# The 50 Mpc Galaxy Catalog (50MGC): Consistent and Homogeneous Masses,
Distances, Colors, and Morphologies

David Ohlson University of Utah
201 James Fletcher Building
115 S. 1400 E.
Salt Lake City, UT 84112, USA  Anil C. Seth University of Utah
201 James Fletcher Building
115 S. 1400 E.
Salt Lake City, UT 84112, USA  Elena Gallo University of Michigan  Vivienne F.
Baldassare Washington State University  Jenny E. Greene Princeton University

(Accepted September 5, 2023)

###### Abstract

We assemble a catalog of 15424 nearby galaxies within 50 Mpc with consistent
and homogenized mass, distance, and morphological type measurements. Our
catalog combines galaxies from HyperLeda, the NASA-Sloan Atlas, and the
Catalog of Local Volume Galaxies. Distances for the galaxies combine best-
estimates for flow-corrected redshift-based distances with redshift
independent distances. We also compile magnitude and color information for
11740 galaxies. We use the galaxy colors to estimate masses by creating self-
consistent color – mass-to-light ratio relations in four bands; we also
provide color transformations of all colors into Sloan $g-i$ by using galaxies
with overlapping color information. We compile morphology information for
13744 galaxies, and use galaxy color information to separate early and late-
type galaxies. This catalog is widely applicable for studies of nearby
galaxies, and placing these studies in the context of more distant galaxies.
We present one application here; a preliminary analysis of the nuclear X-ray
activity of galaxies. Out of 1506 galaxies within the sample that have
available Chandra X-ray observations, we find 291 have detected nuclear
sources. Of the 291 existing Chandra detections, 249 have log(LX)$>$38.3 and
available stellar mass estimates. We find that the X-ray active fractions in
early-type galaxies are higher than in late-type galaxies, especially for
galaxy stellar masses between 109 and 1010.5 M⊙. We show that these
differences may be due at least in part to the increased astrometric
uncertainties in late-type galaxies relative to early-types.

Galaxies — AGN — SMBH

††journal: AAS

##  1 Introduction

The galaxies in the nearby Universe provide our best laboratory for studying a
wide range of scientific questions. Most fundamentally, the properties of
nearby galaxies are the outcome of the process of galaxy evolution over the
lifetime of our Universe. We can use our detailed view of nearby galaxies to
quantify their morphology, kinematics, and star formation histories.

Our most complete census of galaxies comes from the local measurements. Only
within the Local Group can the faintest galaxies be found (e.g. Willman et
al., 2005; McConnachie, 2012; Simon, 2019), and even with modern surveys like
the Sloan Digital Sky Survey (SDSS), we can get a census of bright dwarf
galaxies with masses ($M_{\star}$) $\sim$109 M⊙ only out to $\sim$50 Mpc (e.g.
Blanton et al., 2005). The detailed structures within nearby galaxies can
reveal many aspects of their history and evolution, including their faint
stellar halos (e.g. Crnojević et al., 2016), star cluster populations (e.g.
Krumholz et al., 2019; Hughes et al., 2021), dwarf galaxy satellites (e.g.
Carlsten et al., 2022), central stellar structures (e.g. Erwin et al., 2015;
Neumayer et al., 2020), and their supermassive black holes (e.g. Saglia et
al., 2016). Nearby galaxies also provide a unique laboratory for stellar and
high-energy astrophysics, such as quantifying rare stages of stellar evolution
that aren’t represented in the Milky Way (e.g. Melbourne et al., 2012),
finding and characterizing rare sources like ultraluminous X-ray sources
(Kaaret et al., 2017), and transient events including gravitational wave
events (Abbott et al., 2017) and rare types of supernovae (e.g. Tartaglia et
al., 2018). Finally, nearby galaxies provide a baseline for distance
measurements in the Universe and measurement of their distances are key for
understanding cosmology (e.g. Freedman, 2021).

A number of existing catalogs of nearby galaxies are regularly used in
selecting sources for these science explorations. The Karachentsev et al.
(2013) Local Volume Galaxy
catalog111https://www.sao.ru/lv/lvgdb/introduction.php is a regularly updated
and very complete catalog of the nearest galaxies with distance $<$11 Mpc and
includes distance and photometric information. Beyond this distance, the most
widely used sources of galaxy information are HyperLeda222http://leda.univ-
lyon1.fr/ (Makarov et al., 2014) and the NASA Extragalactic Database
(NED)333http://ned.ipac.caltech.edu/. In addition, many studies make use of
catalogs of the nearest rich clusters of galaxies, Virgo (Binggeli et al.,
1985; Kim et al., 2014). Distance measurements to nearby galaxies has been a
subject of intense study, and much of this information for nearby galaxies has
been compiled within a subsection of NED (Steer et al., 2017). All of these
works combine heterogeneous photometric, distance, and morphological
measurements, and make some attempt to synthesize and homogenize these. Modern
studies of galaxies typically characterize galaxies based on their stellar
masses and colors (e.g. Baldry et al., 2004). Unfortunately, there is no
uniform and homogeneous estimates of these quantities for nearby galaxies, and
the varying photometric data as well as heterogeneous distance estimates
available for these galaxies complicates their comparison to more distant
galaxy samples.

The goal of this paper is to provide the first homogenized source of nearby
galaxy masses, distances, colors, and morphologies. We have chosen a distance
limit of $\sim$50 Mpc for this catalog, as many of the existing studies of
nearby galaxies are limited to galaxies within this distance limit. We find in
§4 that these galaxies have stellar masses that range from
log($M_{\star}/M_{\odot}$) of 6.6 to 10.9 (at the 99%ile). We also present one
application that motivated the creation of this catalog; a comparison of
nuclear X-ray activity between early and late-type galaxies. These results are
the first step to determining constraints on the occupation fraction of
central massive black holes in nearby galaxies (Miller et al., 2015; Gallo &
Sesana, 2019), which will be presented in a follow-up paper (Gallo et al., in
prep).

This paper is organized as follows: we describe our sample selection and
photometry in §2. We discuss distance estimates to the sample galaxies in §3,
determine their masses based on consistent color-mass-to-light ratio
measurements in §4, and describe their morphologies and group membership in
§5. Finally in §6, we apply the catalog by combining it with Chandra archival
data to present our X-ray active fraction measurement before concluding. The
full galaxy catalog is in the Appendix in Table 2. This table is available
through the publisher and (potentially updated) at
https://github.com/davidohlson/50MGC. Throughout the paper, when we refer to
column names without other clarifying information, we refer to the full
catalog; columns are indicated through their font, i.e. column_font_format.

##  2 Sample Selection & Photometry

In this section, we detail the construction of our sample from multiple
sources as well as the compilation of photometry that enables luminosity and
mass estimates for each galaxy.

###  2.1 Base Catalogs & Combination

Our catalog is formed from three base sources, the Local Volume Galaxy (LVG)
catalog (Karachentsev et al., 2013), HyperLeda (Makarov et al., 2014), and the
NASA-Sloan Atlas (NSA) 444https://www.sdss.org/dr13/manga/manga-target-
selection/
nsa/. All three contain a wide variety of information including redshifts and
photometry. The strength of the NSA catalog is its uniform Sloan Digital Sky
Survey photometry extending to quite faint sources, however, this photometry
only extends over $\sim$1/3rd of the sky. The LVG and HyperLeda catalogs
combine hetereogenous data sets and are continuously maintained and updated.
The LVG catalog contains nearly complete measurements of galaxies brighter
than $M_{B}\sim-12$ within 10 Mpc, however it lacks optical color information
for the galaxies. Both NSA and HyperLeda extend to greater distances and have
optical color information. Combined, these catalogs can provide the data
needed to obtain distances, masses, and Hubble types for galaxies out to
maximum target distance of 50 Mpc.

From HyperLeda, we initially began with a subsample of 18726 objects
designated as galaxies and with radial velocity $<3500$ km s-1. We chose to
remove the $\sim$20% of objects which were missing absolute B-band magnitude
values in column mabs from our sample, because HyperLeda’s calculation of
absolute magnitude relied upon apparent B-band magnitude, distance modulus,
and extinction. Removal of these objects from the sample ensured the inclusion
of the most essential galaxy measurements. This cut the initial HyperLeda
sample down to 14941 objects.

For the LVG sub-sample, we combined the "Catalog of Nearby Galaxies" and
"Global Parameters of the Nearby Galaxies" tables available from the LVG
catalog website555http://www.sao.ru/lv/lvgdb/tables.php. The combined tables
totalled 1240 galaxies666The version used was accessed 8/4/2020.

Finally for the NSA data, we used the nsa_v1_0_1 catalog and found 8242
objects with $cz<3500$ km s-1. We also added available SDSS spectral line flux
data to these objects, matched by plate and fiber IDs. We note that the NSA
catalog includes not just objects with SDSS redshift information, but also
galaxies with redshifts measured from other sources (ALFALFA, NED, 6dF, 2dF,
and ZCAT).

####  2.1.1 Combining the Sample

The three subsamples contain both duplicate objects and contaminants. Here, we
describe how we combined the catalogs while eliminating duplicates and
contaminants. We note that the presence of a galaxy in each input catalog is
given by flags in our catalog: hl_obj, lvg_obj, and nsa_obj. If a galaxy is
present in that catalog the flag has a value of 1, while if it is not, it has
a 0 value.

The HyperLeda sample included some duplicate galaxy entries; to remove these
we crossmatched each entry to its nearest neighbor within the sample. We
followed up on galaxies with nearby neighbors within a 2′ separation radius
for galaxies within 20 Mpc, and a 1′ separation for galaxies between 20-50
Mpc. For the 580 potential duplicate galaxies we examined available data and
flagged them as follows -- 0: individual galaxy with the best available data,
1: duplicated galaxy with less available data, 2: off-nuclear position in
duplicate galaxy, 3: image shows a bright or star-forming region within a
larger galaxy. We include only objects flagged with 0, which removed 183/580
galaxies. This cut resulted in an initial HyperLeda sample of 14758 galaxies.

We first created a combined LVG and HyperLeda sample. We used
astropy.coordinates.SkyCoord to conduct a coordinate-based crossmatch with a
maximum separation distance of 30″. Of the 1240 galaxies in the LVG, 744 were
found in HyperLeda. The 496 unmatched LVG galaxies are almost entirely very
low-luminosity. This explains their exclusion, as these galaxies are likely to
lack the photometric and redshift measurements required to have been included
in our initial HyperLeda sample. When combining sources between these
catalogs, we prioritize the LVG galaxy properties in almost all cases, i.e.
radial velocities, $B$ band magnitudes, and distances (see §3).

We then crossmatched NSA catalog objects to our combined HyperLeda and LVG
samples using a maximum separation distance of 30″. Of the 8242 NSA objects,
6966 matched the combined HyperLeda & LVG catalog. For the the 1276 unmatched
objects, we found that many were contaminants. To distinguish these
contaminants from real galaxies, we visually inspected each galaxies’ SDSS
imaging and compared redshifts to those listed in the NASA Extragalactic
Database (NED). At the lowest redshifts, the contaminants included foreground
stars on top of image artifacts or distant galaxies. At larger distances,
bright portions of galaxies with separate spectroscopic measurements from SDSS
were often included as separate catalog entries. Some contaminants also were
just image artifacts, and a small number contained no obvious image source
whatsoever. We flagged each of the 1276 unmatched NSA objects, based on our
findings -- 1: image shows a bright or star-forming region within a larger
galaxy. 2: image shows no object, an imaging artifact, a star, or a foreground
star over a high redshift galaxy. 3: Duplicates of otherwise good galaxies, of
which we flagged the entry with the most reliable redshift source to keep, in
order: SDSS, NED, ZCAT. 4: Good imaging of a galaxy with redshift matching
NED. These qualtity flags are given for all 1276 galaxies in Table 1. Only
objects in category 4 are considered as possible unique new NSA galaxies to
add to the catalog.

Finally, to minimize the potential for duplicate galaxies, we wanted to ensure
none of the unique NSA objects in category 4 were observations of the edge of
a galaxy already included in our sample. To do this, we first needed uniform
angular diameter measurements for each sample galaxy.

Figure 1: Difference between HyperLeda galaxies with overlapping total
($(B-V)_{t,0}$) and effective ($(B-V)_{e,0}$) colors vs their $(B-V)_{e,0}$
value. The red line shows a linear correction applied to $(B-V)_{e,0}$ values
to correct for the slight color-dependent offset to allow combination of
colors into a single field. Correction coefficients are based on a linear fit
from $0.2\leq(B-V)_{e,0}\leq 1.0$.  Figure 2: Examples of SDSS imaging of NSA
contaminant objects which are removed from our catalog. (a) Imaging shows a
bright, star forming region of a galaxy (NSAID: 177945). (b) A foreground star
centered on a distant galaxy which causes incorrect redshift values for the
galaxy(NSAID: 401970). (c) An imaging artifact treated as a galaxy by NSA
(NSAID: 460798). (d) An artifact from a bright star in the top left surrounds
a nearby star in the center, which causes both classification as a galaxy and
provides redshift of the star (NSAID: 460798).

HyperLeda gives galaxy diameter measurements as the major axis using a
standard $25~{}$mag arcsec-2 isophote ($d25$), whereas LVG uses a diameter
measurement corresponding to the Holmsberg isophote ($\sim 26.5~{}$mag
arcsec-2; which we call $d26$). We compared $d25$ HyperLeda diameters to $d26$
LVG diameters for galaxies found in both source catalogs to find the typical
ratio between these, and then multiplied the LVG diameters by $\sim 0.838$ to
approximate $d25$ HyperLeda values for all the nearest neighbor galaxies.

For each NSA object, we then divided the angular separation from its nearest
neighbor’s angular diameter to create a "match separation ratio",
$\delta_{sep}/d25$. Of the galaxies confirmed to include in our sample, we
only included those with a ratio $\delta\geq 0.75$ (i.e. lying beyond 1.5
galaxy radii). Of the 208 objects in category 4, 170 galaxies passed this cut
and were added to our final sample. These galaxies have a median distance of
29.9 Mpc and a median mass of $8.08\times 10^{7}$ M⊙ ($log(M)\sim$8), and thus
add significantly to our sample.

We note that many studies (e.g. Geha et al., 2012; Reines et al., 2013) have
used the NSA catalog as a sample of nearby galaxies without removing
contaminants. We find that $\sim$13% of nearby NSA galaxies (1106/8242) are
contaminants. Most contaminants show a low mass estimate from NSA redshift
distances and photometry, with a median mass of $2.6\times 10^{7}$ M⊙. We
therefore include the full list of NSA objects that don’t match HyperLeda &
LVG and the criteria we used to cut these down in Table 1 for future users.

####  2.1.2 Galaxy Coordinates

We want the coordinates in our catalog to be centered as accurately as
possible on galaxies’ nuclear regions for the purpose of AGN selection and
observation. Our catalog combines multiple subsamples, each with slightly
different astrometric methods and sources. HyperLeda applies a homogenized
position computed by averaging published measurements weighted by an assigned
quality flag. NSA provides coordinates of the measured object’s photometric
center in the SDSS. The source of LVG coordinates was not given. Each time we
combined subsamples with different astrometric sources, we compared the
coordinates for galaxies in both. We visually inspected 150 galaxies with
overlap between HyperLeda and either LVG (739 galaxies) or NSA (7136
galaxies). We randomly selected these galaxies to have a range of offsets
between 1″ and 10″. We compared the galaxy positions to imaging with SDSS and
2MASS, both of which have good astrometry (Pier et al., 2003; Skrutskie et
al., 2006). This visual inspection suggests that in general the HyperLeda
positions are better aligned with the photocenter than the LVG and NSA
samples. We therefore use the following priority order for the ra and dec in
our catalog — HyperLeda, LVG, and NSA.

In addition to the comparison and selection between position sources from
multiple catalog sources, we also compared our selected Right Ascension and
Declination values to the primary position provided by NED. We retrieved NED
positions using a name-based query. After removing 25 matches with separation
$>$5′ due to misnamed entries in HyperLeda and NED, we reliably matched
positions for 14412 catalog galaxies ($\sim 93\%$). We used the visual
inspection process described in the previous paragraph to determine whether
NED coordinates are noticeably preferable to our catalog position. We found
our positions were generally better than those from NED, with a larger spread
between our catalog positions and NED than between HyperLeda, LVG, and NSA. We
find 1680 galaxies have a separation $>3\arcsec$ between our catalog position
and the NED position. We investigate the relation between coordinate offsets,
mass, and galaxy type in more detail in §6. Because of the variation between
positions from different sources, we choose to include columns for NSA and NED
positions in the final catalog, in addition to our chosen compilation of
coordinates described above.

Table 1: NSA objects that don’t match galaxies in the Hyperleda and LVG catalogs. NSAID | RA | DEC | Quality_FLAG | MATCH_RATIO
---|---|---|---|---
30129 | 343.8028213 | -0.350540038 | 2 | 191.944
34438 | 13.23646298 | -1.174127269 | 2 | 225.389
36914 | 24.69818421 | 1.071791959 | 2 | 371.004
38419 | 32.6382837 | 0.738166461 | 4 | nan
41803 | 8.850357094 | 14.79454207 | 2 | 122.785
42629 | 14.93267592 | 14.56776754 | 2 | 331.128
46179 | 116.1573335 | 40.88379846 | 4 | 151.729
50066 | 129.4311915 | 51.64175054 | 1 | 0.752
51666 | 134.7746823 | 53.76023531 | 1 | 0.707
52302 | 138.5671796 | 57.04441951 | 4 | 318.963

###  2.2 Color and Luminosity

The luminosities and colors of our galaxies are two crucial parts of our
catalog. In addition to being intrinsically useful, the colors are also
important for calculating stellar masses (§4) and for assessing the galaxy
type (early vs. late; §5). To obtain these estimates, we use four different
sources for colors and associated luminosities: (1) HyperLeda, (2) NSA, (3)
the Siena Galaxy Atlas (SGA), (4) NASA/IPAC Extragalactic Database (NED). All
luminosity calculations utilize the best distances described in 3.

Extinction Correction: For our foreground extinction corrections, we uniformly
apply the Schlafly & Finkbeiner (2011) corrections; all our sources have
Schlegel et al. (1998) extinction estimates available, and we multiply these
by 0.86 to convert them to Schlafly & Finkbeiner (2011)
values777https://irsa.ipac.caltech.edu/applications/DUST/. For HyperLeda, we
used $B$ and $V$ magnitudes, and scaled their $A_{B}$ as described above; then
to determine $A_{V}$ values we assumed an $A_{V}/A_{B}=0.769$ (Cardelli et
al., 1989). For NSA, we used the provided Schlegel et al. (1998) extinction
values for each band and scale these by 0.86. We retrieved a table of
location-based extinction values for our entire catalog from the IPAC Galactic
DUST web service and added the table’s Schlafly & Finkbeiner (2011) $E(B-V)$
values to the catalog as EBV_irsa. For SGA, we then created band-specific
extinction arrays by multiplying EBV_irsa by the proper $A/E(B-V)$ values:
$g$=3.303, $r$=2.285. Finally, for NED, we use $B$ and $R$ band magnitudes
from the same source for consistency -- these primarily come from the southern
ESO Uppsala survey (Lauberts & Valentijn, 1989); we use the HyperLeda B-band
extinction, and assume an $A_{R}/A_{B}=0.628$ based on the extinction curves
of Cardelli et al. (1989) and O’Donnell (1994) taken from the Padova CMD
website 888http://stev.oapd.inaf.it/cgi-bin/cmd_3.4.

We did not apply any internal extinction corrections, thus the luminosities
and colors presented here are only foreground extinction corrected. Internal
extinction values are available from both HyperLeda and LVG. However, we found
no correlation between these two.

Luminosities & Colors from HyperLeda ($(B-V)_{0}$, $L_{V}$, $L_{B}$):
HyperLeda provides two different $B-V$ colors: (1) the total asymptotic
corrected color, bvtc which is corrected for both internal and foreground
extinction, and (2) color measured within the effective aperture in which half
the total B-flux is emitted, bve, which is not corrected for extinction.
Various HyperLeda galaxies have one or both measurements available. Our first
step to obtain consistent values was to undo the HyperLeda extinction
corrections on the bvtc column, and apply the Schlafly & Finkbeiner (2011)
foreground extinction correction to both the total and effective colors -- we
call these extinction corrected quantities $(B-V)_{t,0}$ and $(B-V)_{e,0}$
respectively. Because we are using the colors to calculate total masses, we
also attempt to correct the small offset between the $(B-V)_{t,0}$ and
$(B-V)_{e,0}$ \-- the difference between these colors for galaxies that have
both values are shown in Fig. 1. The offset is clearly color dependent, and
the line in that figure shows the color dependent correction appslied to the
$(B-V)_{e,0}$ to get our final $(B-V)_{0}$ values for galaxies that only had
bve data available. We flagged these data using the bvcolor_f value in our
table.

For calculating luminosities and total magnitudes in $B$ and $V$ bands, we
found that the given bt and vt columns were not consistent with the given
colors (after correcting for the different extinction corrections applied) --
this is likely due to the heterogeneity of data sources used. Given the
ubiquity of btc values, we first calculate the $B$ total magnitude by removing
the extinction corrections and applying Schlafly & Finkbeiner (2011)
foreground extinction corrections to get total $B_{t,0}$ values. We then
derived the total V band magnitude by using our $B_{t,0}$ and subtracted the
$(B-V)_{t,0}$ value to get a foreground extinction $V$ band magnitude,
$V_{t,0}$. We note the use of $B_{t,0}$ values throughout this paper when
plotting galaxy luminosities. These are transformed to luminosities using the
Vega-based absolute magnitudes of the Sun from http://mips.as.arizona.edu/
cnaw/sun.html.

Luminosities & Colors from NSA ($(g-i)_{0}$, $L_{i}$, $L_{B}$): NSA provides
Petrosian flux and extinction values for each $FNugriz$ band. We calculate the
Pogson $g$-, $r$\- and $i$-band magnitudes using the SDSS
relation999https://www.sdss.org/dr12/algorithms/magnitudes/:
$m=22.5-2.5log_{10}f$. We then extinction correct all individual magnitudes as
described above. For calculating galaxy masses (§4), we use the $g_{0}$ and
$i_{0}$ magnitudes to calculate an extinction corrected $(g-i)_{0}$ color. We
also calculate and $i$-band luminosity for the galaxies ($L_{i}$) using the
solar AB magnitude from http://mips.as.arizona.edu/c̃naw/sun.html. To analyze
a single luminosity distribution for our entire sample, we also estimate the
$B$-band luminosity $L_{B}$ from the SDSS magnitudes using the stellar
transformation $B_{0}=g_{0}+0.39*(g-r)_{0}+0.21$ from Jester et al. (2005).

Luminosities & Colors from SGA ($(g-r)_{0}$, $L_{r}$, $L_{B}$): A
complementary source of photometry for primarily southern galaxies is the
Siena Galaxy Atlas (SGA101010https://www.legacysurvey.org/sga/sga2020/). We
matched our catalog to the SGA catalog after removing sources with known
redshifts above 3500 km s-1 using a maximum separation distance of 30″. In
cases of multiple galaxies within this threshold, we matched the galaxy with
the smallest angular separation. This gave us $(g-r)_{0}$ data for 7681
galaxies, 1616 of which otherwise lacked color.

The SGA catalog contains a number of different magnitude measurements. To
ensure consistency, we compared overlapping sources with NSA and found
[G,R,Z]_MAG_SB26 best matched the NSA magnitudes. We then calculated
reddenings and extinction using the IRSA online
portal111111https://irsa.ipac.caltech.edu/applications/DUST/  to get Schlafly
& Finkbeiner (2011) extinction values. We corrected for these values to
calculate the $(g-r)_{0}$ color, $L_{r}$, and estimate the $B$-band luminosity
$L_{B}$ using the same Jester et al. (2005) equation applied to NSA
photometry.

Luminosities & Colors from NED ($(B-R)_{0}$, $L_{R}$, $L_{B}$): To expand our
mass estimations for southern galaxies, we used astroquery to request NED
photometry for each sample object. We added available $B$ and $R$ data to the
sample, specifically objects with an observed passband of B (Cousins) (B_25)
and R (Cousins) (R_25). If multiple magnitude measurements were available for
a single object, we included a mean value. Queries returned a uniform $B$ and
$R$ magnitude error of 0.09 for all objects. We used these $B$ and $R$ band
magnitudes, corrected for foreground extinction, to calculate the $(B-R)_{0}$
color, $L_{R}$, and $L_{B}$ where available.

Combined Color and Luminosity Estimates: In section 5 we describe color
transformations between $(g-i)_{0}$ and other colors. We use these to derive
an estimate of the $(g-i)_{0}$ color for all galaxies with colors. This is
contained in column gi_color. For galaxies with multiple available colors, we
give precedence to source colors in order: NSA $(g-i)_{0}$, and then
synthesized $(g-i)_{0}$ values based on $(g-r)_{0}$, $(B-V)_{0}$, and
$(B-R)_{0}$. We compile the $L_{B}$ values from all sources in column B_lum;
most of these are taken directly from HyperLeda, while a handful are
synthesized from other sources as described above with the precedence order
being HyperLeda, NED, NSA, SGA. A vast majority of $L_{B}$ estimates come
directly from Hyperleda.

###  2.3 Cleaning the Photometric Measurements Used for Mass Estimates

Due to a number of objects with both NSA and HyperLeda data available, we
wanted to test which source provided more reliable photometry when both were
available. We compared $g$ vs $B$ magnitudes, due to their similar wavelength,
and investigated galaxies with significantly differing magnitudes to available
NED values. Generally, by comparing to available photometry compiled in NED,
we find that NSA photometry is more reliable in cases where it is discrepant
from HyperLeda and therefore typically prioritize NSA data in estimating
galaxy masses. An exception to this is for a selection of galaxies with NSA
$g>16$ and $B-g>1$; for these galaxies we use Hyperleda values preferentially,
as the HyperLeda values more closely matched other measurements from NED.
Based on this analysis, we created the mag_flag column to signify which source
provided the more accurate measurement at a given magnitude -- 0: the
difference between $B$ and $g$ are within one magnitude, so NSA values are
used for calculating masses. 1: $B$ and $g$ are discrepant; NSA photometry is
more reliable. 2: $B$ and $g$ are discrepant; HyperLeda photometry is more
reliable, and is used for estimating masses.

There are 446 additional objects where the NSA photometry is not used. These
include objects with flags indicating unreliable photometry, specifically
those with the 5th or 6th bitwise flag set in the NSA DFLAGS field (Blanton,
M., private communication). We also don’t use NSA photometry for 20 objects
with $g$ or $i$ petrosian flux $\leq 0~{}nanomaggies$. Of these 446 galaxies,
306 had other colors and luminosities available for calculating masses.

Figure 3: The distribution of the B-band luminosities $log(L_{B})$ for the
entire catalog (shaded histogram), along with the subset that have available
masses (dark gray line). The colored lines indicate the source of photometry
used in estimating the galaxy masses (see §4.)

##  3 Deriving Best Distances

In this section we discuss our derivation of the best available distance
estimates across our full galaxy sample. These distance estimates fall into
three broad categories: (i) redshift-based distances determined using the
Cosmicflows-3 Distance--Velocity Calculator (Kourkchi et al., 2020), (ii)
redshift-independent distances taken from the LVG and from the NASA/IPAC
Extragalactic Database (NED Steer et al., 2017; Steer, 2020), and (iii) Virgo
Cluster distances, for which objects matching Virgo samples and catalogs were
assigned either measured distances or an appropriate mean distance. In
general, we prioritize the redshift independent and Virgo distances except in
cases where the available redshift based distances are more accurate. $\sim
20\%$ of redshift distances are more accurate, once distances reach
$>30~{}$Mpc and error becomes a lower fraction of total distance.

###  3.1 Redshift Based Distances

Due to the peculiar motions of galaxies, in the nearby Universe, redshift
based distances are not always accurate. However, this accuracy can be
improved by using models of the local mass density based on the measured
peculiar motions of galaxies (Courtois et al., 2012). We chose to use the
Cosmicflows-3 Distance--Velocity Calculator (Kourkchi et al., 2020) because it
covered the full distance range of our sample using an up-to-date local mass
density model (Graziani et al., 2019). We entered Heliocentric velocities into
the calculator to retrieve distance estimates corrected for the local mass
density. Velocity measurements are given precedence in the order NSA, LVG, and
HyperLeda where available.

To estimate errors on the redshift based distances, we first estimated a
typical peculiar velocity for galaxies in the Local Volume. We based this on
galaxies with redshift independent measurements (see next subsection) with
errors $<$10%. We then used the CMB-relative velocity measurements from
HyperLeda to calculate the peculiar velocity of each galaxy using
$v_{pec}=v_{rad}-H_{0}d$ assuming $H_{0}=70$. We then measured the width of
this distribution using the 16th and 84th percentiles, taking half the width
to get a typical peculiar velocity of $\sim$400 km s-1.

Our redshift based distance errors were then calculated by adding and
subtracting this typical peculiar velocity from the Heliocentric velocity and
recalculating the distance using the Cosmicflows-3 Distance--Velocity
Calculator. The final redshift-dependent distance error uses a symmetrization
of the difference between upper- and lower-limit corrected distances. These
distance errors are typically $\sim$65.3% at 10 Mpc and $\sim$17.4% at 40 Mpc.

###  3.2 Redshift Independent Distances

We use redshift independent distances from three sources: (1) the updated LVG
catalog distances and (2) the compilation of redshift independent distances in
the NED distance database (NED-D Steer et al., 2017). We chose to give highest
priority to the LVG catalog distances, as these distances have typically
represent the best available measurements and are kept up to date. We manually
examined distance estimates where the values from LVG conflicted with those
NED-D and NED-MED, and found LVG to typically be superior; for instance the
distances to the Maffei group galaxies were based on the recently updated TRGB
distances presented in Anand et al. (2019).

For galaxies in NED-D, we chose our distances using the following list of
primary indicators, in order of precedence based on the order given in Steer
(2020): Cepheids, TRGB, RR Lyrae, Red Clump, SNIa, FGLR, Horizontal Branch,
SBF, GCLF, CMD, Type II Cepheids, Miras, PNLF, AGB, Carbon Stars, SNIa SDSS.
We use the single most recent preferred distance estimate of the preferred,
except if two preferred measurements were from the same year, in which case we
apply an average of these preferred distances and errors. If NED-D provided
more than two older measurements from the best primary indicator, we ensured
that the newest distance was not greater than two standard deviations from the
averaged older distances. For galaxies with large spreads in their preferred
distances, we use the mean of all preferred distances with the best primary
indicator. We encode this information in the field ’dist_nedd_flag’ - 0: uses
single best NED-D measurement, 1: best NED-D distance averaged from multiple
measurements, 2: NED-D best indicator mean used, due to discrepancy, as
previously described.

###  3.3 Galaxies in Virgo

Due to the nearby Virgo Cluster’s large velocity dispersion, redshift based
distances to Virgo are much more inaccurate than for galaxies in the rest of
our sample. Because of this, we treated Virgo as a special case for distance
purposes. We matched and compared our sample to galaxies from Mei et al.
(2007), the Extended Virgo Cluster Catalog (EVCC; Kim et al., 2014) \-- we
included "certain members" from their catalog as special cases to have their
distances revised. These galaxies have velocities within the range of galaxies
gravitationally bound to Virgo at a given projected radius.

For these galaxies, we first combined the separate redshift-based and
redshift-independent distance lists, using whichever available option had the
lowest fractional error. Galaxies with surface brightness fluctuation
distances from from Mei et al. (2007) are assigned that distance and error.
Other Virgo galaxies with only redshift based distances available are assigned
the average distance of $16.5\pm 1.1~{}$Mpc given by Mei et al. (2007). For
galaxies with redshift-independent distances, we found more divergent values
than expected, including some that didn’t agree with Virgo distances. After
careful evaluation of individual cases, we assigned the $16.5\pm 1.1~{}$Mpc
distance for most objects, keeping the redshift independent distance only if
$|D-16.5|>3\sigma$, where $\sigma$ is the error on the distance; this is a
total of 862 objects.

###  3.4 Final Compilation of Distances

For galaxies with multiple distance sources, our aim was to identify the more
reliable and precise distance source. Our distance sources included: Virgo
distances, LVG distances, NED-D distances with lowest error, redshift-based
distances from Cosmicflows-3 calculator. We prioritized Virgo distances, and
otherwise chose the distances with the lowest error between NED-D and CF3
z-distances.

Of the 55 objects which had no assigned distance from any of our sources, 29
are assigned the given distances and errors from HyperLeda. The remaining 26
are NSA-only objects for which velocity measurements returned a Cosmicflows-3
corrected distance $D\leq 0$; we don’t supply a distance in this case.

Figure 4: Distribution of our chosen best distances for the entire catalog
(shaded histogram). Colored lines indicate the source (bestdist_source) used
for the final compilation of distances (see §3).

As well as best distances (the bestdist column) and errors (bestdist_error),
each galaxy in the catalog has a field for specific distance indicator if
available. We have also included the field bestdist_source, the values of this
are -- 0: Virgo, 1: LVG, 2: NED-D, 3: redshift-based, 4: HyperLeda, ‘nan’:No
Distance. The final distribution of galaxy distances, separated using these
classifications, is shown in §4.

##  4 Mass Estimation

Our goal in this section is to estimate consistent stellar masses across the
full sample based on galaxy colors and luminosities. We use the $g-i$ vs.
$M/L_{i}$ color-M/L relation from Taylor et al. (2011) as the basis for our
measurements and derive self-consistent relations in other colors using
galaxies with overlapping measurements.

We use the $g-i$ vs. $M/L_{i}$ relation as our preferred relation as previous
work has found it is accurate to $\sim$0.1 dex due to the alignment of the
extinction and stellar population effects (Zibetti et al., 2009; Taylor et
al., 2011). We note that while NIR photometry is widely used to estimate
stellar masses assuming a constant $M/L$ (e.g. Meidt et al., 2014), this
approach is compromised in galaxies with younger stellar populations by its
sensitivity to difficult to model TP-AGB stars and hot dust emission (e.g.
Kriek et al., 2010; Taylor et al., 2011; McGaugh & Schombert, 2014; Telford et
al., 2020).

One challenge in deriving stellar masses in our sample is the different
photometry available for different galaxies. This creates two issues, first,
McGaugh & Schombert (2014) has shown that individual color-M/L relations are
not consistent across multiple bands, i.e. that using different colors in the
Bell et al. (2003) or Zibetti et al. (2009) relations gives systematically
different masses for observed galaxy colors. Second, many available relations
are not available across both the Sloan and Johnson photometry that we are
using for our galaxy sample (e.g. Roediger & Courteau, 2015). To solve both of
these issues, we therefore translate the Taylor et al. (2011) $g-i$ vs.
$M/L_{i}$ into the other colors used ($(g-r)_{0}$, $(B-V)_{0}$, and
$(B-R)_{0}$) using overlapping galaxies to ensure self-consistency.

We first used Equation (7) from Taylor et al. (2011):

${\rm log}(M_{\star,i}/L_{i})=-0.68+0.70(g-i)$ (1)

to calculate galaxy stellar mass estimates (log($M_{\star,i}/L_{i}$)) using
$(g-i)_{0}$ and $L_{i}$ derived from NSA data. To translate this relation into
other bands, we used overlapping samples. First, for 6677 NSA galaxies with
both $(g-i)_{0}$ and $(g-r)_{0}$ color available, we compared the stellar
masses calculated from $i-$band to the $r-$band luminosities, i.e.
log($M_{\star,i}/L_{r}$). We then use this to fit the $M_{\star,r}/L_{r}$ vs.
$(g-r)_{0}$ relationship using the RANSAC fitting from sklearn.linear_model to
create a linear fit to this comparison while ignoring outliers as shown in
Fig. 7. We followed the same process with 986 overlapping galaxies between NSA
and HyperLeda to translate the color--M/L relation to $(B-V)_{0}$. Our sample
lacks significant overlap between galaxies with both $(g-i)_{0}$ and
$(B-R)_{0}$, due to the latter coming primarily from southern sky
observations. We therefore used the same method on 822 galaxies with
$(g-r)_{0}$ and $(B-R)_{0}$ to bootstrap an appropriate and self-consistent
$M/L$ relation. The resulting equations from these transformations are:

${\rm log}(M_{\star,r}/L_{r})=-0.66+1.20(g-r)_{0}$ (2) ${\rm
log}(M_{\star,V}/L_{V})=-0.72+1.07(B-V)_{0}$ (3) ${\rm
log}(M_{\star,R}/L_{R})=-0.51+0.51(B-R)_{0}$ (4)

We multiplied the color-based M/L ratios by their corresponding luminosity to
calculate a stellar mass estimate. We created separate columns for mass
estimates from each color: $g-i$, $g-r$, $B-V$, and $B-R$. To compile our
final column of "best" mass estimates for 11740 galaxies, we gave precedence
when available to (1) 6626 log($M_{\star,i})$ values calculated using NSA data
using Equation 1, (2) 2705 log($M_{\star,r})$ values from SGA data using Eq.
2, (3) 1182 log($M_{\star,V})$ values from HyperLeda using Eq. 3, (4) 1227
log($M_{\star,R})$ values from retrieved NED photometry using Eq. 4.

To obtain errors on the mass estimates, we use the scatter from multiple
estimates for the same objects. To do this, we take the sample of 986 galaxies
with both $(B-V)_{0}$ and $(g-i)_{0}$ colors, and compare the masses derived
with these two separate colors. This comparison includes scatter in the mass
estimates due to different bandpass information and independent photometric
measurements. The 68 percentile of the mass differences is 0.136. We use this
value as an assumed floor on the log($M_{\star}/M_{\odot}$) error. We also
propagated distance errors by calculating high and low masses adding and
subtracting bestdist_error. For our final logmass_error, we use the larger of
these two error estimates.

###  4.1 Mass Comparisons

Our mass estimates are based on those of Taylor et al. (2011) and thus should
be consistent with the widely used GAMA mass functions (Baldry et al., 2012;
Kelvin et al., 2014; Wright et al., 2017; Driver et al., 2022) that provide
the deepest mass function estimates for galaxies in the local Universe. The
Taylor et al. (2011) mass estimates are based on the Bruzual & Charlot (2003)
stellar population models and assume a Chabrier (2003) mass function and the
dust extinction law from Calzetti et al. (2000). There is an extensive
comparison of how this color-M/L relation compares to earlier color-M/L
relations in Taylor et al. (2011). In particular, their Fig. 13 shows a their
relation is $\sim$0.2 dex below the Bell et al. (2003) relation (due to IMF
differences) and a similar normalization but significantly different slopes
from the Zibetti et al. (2009) relationship due to differences in the
weighting of stellar population models. Here we compare our derived masses
against dynamical mass estimates from ATLAS3D Cappellari et al. (2011) and IR
based mass estimates from the S4G survey (Sheth et al., 2010).

####  4.1.1 ATLAS3D Dynamical Mass Comparison

ATLAS3D is a volume-limited sample of 260 nearby early-type galaxies
Cappellari et al. (2011) within 42 Mpc. Dynamical masses were derived using
Jeans anisotropic modeling of the integral field kinematics in Cappellari et
al. (2013a) and Cappellari et al. (2013b). We compare our masses to those
derived from the stellar-only dynamical (M/L)stars estimates from Cappellari
et al. (2013b), shown in Fig. 5. The ATLAS3D masses are mostly larger than our
masses, with a median offset of 0̃.34 dex. The offset is near zero in the
lowest mass galaxies (log($M_{\star}/M_{\odot}$)$<$10), and is highest (nearly
0.5 dex) at the highest masses. These offsets are consistent with the findings
of Cappellari et al. (2012), who found that a vast majority of ATLAS3D
galaxies had dynamical mass estimates that were heavier than stellar
population model estimates based on a Chabrier (2003) IMF, with a typical
offset roughly a factor of 2 (0.3 dex) higher. They found that this offset
strongly depends on the observed velocity dispersion, with the highest
dispersion and mass galaxies having the largest offsets, just as observed
here. So overall, this comparison suggests that our mass estimates are
accurate for their assumption of a Chabrier IMF, but that assumption may be a
poor one especially for the most massive early-type galaxies, and our stellar
mass estimates may significantly underestimate the masses of these galaxies.

####  4.1.2 S4G IR-based Mass Comparison

The Spitzer Survey of Stellar Structure in Galaxies (S4G) is a volume-,
magnitude-, and size-limited survey of over 2300 nearby galaxies at 3.6 and
4.5 µm (Sheth et al., 2010). The available mass estimates are based on IR
luminosities using the relation of Eskew et al. (2012). This relation uses the
resolved star stellar mass estimates assuming a Salpeter IMF of the LMC from
Harris & Zaritsky (2009) and combines this with IR fluxes 3.6 and 4.5 $\mu$m
to get a luminosity to stellar-mass conversion. This relation was applied to
all the S4G galaxies to derive stellar masses available in the v. 2 S4G
catalog121212Available at https://vizier.u-strasbg.fr/viz-bin/
VizieR?-source=J/PASP/122/1397/s4g. Using these values, we match 2346/2352
galaxies (99.7%) and plot our derived masses vs. the S4G Eskew et al. (2012)
masses (after subtracting 0.24 dex to account for the difference expected in
mass between a Chabrier and Salpeter IMF Cappellari et al., 2012) in Fig.5.
After the correction for the Salpeter IMF, we see very close agreement between
our masses with just a bias of just 0.03 dex (with S4G masses being very
slightly more massive). The one-to-one relation extends over a wide range of
galaxy masses, and the 1$\sigma$ scatter around this relation is just 0.24
dex. Overall, we find good agreement between the S4G masses and the masses we
derive after accounting for different IMF assumptions.

Figure 5: Comparison of our mass estimates (logmass_best) against those from
S4G (blue; Sheth et al., 2010; Eskew et al., 2012) and ATLAS3D dynamical
stellar mass estimates (red; Cappellari et al., 2011). The S4G masses are
corrected from a Salpeter to Chabrier IMF by subtracting 0.24 dex for
consistent comparison to our catalog. The solid line shows the one-to-one
relation, while dashed lines indicate an 0.5 dex offset.

###  4.2 Mass Completeness

Many science applications benefit from complete, volume-limited samples of
galaxies. To gauge the completeness of our sample, we compare our galaxy
catalog to mock galaxy samples created from galaxy stellar mass functions.
Above, we have derived masses for our galaxies based on the color--stellar
mass relations used in the GAMA survey (Baldry et al., 2010; Taylor et al.,
2011); we therefore use the GAMA galaxy stellar mass functions to evaluate the
completeness of our catalog.

For the full sample of galaxies, we use the most recent stellar mass function
published by GAMA from Driver et al. (2022), which extends down to
$M_{\star}\sim 10^{7}M_{\odot}$. We simulate a galaxy population with a volume
of 4/3$\pi$(50 Mpc)3 based on the Driver et al. (2022) mass function. We then
compare the number of galaxies with available masses in our catalog to this
mock galaxy sample in 0.2 dex mass bins to calculate the fraction of the
expected number of galaxies that we detect. We then translate this fraction to
estimate the "Volume Completeness Radius", which we define as the radius out
to which we’d have a complete sample based on the total number of galaxies in
our sample. For instance, if we detect 50% of the number of expected galaxies,
this translates to a Volume Completeness Radius of $\sim$40 Mpc. Fig. 6 shows
that the catalog is $>$50% complete at $M_{\star}\gtrsim 10^{8.5}M_{\odot}$,
which corresponds to an effective volume with radius $R\sim 40$ Mpc. At lower
masses, the completeness drops significantly. At the highest masses, the
sample also appears to become less complete, a point which we come back to
below.

The Driver et al. (2022) paper doesn’t have comparable morphology or color
separation to what we have done here, so we use an earlier GAMA paper by
Baldry et al. (2012), which is more directly comparable to our color-based
morphology separation (i.e. the color_type in our sample). We note that the
color-separation used here and in the Baldry et al. (2012) are similar but not
identical. Fig. 6 shows that for the late-type galaxies, the Volume
Completeness Radius rises to the expected 50 Mpc at stellar masses of
$\sim$1010 M⊙. The early-types achieve a similar completeness at much lower
masses, but then the completeness declines at the highest masses. Values
greater than one in this figure could be due to: (1) a real difference in the
number density of galaxies in the local Universe than in the regions used for
the Baldry et al. (2012) mass function, or (2) differences in the color
separation between our study and theirs. Note that a direct comparison using
identical color separations isn’t possible due to their use of $u-r$ colors in
separating their populations, and the lack of $u-r$ colors for many galaxies
in our sample. The bottom panel of Fig. 6 shows the fraction of early-type
galaxies as a function of stellar mass in our sample compared to the Baldry et
al. (2012) mass function. This shows that the fraction of red galaxies is
higher than expected in the local Universe at low masses, but lower at higher
masses.

The lack of high mass galaxies overall, and specifically high mass early-type
galaxies is a surprising result, because we expect to be complete at the
highest masses. This therefore implies that these galaxies are underdense in
the local Universe relative to the volumes probed in the GAMA survey. We re-
emphasize that our stellar mass estimates should be consistent with GAMA, thus
making the difference more likely to represent a real deficit of massive
early-type galaxies locally relative to the GAMA volume.

Figure 6: Completeness of our catalog as a function of stellar mass. Top--
Mass completeness of our catalog, using 0.2 dex bins. Y-axes show the radius
of effective volume left and completeness fraction right. The black line
compares all catalog galaxies against a $<50$ Mpc mock sample created using
the Driver et al. (2022) galaxy stellar mass function. Colored lines show
comparisons between color_type separated catalog galaxies against color-
separated mock sample created using the Baldry et al. (2012) galaxy stellar
mass functions. Bottom-- Percent of galaxies classified as "Red" by our
catalog and the Baldry et al. (2012) sample in each bin.  Figure 7: An example
of how we transform the Taylor et al. (2011) $(g-i)_{0}$\--$M/L_{i}$ relation
to $(g-r)_{0}$\--$M/L_{r}$. The y-axis is determined by taking masses
calculated from the NSA data using the $(g-i)_{0}$\--$M/L_{i}$ relation, and
then dividing by the NSA $L_{r}$ values. This is then plotted against NSA
$(g-r)_{0}$ values and fit using a robust outlier-rejection algorithm, RANSAC.
Also shown is the somewhat steeper relation from Zibetti et al. (2009). Our
translation of the Taylor et al. (2011) relation to other colors to ensures
consistency between masses calculated in different bands. We note that
although this $(g-r)_{0}$\--$M/L_{r}$ relation is used for galaxies with SGA
data, we fit this relation with just NSA photometry to ensure a large sample
of galaxies and minimize photometric outliers. A similar process was used to
also create the other color-$M/L$ relations given in §4. Figure 8:
Distribution of log($M_{\star}/M_{\odot}$) estimates for the entire catalog
(shaded histogram). Colored lines indicate which colors are used to estimate
stellar mass, using color -- mass-to-light ratio relations and precedence as
described in §4.

##  5 Galaxy Morphologies & Group Membership

###  5.1 Galaxy Morphologies

Figure 9: Morphological classifications of our galaxies in the color--mass
plane. Left -- a color--mass diagram of 6677 galaxies with $g-i$ data from NSA
and morphologies from HyperLeda and LVG. Right -- the same color--mass
distribution (early-types in red contours, late-types in blue), with the black
line showing our linear fit to most cleanly separate early- and late-type
galaxies; this fit is extrapolated to larger and smaller masses (gray line).
The dotted line and blue shaded regions shows where we reclassify blue early-
type galaxies as late-type based on visual inspection of these galaxies (see
§5).

Many galaxies in the nearby universe have been morphologically classified
using the Hubble and de Vaucouleurs classification systems. Galaxies can be
separated into two broad categories: early-types are bulge-dominated,
elliptical or lenticular in shape, and host little-to-no current star
formation. Late-types have a spiral disk shape with smaller central bulges and
undergo current star formation. Numerical Hubble T classification ranges from
-6 to 10, with negative values corresponding to early-type galaxies and
positive values to late-types. In addition, early- and late-type galaxies are
often separated using galaxy color-magnitude or color-mass diagrams. At a
given stellar mass, early-type galaxies are typically associated with redder
colors, while late-types are associated with bluer colors. In this section, we
discuss the source of our morphological type information. We then also
describe how we translate this into a color-based separation of early and
late-type galaxies. We then evaluate the X-ray active fraction as a function
stellar mass and morphology/color in §6.

We use the numerical $T$-type translation of the morphological classifications
in this work. Both HyperLeda and LVG provide numerical $T$-types for 13744 of
the 15611 (89%) galaxies in our catalog. Of the remaining galaxies, 603 sample
galaxies have available color information. Furthermore, as can be in the left
panel of Fig. 9, the early- and late-type galaxies (a.k.a. the red sequence
and blue cloud) show considerable overlap, especially for low-mass ($<$109 M⊙)
galaxies. In order to give all sample objects an appropriate morphology, we
worked to minimize the potential for morphological misclassification and
calculated a color-based type for all galaxies with available color
information.

To separate galaxies into early- and late-types using the color-mass diagram,
we first calculated a morphological separation line by minimizing the
misclassification of galaxies with both morphological $T$-types and NSA
colors. We separated galaxies as early- and late-type based on their $T$-type
(with $T>0$ being late-type), fit a line to the color-mass diagram that
minimizes the number of misclassifications, i.e. late-types redder than the
line, and early-types bluer than the line. To fit this line, we first divided
the data into 0.25 dex mass bins and calculated the fraction of
misclassifications as a function of $(g-i)_{0}$ color. We then plotted the
$(g-i)_{0}$ value with minimum misclassification in each bin and found the
best-fit line to to establish our color-based morphological separation line.
We note that we only fit the line between log($M_{\star}$/$M_{\odot}$) of 8.5
to 10.5 where galaxy mass estimates are more robust. This line’s equation is:

$(g-i)_{0}=0.133*{\rm log}(M_{\star}/M_{\odot})-0.294$ (5)

This equation appears as the upper dashed line in the right panel of Fig. 9.

Visual inspection of SDSS imaging of the misclassified galaxies using our
morphological separation line suggested that while most red late-types were
showed distinct signatures of being dusty late-type galaxies (i.e. spiral arms
or clumpy regions of active star formation), many blue early-types were in
fact not properly classified and also showed these signatures of ongoing star
formation. This was especially true for early-type galaxies that had colors
much bluer than our morphological separation line. To better separate early-
and late-type galaxies we decided to reclassify very blue early-type galaxies.
We created a second reclassification cutoff line below which we reclassified
these galaxies in our best_type column (as described below) . To create this
line, we sorted the early-type contaminants by color in 0.25 dex mass bins.
For each mass bin, we visually classified these contaminants as early- or
late-type and then calculated threshold color such that legitimate
misclassification was minimized within the cutoff. Then, as above, we fit
reclassification cutoff line to the colors determined in each bin to get:

$(g-i)_{0}=0.124*{\rm log}(M_{\star}/M_{\odot})-0.344$ (6)

This equation appears as the lower dotted line in the right panel of Fig. 9.

To apply our color-based reclassification across our full sample, we needed to
transform our morphological separation and reclassification cutoff lines to
$(g-r)_{0}$, $(B-V)_{0}$, and $(B-R)_{0}$. Note that for deriving the
$(B-R)_{0}$ we added $(B-R)_{0}$ data from Cook et al. (2014) for galaxies in
our sample. Using overlapping galaxies and using RANSAC as described above, we
used the following color transformations.

$(g-r)_{0}=0.562*(g-i)_{0}+0.109$ (7) $(B-V)_{0}=0.625*(g-i)_{0}+0.131$ (8)
$(B-R)_{0}=0.898*(g-i)_{0}+0.133$ (9)

Applying these color-transformations we get the following morphological
separation lines:

$(g-r)_{0}=0.075*{\rm log}(M_{\star}/M_{\odot})-0.056$ (10)
$(B-V)_{0}=0.083*{\rm log}(M_{\star}/M_{\odot})-0.052$ (11)
$(B-R)_{0}=0.119*{\rm log}(M_{\star}/M_{\odot})-0.131$ (12)

and the reclassification lines:

$(g-r)_{0}=0.070*{\rm log}(M_{\star}/M_{\odot})-0.126$ (13)
$(B-V)_{0}=0.062*{\rm log}(M_{\star}/M_{\odot})-0.098$ (14)
$(B-R)_{0}=0.111*{\rm log}(M_{\star}/M_{\odot})-0.242$ (15)

For our final catalog, we provide several morphological types. In addition to
our catalog columns for numerical $T$-types and their source, we created two
type columns which broadly categorize galaxies into ’early’ or ’late’. Our
color_type classifies a galaxy as early if its color lies above the
morphological separation line, and late if it lies below the line. Our
recommended best_type column follows this process:
(1) most galaxies are classified based on their $T$-type from HyperLeda or
Karachentsev et al. (2013) with $t\leq 0$ = ’early’ and $t>0$ = ’late’.
(2) The 387 early-type galaxies that have colors bluer than the
reclassification cutoff are reclassified as late-type.
(3) 603 galaxies without a morphology are assigned their color-based type. We
reclassify each galaxy based on the color used to estimate mass in the final
catalog.

In total, we have best_type values for 14347 out of 15611 galaxies, and
color_type values for 11740 galaxies. We show the masses of galaxies separated
into early- and late-type in the left panel of Fig. 10, and also include the
masses of all galaxies with $T$-types in the right panel.

###  5.2 Galaxy Environments: Group Catalog Assignment

The galaxies in the local Universe span a wide range of environments, from
dense clusters like Virgo to galaxies in voids. One common way of identifying
galaxies in dense environments is through group catalogs. To enable easy
identification of galaxies in groups in our survey, we use the Lambert et al.
(2020) group catalog based on the 2MRS survey. This group catalog identifies
3022 groups from the 2MRS catalog (which extends well beyond our 50 Mpc limit)
using a friends-of-friends algorithm.

To match our galaxy catalog, we first used Table 3 from Lambert et al. (2020),
which gives individual galaxies that are part of a group or subgroup using the
paper’s friends-of-friends algorithm. We crossmatched our sample to galaxies
with $v_{cmb}<3867$ km s-1 (based on our highest redshift galaxy), resulting
in 2463/2864 unique matches within 30″. We wanted to ensure we also matched
low-mass sample galaxies which might not be included in the Lambert et al.
(2020) catalog due to the magnitude limit of 2MRS. To do this, we used Table 1
from Lambert et al. (2020), which includes group positions, comoving
distances, velocity dispersion, and radius/$R_{200}$ estimates. We
crossmatched our entire sample to find the nearest group for each galaxy,
considering sample galaxies to be part of a group if they fulfilled two
requirements: (i) the velocity for our sample galaxy must be within 2$\times$
the velocity dispersion of the group, and (ii) the angular separation between
between the galaxy and its nearest group matches was smaller than the angular
radius of that group, ($d\leq R_{200}$). Using this three-dimensional
approximation, we flagged an additional 790 sample galaxies galaxies as likely
group members. As expected, the median log($M_{\star}/M_{\odot}$) for the
distinct group method matches is $\sim$8.4, compared to $\sim$10 for the
direct galaxy matches.

We assigned Lambert et al. (2020) Group IDs to 3253 sample galaxies, giving
precedence to those with direct galaxy matches. This simultaneously flags
galaxies as being part of a group, and easily allows users to seek further
group information (including group radius and velocity dispersion) by cross-
correlating our catalog with the catalogs available from Lambert et al. (2020)
on the publisher’s website.

The Virgo cluster is indicated in the Lambert et al. (2020) catalog as cluster
2987 -- of the 347 galaxies with that group identification, we find 276 of
them (79%) are included in Virgo based on distance sources in §3. This is a
small fraction of the 907 total galaxies that we identify as being part of
Virgo in our catalog because the Lambert et al. (2020) catalog is based on
2MASS and thus misses many fainter Virgo galaxies.

Figure 10: Mass distribution of galaxies as a function of their morphology.
Left -- Distribution of log($M_{\star}/M_{\odot}$) for the full catalog
(shaded histogram), using best_type as described in §5.1 to separate into
early- (red) and late-types (blue). Right -- Comparison of numerical T-Type vs
log($M_{\star}/M_{\odot}$) for all catalog galaxies with an available t_type.
Color gradient is included for visual representation of galaxy type and
coincides with T-Type value.

##  6 X-ray Active Fraction Measurement

###  6.1 Background

The initial motivation for creating this sample was to quantify the
demographics of central black holes in nearby lower mass galaxies. These
demographics can provide constraints on the formation mechanisms of central
massive black holes (see recent review by Greene et al., 2020). X-ray
measurements of AGN in nearby galaxies $\lesssim 50$ Mpc can provide deep and
clean enough observations to constrain the occupation fraction of black holes
in galaxies below 1010 M⊙. Results on large samples of early-type galaxies
(Miller et al., 2015; Gallo & Sesana, 2019) suggest a high occupation fraction
for 109 to 1010 M⊙ galaxies, in agreement with dynamical measurements in a
handful of nearby early-type galaxies (Nguyen et al., 2018, 2019). However, a
majority of galaxies below 1010 M⊙ are late-type galaxies (Baldry et al.,
2012), and thus far no suitable galaxy sample exists to compare the X-ray
active fraction (and thus occupation fraction) of late-type galaxies to the
studies of early-type galaxies at masses below $M_{\star}\sim$1010 M⊙. Recent
compilations of archival X-ray data in nearby galaxy samples cut out most low
mass nearby galaxies by requiring redshift independent distance measurements
(She et al., 2017a, b), or by including only brighter samples dominated by
massive galaxies (Bi et al., 2020). The X-ray active fractions of more distant
dwarfs (between $z$ of 0.08 and 2.4) have also been studied by Mezcua et al.
(2018) in the COSMOS field. Our new 50 Mpc Galaxy Catalog provides the best
starting point for constraining the local X-ray active fraction and the
occupation fraction of central black holes. We focus specifically on trying to
get as large and complete a sample as possible down to lower masses (i.e.
$10^{8}$ $M_{\odot}$), with stellar mass estimates and Hubble type
measurements based on color index. Below we quantify the X-ray active fraction
using our new catalog.

Detection of a nuclear X-ray source above $10^{42}~{}$erg s-1 (the "classical"
AGN threshold) is invariably associated with accretion-powered emission from a
massive (i.e., non-stellar) black hole. Below this threshold, and even more so
below $10^{40}~{}$erg s-1, emission from bright X-ray binaries could
contribute to the detected signal. The expected luminosity from low-mass X-ray
binaries in particular scales with stellar mass (Lehmer et al., 2019), which
makes spatial resolution key for discriminating between low-Eddington ratio
massive BHs vs. contaminants. Owing to the factor $\sim$4 higher resolution,
Chandra is thus preferable to XMM-Newton for searching and identifying massive
black holes with luminosities lower than traditional AGN.

We present the Chandra-based X-ray active measurements of our galaxy catalog
here, without an attempt to model the distribution or account for
contamination from X-ray binaries. However, in a subsequent paper, we will use
this data to constrain the $L_{X}-M_{\star}$ relationship and the occupation
fraction as a function of galaxy stellar mass including estimates of
contamination from X-ray binaries (Gallo et al. in prep).

###  6.2 50 Mpc Active Fraction Measurement

We determined which galaxies in our catalog have available Chandra X-ray data
using the Chandra Source Catalog 2.0 (Evans et al., 2010, and Evans in prep),
which includes Chandra observations made public before the end of 2014. We
search for X-ray sources coincident with our galaxies’ nuclei using their
catalog ra and dec. Note that our choice of coordinates have been discussed in
§2.1.2, and the effect of uncertainties on these central coordinates is
considered in more detail in §6.2.2. Using Chandra’s
CSCView131313http://cda.cfa.harvard.edu/cscview/, we retrieved two tables from
a crossmatch with our catalog: (1) a table of limiting sensitivities for all
1506 objects with Chandra data, and (2) a table of detections within a given
angular separation (1--3″) of the galaxies’ ra and dec. The second table of
detections contains 291 unique galaxies with matching detections when using a
1″ search radius (note that 20 objects returned multiple sources). We use our
distance estimates (best_dist) to calculate X-ray luminosities from the 0.5-7
keV fluxes. These have a median log(LX/erg s-1) of 39.21 (hereafter we just
refer to this as log(LX)), with a full range of log(LX) from 33.19--42.26.
While some of these sources are likely contaminants from X-ray binaries and
not accreting massive black holes, we attempt to minimize this contamination
in two ways: (1) we restrict our X-ray activity analysis below to the 249
(85%) of sources that have available log($M_{\star}/M_{\odot}$) and
log(LX)$>$38.3 (as in Miller et al., 2015; Gallo & Sesana, 2019), and (2) we
use a 1″ match as our default matching radius. We note that previous work on
X-ray binary contamination in similar Chandra observations has shown that only
a small fraction ($\sim$10%) of nuclear X-ray detections with log(LX)$>$38.3
are likely to be contaminants (e.g. Gallo et al., 2010; Miller et al., 2012;
Foord et al., 2017). We include the columns chandra_observation and
chandra_detection to flag matched catalog galaxies using our default 1″
matching, and also include a chandra_detection_3arcsec for galaxies with
sources within 3″ (see §6.2.2); the X-ray luminosity is given in the log_lx
column.

Of the 249 galaxies used for our active fraction analysis, 159 have available
SDSS imaging. For visualizing the X-ray sources in our sample, we show 42 of
these galaxies in Fig. 11. In the first three rows of the figure we show the
18 lowest-mass SDSS galaxies, and a sampling of galaxies at higher masses in
the bottom half. The 18 lowest mass galaxies have log($M_{\star}/M_{\odot}$)
from 7.99--9.61; there are an additional 4 galaxies in this mass range without
available SDSS imaging.

Figure 11: Images of catalog galaxies with Chandra sources within 1″ of their
centers that have log(LX)$>$38.3 and have available SDSS imaging. The white
circle is centered on the catalog’s galaxy coordinates and shows the 1″ radius
used to match to Chandra detections, while the red dot marks the Chandra
source position. Each image has a width of 20″. The galaxies are arranged in
order of increasing log($M_{\star}/M_{\odot}$). The top three rows show the 18
galaxies with the lowest log($M_{\star}/M_{\odot}$), and represent 18/22
lowest mass galaxies with X-ray sources. The bottom four rows show galaxies at
higher masses sampled evenly in log($M_{\star}/M_{\odot}$). Image labels
provide catalog objname, logmass, and best_type (red are early-types, and blue
are late-types).

####  6.2.1 Local Active Fraction as a Function of Galaxy Mass and Type

We calculate the X-ray active fraction by dividing the number of galaxies with
Chandra detections by the number of galaxies with Chandra observations. We use
a 1″ matching radius, to limit the impact of X-ray binary contamination and
take advantage of Chandra’s excellent resolution (e.g. Gallo et al., 2010),
however, we examine the results of larger matching radii in the next
subsection. Results of this X-ray active fraction binned by mass and separated
by morphological type, are shown in Fig. 12. Although some detections may be
caused by nuclear X-ray binaries, this fraction serves as an upper limit of
AGN occupation for our catalog.

Figure 12:  X-ray active fraction of catalog galaxies as a function of galaxy
mass and type, using a 1″matching radius. Top row \-- the
log($M_{\star}/M_{\odot}$) distribution of catalog galaxies with Chandra data.
The solid line shows galaxies with Chandra observations, and the filled
histogram shows galaxies with Chandra detections with log(LX)$>$38.3 for (a)
early-types and (b) late-types. Panel c -- X-ray detection fraction as a
function of log($M_{\star}/M_{\odot}$) and separated by best_type, a
combination of morphological and color-based types (see §5). Panel d -- X-ray
detection fraction as a function of log($M_{\star}/M_{\odot}$) and separated
using color_type. Shaded areas in bottom figures show Agresti-Coull binomial
confidence intervals.

We observe an apparent difference in detection fraction between early- and
late-type galaxies, particularly within the mass range $9\lesssim
log(M_{\star}/M_{\odot})\lesssim 10.5$. We examine the significance of this
difference following the methods of Hoyer et al. (2021). We calculate
$p$-values under the null hypothesis that both early- and late-type galaxies
have the same X-ray active fraction. We split data into 0.25 dex bins from
$7.75\leq log(M_{\star}/M_{\odot})\leq 11.25$. We use the total X-ray active
fraction in each bin as an estimator of the binomial success probability.
Using this fraction, we draw $n$ samples from a binomial distribution where
$n$ is the number of galaxies per bin in both the early- and late-type
subsamples. We repeat this exercise $10^{6}$ times, and use the fraction of
estimates that match or exceed our observed difference between early- and
late-type galaxies to estimate a $p$-value for each bin. Using Fisher’s method
(Fisher 1992), we combine all $p$-values into a single parameter, under the
assumption that the null hypothesis is true and that the data in each mass bin
are independent. The final $p$-value is 0.0004 over the entire mass range,
thus suggesting that the observed enhancement of early-type galaxies is
significant at a level $>$3$\sigma$. This significance increases if, for
instance, we consider just the galaxies between $9\leq
log(M_{\star}/M_{\odot})\leq 10.5$. Thus, it appears there is a significant
enhancement of X-ray detections in early-type galaxies versus late-type
galaxies. We examine whether this result is due to a higher AGN fraction in
early-type galaxies below.

####  6.2.2 Local Active Fraction Uncertainties and Systematics

One significant potential uncertainty is the locations of galaxy centers, and
whether our catalog coordinates accurately reflect the true center of each
galaxy. To gauge the impact of this uncertainty, we conducted the same
analysis using a 2″ and 3″ separation constraint for Chandra detection
matching. As shown in the left panel of Fig. 13, larger matching radii
increase the X-ray active fraction for both types overall. The difference
between early- and late-type active fraction depicted in Fig. 12 still exists
for larger separation constraints, but the statistical significance decreases
to $p=0.0019$ for 2″ and $p=0.0104$ for 3″.

The decreasing difference in X-ray active fractions between early- and late-
types with larger matching radii could be explained if the centers of late-
type galaxies were less certain due to their lower surface brightness and more
complicated morphology. The central surface brightness does depend on galaxy
mass & luminosity (e.g. Ferrarese et al., 2006; Blanton et al., 2003), with
decreasing surface brightness in lower mass galaxies below roughly the Milky
Way’s luminosity/mass. Late-type galaxies also generally have lower surface
brightness than early-type galaxies (e.g. Blanton et al., 2003). In addition,
many of our late-type galaxies are irregulars; these dominate our late-type
galaxy number counts below log($M_{\star}/M_{\odot}$)$=$8.0. Finally, distance
plays a role in our ability to accurately locate the angular center of our
galaxies, with more distant galaxies likely having more accurate angular
coordinates.

As noted in §2.1.1, we see significant discrepancies between our catalog
positions (mostly from HyperLeda) and the best available positions from NED.
These offsets can give us a sense of how uncertain the nuclear positions are
of each galaxy. In the right panel of Fig. 13, we show the fraction of
galaxies with offsets larger than 1″, 2″, and 3″ as a function of galaxy type
(early vs. late) and stellar mass. The fraction of galaxies with significant
position differences between NED and our catalog is noticeably larger for
late-type galaxies than early-type galaxies in the mass range $7.5\lesssim
log(M_{\star}/M_{\odot})\lesssim 9.5$. For a 1″ position match, the
differences are large enough ($>$20% at some masses between early- and late-
types), to explain the lower active fractions we see in late-type galaxies.
These larger positional uncertainties are likely tied to the lower surface
brightness and irregular morphology amongst our late-type galaxies. However,
at higher masses ($9.5\lesssim log(M_{\star}/M_{\odot})\lesssim 10.5$), the
positional uncertainty differences become smaller than the active fraction
differences. To further examine the possibility of uncertain nuclear
coordinates impacting our active fraction measurements, we visually examined
all 17 galaxies (16 of which are late-type) with
$log(M_{\star}/M_{\odot})\lesssim 9.5$ that had X-ray sources within 3″ of
their centers, but not within 1″. In 11 of these 17 galaxies, the X-ray
sources are clearly offset from the galaxy centers. In another five galaxies
it was ambiguous whether the X-ray source was nuclear or not, while in only
one galaxy (NGC 598/M33), it was clear that the X-ray source was nuclear and
our nuclear position is offset by $\sim$1″from the true center of the galaxy
(this galaxy has a known nuclear star clusters that is visible in SDSS
imaging). This shows that accurate nuclear positions are an important factor
to consider when examining the presence of AGN in nearby galaxies, especially
in lower-mass late-type galaxies. However, it doesn’t appear that errors in
nuclear positions fully account for the difference we see between early and
late-type galaxy active fractions.

Figure 13:  Position offsets and their potential effects on active fraction as
a function of galaxy mass and type. Left -- Comparison between the X-ray
active fractions calculated using different separation radii to crossmatch
with Chandra observations. The solid line shows active fraction using a
matching radius of $1~{}\arcsec$, dashed lines use a radius of $2~{}\arcsec$,
and dotted lines with a radius of $3~{}\arcsec$. Red and blue lines show
early- and late-type galaxies respectively. Right -- Fraction of catalog
galaxies with large position offsets between our catalog and NED as a function
of mass and type. The solid line shows fraction with offset $>1~{}\arcsec$,
dashed lines with an offset $>2~{}\arcsec$, and dotted lines with offset
$>3~{}\arcsec$. Large offsets are seen much more commonly in late-type
galaxies than in early-type galaxies, especially at lower masses.

####  6.2.3 Local Active Fraction as a Function of Galaxy Mass and Color

We also repeated the process to calculate X-ray active fractions using only
color information to separate early- and late-type galaxies (using the
color_type described in §5.1). This is shown in the lower-right panel of Fig.
12. When classifying type based only on color, X-ray active fractions appear
similar in all mass bins. This change from our primarily morphological-based
types results from a suppression of the X-ray active fraction in red galaxies
relative to early-type galaxies. This is primarily caused by galaxies
classified as late-type, but with red color which lie above our color-based
type separation (Fig. 9). Visual inspection shows that the majority of these
red late-type galaxies are dusty spiral galaxies, many of which are viewed at
high inclinations. This suggests that despite the potential issues with visual
morphological classification (see §5.1), that they provide valuable
information on the galaxy type over and above the color-information. This is
particularly true for red late-type galaxies, which are quite numerous and
have accurate morphological classifications.

The active fraction measurement we present here is the first step in a more
sophisticated occupation fraction analysis (including accounting for XRB
contamination) that will be presented in a follow-up paper (Gallo et al., in
prep).

##  7 Conclusions

In this paper, we present a robust catalog of 15424 nearby galaxies within 50
Mpc with self-consistent distance, mass, and morphological type measurements.
This catalog was constructed primarily to enable X-ray active fraction and
occupation fraction measurements, but the utility of this catalog spans a much
broader range of possible applications. In particular, it provides a more
complete catalog of lower mass galaxies than were included in recent similar
studies (She et al., 2017a; Bi et al., 2020).

Our catalog combines galaxies from HyperLeda, the NASA-Sloan Atlas, and the
Local Volume Galaxy catalog. We also include additional photometry from Siena
Galaxy Atlas and NED. For galaxies with data from multiple sources, we
compared values to the literature and include best possible measurements in
the catalog. We extensively cleaned the catalog, including individual
investigation into extreme outliers. Through individual investigation of SDSS
imaging for NSA non-matches, we discovered that $\sim 13\%$ of nearby NSA
galaxies are contaminants.

We compile extinction-corrected magnitude, luminosity, and color information
for 11674 galaxies (76% of our full sample). We use the galaxy colors to
estimate masses by creating self-consistent color – mass-to-light ratio
relations in four bands. We also provide errors on our mass estimates that
include both the scatter around our color-$M/L$ relations and distance errors.
We also compare our masses to photometric masses from S4G and dynamical masses
from ATLAS3D.

Galaxy morphologies are available for 13744 galaxies. We combine this
information with galaxy masses and colors to fit a line that optimizes the
separation of early- and late-type galaxies. Through visual inspection, we
find that early-type galaxies bluer than this line are frequently incorrectly
classified, and we therefore reclassify those that fall more than $\sim 0.13$
mags below our morphological separation line. Red late-type galaxies are
typically truly late-type; many of these are edge-on dusty galaxies.

In addition we:

  * •

combine best estimates for flow-corrected redshift-based distances with
redshift independent distances, with a focus on minimizing distance error, and
including special treatment for galaxies in the Virgo Cluster.

  * •

use galaxies with overlapping color information to calculate and provide
empirical transformations of $B-V$, $B-R$, and $g-r$ colors into Sloan $g-i$.

  * •

identify galaxies in the catalog which belong to dense groups with the help of
the Lambert et al. (2020) group catalog.

Lastly, we present a preliminary analysis of X-ray sources and find that 249
out of the 1506 galaxies with existing Chandra observations have X-ray
detections with log(LX)$>$38.3 and available stellar mass estimates. We
present evidence that the X-ray active fractions for early- and late-type
galaxies differ. In particular, we find the active fraction is higher for
early-types within the mass range $9<$log($M_{\star}/M_{\odot}$)$<10.5$. We
show that increased astrometric uncertainties in late-type galaxies than
early-types may be partially responsible for this difference in active
fractions by galaxy type. In an upcoming paper (Gallo et al., in prep) we use
this catalog to conduct the first robust analysis of AGN occupation fraction
down to log($M_{\star}/M_{\odot}$)=8 both overall, and separated by
morphological type.

We would like to thank Michael Blanton for his useful clarifications on NSA
photometry. We also thank Ian Steer for helpful correspondence and assistance
concerning NED-D. A.C.S. acknowledges support from NSF grant AST-2108180. This
research has made use of the NASA/IPAC Extragalactic Database (NED),which is
operated by the Jet Propulsion Laboratory, California Institute of Technology,
under contract with the National Aeronautics and Space Administration. We
acknowledge the usage of the HyperLeda database (http://leda.univ-lyon1.fr).
We acknowledge the usage of the Siena Galaxy Atlas, based on the Legacy
Survey. The Legacy Surveys consist of three individual and complementary
projects: the Dark Energy Camera Legacy Survey (DECaLS; Proposal ID
#2014B-0404; PIs: David Schlegel and Arjun Dey), the Beijing-Arizona Sky
Survey (BASS; NOAO Prop. ID #2015A-0801; PIs: Zhou Xu and Xiaohui Fan), and
the Mayall z-band Legacy Survey (MzLS; Prop. ID #2016A-0453; PI: Arjun Dey).
DECaLS, BASS and MzLS together include data obtained, respectively, at the
Blanco telescope, Cerro Tololo Inter-American Observatory, NSF’s NOIRLab; the
Bok telescope, Steward Observatory, University of Arizona; and the Mayall
telescope, Kitt Peak National Observatory, NOIRLab. Pipeline processing and
analyses of the data were supported by NOIRLab and the Lawrence Berkeley
National Laboratory (LBNL). The Legacy Surveys project is honored to be
permitted to conduct astronomical research on Iolkam Du’ag (Kitt Peak), a
mountain with particular significance to the Tohono O’odham Nation141414full
acknowledgment available at
https://www.legacysurvey.org/acknowledgment/
#siena-galaxy-atlas

. The Siena Galaxy Atlas was made possible by funding support from the U.S.
Department of Energy, Office of Science, Office of High Energy Physics under
Award Number DE-SC0020086 and from the National Science Foundation under grant
AST-1616414.

##  Appendix A Full Catalog

Table 2: Stub of catalog, shortened to include only the most important columns

objname | ra | dec | v_h | t_type | best_type | bestdist | bestdist_error | logmass | logmass_error | logmass_src
---|---|---|---|---|---|---|---|---|---|---
| (deg) | (deg) | (km/s) |  |  | (Mpc) | (Mpc) |  ($M_{\odot}$) |  ($M_{\odot}$) |
NGC2788B | 135.898976 | -67.969798 | 1444.9 | 3.2 | late | 17.451 | 6.903 | 9.1114 | 0.3634 | B-V
NGC2793 | 139.194264 | 34.431685 | 1724.49 | 8.7 | late | 26.016 | 7.16 | 9.1861 | 0.2454 | g-i
NGC2798 | 139.345353 | 41.99981 | 1726.0 | 1.1 | late | 26.745 | 6.563 | 9.983 | 0.2176 | g-i
NGC2799 | 139.379054 | 41.994172 | 1870.86 | 8.5 | late | 30.689 | 6.964 | 9.3076 | 0.2006 | g-i
NGC2805 | 140.084902 | 64.102968 | 1730.35 | 6.9 | late | 27.77 | 6.506 | 9.8968 | 0.2073 | g-i
NGC2810 | 140.518791 | 71.843972 | 3479.7 | -4.9 | early | 51.345 | 6.415 | 10.8048 | 0.1354 | g-r
NGC2811 | 139.046301 | -16.312679 | 2355.6 | 1.1 | late | 28.1 | 1.29 | 10.617 | 0.1354 | B-V
NGC2814 | 140.297846 | 64.25236 | 1592.0 | 3.1 | late | 25.786 | 6.638 | 9.1279 | 0.2287 | g-r
NGC2815 | 139.082217 | -23.633299 | 2545.3 | 2.9 | late | 36.977 | 5.217 | 10.702 | 0.1354 | B-V
NGC2820 | 140.441039 | 64.257886 | 1575.01 | 5.3 | late | 25.507 | 6.642 | 9.7143 | 0.2315 | g-i

Note. — objname \- Common galaxy name; ra \- Right Ascension from combined
sources; dec \- Declination from combined sources; v_h \- Heliocentric radial
velocity, see Section 3.1; t_type \- Numerical Hubble T-Type; best_type \-
Combined galaxy type, see Section 5.1; bestdist \- Our chosen best distance
estimate; bestdist_error \- error for best distance estimate; logmass \-
Compiled best log$(M_{*}/M{\odot})$ estimate; logmass_error \- Error on
logmass; logmass_src \- Color used for best mass estimate: $g-i$, $g-r$,
$B-V$, $B-R$;

Columns found in full catalog, available from the publisher and at
https://github.com/davidohlson/50MGC: pgc \- PGC number (from HyperLeda);
nsa_id \- NASA-Sloan Atlas NSAID number; group_id \- Galaxy is member of
group, number based on Lambert et al. 2020 group catalog; ra_nsa \- Right
Ascension provided by NSA; dec_nsa \- Declination provided by NSA; ra_ned \-
Primary Right Ascension provided by NED; dec_ned \- Primary Declination
provided by NED; d25 \- Apparent Diameter from Karachentsev and HyperLeda;
v_cmb \- Radial velocity with respect to CMB radiation; v_source \- Original
source for compiled velocity: HyperLeda, LVG, NSA; hl_obj \- True for objects
in HyperLeda; lvg_obj \- True for objects in Karachentsev’s Catalog of Local
Volume Galaxies; nsa_obj \- True for objects in NASA-Sloan Atlas; sga_obj \-
True for objects in Siena Galaxy Atlas; color_type \- Color-based Type, see
Section 5.1; a_B_leda \- B-band extinction from HyperLeda multiplied by 0.86
to translate to Schlafly, $A_{V}=0.769*a_{B_{leda}}$ and
$A_{R}=0.629*a_{B_{leda}}$; a_g_nsa \- g-band extinction from NASA-Sloan
Atlas, the i-band extinction used is $=0.550*a_{g_{nsa}}$; EBV_irsa \-
$E(B-V)$ value from IRSA dust website Schlafly values for all galaxies. Used
only for the Siena Galaxy Atlas sources, $A_{g}/EBV_{sga}=3.303$,
$A_{r}/EBV_{sga}=2.285$; Bt0_leda \- Extinction corrected total B band
magnitude from HyperLeda; BV_color_leda \- $(B-V)$ color from HyperLeda; B_lum
\- B-band Luminosity — derived for non-HyperLeda sources as described in
Section 2.2; gi_color_nsa \- Extinction corrected $(g-i)$ color from NASA-
Sloan Atlas; i_lum_nsa \- i-Band Luminosity, calculated using
$M_{i,\odot}=4.53$; gr_color_sga \- Extinction corrected $(g-r)$ color from
Siena Galaxy Atlas; r_lum_sga \- r-band Luminosity, calculated using
$M_{r,\odot}=4.65$; BR_color_ned \- Extinction corrected $(B-R)$ color from
NED; R_lum_ned \- R-band Luminosity from NED, calculated using
$M_{R,/odot}=4.60$; BMag \- Estimated absolute B-Band Magnitude for all
galaxies; gi_color \- Estimated $g-i$ color for all galaxies with color
measurements; mag_flag \- Flags galaxies with magnitude preference exceptions
as described in Section 2.3; cf3_dist \- Distances from CosmicFlows3
Calculator; cf3_dist_error \- Error on cf3_dist; zind_dist \- Redshift
independent distances; zind_dist_error \- Error on zind_dist; zind_indicator
\- Redshift independent distance indicator listed in NEDD; bestdist_method \-
General method used for best distances: CF3-Z, Karachentsev, NED-D, Mei,
Cantiello, EVCC, HyperLeda; bestdist_source \- Source/Reference for bestdist,
see Section 3; dist_ned_flag \- Flags galaxies with NED-D best distance
exceptions as described in Section 3.2; logmass_gi \- log$(M_{*}/M{\odot})$
from $(g-i)$; logmass_gr \- log$(M_{*}/M{\odot})$ from $(g-r)$; logmass_BV \-
log$(M_{*}/M{\odot})$ from $(B-V)$; logmass_BR \- log$(M_{*}/M{\odot})$ from
$(B-R)$; chandra_observation \- True if CSCView crossmatch returned a limiting
sensitivity at matching position as described in Section 6; chandra_detection
\- True if CSCView crossmatch returned flux information within a 1″ matching
radius as described in Section 6; log_lx \- X-ray luminosity log(LX/erg/sec)
calculated from the CSC 0.5-7 keV flux, using a 1″ radius;
chandra_detection_3arcsec \- True if CSCView crossmatch returned flux
information within a 3″ matching radius as described in Section 6;
log_lx_3arcsec \- X-ray luminosity log(LX/erg/sec) calculated from the CSC
0.5-7 keV flux, using a 3″match radius.

## References

  * Abbott et al. (2017) Abbott, B. P., Abbott, R., Abbott, T. D., et al. 2017, Phys. Rev. Lett., 119, 161101, doi: 10.1103/PhysRevLett.119.161101
  * Anand et al. (2019) Anand, G. S., Tully, R. B., Rizzi, L., & Karachentsev, I. D. 2019, ApJ, 872, L4, doi: 10.3847/2041-8213/aafee6
  * Baldry et al. (2004) Baldry, I. K., Glazebrook, K., Brinkmann, J., et al. 2004, ApJ, 600, 681, doi: 10.1086/380092
  * Baldry et al. (2010) Baldry, I. K., Robotham, A. S. G., Hill, D. T., et al. 2010, MNRAS, 404, 86, doi: 10.1111/j.1365-2966.2010.16282.x
  * Baldry et al. (2012) Baldry, I. K., Driver, S. P., Loveday, J., et al. 2012, MNRAS, 421, 621, doi: 10.1111/j.1365-2966.2012.20340.x
  * Bell et al. (2003) Bell, E. F., McIntosh, D. H., Katz, N., & Weinberg, M. D. 2003, ApJS, 149, 289, doi: 10.1086/378847
  * Bi et al. (2020) Bi, S., Feng, H., & Ho, L. C. 2020, ApJ, 900, 124, doi: 10.3847/1538-4357/aba761
  * Binggeli et al. (1985) Binggeli, B., Sandage, A., & Tammann, G. A. 1985, AJ, 90, 1681, doi: 10.1086/113874
  * Blanton et al. (2005) Blanton, M. R., Lupton, R. H., Schlegel, D. J., et al. 2005, ApJ, 631, 208, doi: 10.1086/431416
  * Blanton et al. (2003) Blanton, M. R., Hogg, D. W., Bahcall, N. A., et al. 2003, ApJ, 594, 186, doi: 10.1086/375528
  * Bruzual & Charlot (2003) Bruzual, G., & Charlot, S. 2003, MNRAS, 344, 1000, doi: 10.1046/j.1365-8711.2003.06897.x
  * Calzetti et al. (2000) Calzetti, D., Armus, L., Bohlin, R. C., et al. 2000, ApJ, 533, 682, doi: 10.1086/308692
  * Cappellari et al. (2011) Cappellari, M., Emsellem, E., Krajnović, D., et al. 2011, MNRAS, 413, 813, doi: 10.1111/j.1365-2966.2010.18174.x
  * Cappellari et al. (2012) Cappellari, M., McDermid, R. M., Alatalo, K., et al. 2012, Nature, 484, 485, doi: 10.1038/nature10972
  * Cappellari et al. (2013a) Cappellari, M., Scott, N., Alatalo, K., et al. 2013a, MNRAS, 432, 1709, doi: 10.1093/mnras/stt562
  * Cappellari et al. (2013b) Cappellari, M., McDermid, R. M., Alatalo, K., et al. 2013b, MNRAS, 432, 1862, doi: 10.1093/mnras/stt644
  * Cardelli et al. (1989) Cardelli, J. A., Clayton, G. C., & Mathis, J. S. 1989, ApJ, 345, 245, doi: 10.1086/167900
  * Carlsten et al. (2022) Carlsten, S. G., Greene, J. E., Beaton, R. L., Danieli, S., & Greco, J. P. 2022, ApJ, 933, 47, doi: 10.3847/1538-4357/ac6fd7
  * Chabrier (2003) Chabrier, G. 2003, PASP, 115, 763, doi: 10.1086/376392
  * Cook et al. (2014) Cook, D. O., Dale, D. A., Johnson, B. D., et al. 2014, MNRAS, 445, 881, doi: 10.1093/mnras/stu1580
  * Courtois et al. (2012) Courtois, H. M., Hoffman, Y., Tully, R. B., & Gottlöber, S. 2012, ApJ, 744, 43, doi: 10.1088/0004-637X/744/1/43
  * Crnojević et al. (2016) Crnojević, D., Sand, D. J., Spekkens, K., et al. 2016, ApJ, 823, 19, doi: 10.3847/0004-637X/823/1/19
  * Driver et al. (2022) Driver, S. P., Bellstedt, S., Robotham, A. S. G., et al. 2022, MNRAS, 513, 439, doi: 10.1093/mnras/stac472
  * Erwin et al. (2015) Erwin, P., Saglia, R. P., Fabricius, M., et al. 2015, MNRAS, 446, 4039, doi: 10.1093/mnras/stu2376
  * Eskew et al. (2012) Eskew, M., Zaritsky, D., & Meidt, S. 2012, AJ, 143, 139, doi: 10.1088/0004-6256/143/6/139
  * Evans et al. (2010) Evans, I. N., Primini, F. A., Glotfelty, K. J., et al. 2010, ApJS, 189, 37, doi: 10.1088/0067-0049/189/1/37
  * Ferrarese et al. (2006) Ferrarese, L., Côté, P., Jordán, A., et al. 2006, ApJS, 164, 334, doi: 10.1086/501350
  * Foord et al. (2017) Foord, A., Gallo, E., Hodges-Kluck, E., et al. 2017, ApJ, 841, 51, doi: 10.3847/1538-4357/aa6d63
  * Freedman (2021) Freedman, W. L. 2021, arXiv e-prints, arXiv:2106.15656.  https://arxiv.org/abs/2106.15656
  * Gallo & Sesana (2019) Gallo, E., & Sesana, A. 2019, ApJ, 883, L18, doi: 10.3847/2041-8213/ab40c6
  * Gallo et al. (2010) Gallo, E., Treu, T., Marshall, P. J., et al. 2010, ApJ, 714, 25, doi: 10.1088/0004-637X/714/1/25
  * Geha et al. (2012) Geha, M., Blanton, M. R., Yan, R., & Tinker, J. L. 2012, ApJ, 757, 85, doi: 10.1088/0004-637X/757/1/85
  * Graziani et al. (2019) Graziani, R., Courtois, H. M., Lavaux, G., et al. 2019, MNRAS, 488, 5438, doi: 10.1093/mnras/stz078
  * Greene et al. (2020) Greene, J. E., Strader, J., & Ho, L. C. 2020, ARA&A, 58, 257, doi: 10.1146/annurev-astro-032620-021835
  * Harris & Zaritsky (2009) Harris, J., & Zaritsky, D. 2009, AJ, 138, 1243, doi: 10.1088/0004-6256/138/5/1243
  * Hoyer et al. (2021) Hoyer, N., Neumayer, N., Georgiev, I. Y., Seth, A. C., & Greene, J. E. 2021, MNRAS, 507, 3246, doi: 10.1093/mnras/stab2277
  * Hughes et al. (2021) Hughes, A. K., Sand, D. J., Seth, A., et al. 2021, ApJ, 914, 16, doi: 10.3847/1538-4357/abf63c
  * Jester et al. (2005) Jester, S., Schneider, D. P., Richards, G. T., et al. 2005, AJ, 130, 873, doi: 10.1086/432466
  * Kaaret et al. (2017) Kaaret, P., Feng, H., & Roberts, T. P. 2017, ARA&A, 55, 303, doi: 10.1146/annurev-astro-091916-055259
  * Karachentsev et al. (2013) Karachentsev, I. D., Makarov, D. I., & Kaisina, E. I. 2013, AJ, 145, 101, doi: 10.1088/0004-6256/145/4/101
  * Kelvin et al. (2014) Kelvin, L. S., Driver, S. P., Robotham, A. S. G., et al. 2014, MNRAS, 444, 1647, doi: 10.1093/mnras/stu1507
  * Kim et al. (2014) Kim, S., Rey, S.-C., Jerjen, H., et al. 2014, ApJS, 215, 22, doi: 10.1088/0067-0049/215/2/22
  * Kourkchi et al. (2020) Kourkchi, E., Courtois, H. M., Graziani, R., et al. 2020, AJ, 159, 67, doi: 10.3847/1538-3881/ab620e
  * Kriek et al. (2010) Kriek, M., Labbé, I., Conroy, C., et al. 2010, ApJ, 722, L64, doi: 10.1088/2041-8205/722/1/L64
  * Krumholz et al. (2019) Krumholz, M. R., McKee, C. F., & Bland-Hawthorn, J. 2019, ARA&A, 57, 227, doi: 10.1146/annurev-astro-091918-104430
  * Lambert et al. (2020) Lambert, T. S., Kraan-Korteweg, R. C., Jarrett, T. H., & Macri, L. M. 2020, MNRAS, 497, 2954, doi: 10.1093/mnras/staa1946
  * Lauberts & Valentijn (1989) Lauberts, A., & Valentijn, E. A. 1989, The surface photometry catalogue of the ESO-Uppsala galaxies
  * Lehmer et al. (2019) Lehmer, B. D., Eufrasio, R. T., Tzanavaris, P., et al. 2019, ApJS, 243, 3, doi: 10.3847/1538-4365/ab22a8
  * Makarov et al. (2014) Makarov, D., Prugniel, P., Terekhova, N., Courtois, H., & Vauglin, I. 2014, A&A, 570, A13, doi: 10.1051/0004-6361/201423496
  * McConnachie (2012) McConnachie, A. W. 2012, AJ, 144, 4, doi: 10.1088/0004-6256/144/1/4
  * McGaugh & Schombert (2014) McGaugh, S. S., & Schombert, J. M. 2014, AJ, 148, 77, doi: 10.1088/0004-6256/148/5/77
  * Mei et al. (2007) Mei, S., Blakeslee, J. P., Côté, P., et al. 2007, ApJ, 655, 144, doi: 10.1086/509598
  * Meidt et al. (2014) Meidt, S. E., Schinnerer, E., van de Ven, G., et al. 2014, ApJ, 788, 144, doi: 10.1088/0004-637X/788/2/144
  * Melbourne et al. (2012) Melbourne, J., Williams, B. F., Dalcanton, J. J., et al. 2012, ApJ, 748, 47, doi: 10.1088/0004-637X/748/1/47
  * Mezcua et al. (2018) Mezcua, M., Civano, F., Marchesi, S., et al. 2018, MNRAS, 478, 2576, doi: 10.1093/mnras/sty1163
  * Miller et al. (2012) Miller, B., Gallo, E., Treu, T., & Woo, J.-H. 2012, ApJ, 747, 57, doi: 10.1088/0004-637X/747/1/57
  * Miller et al. (2015) Miller, B. P., Gallo, E., Greene, J. E., et al. 2015, ApJ, 799, 98, doi: 10.1088/0004-637X/799/1/98
  * Neumayer et al. (2020) Neumayer, N., Seth, A., & Böker, T. 2020, A&A Rev., 28, 4, doi: 10.1007/s00159-020-00125-0
  * Nguyen et al. (2018) Nguyen, D. D., Seth, A. C., Neumayer, N., et al. 2018, ApJ, 858, 118
  * Nguyen et al. (2019) \---. 2019, ApJ, 872, 104
  * O’Donnell (1994) O’Donnell, J. E. 1994, ApJ, 422, 158, doi: 10.1086/173713
  * Pier et al. (2003) Pier, J. R., Munn, J. A., Hindsley, R. B., et al. 2003, AJ, 125, 1559, doi: 10.1086/346138
  * Reines et al. (2013) Reines, A. E., Greene, J. E., & Geha, M. 2013, ApJ, 775, 116, doi: 10.1088/0004-637X/775/2/116
  * Roediger & Courteau (2015) Roediger, J. C., & Courteau, S. 2015, MNRAS, 452, 3209, doi: 10.1093/mnras/stv1499
  * Saglia et al. (2016) Saglia, R. P., Opitsch, M., Erwin, P., et al. 2016, ApJ, 818, 47, doi: 10.3847/0004-637X/818/1/47
  * Schlafly & Finkbeiner (2011) Schlafly, E. F., & Finkbeiner, D. P. 2011, ApJ, 737, 103, doi: 10.1088/0004-637X/737/2/103
  * Schlegel et al. (1998) Schlegel, D. J., Finkbeiner, D. P., & Davis, M. 1998, ApJ, 500, 525, doi: 10.1086/305772
  * She et al. (2017a) She, R., Ho, L. C., & Feng, H. 2017a, ApJ, 835, 223, doi: 10.3847/1538-4357/835/2/223
  * She et al. (2017b) \---. 2017b, ApJ, 842, 131, doi: 10.3847/1538-4357/aa7634
  * Sheth et al. (2010) Sheth, K., Regan, M., Hinz, J. L., et al. 2010, PASP, 122, 1397, doi: 10.1086/657638
  * Simon (2019) Simon, J. D. 2019, ARA&A, 57, 375, doi: 10.1146/annurev-astro-091918-104453
  * Skrutskie et al. (2006) Skrutskie, M. F., Cutri, R. M., Stiening, R., et al. 2006, AJ, 131, 1163, doi: 10.1086/498708
  * Steer (2020) Steer, I. 2020, AJ, 160, 199, doi: 10.3847/1538-3881/abafba
  * Steer et al. (2017) Steer, I., Madore, B. F., Mazzarella, J. M., et al. 2017, AJ, 153, 37, doi: 10.3847/1538-3881/153/1/37
  * Tartaglia et al. (2018) Tartaglia, L., Sand, D. J., Valenti, S., et al. 2018, ApJ, 853, 62, doi: 10.3847/1538-4357/aaa014
  * Taylor et al. (2011) Taylor, E. N., Hopkins, A. M., Baldry, I. K., et al. 2011, MNRAS, 418, 1587, doi: 10.1111/j.1365-2966.2011.19536.x
  * Telford et al. (2020) Telford, O. G., Dalcanton, J. J., Williams, B. F., et al. 2020, ApJ, 891, 32, doi: 10.3847/1538-4357/ab701c
  * Willman et al. (2005) Willman, B., Dalcanton, J. J., Martinez-Delgado, D., et al. 2005, ApJ, 626, L85, doi: 10.1086/431760
  * Wright et al. (2017) Wright, A. H., Robotham, A. S. G., Driver, S. P., et al. 2017, MNRAS, 470, 283, doi: 10.1093/mnras/stx1149
  * Zibetti et al. (2009) Zibetti, S., Charlot, S., & Rix, H.-W. 2009, MNRAS, 400, 1181, doi: 10.1111/j.1365-2966.2009.15528.x