GALEX-Gaia White DwarfsGALEX-Gaia White Dwarfs

# The GALEX-Gaia-EDR3 Catalogue of Single and Binary White Dwarfs

Ryan Jackim1 0000-0002-5064-2760 Jeremy Heyl1 0000-0001-9739-367X Harvey
Richer1 0000-0001-9002-8178 1Department of Physics and Astronomy, University
of British Columbia, Vancouver, B.C. V6T 1Z1, Canada

###### Abstract

We present a catalogue of white dwarf candidates constructed from the GALEX
and Gaia EDR3 catalogues. The catalogue contains 332,111 candidate binary
white dwarf systems and 111,996 candidate single white dwarfs. Where
available, the catalogue is augmented with photometry from Pan-STARRS DR1,
SDSS DR12 and classifications from StarHorse. We fit photometric data with
modeled white dwarf cooling sequences to derive mass, age and effective
temperature of the white dwarf as well as mass estimates for the companion. We
test our classifications against StarHorse, the Gentile-Fusillo Gaia EDR3
catalogue, and white-dwarf-main-sequence binaries identified in SDSS DR12.
This catalogue provides a unique probe of the binarity of white dwarfs as well
as the abundance of white-dwarf giant binaries and large mass-ratio stellar
binaries which are difficult to probe otherwise.

###### keywords:

White Dwarf – Binaries: general – Catalogues

##  1 Introduction

White dwarfs (WD) are stellar remnants originating from stars with mass less
than 8 $\textrm{M}_{\odot}$ which have reached the end of their evolutionary
track. White-dwarf-main-sequence (WDMS) binaries are stellar systems with a WD
that has formed from the initial primary star and a main sequence (MS) star.
Similarly, white-dwarf-giant (WDG) binaries are systems where a WD has formed
from the initial primary star and the secondary is now a giant star. We will
refer to both types of systems generically as white-dwarf-non-degenerate
(WDND) binaries (in contrast with double-degenerate, DD, binaries). WDs have
low luminosity in the optical. When paired with a MS star more massive than a
G star, it is difficult to identify the WD in the system due to the
comparatively high luminosity of the companion. WDND systems offer the ability
to construct studies on comprehensive binary population synthesis as well as
insights into pre-cataclysmic variables (Willems & Kolb, 2004). The evolution
of WDND systems is largely controlled by the separation between the two stars.
Three-quarters of WDND systems have orbital periods wide enough that the
progenitor of the WD evolved independently of the other star in the system.
Typically these systems will have an orbital period longer than 400 days
(Willems & Kolb, 2004). The remaining twenty five percent are close enough to
each other to exchange mass during their stellar evolution. In these close
binary systems, the more massive companion evolves off the main sequence first
and moves onto the red giant branch (RGB). The RGB’s outer atmosphere envelops
the lower mass companion star and transfers mass. This is referred to as the
common envelope (CE) stage. The RGB star’s envelope is eventually ejected and
it evolves into a WD creating a WDND system (Ivanova et al., 2013).

The atmospheres of WDs are generally found as either hydrogen-rich (DA) or
helium-rich (DB). The fraction of WDs with helium-rich atmospheres increases
significantly when $T_{\text{\scriptsize eff}}$ of the WD is below 10,000 K.
The reason for this change is likely caused by convection mixing the upper
hydrogen atmosphere with the more massive helium envelope beneath. It is
expected that this convection process will turn all hydrogen-rich atmosphere
WDs into helium-rich atmosphere WDs (Bergeron et al., 1997).

There have been several previous surveys that have attempted to gather samples
of the WDND systems. The Sloan Digital Sky Survey (SDSS) identified WDND
binary systems from Radial Velocity (RV) measurements of optical spectra.
Using this technique, just over 3,300 WDND binaries have been identified
(Rebassa-Mansergas et al., 2016). SDSS was primarily developed to identify
quasars and galaxies, leading to most of the WDND systems identified being the
same colour as quasars. This creates a bias towards hot
($\gtrsim$10,000-15,000 K) WDs with M-dwarf spectral type (¿M3–4) companions
(Rebassa-Mansergas et al., 2016; Rebassa-Mansergas et al., 2013). A correction
done by Rebassa-Mansergas et al. (2013) attempted to use photometric data to
identify WDND systems with colder WDs. Of the 3,419 candidates selected, 71%
of the sources were expected to have a cool ($\lesssim$ 10 000-15 000 K) WD
and a M-dwarf secondary ($\sim$M2-3). A similar method was employed by the
Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) Survey
which has identified 876 WDND systems (Ren et al., 2018). These previous
studies of WDND systems have only observed WDND systems with M-dwarf
companions. A study done by Rebassa-Mansergas et al. (2021) reported 112 WDND
binary systems within 100 pc, located in the gap between the WD and MS region
in the visual CMD. Their study did not include WDND systems lying within the
MS region where the MS star dominates the light of the WD. They were able to
conclude that their identification only accounted for roughly 9% of WDND
systems and that 91% of WDND binaries remain obscured in the MS region. Thus,
it is imperative that we identify alternative methods for assessing WDND
systems enhance our understanding of the evolution of binary systems.

To attempt to identify WDND systems, we used several photometric survey’s that
contain optical and UV data. Two primary catalogues were utilized. The Galaxy
Evolutionary Explorer (GALEX) is a NASA explorer mission designed to carry out
a space-based UV sky survey. The GALEX survey allows for UV detections of
objects as faint as 23rd magnitude (Martin et al., 2005). WDs with a
temperature range of 30,000 K to less than 4,500 K will emit in the UV
(Bergeron et al., 2019). This makes the GALEX survey an excellent tool in
which to identify WD systems. Gaia Early Data Release 3 (EDR3) (Gaia
Collaboration et al., 2021) is a catalogue containing 1.8 billion stars
limited to objects brighter than 21st G-magnitude. It contains improved
photometry relative to Gaia Data Release 2 and detailed distance measurements
of objects through observation of their parallactic shift. Gaia EDR3 contains
the same astrometric data as Gaia DR3; therefore, the third data release does
not add additional data for the match. The combined use of both these
catalogues allows us to detect WDs through GALEX and collect both photometric
and distance information of the systems through Gaia EDR3.

Using WD stars identified in GALEX and Gaia EDR3, we constructed a catalogue
containing a total of 444,107 candidate single and binary WDs. Of these,
111,996 candidates match to WD objects identified by Gentile Fusillo et al.
(2021). The remaining 309,584 are candidate WDND systems where the GALEX UV
photometry reports values similar to WD, but Gaia EDR3 reports values similar
to MS or giant stars. This was accomplished by identifying all GALEX entries
near the WD sequence in the UV and cross-matching them with Gaia EDR3 entries.
We then attempted to gather stellar parameter estimates for both the WD and
non-degenerate secondary by applying a two-star fitting algorithm based on the
stellar isochrones of Bressan et al. (2012) and the WD cooling models of
Bédard et al. (2020). We analyzed the resulting masses for all the stars in
the system and reported our estimated masses in our catalogue. Mass estimates
from the StarHorse MS catalogue (Anders et al., 2021) and Gentile Fusillo et
al. (2021) WD catalogue were included to supplement our estimates.

##  2 Catalogue Construction

###  2.1 Data Selection

The procedure to select WDND systems is similar to the Gentile Fusillo et al.
(2021) selection of WD systems in the Gaia EDR3 data set. We sought to expand
our list of potential WDND identifications by including likely UV-WDs as one
of the components in the binary system. To begin, we produced a cross-match
between GALEX and Gaia EDR3 using Vizier. The purpose behind this cross-match
was to identify stellar systems with UV excesses that resembled WD stars. If
these systems had an excess similar to a WD, it is likely that there is a WD
present. We then implemented a broad cut which defines an area of the colour-
magnitude diagram (CMD) space where WDs or WDND systems are located. At this
stage, the cross-match was focused on gathering a wide selection of potential
objects with little consideration for contaminants:

ADQL 1: Photometric Cut

⬇

NUVmag-DM-0.6*(FUVmag-NUVmag) > 8 OR

NUVmag-DM\-

2*(NUVmag-phot_bp_mean_mag) > 8 OR

phot_g_mean_mag-DM-3*bp_rp > 8

where DM is the distance modulus determined using the Gaia EDR3 parallax. Our
selection focused on locating white-dwarf-like objects within one of three
wavelength regions. These three regions are: the far-UV (FUV) and near-UV
(NUV) defined exclusively by the GALEX data-set in the left plot of Figure 1,
the near-UV from GALEX to the Gaia Bp filter described in the center plot of
Figure 1, and the Gaia EDR3 visual magnitudes described by the Bp and Rp
filters shown in the right plot of Figure 1. The black line in Figure 1
depicts a cut where everything below the line is considered a WD or a WDND.
Everything above the line is excluded from our catalogue. If an object was
below the black line in one of the wavelength regions, it was considered a WD
or WDND regardless of whether it succeeded in the other two regions.

A large fraction of our collected data had unreliable astrometric or
photometric data. We apply the quality cuts in ADQL 2 to our sample, which
excluded non-WD contaminants and entries with poor photometry.

ADQL 2: Error Cut

⬇

parallax_over_error >= 4 AND

(astrometric_excess_noise_sig < 2 OR

astrometric_excess_noise < 1.5 OR

astrometric_params_solved < 32)

Because of the potential presence of a companion star contaminating the
photometry of the WD, the quality cuts were generous enough to allow for this
circumstance.

Figure 1: The distribution of GALEX-Gaia EDR3 cross-match is shown in the
above three figures. The left image depicts the objects that succeeded our
cross-match in the GALEX FUV-NUV filters. The middle figure depicts the same
objects in the GALEX NUV-Gaia Bp filters. The right figure depicts these
objects in the Gaia Bp-Rp filters. The black line represents our cut described
in ADQL 1. Objects that lie below the black line in any of these panels are
considered WDs or WDNDs and included in our catalogue.

Two additional cross-matches were done to identify if the objects had data
from SDSS DR12 or Pan-STARRS DR1. If the objects matched SDSS or Pan-STARRS
entries, the data were included in the catalogue. Those that did not were left
blank.

After quality cuts were applied, 444,107 possible WD systems remain. Figure 2
describes the distribution of these 444,107 objects in the Gaia EDR3
photometric filters. The blue region signifies objects that were similar to
WDs in Gaia EDR3 and the red signifies those that did not look like WDs in
Gaia EDR3, but were similar to WDs in other wavelengths. Upon inspection of
Figure 2, it is clear that there is a large variety of objects identified as
WD across the MS and the evolved branch. The total numerical distribution of
objects that succeeded our three different WD cuts can be seen in the weighted
Venn diagram of Figure 3. The magenta circle depicts objects that look like
WDs only in the GALEX FUV-NUV filters and not WDs in the GALEX NUV-Gaia Bp or
Gaia Bp-Rp filters. The yellow circle describes objects that look like WDs
only in GALEX NUV-Gaia Bp and not WDs in the GALEX FUV-NUV or Gaia Bp-Rp
filters. Finally, the cyan slice (2199 stars), defines the number of objects
that were identified as a WD in only the Gaia filters and not WDs in the NUV-
Bp or FUV-NUV filters. A majority of the objects in the cyan slice, did not
contain FUV data or were objects that existed above the WD sequence in the
Gaia filters. If one of the circles has been overlapped by another, then the
object looks like a WD in two filters. The central section where all three
circles overlap describes the number of objects that look like WDs in all
three filters. The cyan Gaia EDR3 circle in Figure 3, including all
overlapping sections, directly depicted the blue region in Figure 2, totaled
134,523 objects. The yellow, pink and magenta circles correspond to objects
that were identified as WDs in GALEX UV but not Gaia optical bands and totaled
309,584 objects. The red region in Figure 2 corresponds to these 309,584
objects. By observing this red region in Figure 2, it is clear that there are
many objects that appear to be MS or giant stars that look like WDs in the
GALEX UV. Thus, we consider these 309,584 objects to be candidate WDND
systems.

A number of entries in Figure 2 appear to lie within the WD sequence in the
Gaia filters. To attempt to identify these possible single WDs, a cross-match
was done with the Gentile Fusillo et al. (2021) WD catalogue. Of the 444,107
entries, 111,996 matched to WDs previously identified by Gentile Fusillo et
al. (2021). A total of 332,111 objects remain unmatched.

Figure 2: Gaia absolute G magnitude and Bp-Rp of the 444,107 entries in the
catalogue. The blue region represents objects that lie within or near the WD
region in Gaia and the red region represents objects identified as a WD in the
GALEX filters. The blue region contains 134,523 entries while the red region
contains 309,584 entries. Figure 3: This figure describes the number of
entries in our catalogue that survived at least one of our cuts in the colour-
magnitude diagram. The cyan slice (2,199 stars) indicates the number of
objects that appear as WDs only in the Gaia CMD and not the GALEX NUV-Gaia Bp
or GALEX FUV-NUV CMD. The yellow circle describes the number of objects only
identified as WDs in GALEX NUV-Gaia Bp filters and not the Gaia Bp-Rp or GALEX
FUV-NUV CMD. The magenta circle illustrates the number of objects only
identified as WDs in the GALEX FUV-NUV filters and not the Gaia Bp-Rp or GALEX
NUV-Gaia Bp CMD. The intersections of the circles indicate objects which have
two or more WD identifications. The 168 objects identified as a WD only in
Gaia EDR3 and GALEX FUV-NUV CMD and not in the GALEX NUV-Gaia Bp CMD, are
depicted in darker purple (168). Over half of the objects that we identify as
WDs are not identified as WDs in the Gaia ED3 filters, indicating that they
appear as non-WD-like objects in the Gaia EDR3 filters.

###  2.2 Extinction

We estimate a value for the reddening in Gaia EDR3 and GALEX filters using a
method similar to Gentile Fusillo et al. (2019). An estimated Galactic
extinction is identified from the Schlegel et al. (1998) dust map for each
object individually. The extinction coefficients for the GALEX filters were
derived by Wall et al. (2019), while coefficients similar to Bédard et al.
(2020) were utilized for the Gaia EDR3 filters. Formulae for absolute
magnitude given the apparent magnitude, distance modulus and extinction
(assumed uniform along the line of sight) are shown below.

$G_{\textrm{\\!\scriptsize star}}=G_{\textrm{\\!\scriptsize
obs}}-DM-0.835A_{V}\left[1-\exp\left({-\frac{\sin{|b|}}{200\varpi}}\right)\right]$
$G_{\textrm{\scriptsize Bp,star}}=G_{\textrm{\scriptsize
Bp,obs}}-DM-1.364A_{V}\left[1-\exp\left({-\frac{\sin{|b|}}{200\varpi}}\right)\right]$
$G_{\textrm{\scriptsize Rp,star}}=G_{\textrm{\scriptsize
Rp,obs}}-DM-0.778A_{V}\left[1-\exp\left({-\frac{\sin{|b|}}{200\varpi}}\right)\right]$

Here, $G_{\textrm{\\!\scriptsize obs}}$ is the observed magnitude, $A_{V}$ is
the individual Galactic extinction identified from the Schlegel et al. (1998)
dust maps in the $V-$band, $b$ is the Galactic latitude, and $\varpi$ is the
parallax in arcsec. Further details concerning this function can be found in
Gentile Fusillo et al. (2019).

###  2.3 Characterization of WD Single and WDND Systems

Using the processed photometry of the systems, an estimate of the WD mass and
age was attempted. WD candidates identified below the Gaia EDR3 optical cut
are assumed to be WDs without a companion. These single WD stars were
characterized using WD cooling models developed by Bédard et al. (2020). Using
Bédard et al. (2020) WD cooling models and initial estimates for the mass and
cooling age, photometric models of the system in all potential filters were
constructed. These photometric models were compared to the photometric data in
a $\chi^{2}$ test. The mass and cooling age of the WD was allowed to change in
order to minimize the $\chi^{2}$. The processed photometry and modeled
photometry used GALEX FUV NUV bands, Gaia EDR3 bands and all the SDSS filters
available. Where data were not present, or a value of 0 was reported, the band
or filter data were ignored in the $\chi^{2}$ minimization. The mass of the
secondary star for these objects is reported as 0 in the case of single WDs.
The estimated mass of the WD was limited by the models to be within 0.2-1.3
$\textrm{M}_{\odot}$. The estimated masses and cooling ages were applied to
the Bédard et al. (2020) WD cooling models to develop an estimate for the
temperature of the WD.

For the WD binary candidates, a similar method $\chi^{2}$ minimization fit was
carried out. However, we modified the minimization to attempt to fit a WD
mass, a WD cooling age and a MS or giant secondary mass by adding the modeled
photometry of the WD with the modeled photometry of a MS or giant star. Padova
isochrones (Bressan et al., 2012) were used to model the MS or giant
photometry for the fit. Due to the presence of both high-mass stars and
evolved stars in the sequence, two different isochrones were chosen to model
the data. A cut was done in the optical regions to divide the evolved stars
from the rest of the MS objects. The evolved stars were fitted to an isochrone
with an age of 10 billion years. It is assumed that all the evolved stars fit
on this one isochrone in order to get a rough estimate of mass for these
stars. All the stars in this catalogue were cross-matched with the StarHorse
catalogue (Anders et al., 2021) to provide an additional estimate for the mass
of the secondary star.

A mass for the single WD fit and binary WD of 0.3 $\textrm{M}_{\odot}$ was
used for an initial mass estimate. The expected mass of most WDs is between
0.4-0.6 $\textrm{M}_{\odot}$. However, a smaller initial mass was used to
attempt to identify incidences where the fitting function failed to deviate
from the initial mass provided. Figure 4 reveals no such failure to deviate,
whereas WDND WD mass estimates showed a significant number of entries that
failed to deviate from the initial mass.

A correction had to be applied to our data while attempting to fit GALEX NUV
filter for candidate binary systems. When the best fit MS or giant star was
chosen out of Gaia EDR3 filters, the modeled GALEX NUV magnitude for the MS or
giant star was smaller than the NUV filter data. This means that the secondary
star is emitting more NUV light than we expect for a MS or giant star of this
type. We attribute this anomaly to poor MS and giant photometry models in the
GALEX NUV and exclude the NUV filter in our fits where the anomaly is present.

This catalogue reports the information present in the GALEX and Gaia EDR3
cross-match as well as SDSS DR12 and Pan-STARRS DR1 data, if any are present.
It also reports the cross-matched StarHorse catalogue entries as well as the
estimated WD mass, WD cooling age and MS star’s mass for both DA and DB
atmosphere WDs.

##  3 Catalogue Contents

The catalogue contains 444,107 unique candidate WD systems. These systems were
identified as candidate WDs using GALEX and Gaia EDR3 data. The first section
contains identification information for each star system (Right Ascension and
Declination). GALEX data follows the identification section. The GALEX portion
of the table contains all the GALEX data for each entry. The Gaia EDR3 section
contains information regarding each star based on the Gaia EDR3 data. Pan-
STARRS and SDSS DR12 data are listed next. Entries will be empty if the star
did not have either Pan-STARRS or SDSS DR12 data to correspond with the Gaia
EDR3 data. Entries that were cross-matched to StarHorse (Anders et al., 2021)
follow after the SDSS and Pan-STARRS data. The entries having no StarHorse
data were left blank. Entries that matched with the Gentile Fusillo et al.
(2021) WD catalogue are listed after StarHorse. Following this section are
three boolean columns that are true if the star passed our photometry cut for
either the GALEX FUV-NUV (fuvwd), GALEX NUV - Gaia EDR3 Bp (nuvwd) and Gaia
EDR3 (gaiawd). The final section of the catalogue is the mass estimate of the
WD and the non-degenerate star for both DA model estimates and DB model WD
estimates. The secondary star column will have an entry of 0 for WDs that are
assumed to be without a companion. Entries without a value were reported as
99.9 or 99.0. See Appendix A for a detailed list of all the columns.

##  4 Analysis

###  4.1 Single White Dwarfs

Figure 4: The estimated mass distribution of WDs identified as WD stars in
Gaia filters assuming hydrogen atmospheres (DA models). The sudden spikes on
the tails of the distribution are likely WDNDs mixed into the single WD data
and did not properly fit our models. Figure 5: The estimated mass distribution
of WDs identified as WD stars in Gaia filters assuming helium atmospheres (DB
models). The sudden spikes on the tails of the distribution are likely WDNDs
mixed into the single WD data and did not properly fit our models. Figure 6:
The estimated WD mass from Gentile Fusillo et al. (2021) WD catalogue compared
to the estimated WD mass from our catalogue (DA models). The stars shown in
the figure are a one-to-one match with those in the Gentile Fusillo et al.
(2021) catalogue and contain only single WDs. The black line describes a one-
to-one mass relationship between Gentile Fusillo et al. (2021) and our
catalogue’s estimated WD mass. Figure 7: The estimated WD mass from Gentile
Fusillo et al. (2021) WD catalogue compared to the estimated WD mass from our
catalogue (DB models). The stars shown in the figure are a one-to-one match
with those in the Gentile Fusillo et al. (2021) catalogue and contain only
single WDs. The black line describes a one-to-one mass relationship between
Gentile Fusillo et al. (2021) and our catalogue’s estimated WD mass. Figure 8:
WD mass distribution of objects that only have entries in the Gentile Fusillo
et al. (2021) catalogue (DA models). The blue represents our mass estimate and
the orange represents the mass reported by Gentile Fusillo et al. (2021).
There is a clear shift to higher mass WDs in our estimated WD mass than those
reported in Gentile Fusillo et al. (2021). Figure 9: WD mass distribution of
objects that only have entries in the Gentile Fusillo et al. (2021) catalogue
(DB models). The blue represents our mass estimate and the orange represents
the mass reported by Gentile Fusillo et al. (2021). Similar to Figure 8, there
is a clear shift to higher mass WDs in our estimated WD mass than those
reported in Gentile Fusillo et al. (2021).

Figure 10: The four figures above depict the mass distribution of single WD
stars in our catalogue that were previously identified in Gentile Fusillo et
al. (2021) with hydrogen DA models. The top two figures depict the mass
distribution reported by Gentile Fusillo et al. (2021) for both the visual
magnitude in Gaia EDR3 on the left and the UV magnitude in GALEX on the right.
The two bottom figures depict the mass distribution of WDs that we estimated
considering the UV magnitude. The left figure depicts the visual magnitude in
Gaia EDR3 on the left and the UV magnitude in GALEX on the right.

A significant portion of our catalogue contains single WDs. An attempt to
estimate the mass, temperature and cooling age of the WD was performed using
the photometric data and non-binary WD models. The mass distribution of these
estimates, using DA model assumptions on the WD, can be observed in Figure 4
and using DB model assumptions in Figure 5. The increase in objects at
$0.2\textrm{M}_{\odot}$ is likely caused by a fitting failure. This failure
may have occurred due to the assumption that the WD is a single star, when in
fact these WD systems may be WDND binary systems that were indistinguishable
from those on the WD cooling sequence. The area used to define the single WD
region is described by the blue area in the lower left corner of Figure 2. One
can observe a part of the WDND region is contained within the blue area,
giving credibility to the idea that our single WD assumption is not accurate
for all these systems. A similar problem may be occurring at
$1.3\textrm{M}_{\odot}$. To ensure our fitting function is accurate, we used a
cross-match with the Gentile Fusillo et al. (2021) WD catalogue. This
catalogue is primarily based on single WD systems gathered from Gaia EDR3 and
estimates the WD mass in a similar $\chi^{2}$ minimization analysis. The
matched entries were primarily single WDs and represent 111,996 entries in our
catalogue. Figure 6 shows the distribution of DA WD mass estimates using GALEX
and Gaia EDR3 data compared to the mass reported by Gentile Fusillo et al.
(2021). Figure 9 shows the same distribution assuming DB WDs. The distribution
of WD masses implies similar WD mass values to those found in Gentile Fusillo
et al. (2021) with a few deviations. In Figure 8, the overall distribution is
shifted towards somewhat higher mass WDs when compared to Gentile Fusillo et
al. (2021). This discrepancy between the masses in the two catalogues is
likely caused by the inclusion of all available photometry from GALEX, SDSS
and Gaia EDR3, where Gentile Fusillo Gentile Fusillo et al. (2021) only
considers Gaia EDR3 data. There is also an anomaly in the Gentile Fusillo et
al. (2021) data that reports an unusually large number of WDs as having a mass
of roughly 0.46 $\textrm{M}_{\odot}$. The source of this anomaly is unknown.
We calculate the reduced $\chi^{2}$ between our WD mass estimates and Gentile
Fusillo et al. (2021) WD mass estimates

$\chi^{2}_{\textrm{\scriptsize
red}}=\frac{1}{N}\sum_{i}\left[\frac{\left(M_{\textrm{\scriptsize Fusillo,
H}}-M_{\textrm{\scriptsize our,
H}}\right)^{2}}{{\sigma^{2}_{M,\textrm{\scriptsize Fusillo,
H}}+\sigma^{2}_{M,\textrm{\scriptsize our, H}}}}\right]$

over the WDs in both samples excluding the anomalous regions of
$0.46\textrm{M}_{\odot}<M_{\textrm{\scriptsize Fusillo,
H}}<0.47\textrm{M}_{\odot}$, $M_{\textrm{\scriptsize our,
H}}<0.21\textrm{M}_{\odot}$ and $M_{\textrm{\scriptsize our,
H}}>1.29\textrm{M}_{\odot}$ and obtain 2.5. The median value reported by our
WD mass estimates excluding the anomalous regions is 0.66 and the median value
reported by Gentile Fusillo et al. (2021) is 0.62, leading to a difference of
0.04. These values indicate that the WD mass estimates from our catalogue and
Gentile-Fusillo catalogue generally agree within their respective
uncertainties.

The WD DA estimated masses reported by Gentile Fusillo et al. (2021) are
plotted onto both Gaia EDR3 CMDs and GALEX CMDs in the top two plots,
alongside our estimates for DA WDs on the bottom two plots of Figure 10. The
bottom left plot in Figure 10 depicts single WDs with a broader distribution
of WDs between 0.6-0.8 $\textrm{M}_{\odot}$ compared to the top left plot. In
the top right plot, there is a far less homogeneous distribution in the UV for
the reported values of WDs in Gentile Fusillo et al. (2021). In particular,
the high mass WD region has a high degree of scatter. However, in the bottom
right plot of Figure 10 there is a spread of mass in the WDs with closer
consistency to modeled WD masses. A similar pattern is observed with the DB
WDs in Figure 11. It is uncertain how accurate the GALEX UV data is when
predicting the WD masses, but Figure 10 indicates that there is a significant
change when including UV data.

By construction, each object in our catalogue has a measured UV flux either in
the NUV or the FUV. This information clearly influences the masses determined
through the fitting process as our fitted masses better correlate with a
star’s position in the UV CMD than the masses from Gentile Fusillo et al.
(2021) as shown in Fig. 10 and 11. The additional UV data provides both an
opportunity in that the UV fluxes depend strongly on the temperature of the
white dwarfs and a potential pitfall in that the observed UV fluxes typically
have larger statistical and systematic uncertainties than the Gaia DR3 fluxes.
As Fig. 6 through 9, our fitted masses (whether using DA or DB models) are
systematically slightly larger than those obtained by Gentile Fusillo et al.
(2021). Adding the UV data to the visual data from Gaia used by Gentile
Fusillo et al. (2021) may yield more accurate temperature estimates and,
therefore, more accurate mass estimates. However, there is a bias in that the
GALEX flux measurements have larger uncertainties than the Gaia measurements.
These uncertainties, when combined with the flux distribution of WDs (fainter
WDs), will systematically cause the UV fluxes to be overestimated, thereby the
temperatures also to be overestimated and finally also the masses to be
overestimated.

Figure 11: The four figures above depict the mass distribution of single WD
stars in our catalogue that were previously identified in Gentile Fusillo et
al. (2021) to have helium DB models. The top two figures depict the mass
distribution reported by Gentile Fusillo et al. (2021) for both the visual
magnitude in Gaia EDR3 on the left and the UV magnitude in GALEX on the right.
The two bottom figures depict the mass distribution of WDs that we estimated
considering all available photometric data. The left figure depicts the visual
magnitude in Gaia EDR3 and the UV magnitude in GALEX on the right.

###  4.2 White Dwarfs in Binaries

WDND WD mass estimates were less successful than single WD mass estimates. A
large portion of the WD mass estimates for WDNS systems were approximately
$0.2\textrm{M}_{\odot}$, $0.3\textrm{M}_{\odot}$ and $1.3\textrm{M}_{\odot}$.
The large fraction of WD estimates at $0.3\textrm{M}_{\odot}$ is related to
the initial guess used in the $\chi^{2}$ fitter for the WD mass which was
given as $0.3\textrm{M}_{\odot}$. All three values are failed WD mass
estimates where the fitter was unable to gather reliable mass estimates. In
areas outside these three values, the masses returned by our fitting were
uniform over the allowed range in the fitting. The WD model takes up any UV
excess in the data relative to the MS model, so discrepancies in the UV data,
the MS or giant models, the WD models or all three yield inaccurate WD mass
estimates. As stated in Section 2.3, the models used to calculate the GALEX
NUV output of MS stars fitted with Gaia EDR3 data exhibited considerably more
NUV emission than observed. Consequently, it is likely that the poor
reliability of many of our estimates is related to a lack of suitable models
for the GALEX FUV and NUV magnitudes of the stars. We cannot estimate the mass
of WDs in WDNDs with photometry alone and spectra are required to have
certainty of WDND WD mass estimates.

These WDND candidates are MS or evolved stars whose photometry exhibits a UV
excess similar to WD stars. If these are WDND systems, we expect the WD UV
magnitude to either dominate the UV light observed or have an additional
contribution of UV flux from the non-degenerate star. This would move the
object off the WD sequence described below the black line in the left plot of
Figure 1 and toward the top right of the figure. Below the WD region depicted
in the left plot of Figure 1, the catalogue does not likely contain high mass
WDs. Instead, there is a possibility that these are low flux readings from MS
stars or flaring M-dwarf stars. This is consistent with SDSS spectra
classifications shown in Figure 16 where M-dwarf stars are identified in our
catalogue based on their UV data, not their optical data. This suggests that a
certain fraction of the catalogue contains M-dwarf stars with excess UV. A
$\chi^{2}$ minimization fit was performed to attempt to identify the masses of
these non-degenerate systems assuming a potential WD was contributing to the
UV flux. The distribution of non-degenerate mass estimates can be seen in
Figure 12. A cross-match with the StarHorse MS catalogue (Anders et al., 2021)
was performed to supplement our non-degenerate mass estimates. Of the 309,584
entries, 193,221 objects matched to StarHorse and reported a non-degenerate
mass estimate. Figure 14 describes the difference between our non-degenerate
mass estimates and StarHorse’s reported MS mass. The black line in the figure
represents a one-to-one relationship. The stars above the black line are stars
where StarHorse reported masses higher than our own estimates and below the
black line represents stars where StarHorse reported a lower mass than our
estimates. From Figure 14, there exists a one-to-one correlation between our
estimated masses and the StarHorse reported masses up until 0.9
$\textrm{M}_{\odot}$. A discontinuity exists at 1.04 $\textrm{M}_{\odot}$.
This is a known degeneracy in our fitting caused by the upper mass limit of
one of the isochrones used. In order to develop a complete look at the non-
degenerate mass distribution of the catalogue, we utilize a mass cut. For
stars with masses greater than $0.9\textrm{M}_{\odot}$, StarHorse is likely
the more accurate estimate for the non-degenerate mass. This is due to the
uncertainty in the UV flux of the models we used, whereas for stars less
massive than $0.9\textrm{M}_{\odot}$, we assume that StarHorse will
incorrectly estimate the mass of the non-degenerate star if there is a WD in
the system. This is due to the non-degenerate star being faint enough to allow
for the WD to contribute significantly to the visual flux of the system.
Figure 15 depicts the distribution of non-degenerate star masses using our
mass estimates for non-degenerate stars with a mass less than
$0.9\textrm{M}_{\odot}$ (shown in blue) and StarHorse mass estimates for stars
with a mass greater than $0.9\textrm{M}_{\odot}$ (shown in orange). The slopes
of the non-degenerate mass distribution of Figure 15 change considerably. On
the low mass end, the slope incrementally increases, then flattens out to an
exponent of 1.4. A dearth of objects exists at $0.65\textrm{M}_{\odot}$. On
the high mass end, the distribution displays another sudden lack of objects at
around $1.5\textrm{M}_{\odot}$. After the dip, the slope of the distribution
is a $-4$ exponent before becoming a slope of $-8$ exponent. We calculate that
for binary systems where one star has evolved into a WD, the initial mass
function (IMF) should tend toward a slope of $-7.5$ exponent. Moe & Di Stefano
(2017) reported that in recent observations of binary systems IMFs with
smaller mass ratios (0.1-0.3), the power-law of the mass ratio distribution
flattens towards shallower slopes.

Figure 12: The distribution of mass estimates of non-degenerate stars with a
WD companion contributing to the UV of the system. For objects above 0.9
$\textrm{M}_{\odot}$, the distribution of estimated stellar mass becomes
unreliable. This is due to the assumed WD in the system dominating the UV,
forcing the non-degenerate mass estimate to be smaller than what can be seen
in the optical. There is also a discontinuity in our fitting due to the
difficulty with the isochrones for giant stars that can be observed as the
peaks in Figure 14. Figure 13: Comparison of the StarHorse mass distribution
of stars found in our candidate WDND systems correlate to the red
distribution. A general distribution of StarHorse main sequence masses are
observed in green. Each distribution contains the same number of stars. For
objects below 0.9 $\textrm{M}_{\odot}$, the distribution of estimated stellar
mass becomes unreliable. This is due to the StarHorse catalogue assuming that
the system is a single star system.  Figure 14: Comparison of the difference
in our fitted secondary star masses and masses in StarHorse. The black line
describes the situation where the mass we have fitted perfectly matches the
StarHorse mass. Objects below the black line are stars where we fit to higher
masses than StarHorse and above the black line are stars where we fit a
smaller mass than StarHorse. The spiked lines at 1$\textrm{M}_{\odot}$ and
1.04$\textrm{M}_{\odot}$ are known issues with the isochrones used being
incapable of correctly estimating the mass of evolved stars. Figure 15: This
distribution represents our estimated non-degenerate mass in the blue with
StarHorse estimated mass in the red from 0.09 $\textrm{M}_{\odot}$ to 9.53
$\textrm{M}_{\odot}$. A cutoff between the two distributions is performed at
0.9 $\textrm{M}_{\odot}$ where we believe that StarHorse MS mass estimates
will be less reliable below 0.9 $\textrm{M}_{\odot}$ due to the presence of a
WD in the system.

###  4.3 SDSS Spectroscopy

All entries with available SDSS photometry and spectral data have been
included in the catalogue. A total of 11,607 entries have SDSS spectral
classifications. Figure 16 depicts the distribution of the various spectral
types identified by SDSS. The distribution of SDSS classifications is heavily
biased to WD identifications with 6,432 of the 11,607 objects being single
WDs. Of the remaining entries, 1,151 were listed as cataclysmic variables
(CVs). These objects can be seen in the Gaia EDR3 CMD in Figure 17. There is a
clear concentration of objects identified as CVs on the WD sequence. Upon
investigating their spectra, a large portion of these objects do not appear to
have clear emission lines. The SDSS DR12 website provides the following
caveat: “Although sub-classifications are provided, they are not optimized for
accuracy. In particular, the CV star templates have more degrees of freedom
than other stellar templates, which can result in unphysical solutions where
negative PCA components of the CV templates can fit absorption features of
White Dwarfs” (Aguado et al., 2015). It is unlikely that the 1,151 listed CVs
are accurately identified; therefore, a significant portion of CVs listed are
likely single WDs. A total of 270 or 2.3 percent of the SDSS entries that
matched with our catalog were listed as galaxies. Using the SDSS optical
spectrum archive, we examined the top ten galaxy entries with the highest
signal-to-noise ratio to further understand if there was a large contaminant
of galaxies in our data. Of these ten entries, nine were misidentified.
Spectra with clear WD features often have their hydrogen absorption lines
misidentified as oxygen II emission lines, giving several entries a consistent
redshift of roughly $0.7$. There is also misidentification of M-dwarf stars as
galaxies due to the presence of $H_{\alpha}$ and $H_{\beta}$ in the spectrum.
The presence of the hydrogen spectral lines in the M-dwarf spectra likely
causes the spectral identifier to assume the system contains many stars and
is, thus, a galaxy. It is also likely that the source of these hydrogen
spectral lines could be coming from a WD in the system and not a star-rich
galaxy. Furthermore, several entries had a redshift close to zero or were
actually negative, meaning they are likely misidentified stars, not galaxies.
After examining the spectra of the 270 listed galaxies, it seems that a large
fraction of them are misidentified sources. It is worth noting that these 270
objects make up only a tiny fraction of the 791,977 galaxy identifications in
the SDSS DR12 catalogue (Alam et al., 2015).

Figure 16: The distribution of the 11,607 different SDSS DR12 spectral
classifications. There are 6,432 WD spectral classifications that are not
listed in the figure above. The orange depicts the distribution of SDSS
classifications that appear to be WDs in Gaia EDR3 photometry, the green
depicts the distribution of objects with SDSS classification that appear to be
WDs in the GALEX NUV and Gaia EDR3 Bp region and the blue depicts the SDSS
classification that appear to be WDs in the GALEX photometry alone. The
distribution is dominated by over 6,000 WD identifications which are not
depicted in histogram. Figure 17: The depiction of CV objects in our catalogue
CMD in Gaia EDR3 magnituddes. All objects in the catalogue are marked in grey.
The red points mark the objects that were classified by SDSS DR12 as CVs.

##  5 Conclusions

The aim of the GALEX-Gaia-EDR3 catalogue is to identify and characterise
candidate white-dwarf-non-degenerate binary stars to be used for additional
study or for follow-up observation. The GALEX-Gaia-EDR3 catalogue was
constructed from GALEX and Gaia EDR3 catalogues containing 332,111 candidate
white dwarf binary systems and 111,996 candidate single white dwarfs. When
available, data from SDSS DR12, Pan-STARRS DR1, StarHorse MS catalogue and
Gentile-Fusillo Gaia EDR3 white dwarf catalogue augment the catalogue to
provide supplementary data. Estimates for the white dwarf’s mass, age,
effective temperature and the non-degenerate secondary mass were determined
through fitting the photometry of objects in our catalogue with modeled white
dwarf cooling sequences from Bédard et al. (2020). Upon comparing our results
with the 111,996 candidate single white dwarfs with the Gentile-Fusillo Gaia
EDR3 white dwarf catalogue, we found that our estimates for the single white
dwarf’s mass tended to be skewed slightly towards higher-mass white dwarfs,
but generally agree within their respective uncertainties. Mass estimates for
the white dwarfs in binary systems have a high degree of inaccuracy due to the
presence of the non-degenerate star significantly contributing to the
photometry of the system.

## Acknowledgements

This research has made use of the VizieR catalogue access tool, CDS,
Strasbourg, France (DOI : 10.26093/cds/vizier). The original description of
the VizieR service was published in 2000, A&AS 143, 23

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

Funding for SDSS-III has been provided by the Alfred P. Sloan Foundation, the
Participating Institutions, the National Science Foundation, and the U.S.
Department of Energy Office of Science. The SDSS-III web site is
http://www.sdss3.org/.

SDSS-III is managed by the Astrophysical Research Consortium for the
Participating Institutions of the SDSS-III Collaboration including the
University of Arizona, the Brazilian Participation Group, Brookhaven National
Laboratory, Carnegie Mellon University, University of Florida, the French
Participation Group, the German Participation Group, Harvard University, the
Instituto de Astrofisica de Canarias, the Michigan State/Notre Dame/JINA
Participation Group, Johns Hopkins University, Lawrence Berkeley National
Laboratory, Max Planck Institute for Astrophysics, Max Planck Institute for
Extraterrestrial Physics, New Mexico State University, New York University,
Ohio State University, Pennsylvania State University, University of
Portsmouth, Princeton University, the Spanish Participation Group, University
of Tokyo, University of Utah, Vanderbilt University, University of Virginia,
University of Washington, and Yale University.

## Data Availability

The catalogue presented in this work can be downloaded
https://doi.org/10.5281/zenodo.8145200HERE or at the url:
https://doi.org/10.5281/zenodo.8145200. The catalogue will also be made
available via the VizieR Service for Astronomical Catalogues. The original
data in this article is publicly available from the relevant survey archives.

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##  Appendix A Column Descriptions

ra_epoch2000, Right Ascension of the object taken from Gaia at Epoch2000

dec_epoch2000, Declination of the object taken from Gaia at Epoch2000

ra_epoch2000_error, Error of the Right Ascension of the object taken from Gaia
at Epoch2000

dec_epoch2000_error, Error of the Declination of the object taken from Gaia at
Epoch2000

ra_dec_epoch2000_corr, Correlation between right ascension and declination

source_id, a Unique source identifier within a Gaia data release

name, GALEX object name

objid, GALEX identifier in JHHMMSS.s+DDMMSS format

Cat, Survey type (Always AIS-all-sky imaging survey)

FUVexp, FUV exposure time.

NUVexp, NUV exposure time

GLON, Galactic Longitude

GLAT, Galactic Latitude

tile, Title number

img, Image number

sv, Number of subsists if exposure was divided

r.fov, Distance of source from center of the field

Obs, Observation type

b, Bands: 1=NUV, 2=FUV, 3 both

E(B-V), Galactic reddening inferred from 100um dust emission maps (E_bv)

Sp?, 1 if a spectrum exists (istherespectrum)

chkf, Astrometry check type

FUVmag, Galex FUV Calibrated magnitude in AB system

e_FUVmag, FUV mag error

NUVmag, Galex NUV calibrated magnitude in AB system

e_NUVmag, NUV mag error

FUV.a, FUV Kron-like elliptical aperture magnitude

e_FUV.a, FUV RMS error for AUTO magnitude

NUV.a, NUV Korn-like elliptical aperture

e_NUV.a, NUV RMS error for AUTO magnitude

FUV.4, FUV mag aperture 8 pix diameter

e_FUV.4, FUV mag aperture error (8 pix diameter)

NUV.4, NUV mag aperture (8 pix diameter)

e_NUV.4, NUV mag aperture error (8 pix diameter)

FUV.6, FUV mag aperture (17 pix diameter)

e_FUV.6, FUV mag aperture error (17 pix diameter)

NUV.6, NUV mag aperture (17 pix diameter)

e_NUV.6, NUV mag aperture error (17 pix diameter)

Fafl, FUV artifact Flag (logical OR near source)

Nafl, NUV artifact Flag (logical OR near source)

Fexf, Extraction flags for FUV band

Nexf, Extraction flags for NUV band

Fflux, FUV calibrated Flux

e_Fflux, FUV flux error

Nflux, NUV calibrated flux

e_Nflux, NUV flux error

FXpos, Source position in FUV image along x

FYpos, Source position in UV image along y

NXpos, Source position in NUV image along x

NYpos, Source position in NUV image along y

Fima, Source FWHM in FUV assuming a gaussian core

Nima, Source FWHM in NUV assuming a gaussian core

Fr, Source FWHM in FUV assuming a gaussian core

Nr, Source FWHM in NUV assuming a gaussian core

nS/G, Star/galaxy classifier from NUV

fS/G, Star/galaxy classifier from FUV

nell, 1-b/a in NUV

fell, 1-b/a in FUV

nPA, Position angle in NUV

e_nPA, Position angle error in NUV

fPA, Position angle in FUV

e_fPA, Position angle error in FUV

Fnr, FUV FWHM IMAGE value from -fd-ncat.fits

F3r, FUV flux radius

Nar, Kron apertures in units of a or b for NUV

Narms, Profile RMS along major axis for NUV

Nbrms, Profile RMS along minor axis for NUV

Far, Kron apertures in units of a or b for FUV

Farms, Profile RMS along major axis for FUV

Fbrms, Profile RMS along minor axis for FUV

w_NUV, NUV effective exposure (flat-field response value) at the source
position

w_FUV, FUV effective exposure

Prob, Probability of the FUVxNUV match

Sep, Separation between FUV and NUV position of the source in the same
observation

Nerr, Position error of the source in the NUV image

Ferr, Position error of the source in the FUV image

Ierr, Inter-band position error

Nperr, NUV poisson position error

Fperr, FUV poisson position error

CV, Whether the source comes from a coda or visit. (corv)

G, Neighbours rank

N, Number of sources within 2.5”, if this is a primary.

primid, objid of the closest primary, based on distance criterion

groupid, Objid’s of all sources (AIS) within 2.5”, concatenated by “+”

Gd, as for “G” (grank), but based on distance criterion (granddist)

Nd, as for “N” (ngrank), but based on distance criterion

OName, Extended object if not:”N”

parallax, Absolute stellar parallax

parallax_error, standard error of parallax

pm, Total proper motion (pm)

pmra, proper motion in right ascension direction

pmra_error, standard error of proper motion in right ascension direction

pmdec, proper motion in declination direction

pmdec_error, standard error of proper motion in declination direction

astronomic_n_good_obs_al, number of good observations AL

astronomic_gof_al, Goodness of fit statistic of model wrt along-scan
observations

astrometric_chi2_al, AL chi-squared value

astrometric_excess_noise, Excess noise of the source

astrometric_excess_noise_sig, Significance of excess noise

astrometric_params_solved, Which parameters have been solved for

pseudocolour, astrometrically determined pseduocolour of the source

pseudocolour_error, standard error of pseudo color of source

visibility_periods_used, Number of visibility periods used in astronomic
solution

ruwe, Gaia dr2 re-normalised unit-weight error (Lindegren et al., 2018)

duplicated_source, Source with duplicate sources

phot_g_mean_flux, Mean G Flux

phot_g_mean_flux_error, Mean G Flux error

phot_g_mean_mag, mean G magnitude

phot_g_mean_mag_error, Mean G magnitude error

phot_bp_mean_flux, Integrated mean flux in BP band

phot_bp_mean_flux_error, Error of the integrated BP mean flux

phot_bp_mean_mag, Integrated BP mean magnitude (Vega)

phot_bp_mean_mag_error, error of the integrated BP mean magnitude

phot_rp_mean_flux, Integrated RP mean flux

phot_rp_mean_mag, Integrated RP mean magnitude (Vega)

phot_rp_mean_mag_error, error on integrated RP mean magnitude

phot_bp_rp_excess_factor, BP/RP excess factor

bp_rp, BP-RP Gaia colour

phot_g_mean_mag_corrected, Calibration corrected G magnitude (added by CDS)

phot_g_mean_mag_error_corrected, Standard error of calibration corrected G
magnitude (added by CDS)

phot_g_mean_flux_corrected, Calibration corrected G-band mean flux (added by
CDS)

phot_bp_rp_excess_factor_corrected, Calibration corrected BP/RP excess factor
(added by CDS)

dr2_radial_velocity, Radial velocity from Gaia DR2

dr2_radial_velocity_error, Radial velocity error from Gaia DR2

dr2_rv_nb_transits, Number of transits used to compute radial velocity in Gaia
DR2

dr2_rv_template_teff, Teff of the template used to compute radial velocity in
Gaia DR2

dr2_rv_template_logg, logg of the template used to compute radial velocity in
Gaia DR2

urat1, URAT1 cross-id name (urat1)

objID_psdr1, Unique object identifier for Pan-STARRS

f_objID_psdr1, Information flag bitmask indicating details of the photometry.

Qual_psdr1, Subset of objInfoFlag denoting whether this object is real or
likely false positive.

Epoch_psdr1, Modified Julian date of the mean Epoch

Ns_psdr1, Number of stack detection

gmag_psdr1, Mean PSF AB magnitude from g filter (4866 angstroms) detection

e_gmag_psdr1, error in gmag

gKmag_psdr1, mean Kron AB magnitude from g filter detection

e_gKmag_psdr1, error in gKmag

gFlags_psdr1, Information flag bitmask for mean object from g filter detection

rmag_psdr1, Mean PSF AB magnitude from r filter (6215 angstroms) detection.

e_rmag_psdr1, error in rmag

rKmag_psdr1, Mean Kron AB magnitude from r filter detection

e_rKmag_psdr1, Error in rKmag

rFlags_psdr1, Information flag bitmask fro mean object from r filter detection

imag_psdr1, mean PSF AB magnitude from i filer (7545 angstroms) detection.

e_imag_psdr1, error in imag

iKmag_psdr1, Mean Kron AB magnitude from i filter detection

e_iKmag_psdr1, error in iKmag

iFlags_psdr1, Information flag bitmask fro mean object from i filter detection

zmag_psdr1, Mean PSF AB magnitude from z filter (8679 angstroms) detection

e_zmag_psdr1, error in zmag

zKmag_psdr1, Mean Kron AB magnitude from z filter detection

e_zKmag_psdr1, Error in zKmag

zFlags_psdr1, Information flag bitmask for mean object from z filter detection

ymag_psdr1, Mean PSF AB magnitude from y filter (9633 angstroms) detection

e_ymag_psdr1, Error in ymag

yKmag_psdr1, Mean Kron AB magnitude from y filter detection

e_yKmag_psdr1, error in yKmag

yFlags_psdr1, Information flag bitmask for mean object from y filter detection

objID_sdssdr12, SDSS unique object identifier

mode_sdssdr12, 1: primary (469,053,874 sources), 2: secondary (324,960,094
sources)

q_mode_sdssdr12, ’+’ indicates clean photometry (310,190,812 sources with mode
1+) (clean)

class_sdssdr12, Type of object (3=galaxy, 6=star)

SDSS12_sdssdr12, SDSS-DR12 name, based on J2000 position

m_SDSS12_sdssdr12, The asterisk indicates that 2 different SDSS objects share
the same SDSS12 name

flags_sdssdr12, Photo Object Attribute flags

ObsDate_sdssdr12, Mean Observation date

Q_sdssdr12, Quality of the observation

umag_sdssdr12, model magnitude in u filter, AB scale

e_umag_sdssdr12, mean error on umag

gmag_sdssdr12, model magnitude in g filter, AB scale

e_gmag_sdssdr12, Mean Error on gmag

rmag_sdssdr12, Model magnitude in r filter, AB scale

e_rmag_sdssdr12, Mean error on rmag

imag_sdssdr12, Model magnitude in i filter, AB scale

e_imag_sdssdr12, Mean error on imag

zmag_sdssdr12, Model magnitude in z filter, AB scale

e_zmag_sdssdr12, Mean error on zmag

zsp_sdssdr12, Spectroscopic redshift (when SpObjID¿0)

e_zsp_sdssdr12, Mean error on zsp

f_zsp_sdssdr12, “zwarning” flag

zph_sdssdr12, Photometric redshift; estimated by robust fit to nearest
neighbors in a reference set.

e_zph_sdssdr12, estimated error of the photometric redshift

avg_zph_sdssdr12, Average redshift of the nearest neighbors; if significantly
different from mph this might be a better estimate than zph.

pmRA_sdssdr12, Proper motion along Right Ascension

e_pmRA_sdssdr12, Mean error on pmRA

pmDE_sdssdr12, Proper motion along Declination

e_pmDE_sdssdr12, mean error on pmDE

SpObjID_sdssdr12, Pointer to the spectrum of object or 0

spType_sdssdr12, Source type (sourceType)

spCl_sdssdr12, Spectroscopic class: GALAXY, QSO, STAR

subClass_sdssdr12, spectroscopic subclass

dist05, StarHorse distance, 5th percentile

dist16, StarHorse distance, 16th percentile

dist50, StarHorse distance, 50th percentile

dist84, StarHorse distance, 85th percentile

dist95, StarHorse distance, 95th percentile

av05, StarHorse line-of-sight extinction at lambda = 5420 angstrom, 5th
percentile

av16, StarHorse line-of-sight extinction at lambda = 5420 angstrom, 16th
percentile

av50, StarHorse line-of-sight extinction at lambda = 5420 angstrom, 50th
percentile

av84, StarHorse line-of-sight extinction at lambda = 5420 angstrom, 84th
percentile

av95, StarHorse line-of-sight extinction at lambda = 5420 angstrom, 95th
percentile

teff16, StarHorse effective temperature, 16th percentile

teff50, StarHorse effective temperature, 50th percentile

teff84, StarHorse effective temperature, 84th percentile

logg16, StarHorse Surface Gravity, 16th Percentile

logg50, StarHorse Surface Gravity, 50th Percentile

logg84, StarHorse Surface Gravity, 84th Percentile

met16, StarHorse metallicity, 16th percentile

met50, StarHorse metallicity, 50th percentile

met84, StarHorse metallicity, 84th percentile

mass16, StarHorse Stellar Mass, 16th Percentile

mass50, StarHorse Stellar Mass, 50th Percentile

mass84, StarHorse Stellar Mass, 84th Percentile

‘fidelity’, Gaia EDR3 astrometric fidelity flag (Rybizki et al., 2021, MNRAS,
in prep.)

TeffH, Effective temperature from fitting the dereddened G, BP, and RP
absolute fluxes with pure-H model atmospheres (teff_H), from Gentile Fusillo
et al. (2021)

e_TeffH, Uncertainty of teff_H (eteff_H), from Gentile Fusillo et al. (2021)

loggH, Log of surface gravity from the dereddened G, BP, and RP absolute
fluxes with pure-H model atmospheres (logg_H) from Gentile Fusillo et al.
(2021)

e_loggH, Uncertainty on logg_H (elogg_H), from Gentile Fusillo et al. (2021)

MassH, Stellar mass resulting from the adopted mass-radius relation and best
fit parameters (mass_H), from Gentile Fusillo et al. (2021)

e_MassH, Uncertainty on mass_H (emass_H), from Gentile Fusillo et al. (2021)

chisqH, chi square value of the fit (pure-H)(chisq_H), from Gentile Fusillo et
al. (2021)

TeffHe, Effective temperature from fitting the dereddened G, BP, and RP
absolute fluxes with pure-He model atmospheres (teff_He), from Gentile Fusillo
et al. (2021)

e_TeffHe, Uncertainty of teff_He (eteff_He), from Gentile Fusillo et al.
(2021)

loggHe, Log of surface gravity from the dereddened G, BP, and RP absolute
fluxes with pure-He model atmospheres (logg_He) from Gentile Fusillo et al.
(2021)

e_loggHe, Uncertainty on logg_He (elogg_He), from Gentile Fusillo et al.
(2021)

MassHe, Stellar mass resulting from the adopted mass-radius relation and best
fit parameters (mass_He), from Gentile Fusillo et al. (2021)

e_MassHe, Uncertainty on mass_He (emass_He), from Gentile Fusillo et al.
(2021)

chisqHe, chi square value of the fit (pure-He)(chisq_He), from Gentile Fusillo
et al. (2021)

fuvwd, Objects that appear to have a white dwarf in GALEX FUV-NUV

nuvwd, Objects that appear to have a white dwarf in GALEX NUV-Gaia Bp

gaiawd, Objects that appear to have a white dwarf in Gaia Bp-Rp

wd_m_H, Estimated white dwarf mass from fit of white dwarf with pure-H
atmosphere.

wd_m_H_e, Estimated white dwarf mass error for pure-H atmosphere

wd_age_H, Estimated white dwarf age from fit of white dwarf with pure-H
atmosphere.

wd_age_H_e, Estimated white dwarf age error for pure-H atmosphere

wd_teff_H, Estimated white dwarf temperature from fit of white dwarf with
pure-H atmosphere.

sec_m_H, Estimated secondary non-degenerate from fit of white dwarf with
pure-H atmosphere. If no evidence of a companion from the photometry, we
performed single WD fit and report 0 for this value.

sec_m_H_e, Estimated secondary non-degenerate mass error pure-H white dwarf
atmosphere.

chi_sq_H, The resulting chi squared value for the fit for hydrogen
atmospheres.

goodness_H, The probability that the estimated masses are a good fit for the
data for hydrogen atmospheres.

wd_m_He, Estimated white dwarf mass from fit of white dwarf with pure-He
atmosphere.

wd_m_He_e, Estimated white dwarf mass error for pure-He atmosphere

wd_age_He, Estimated white dwarf age from fit of white dwarf with pure-He
atmosphere.

wd_age_He_e, Estimated white dwarf age error for pure-He atmosphere

wd_teff_He, Estimated white dwarf temperature from fit of white dwarf with
pure-He atmosphere.

sec_m_He, Estimated secondary non-degenerate star from fit of white dwarf with
pure-He atmosphere. If no evidence of a companion from the photometry, we
performed single WD fit and report 0 for this value.

sec_m_He_e, Estimated secondary non-degenerate mass error white dwarf pure-He
atmosphere.

chi_sq_He, The resulting chi squared value for the fit for helium atmospheres.

goodness_He, The probability that the estimated masses are a good fit for the
data for helium atmospheres.