs for the LRG and
quasar samples are described in Ross et al. (2020) while the LSS catalog for
the ELG sample is described in Raichoor et al. (2021).

####  7.1.2 eBOSS DR16 Large-scale structure multi-tracer EZmock catalogs

We present 1000 realizations of multi-tracer EZmock catalogs, with redshift
evolution and observational systematics, for each sample of the DR16 LSS data.
These mock catalogs are generated using the EZmock method (Chuang et al.,
2015), and applied the survey footprints and redshift distributions extracted
from the corresponding data. They accurately reproduce the two- and three-
point clustering statistics of the DR16 data, including cross correlations
between different tracers, down to the scale of a few $h^{-1}\,{\rm Mpc}$, and
provide reliable estimates of covariance matrices and analyzes on the
robustness of the cosmological results. Details on the construction and
clustering properties of the EZmock catalogs are presented in Zhao et al.
(2021).

####  7.1.3 eBOSS Quasar Catalog

Beginning with SDSS-I, SDSS has maintained a tradition of releasing a
visually-inspected quasar (or quasi-stellar object; QSO) catalog alongside
major data releases. The new SDSS-DR16Q catalog (DR16Q; Lyke et al. 2020)
represents the most recent, and largest, catalog of known unique quasars
within SDSS. To ensure completeness, quasars from previous catalog releases
(DR7Q; Schneider et al. 2010, DR12Q; Pâris et al. 2017) have been combined
with observations from eBOSS in SDSS-IV. The catalog contains data for more
than 750,000 unique quasars, including redshifts from visual inspections,
principle component analysis (PCA), and the SDSS automated pipeline.
Additionally, the catalog is the first from SDSS to contain both the Hewett &
Wild (2010) DR6 redshift estimates and the Shen et al. (2011) DR7 redshift
estimates that are based on the Hewett & Wild (2010) algorithm. Where
applicable, the catalog also contains information about broad absorption line
(BAL) troughs, damped Lyman-$\alpha$ (DLA) absorbers, and emission line
redshifts (via PCA). As in previous releases, DR16Q also contains properties
for each quasar from GALEX (Martin et al., 2005), UKIDSS (Lawrence et al.,
2007), WISE (Wright et al., 2010), FIRST (Becker et al., 1995), 2MASS
(Skrutskie et al., 2006), ROSAT/2RXS (Boller et al., 2016), XMM-Newton (Rosen
et al., 2016), and Gaia (Gaia Collaboration et al., 2018), when available. To
facilitate analyzes of pipeline accuracy and automated classification, a
superset was also released. This sample contained $\sim 1.4$ million unique
observations for objects targeted as quasars from SDSS-I/II/III/IV.

####  7.1.4 Lyman-$\alpha$ Forest Transmission VAC

This VAC contains the estimated fluctuations of transmitted flux fraction in
the pixels across the Lyman-$\alpha$ and Lyman-$\beta$ spectra region of DR16Q
quasars. In total, 211,375 line-of-sights contribute to the Lyman-$\alpha$
spectral regions and 70,626 to the Lyman-$\beta$ one. This VAC contains
everything needed to compute the three-dimensional auto-correlation of
Lyman-$\alpha$ absorption in two different spectral regions as in du Mas des
Bourboux et al. (2020). When combined with the DR16Q quasar catalog, this VAC
also provides the information to compute the three-dimensional quasar $\times$
Lyman-$\alpha$ cross-correlation. These two measurements are used to measure
the location of the BAO as reported in du Mas des Bourboux et al. (2020).

###  7.2. Other VACs based on Single-Fiber Optical Spectra

The final eBOSS data sample contains more than one million spectra of stars,
galaxies, and quasars obtained during SDSS-IV. This catalog has been used for
a range of studies of astrophysical processes beyond the BAO and RSD
measurements described above. Demonstrating the impact of these data for
additional studies, this release includes a value-added catalog of strong
lensing systems (originally released in July 2020 in a mini data release), a
new catalog of lensed Lyman-$\alpha$ emitting (LAE) galaxies, a new catalog of
the cosmic web, and a catalog of metal absorbers. A description of these
value-added catalogs is as follows:

####  7.2.1 eBOSS Strong Gravitational Lens Detection Catalog

A value-added catalog of 838 likely, 448 probable, and 265 possible candidate
strong galaxy gravitational lens systems was released along with the batch of
cosmology results and value-added catalogs in July 2020 (aka DR16+). These
systems were discovered by the presence of higher redshift background
emission-lines in DR16 eBOSS galaxy spectra. The methodology, including
quantitative explanations of the “likely”, “probably” and “possible”
categorisation is described in full in Talbot et al. (2021); also see Talbot
et al. (2018). This Spectroscopic Identification of Lensing Objects (SILO)
program extends the method of the BOSS Emission-Line Lens Survey (BELLS;
Brownstein et al., 2012) and Sloan Lens ACS (SLACS; Bolton et al., 2006)
survey to higher redshift, and has recently been applied to the spectroscopic
discovery of strongly lensed galaxies in MaNGA (SILO; Talbot et al., 2018,
also see §5.5.8). Although these candidates have not been studied through a
dedicated imaging program, an analysis of existing imaging from the SDSS
Legacy Survey and the DESI Legacy Surveys (Dey et al., 2019) provides
additional information on these systems. This catalog includes the results of
a manual inspection process, including grades and comments for each candidate,
consideration of sky contamination, low signal-to-noise emission-lines,
improper calibration, weak target emission-lines, systematic errors, Gaussian
modeling, and potential lensing features visually identified within the
available imaging.

####  7.2.2 eBOSS ELG-LAE Strong Lens Catalog

The eBOSS ELG-LAE Strong Lens Catalog contains roughly $150$ candidate strong
lens systems selected from the ELG sample of the eBOSS survey released by DR17
using the method presented in Shu et al. (2016). By construction, the lensing
galaxies in this catalog are ELGs up to $z\simeq 1.1$, and the source galaxies
are Ly$\alpha$ emitters at $z>2$. A full description of the catalog will be
presented in A. Filipp et al. (in prep.). This catalog is complementary to the
eBOSS Strong Gravitational Lens Detection Catalog constructed by Talbot et al.
(2021, §7.2.1 above), in which the source galaxies are all [Oii] emitters at
$z<1.7$.

####  7.2.3 Cosmic Web Environmental Densities from Monte Carlo Physarum
Machine

The Monte Carlo Physarum Machine (MCPM) cosmic web reconstruction algorithm
(Burchett et al., 2020; Elek et al., 2021) was employed to characterize the
matter density field from galaxies spectroscopically observed in SDSS,
including those previously included in the NSA (Blanton et al., 2011) and LOWZ
and eBOSS LRG catalogs. The MCPM framework, which is particularly sensitive to
the filamentary structure of the cosmic web, requires as inputs galaxy
coordinates, redshifts, and halo masses, and we leverage stellar mass
measurements included in VACs from previous Data Releases, the NASA-Sloan
Atlas itself and the eBOSS Firefly Value-Added Catalog (Comparat et al.,
2017). Halo masses were then derived by the halo abundance matching relations
from Moster et al. (2013). Using the Polyphorm software (Elek et al., 2020),
in which MCPM is implemented, reconstructions were produced in various
redshift ranges corresponding to the varying depth/completeness of the galaxy
samples. MCPM then provides a proxy metric (known as the trace) for the
environmental density at each point in the fitted volume. These arbitrary
density values are then calibrated to the cosmological matter density relative
to the mean matter density by performing MCPM fits to the Bolshoi-Planck
(Klypin et al., 2016) dark matter-only cosmological simulation’s halo catalog
(Behroozi et al., 2013) and producing a mapping between the MCPM trace and
simulation 3D matter density field. The data products are: (1) galaxy catalogs
with local environmental density values evaluated at their locations from the
full 3D density field and (2) the 3D density field itself, which users may in
turn query for density values at locations away from known galaxy positions.

###  7.3. SPIDERS

Within eBOSS, the SPectroscopic IDentification of ERosita Sources (SPIDERS)
program, dedicated to the characterization of the X-ray sources, was the
largest and most complete spectroscopic follow up of the best all-sky X-ray
surveys available at the time (mainly the ROSAT all-sky survey; Clerc et al.
2016; Dwelly et al. 2017; Salvato et al. 2018). As the acronym suggests, the
original motivation and plans for SPIDERS were driven by the capabilities of
the eROSITA X-ray telescope (on board the Spectrum Roentgen Gamma, or SRG,
satellite), which launched on July 13, 2019 (Predehl et al., 2021),
significantly later than the planned date at the start of SDSS-IV. While
waiting for eROSITA to collect data, the SPIDERS program targeted ROSAT/1RXS
and XMM-Newton Slew Survey sources within the eBOSS footprint. These targets
are expected to make up the bright X-ray flux end of the eROSITA population.
Much of the SDSS spectroscopy from the SPIDERS program was released as part of
SDSS DR16 (Ahumada et al., 2020), including the largest spectroscopic redshift
catalogs of X-ray selected AGN and clusters (Comparat et al., 2020b; Clerc et
al., 2020; Kirkpatrick et al., 2021).

Below, we highlight some recent pre-eROSITA SPIDERS results that are based on
the SDSS DR16 dataset. Then, in §7.5 we present a new SPIDERS dataset (to be
released as part of SDSS
DR17313131https://data.sdss.org/sas/dr17/eboss/spectro/redux/v5_13_2/),
dedicated to the spectroscopic follow-up of X-ray sources discovered in early
SRG/eROSITA performance verification observations. In §7.5.6 we discuss future
plans for eROSITA followup in SDSS-V (also see §8).

###  7.4. SPIDERS Galaxy Cluster Highlights

In this section we describe the SPIDERS cluster sample, the largest catalog of
spectroscopically confirmed X-ray detected clusters to date. While no new data
is released in DR17, several new results have come out since DR16. We refer
the reader to the below papers for details of the ancillary data created and
how to access it.

The bulk of the galaxy cluster population targeted by SPIDERS up to and
including DR16 originated from the ROSAT-based CODEX survey (Finoguenov et
al., 2020), with additions of low-mass systems from the XMM-Newton-based
X-CLASS catalog. The initial SPIDERS CODEX and X-CLASS targeting plans and
survey strategy were described by Clerc et al. (2016). Clerc et al. (2020)
provide complementary details of targeting updates that occurred during the
course of the project. Clerc et al. (2020) also describe the full sample of
acquired targets, the achieved spectral quality, as well as the redshift
measurement success rate (nearly 98%) and precision (20 km s-1 at $z=0.2$
after averaging all galaxies in a cluster). In particular, Clerc et al. (2020)
highlight the very homogeneous nature of targets observed throughout the
program; this specific feature supports a minimally biased spectroscopic
follow-up of clusters.

Galaxy clusters confirmed and validated with spectroscopy from SPIDERS are
presented in Kirkpatrick et al. (2021, 2740 CODEX clusters) and Koulouridis et
al. (2021, 124 X-CLASS clusters). These two catalog papers list confirmed
clusters up to $z\lesssim 0.6$, and present derived X-ray properties that
exploit high precision cluster redshifts, obtained by averaging multiple
SPIDERS member galaxy spectra per cluster. Kirkpatrick et al. (2021) also
provide velocity dispersion estimates for those clusters with more than 15
members, confirming correlations with X-ray luminosity and optical richness
found by other studies. A galaxy cluster number count analysis performed in
the velocity dispersion–redshift parameter space yields constraints on
cosmological parameters $\Omega_{\rm{m}}$ and $\sigma_{8}$ consistent with the
main cosmological analysis of the sample. The main cosmological analysis of
the SPIDERS clusters program is presented by Ider Chitham et al. (2020); it
involves the modeling of the redshift–richness relation of galaxy clusters
spectroscopically confirmed by SPIDERS. The best-fit parameters are found to
be $\Omega_{\rm{m}}=0.34^{+0.09}_{-0.05}$ and $\sigma_{8}=0.73\pm 0.03$. As a
central ingredient in those cosmological studies, the calibration of the
mass–observable relations is presented in two papers: Capasso et al. (2019)
focus on the mass–richness relation and Capasso et al. (2020) on the
mass–X-ray luminosity relation. The large scale clustering of clusters –
highly biased tracers of the underlying dark matter density field – is
presented and discussed by Lindholm et al. (2021). Taking advantage of the
dense and precise spectroscopic sampling of cluster members, Mpetha et al.
(2021) extract the gravitational redshift signal from stacks of SPIDERS
confirmed galaxy clusters. Brightest SPIDERS cluster galaxies are studied by
Furnell et al. (2018) and Erfanianfar et al. (2019), in particular, the
scaling of their stellar mass with the host halo properties is shown to lack
evolution since $z\sim 0.6$.

###  7.5. The eROSITA Final Equatorial-Depth Survey

During the SRG performance verification phase (November 2019), eROSITA was
used to survey a contiguous region of $\sim$140 deg2. This eROSITA Final
Equatorial-Depth Survey (eFEDS; Brunner et al., 2021; Liu et al., 2021c) is
intended as an early representative demonstration of the capabilities of the
eROSITA all-sky survey, which will not be completed until late 2023. In order
to exploit the availability of these X-ray data, we allocated a dedicated set
of 12 SDSS-IV/SPIDERS plates to follow up counterparts of eFEDS X-ray sources,
to be observed at APO during the first quarter of 2020. These special eFEDS
plates, documented here, are released as part of SDSS DR17. Unfortunately,
because of the COVID19-related closure of APO (see §1), only a small fraction
of the originally designed plates were observed, most to shallower than
expected depth. Nevertheless, this program did collect useful data, and the
experience aqcuired has greatly helped the preparation of the eROSITA followup
component of the SDSS-V Black Hole Mapper program (Kollmeier et al., 2017,
also see §8).

The target selection procedure for eFEDS broadly followed the steps carried
out for follow up of ROSAT sources in SPIDERS (Clerc et al., 2016; Dwelly et
al., 2017). First, the X-ray source detection process (described in detail by
Brunner et al., 2021), provides a catalog, including parameters that describe
the detection likelihood, extent likelihood (to distinguish point sources from
extended ones), and astrometrically corrected X-ray position and positional
uncertainty. For the SDSS-IV targeting of eFEDS sources, we used the
preliminary data processing software version available at that date (late
2019, ‘c940’), and only considered eFEDS sources with detection likelihood
$>8$, so that the fraction of spurious detections is kept to a relatively low
rate. Based on simulations (Comparat et al., 2019, 2020a; Liu et al., 2021b),
we expect only around 2% of these X-ray detections to be spurious. Optical/IR
counterparts were associated with the X-ray sources using the methods
described below. Finally, each of these counterparts is identified as being in
one or more target classes (recorded using flags in the targeting bitmask,
Sec. 7.5.4), and then the final target list is created which includes only
objects suited for spectroscopic observations with the BOSS spectrographs.

####  7.5.1 eFEDS: Optical counterparts to extended sources

The identification of SPIDERS clusters and their membership, as well as the
detailed target selection process are described fully by A. Merloni et al. (in
prep.) and J. Ider Chitham et al. (in prep.). Here we document the resulting
targeting bits that describe the contents of the SPIDERS cluster target
catalog:

  1. 1.

EFEDS_EROSITA_CLUS: These are galaxies associated with extended X-ray
emission. A combination of all member galaxies selected using redMaPPer in
scanning-mode (Rykoff et al., 2014) as well as additional BCGs identified
using the multicomponent matched filter cluster confirmation tool (Klein et
al., 2019). BCGs are allocated the highest target priority (0), while all
other members are given a lower priority (33-130). The selection method on
eFEDS extended sources is described fully in Liu et al. (2021a); Klein et al.
(2021). The redMaPPer based selection uses positional X-ray information to
estimate optical properties such as photometric redshifts, centering
information and member selection in a method analogous to that of as described
by Clerc et al. (2016), as used in the SPIDERS DR16 data release. The method
itself is described by Ider Chitham et al. (2020), J. Ider Chitham (in prep.).
The photometric selections of BCGs and cluster members are based on the eighth
data release of the DESI Legacy Imaging Surveys (using $g-r$ and $r-z$ colors)
as well as HSC photometric ($grizy$) data (Dey et al., 2019; Aihara et al.,
2019). Cluster BCG photometric redshifts are within $0.0<z<1.3$, a total of 85
were observed.

  2. 2.

EFEDS_SDSS_REDMAPPER: Publicly available optically selected cluster member
catalog based on SDSS DR8 and CODEX data (Rykoff et al., 2014; Finoguenov et
al., 2020). Cluster photometric redshifts are within $0.0<z<0.6$, a total of
1031 potential cluster galaxies were observed.

  3. 3.

EFEDS_HSC_REDMAPPER: Optically selected cluster member catalog (Rykoff et al.,
2014) based on HSC DR2 data (Aihara et al., 2019). Cluster photometric
redshifts are within $0.3<z<1.1$, a total of 13 cluster galaxies were
observed.

####  7.5.2 eFEDS: Optical counterparts to point sources

The method used to associate optical/IR counterparts to the eFEDS X-ray point-
sources is presented by Salvato et al. (2021). In short, the optical
counterparts to the X-ray sources were identified using a Bayesian association
algorithm NWAY (Salvato et al., 2018), which accounts for both position and
photometric properties of the counterpart. The association algorithm was
informed by a large training sample of XMM-Newton and Chandra X-ray
serendipitous sources with secure optical identifications. Supplementary
optical/IR counterparts were also provided via a Likelihood Ratio approach
(Sutherland & Saunders, 1992; Ruiz et al., 2018). Note that at the time of
selection of targets for the SDSS-IV special plates, only very early versions
of the X-ray catalog and counterpart selection algorithms were available - so
the pool of targets is different from the counterparts presented by Salvato et
al. (2021). The main eFEDS point-like target class is EFEDS_EROSITA_AGN with
2149 observed targets. Despite the name, we expect this target set to also
include a number of stars and (inactive) galaxies.

####  7.5.3 eFEDS: additional targets

To ensure that all fibers available in the designed plates could be allocated
to interesting scientific targets, beyond the eROSITA-selected sources
described above, several additional target classes were defined before the
arrival of the eROSITA data. The largest of these were special classes of AGN
drawn from optical targets selected by a combination of the HSC Subaru
Strategic Program (SSP) imaging and other multi-wavelength catalogs. Note that
due to the high sky density seen in the available eROSITA/eFEDS source
catalog, very few fibers were left for filler targets, and so most of the
defined target classes were allocated zero fibers (unless the same
astrophysical object was also selected as an eROSITA target). However, since
these target selection classes are defined in the DR17 data products, for
completeness of the documentation, we give a brief description here of each
target class.

  1. 1.

EFEDS_HSC_HIZQSO: Optical/NIR High-redshift QSO candidates selected from HSC
photometric data witha drop-out technique targeting $z>4$.

  2. 2.

EFEDS_HSC_WERGS: FIRST-detected bright radio galaxies selected for their
extreme red Optical-NIR colors

  3. 3.

EFEDS_HSC_REDAGN: WISE-detected obscured AGN candidates selected on the basis
of their extremely red Optical-NIR colors.

  4. 4.

EFEDS_COSMOQSO: UV excess/variability selected QSO - following prototype
selection criteria of 4MOST Cosmology Survey.

  5. 5.

EFEDS_WISE_AGN: AGN candidate selected via location in WISE color-magnitude
space, following criteria of Assef et al. (2018) \- with optical counterparts
selected from DESI Legacy Imaging Survey DR8.

  6. 6.

EFEDS_WISE_VARAGN: AGN candidate selected via variability signature in WISE
data - with optical counterparts selected from DESI Legacy Imaging Survey DR8.

  7. 7.

EFEDS_XMMATLAS: Optical counterparts to X-ray sources from the 3XMM/dr8
catalog (Rosen et al., 2016) and from the XMM-ATLAS survey field (Ranalli et
al., 2015).

  8. 8.

EFEDS_CSC2: X-ray sources from the Chandra-CSC2.0 catalog (Evans et al., 2010)
\- with optical counterparts selected from DESI Legacy Imaging Survey (DR8).

  9. 9.

EFEDS_SDSSWISE_QSO: QSO candidates from the Clarke et al. (2020) ML
classification of SDSS-WISE photometric sources

  10. 10.

EFEDS_GAIAWISE_QSO: QSO candidate from the Shu et al. (2019) Random Forest
classification of Gaia-unWISE sources

  11. 11.

EFEDS_KIDS_QSO: QSO candidate from the Nakoneczny et al. (2019) classification
of KiDS+VIKING DR3 sources

  12. 12.

EFEDS_GAIA_WD: Gaia-selected WD binary candidate in the region between the
zero age main sequence and the WD sequence.

  13. 13.

EFEDS_GAIAGALEX_WD: Gaia-GALEX UV excess source - expected to be compact
binary harbouring a WD

  14. 14.

EFEDS_VAR_WD: WD candidate selected on the basis of its optical variability

Further details of the eFEDS target selection process will be presented in A.
Merloni et al. (in prep.).

####  7.5.4 Target classes, target bits

The targets described above for the eFEDS field are assigned to 19 different
target classes. Table 5 describes each of the target classes designed for the
12 plates covering eFEDS. The maskbits used range from 19 to 47 and can be
found in the EBOSS_TARGET1 target bitmask. For clarity, only the bits for
target classes that resulted in observations are reported.

Table 5Different target classes created for the SDSS-IV/eFEDS plates.  EBOSS_TARGET1 | Bit name | N | Description
---|---|---|---
19 | EFEDS_EROSITA_AGN | 2149 | Point-like eROSITA source - target expected to be AGN, star or compact object
20 | EFEDS_EROSITA_CLUS | 85 | Extended eROSITA source - target expected to be galaxy cluster member
22 | EFEDS_HSC_HIZQSO | 34 | High redshift QSO candidate from HSC photometry
23 | EFEDS_HSC_WERGS | 154 | WISE bright, optically faint radio galaxy from HSC photometry
24 | EFEDS_HSC_REDAGN | 108 | Obscured AGN selected in HSC+WISE and/or HSC+VIKING photometry
25 | EFEDS_COSMOQSO | 1056 | UVX/variability QSO selection
26 | EFEDS_WISE_AGN | 649 | WISE color-selected AGN
27 | EFEDS_WISE_VARAGN | 508 | WISE variability-selected AGN
28 | EFEDS_XMMATLAS | 88 | X-ray selected target from the XMM-ATLAS coverage in the eFEDS field
29 | EFEDS_CSC2 | 6 | X-ray selected target from the Chandra Source Catalog in the eFEDS field
35 | EFEDS_SDSSWISE_QSO | 395 | QSO candidate from UMAP classification of SDSS+WISE photometric sources
36 | EFEDS_GAIAWISE_QSO | 651 | QSO candidate from RF classification of Gaia-unWISE sources
37 | EFEDS_KIDS_QSO | 565 | QSO candidate from classification of KiDS+VIKING DR3 sources
38 | EFEDS_SDSS_REDMAPPER | 1031 | RedMaPPER galaxy cluster candidate with SDSS photometry
39 | EFEDS_HSC_REDMAPPER | 13 | RedMaPPER galaxy cluster candidate with HSC photometry
45 | EFEDS_GAIA_WD | 55 |  Gaia-selected WD binary candidate in the region between the ZAMS and the
|  |  | WD sequence.
46 | EFEDS_GAIAGALEX_WD | 9 |  Gaia-GALEX UV excess source - expected to be compact binary harbouring a WD
47 | EFEDS_VAR_WD | 3 | Optical variability selected WD candidate
††footnotetext: These bits are located within the EBOSS_TARGET1 targeting bitmask. In the column ‘N’, we report the number of observed targets falling in a category. Note that targets can belong to several categories. |  |  |

####  7.5.5 eFEDS Observations

Observations were carried out in March 2020, but due to a combination of poor
weather and Covid-19 shut down (on MJD 58932), the program could unfortunately
not be completed. The initial success criteria for a plate to be considered
complete were SN2_G1,2 $>20$, SN2_I1,2 $>40$, where the SN2 are estimates of
the squared SNR, at fiducial 2″ fiber magnitudes of $g=21.2$ and $i=20.2$,
averaged over targets in each of the four BOSS spectrograph arms (G1, I1, G2,
I2). These SN2 thresholds are approximately double those adopted for regular
eBOSS observations (Dawson et al., 2016). This guarantees a highly complete
redshift measurements for faint AGN, and allows the measurement of the
properties of the AGN and of the host galaxy.

Table 6 details the plate numbers and the meta data related to their
observations. Only seven out of 12 plates were actually observed in the period
MJD 58928–58932; of these seven, only one plate reached nominal depth, three
received significant exposure beyond the typical eBOSS limit, and three were
only partially exposed. In Table 6 these are marked as good (G), fair (F) and
bad (B), respectively.

The redshifts from eFEDS special plates are part of the catalog that describes
the multiwavelength properties of the eROSITA point sources (Salvato et al.,
2021), currently available at
https://erosita.mpe.mpg.de/edr/eROSITAObservations/Catalogues/ and expected to
be available in Vizier in the future.

Table 6Summary of eFEDS observations.  Plate | MJD | RA | DEC | Quality | SN2 | Tile ID
---|---|---|---|---|---|---
Number |  | (deg) | (deg) |  | G1 | I1 | G2 | I2 |
12525 | 58932 | 130.27 | -0.57 | B | 7.6 | 17.8 | 9.2 | 26.0 | 17693
12527 | 58930 | 130.02 | 1.65 | F | 9.1 | 27.6 | 10.8 | 35.0 | 17694
12531 | 58932 | 131.96 | 1.65 | B | 3.2 | 9.1 | 4.0 | 11.5 | 17696
12533 | 58931 | 134.15 | -0.57 | B | 0.4 | 0.9 | 0.6 | 1.4 | 17697
12544 | 58928 | 137.79 | 1.65 | F | 9.3 | 21.9 | 9.8 | 29.7 | 17702
12545 | 58930 | 140.02 | -0.55 | F | 8.3 | 24.6 | 9.4 | 30.9 | 17703
12547 | 58928 | 139.75 | 1.65 | G | 28.8 | 70.3 | 27.1 | 81.4 | 17704
††footnotetext: Plate: plate number. MJD: Modified Julian Date of the observations. RA: Right Ascension of the center of the plate (degrees). Dec: Declination of the center of the plate (degrees). Quality: G: good; F: fair; B: bad. SN2: signal to noise reached in each spectrograph’s arm. |  |  |  |  |  |  |  |  |

####  7.5.6 eFEDs in SDSS-V

The spectroscopic observations of the eROSITA sources in the eFEDS PV field,
reduced at the end of the SDSS-IV program because of the March 2020 closure at
APO, has been resumed within the early (plate-mode operations) phase of SDSS-V
(see §8). A much more complete description of the eFEDS program (SDSS-IV and
SDSS-V) will be provided in A. Merloni et al., (in prep.). Among others, the
more homogeneous coverage of the combined program will enable X-ray AGN
clustering studies and galaxy-galaxy lensing measurements (J. Comparat et al.
in prep) on an unprecedentedly large sample.

Eventually, with the commissioning of the robotic fibre positioners on both
northern and southern SDSS-V sites, SPIDERS will deliver on its original
promises of massive, systematic spectroscopic observations of the sources
detected in the eROSITA all-sky survey as part of the SDSS-V ‘Black Hole
Mapper’ (BHM) program (Kollmeier et al., 2017). The experience and the
scientific results obtained by the SPIDERS team within SDSS-IV, that we have
briefly described above, represent a major milestone for the planning,
execution and exploitation of the BHM survey program.

###  7.6. eBOSS-RM

The Sloan Digital Sky Survey Reverberation Mapping (SDSS-RM) project is a
dedicated multi-object optical reverberation mapping program (Shen et al.,
2015b) that has monitored a single 7 ${\rm deg}^{2}$ field (R.A.
J2000=213.704, decl. J2000=+53.083) since 2014 during both SDSS-III and SDSS-
IV, using the BOSS spectrographs (Smee et al., 2013) at APO. The first-season
SDSS-RM spectroscopic data were taken during January-July 2014 in SDSS-III
(Eisenstein et al., 2011) and consist of a total of 32 epochs with an average
cadence of $\sim 4$ days; each epoch had a typical exposure time of 2 hr. The
SDSS-RM program continued in SDSS-IV (Blanton et al., 2017), with $\sim 12$
epochs per year (2 per month) with a nominal exposure time of 1 hr each during
2015-2017, and $\sim 6$ epochs per year (monthly cadence) during 2018-2020. As
of July 2020, SDSS-RM has obtained a total of 90 spectroscopic epochs over a
spectroscopic baseline of 7 years (2014-2020). All SDSS spectroscopic data
(pipeline reduced and calibrated) from SDSS-RM are included in the data
releases of SDSS-III and SDSS-IV. The full technical details of SDSS-RM are
provided in Shen et al. (2015b).

In addition to optical BOSS spectroscopy, accompanying photometric data in the
$g$ and $i$ bands are acquired with the 3.6-m Canada-France-Hawaii Telescope
(CFHT) and the Steward Observatory 2.3-m Bok telescope (Kinemuchi et al.,
2020). The final photometric baseline spans 11 years (2010-2020) when
including the 2010-2013 photometric light curves from the Pan-STARRS 1 (Kaiser
et al., 2010) Medium Deep survey that covers the entire SDSS-RM field. The
SDSS-RM sample includes 849 broad-line quasars with $i_{\rm PSF}<21.7$ and
$0.1<z<4.5$ without any constraints on quasar properties. The detailed sample
properties are described in Shen et al. (2019b).

The primary science goal of SDSS-RM is to measure reverberation mapping lags
of different broad emission lines covered by optical spectroscopy across the
full range of quasar luminosity and redshift probed by the sample. SDSS-RM has
successfully measured short ($<6\,{\rm months}$) lags for the low-ionization
broad lines (e.g., H$\alpha$, H$\beta$, and Mg II; Shen et al., 2016a; Grier
et al., 2017) based on the 2014 data. Grier et al. (2019) reported results on
C IV lags using the first four years (2014-2017) of imaging and spectroscopy
from SDSS-RM (and for Mg II lags in Homayouni et al., 2020), where the lags
are typically longer than one season in the observed frame. Lag measurements
based on more extended light curves are reported in Shen et al. (2019a), and
the analyzes of the final SDSS-RM dataset are underway.

In addition to the main science goal of RM measurements, SDSS-RM also enables
a diverse range of quasar science. Some notable examples are: measurements of
continuum lags to constrain quasar accretion disk sizes (Homayouni et al.,
2019); constraints on the black hole mass - host galaxy correlations at
$z>0.3$ (Shen et al., 2015a; Matsuoka et al., 2015; Yue et al., 2018; Li et
al., 2021); spectral and variability properties of quasar emission and
absorption lines (Shen et al., 2016b; Sun et al., 2018; Hemler et al., 2019;
Wang et al., 2019, 2020); and extreme variability in quasars (Dexter et al.,
2019).

The SDSS-RM field continues to be monitored with optical imaging and
spectroscopy as part of the BHM program in SDSS-V (Kollmeier et al., 2017, and
§8 below). With more extended light curves, this program will be able to
measure broad-line RM lags for the most luminous quasars at high redshift.

##  8\. Conclusions and Future Plans

This paper documents the final data release from the SDSS-IV collaboration,
and the seventeenth data release from SDSS programs as a whole (DR17). With
this paper we make all SDSS-IV data public, concluding the program described
in Blanton et al. (2017). In the rest of this section, we give an update of
SDSS-V which is now actively observing.

###  8.1. SDSS-V

As SDSS-IV was wrapping up observations (in late 2020 at APO and early 2021 at
LCO), the next generation of SDSS began — SDSS-V323232www.sdss5.org (Kollmeier
et al., 2017), with its first data taken using the existing plug plate system
at APO in late 2020. Building on the legacy of earlier generations, SDSS-V is
a multi-epoch spectroscopic survey to observe nearly six million sources using
the existing BOSS/APOGEE spectrographs and 2.5 m telescopes, as well as very
large swathes of the interstellar medium (ISM) in the Milky Way and Local
Group using new optical spectrographs and small telescopes. SDSS-V operates at
both APO and LCO and will provide the first “panoptic” spectroscopic view of
the entire sky, spanning a wide variety of target types, observing cadences,
and science goals.

The scientific program is overseen by three “Mappers”:

  1. 1.

The Milky Way Mapper (MWM) is targeting millions of stars and stellar remnants
with both the APOGEE and BOSS spectrographs, probing stellar populations from
the immediate solar neighborhood across the MW, to the far side of the
Galactic disk and in the MW’s satellite companions. The MWM’s primary goals
are to explore (1) the formation and evolution of the MW, (2) the interior
physics and evolutionary pathways of stars across all $T_{\rm eff}$ regimes,
from combined asteroseismology and spectroscopy (e.g. Aerts, 2021), and (3)
the architecture of multi-star and planetary systems.

  2. 2.

The Black Hole Mapper (BHM) is targeting nearly half a million accreting SMBHs
and other X-ray sources, including newly discovered systems from the
SRG/eROSITA mission, with the optical BOSS spectrographs. The BHM seeks to
characterize the X-ray sky, improve our understanding of accretion physics,
and trace the evolution and impact of supermassive black holes across cosmic
time.

  3. 3.

The Local Volume Mapper is using a wide-field optical IFU, with new optical
spectrographs (R $\sim$ 4000, $\lambda=3600-9800$Å) fed by a 16cm telescope,
to map $\sim$2800 deg2 of sky. With their tens of millions of spectra sampling
the ISM and embedded stellar populations in the MW and satellite galaxies, the
LVM’s maps will reveal the physics of star formation and the ISM, the complex
interplay between stars and the ISM, and the connections between interstellar
processes on parsec-sized to galaxy-wide scales.

SDSS-V expands upon the operational infrastructure and data legacy of earlier
SDSS iterations with several key developments. Among these are the
installation of robotic fiber positioners in the focal planes of both 2.5 m
telescopes at APO and LCO, which replace the now-retired SDSS plug plate
system. In comparison to earlier SDSS surveys, these focal plane systems (FPS)
enable improved observing efficiency, larger target densities, and more
complex observing cadences for the MWM and BHM programs. In addition, the LVM
is constructing additional small telescopes at LCO (with longer-term plans to
expand to APO) that are linked to new optical spectrographs based on the DESI
design (Martini et al., 2018). SDSS-V is continuing the decades-long SDSS
legacy of open data policies and efficient, well-documented public data
access, boosted with the development of improved data distribution systems to
serve its expansive time-domain, multi-object and integral-field data set to
the world. The eighteenth data release of the SDSS (DR18), which will include
the first SDSS-V data, mostly targeting and other information, is currently
anticipated for 2022.

SDSS-V will also build on the rich educational legacy established by previous
SDSS generations. This will include continuing to distribute as many SDSS plug
plates to schools and science centers as possible, and broadening the reach of
the SDSS Voyages suite of educational activities. SDSS’s large online datasets
are an ideal resource for supporting hands-on activities using real archival
data, at a wide range of ability levels, and have the potential to act as a
gateway for numerous data science and analysis topics.

After twenty-one years of Sloan Digital Sky Surveys, the data released by
SDSS-IV in DR17 has made significant contributions to our understanding of the
MW, galaxy evolution, and the Universe as a whole, and it will continue to
enable new research for years to come. The SDSS-IV project is now complete,
but the new technology and exciting new surveys coming as part of SDSS-V mean
that the future remains bright for the SDSS legacy.

##  9\. Acknowledgements

Funding for the Sloan Digital Sky Survey IV has been provided by the Alfred P.
Sloan Foundation, the U.S. Department of Energy Office of Science, and the
Participating Institutions. SDSS-IV acknowledges support and resources from
the Center for High-Performance Computing at the University of Utah. The SDSS
web site is www.sdss.org.

SDSS-IV is managed by the Astrophysical Research Consortium for the
Participating Institutions of the SDSS Collaboration including the Brazilian
Participation Group, the Carnegie Institution for Science, Carnegie Mellon
University, the Chilean Participation Group, the French Participation Group,
Center for Astrophysics — Harvard & Smithsonian, Instituto de Astrofísica de
Canarias, The Johns Hopkins University, Kavli Institute for the Physics and
Mathematics of the Universe (IPMU) / University of Tokyo, Korean Participation
Group, Lawrence Berkeley National Laboratory, Leibniz Institut für Astrophysik
Potsdam (AIP), Max-Planck-Institut für Astronomie (MPIA Heidelberg), Max-
Planck-Institut für Astrophysik (MPA Garching), Max-Planck-Institut für
Extraterrestrische Physik (MPE), National Astronomical Observatories of China,
New Mexico State University, New York University, University of Notre Dame,
Observatário Nacional / MCTI, The Ohio State University, Pennsylvania State
University, Shanghai Astronomical Observatory, United Kingdom Participation
Group, Universidad Nacional Autónoma de México, University of Arizona,
University of Colorado Boulder, University of Oxford, University of
Portsmouth, University of Utah, University of Virginia, University of
Washington, University of Wisconsin, Vanderbilt University, and Yale
University.

Co-authorship on SDSS-IV data papers is alphabetical by last name and offered
to all collaboration members who have contributed at least 1 month FTE towards
any of the surveys during the period up to the end of data collection; as well
as to any formally approved external collaborator who has contributed at least
1 month FTE to work critical to the data release.

The documentation workshop for DR17 was held over Zoom (“DocuZoom”) in May
2021. This event was the main venue for documentation updates for DR17
(including the start of this paper) and was attended by Anne-Marie Weijmans,
Joel Brownstein, Mike Blanton, Karen Masters, Renbin Yan, Sten Hasselquist,
Michael Talbot, Hannah lewis, Christian Hayes, Ani Thaker, Gail Zasowski,
David Law, Brian Cherinka, Kyle Westfall, Amy Jones, Lewis Hill, Jon Holtzman,
José Sánchez-Gallego, Rachael Beaton, Scott Anderson, Jennifer Johnson,
Caroline Swartz, Jordan Raddick, Julie Imig and a Llama.

Figure 1 made by Joel Browstein; Figure 2 and 3 made by Christian Hayes;
Figure 4 made by Jon Holtzman. Figure 5 by José Sánchez-Gallego; Figure 6 made
by Kyle Westfall; Figure 7 made by Brian Cherinka; Figure 8 made by Renbin
Yan;

This publication uses data generated via the Zooniverse.org platform,
development of which is funded by generous support, including a Global Impact
Award from Google, and by a grant from the Alfred P. Sloan Foundation.

This publication makes use of data products from the Two Micron All Sky
Survey, which is a joint project of the University of Massachusetts and the
Infrared Processing and Analysis Center/California Institute of Technology,
funded by the National Aeronautics and Space Administration and the National
Science Foundation.

This work is based, in part, on observations made with the _Spitzer_ Space
Telescope, which is operated by the Jet Propulsion Laboratory, California
Institute of Technology under a contract with NASA.

This publication makes use of data products from the Wide-field Infrared
Survey Explorer (Wright et al., 2010), which is a joint project of the
University of California, Los Angeles, and the Jet Propulsion
Laboratory/California Institute of Technology, funded by the National
Aeronautics and Space Administration, and NEOWISE, which is a project of the
Jet Propulsion Laboratory/California Institute of Technology. WISE and NEOWISE
are funded by the National Aeronautics and Space Administration

This work has made use of data from the European Space Agency (ESA) mission
Gaia (https://www.cosmos.esa.int/gaia), processed by the Gaia Data Processing
and Analysis Consortium (DPAC,
https://www.cosmos.esa.int/web/gaia/dpac/consortium). Funding for the DPAC has
been provided by national institutions, in particular the institutions
participating in the Gaia Multilateral Agreement.

This research has made use of NASA’s Astrophysics Data System.

This research has made use of the SIMBAD database, operated at CDS,
Strasbourg, France. The original description of the SIMBAD service was
published in Wenger et al. (2000).

This research has made use of the VizieR catalogue access tool, CDS,
Strasbourg, France (DOI: 10.26093/cds/vizier). The original description of the
VizieR service was published Ochsenbein et al. (2000).

This research makes use of data from the Green Bank Observatory. The Green
Bank Observatory is a facility of the National Science Foundation operated
under cooperative agreement by Associated Universities, Inc.

This research made use of astropy, a community-developed core python
(http://www.python.org) package for Astronomy (Robitaille et al., 2013);
ipython (Pérez & Granger, 2007); matplotlib (Hunter, 2007); numpy (van der
Walt et al., 2011); scipy (Jones et al., 2001); and topcat (Taylor, 2005).

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