# Discovery of 16 New Members of the Solar Neighborhood using Proper Motions
from CatWISE2020

Tarun Kota Eastview High School, 6200 140th St W, Apple Valley, MN 55124, USA;
J. Davy Kirkpatrick IPAC, Mail Code 100-22, Caltech, 1200 E. California Blvd.,
Pasadena, CA 91125, USA  Dan Caselden Department of Astrophysics, American
Museum of Natural History, Central Park West at 79th Street, New York, NY
10034, USA  Federico Marocco IPAC, Mail Code 100-22, Caltech, 1200 E.
California Blvd., Pasadena, CA 91125, USA  Adam C. Schneider United States
Naval Observatory, Flagstaff Station, 10391 West Naval Observatory Rd.,
Flagstaff, AZ.86005, USA  Department of Physics and Astronomy, George Mason
University, MS3F3, 4400 University Drive, Fairfax, VA 22030, USA  Jonathan
Gagné Planétarium Rio Tinto Alcan, Espace pour la Vie, 4801 av. Pierre-de
Coubertin, Montréal, Québec, Canada  Institute for Research on Exoplanets,
Université de Montréal, Département de Physique, C.P. 6128 Succ. Centre-ville,
Montréal, QC H3C 3J7, Canada  Jacqueline K. Faherty Department of
Astrophysics, American Museum of Natural History, Central Park West at 79th
Street, New York, NY 10034, USA  Aaron M. Meisner NSF’s National Optical-
Infrared Astronomy Research Laboratory, 950 N. Cherry Avenue, Tucson, AZ
85719, USA  Marc J. Kuchner NASA Goddard Space Flight Center, Exoplanets and
Stellar Astrophysics Laboratory, Code 667, Greenbelt, MD 20771, USA  Sarah
Casewell School of Physics and Astronomy, University of Leicester, University
Road, Leicester LE1 7RH, UK  Kanishk Kacholia Department of Computer Science,
University of Minnesota, 200 Union St SE, Minneapolis, MN 55455, USA  Tom
Bickle Backyard Worlds  Paul Beaulieu  Backyard Worlds  Guillaume Colin
Backyard Worlds  Leslie K. Hamlet Backyard Worlds  Jörg Schümann Backyard
Worlds  Christopher Tanner  Backyard Worlds  The Backyard Worlds: Planet 9
Collaboration

(Received TBD; Revised TBD; Accepted TBD)

###### Abstract

In an effort to identify nearby and unusual cold objects in the solar
neighborhood, we searched for previously unidentified moving objects using
CatWISE2020 proper motion data combined with machine learning methods. We
paired the motion candidates with their counterparts in 2MASS, UHS, and VHS.
Then we searched for white dwarf, brown dwarf, and subdwarf outliers on the
resulting color-color diagrams. This resulted in the discovery of 16 new
dwarfs including two nearby M dwarfs (< 30 pc), a possible young L dwarf, a
high motion early T dwarf and 3 later T dwarfs. This research represents a
step forward in completing the census of the Sun’s neighbors.

stars: distances – solar neighborhood – binaries: close

††journal: AJ††facilities: WISE/NEOWISE, 2MASS, UKIRT, VISTA††software:
Astropy: Astropy Collaboration et al. (2013), Banyan $\Sigma$: Gagné et al.
(2018), TOPCAT: Taylor (2005) XGBoost: Chen & Guestrin (2016), WiseView:
Caselden et al. (2018)

##  1 Introduction

The Wide-Field Infrared Survey Explorer (WISE; Wright et al. 2010), with its
unique full-sky sensitivity, has the potential to answer fundamental
astrophysical questions. From 2009-2011, WISE gave astronomers unparalleled
photometric data on objects across the span of the universe. Using these data,
there were many WISE\- based discoveries including the first Earth Trojan
Asteroid (Connors et al. 2011), the most luminous galaxy (Tsai et al. 2015),
and the coldest and closest known brown dwarfs (Luhman 2013, Luhman & Esplin
2014). Due to cryogen exhaustion in late 2010, WISE was forced to change from
a four-band Survey (W1 (3.4 $\mu$m), W2 (4.6$\mu$m), W3 (12$\mu$m), W4
(22$\mu$m)) into a two band survey (W1, W2). The mission was repurposed as
NEOWISE (Mainzer et al. 2011). Since its launch, NEOWISE has detected over
34,000 minor planets (Mainzer et al. 2014), as near-earth object detection is
the new scientific driver of the mission.

Nevertheless, for astronomers who were interested in identifying motion
objects outside of our solar system, the only available WISE processing that
leveraged the time series data was the AllWISE catalog (Cutri et al. 2013).
However, AllWISE only uses early WISE data (13 months viewing time), which
means it is only able to identify brighter, high motion objects (Kirkpatrick
et al. 2014, Kirkpatrick et al. 2016). With a short time baseline, our ability
to detect fainter moving objects and smaller motion is limited. The key factor
is that fainter objects have larger positional uncertainties, which prohibits
us from finding faint, smaller motion objects.

The CatWISE Preliminary Catalog (Eisenhardt et al. 2020) is a reprocessing of
WISE/NEOWISE data over a 6 year time frame (2010-2016) to measure motions of
more slowly moving objects. Since NEOWISE is only a W1 and W2 survey, CatWISE
Preliminary only has these two filters. CatWISE Preliminary - the richer data
set - enabled motion detection at fainter magnitudes and smaller motions.

CatWISE2020 (Marocco et al. 2021) took this one step further by adding another
two years of data to that used for the CatWISE Preliminary Catalog, bringing
the total to six times as many exposures spanning over 16 times as large a
time baseline as the AllWISE catalog. In addition, the detection list for the
CatWISE2020 Catalog was generated using the unWISE Catalog (Schlafly et al.
2019) instead of the AllWISE detection software used by the CatWISE
Preliminary pipeline. These two factors led to the CatWISE2020 Catalog having
almost twice as many sources as the CatWISE Preliminary Catalog. Although this
improved astrometric accuracy has led to initial discoveries (Kirkpatrick et
al. 2021, Meisner et al. 2021, Rothermich et al. 2021), the CatWISE2020 data
are still largely unexplored.

In this paper, we use the CatWISE2020 proper motions to search for nearby and
unusual cold objects that have otherwise been overlooked. Specifically, due to
their low luminosity, cooler brown dwarfs comprise the majority of gaps in our
knowledge of the solar neighborhood (Kirkpatrick et al. 2019). However, M
dwarfs, which are the most common objects in the solar neighborhood, can also
be missing due to small motions because earlier surveys may have lacked
sufficient time baseline to identify them.

A complete census of astronomical objects in the solar neighborhood is
important in determining the low mass cutoff for star formation (Kirkpatrick
et al. 2019) and would allow researchers to do more targeted searches for
habitable exoplanets around these host objects (Gillon et al. 2017,
Zechmeister et al. 2009).

In this paper, we present the discovery of 16 new objects including two nearby
M dwarfs, a likely young L dwarf, a high motion early T dwarf and 3 additional
late T dwarfs. In § 2 we discuss the method used to select candidates from
CatWISE2020. In § 3 we discuss the identification methods used to select
interesting objects from our candidate list. In § 4 we characterize our most
interesting new discoveries. In § 5 we give brief conclusions.

##  2 Candidate Selection

We used XGBoost (Chen & Guestrin 2016) to identify rows in CatWISE2020 that
may pertain to moving objects. XGBoost is a feature-rich machine learning
package centered around gradient boosting. From XGBoost, we chose to train
binary classification trees that output a single floating point score with
values from 0.0 to 1.0 for each CatWISE2020 row. Lower scores indicate the row
is less like those of moving objects in our training set, whereas higher
scores mean the row is more like them. Typically, we would explore classifier
results by sorting them with higher scores first, and stop exploring results
when we encounter so many false positives that we are no longer effectively
using our time. Although every classifier we trained was different, and had
completely different score distributions, we were usually overwhelmed by false
positives well before scores of 0.5.

Being a binary classification task means that the classifiers are trained to
answer a “yes” or “no” question, or, thought of another way, predict
membership of two classes. For our classifiers, these are the “moving object”
class and the “non-moving object” class. The moving object class asks “does
the object to which this CatWISE2020 row pertains have significant proper
motion?” Significant proper motion, for our methodology, is a proper motion
that a human verifier could confirm by watching an animation of WISE imagery
spanning approximately 10 years. This ends up being around 50 to 150 mas/yr,
but is different for every human verifier and candidate moving object’s
signal-to-noise ratio. The non-moving object class is then the complement of
the moving object class: if the CatWISE2020 row does not pertain to an object
with significant proper motion, it is a member of the non-moving object class.

Figure 1:  These histograms show the distribution of the initial (blue) and
final (green) moving object training sets across W1-W2, W2 and total proper
motion. Note for our W1-W2 histogram we only plotted objects that had W1-W2
values between $-$0.5 and 4.0 mag.

To construct our dataset, we took a random sample of about 200,000 rows from
CatWISE2020 and temporarily assumed that none of them pertained to moving
objects. This was our non-moving object training set. The classifier would
train to output scores that are low for rows like these.

Next, we took the results of a previous candidate search that contained
coordinates for candidate moving objects. These objects had previously been
human reviewed, and in the human’s opinion, they exhibited discernable proper
motion in WISE animations. We cross-matched these candidate moving objects
with CatWISE2020 to obtain CatWISE2020 rows for each candidate. Some
candidates did not have matches, some matched incorrect rows, and some matched
more than one correct row. More than one correct row can happen when
CatWISE2020 fits multiple detections of the same source. We plotted the cross-
match results in various views such as with color, magnitude, and cross-match
angular separation to identify obvious spurious matches by their outlying from
the majority of the dataset, and eliminated these matches from our training
set. We then temporarily assumed that all of the remaining matches were
correct. This left us with about 4000 rows. This was our moving object
training set.

We next assigned class weights to the moving and non-moving object training
set. With an imbalanced training set, weighting is crucial. As XGBoost builds
classifiers, it runs partially built classifiers against the training set and
measures the difference between the classifiers’ predicted versus desired
output. That difference goes into the loss function (we used the default log
loss function), which is then used to calculate loss. Without any weighting,
the non-moving set will contribute $\sim$200,000 / $\sim$4000 $\approx$ 50
times more examples to the loss function than the moving set. As a result, the
classifier will be incentivized to return a low score and will not train as
well. To address this, class weighting is the standard approach. We chose a
“balanced” strategy that equalizes the total weight of all members of each
class. Members of the overpopulated non-moving set get a lower weight, and the
training loss is scaled down by that lower weight. Members of the
underpopulated moving set get a higher weight, and the training loss is scaled
up.

In addition to weighting by class, we also weighted the moving set using the
PSF-fit magnitude in W2 (w2mpro in the CatWISE2020 Catalog). We divided the
moving set into 0.5-mag bins, and equalized the total weight in each bin. In
Marocco et al. (2019), balancing weights by w2mpro was motivated by the
recognition that our moving object set contained few very faint examples, and
we desired to improve the model’s performance on very faint examples. We also
limited rows to w2mpro $>$ 14.0 mag under the assumption that bright objects
had been exhausted by prior searches and we hoped that the reduced scope would
make the model perform better for faint objects. These processes are described
in more detail in §3 of Marocco et al. (2019).

We then removed columns that were inappropriate for classification, such as
coordinates and source identifiers. Then, we added new columns, commonly
called features, calculated from catalog parameters. For example, we added
signal to noise of the W1 versus W2 profile-fit photometry $w1snr-w2snr$;
signal to noise of profile-fit photometry without versus with the CatWISE2020
astrometric solution such as $w1snr-w1snr\\_pm$; and differences in aperture
photometry in the same band but with another aperture size such as 5.5" versus
8.25" $w1mag\\_1-w1mag\\_2$ and 8.25" versus 11" $w1mag\\_2-w1mag\\_3$. We
exhaustively added, removed, and modified features between training countless
classifiers until improvements to the classifier’s performance were no longer
statistically significant across multiple training runs. We are under no
illusion that our feature set is optimal. Instead, we experimentally found
that removing any individual features from the list did not improve
performance, and exhausted our ideas for new features.

Before training the classifier, we removed duplicate rows, randomly shuffled
rows, calculated additional feature columns, and dropped non-feature columns
from the training set. Then we isolated 20% of the training set and set it
aside. This would be the validation set, used to estimate the classifier’s
performance during training. When the classifier’s performance on the
validation set stopped improving, the training process would halt. Halting
training early in this way is a common technique called “early stopping”.
Since each training iteration in XGBoost adds complexity to the classifier,
early stopping prevents classifiers from becoming unnecessarily complex.

Then we ran the trained classifier over all CatWISE2020 rows having w2mpro $>$
14.0 mag. This involves, for each row, calculating additional feature columns,
dropping non-feature columns, and passing it to the classifier’s prediction
function which returns the score. We performed this operation on all
CatWISE2020 rows within one unWISE tile footprint, or about 1/17000th of the
sky, per invocation to realize performance gains. For each row with a
classifier score over 0.5, we added the score to the original CatWISE2020 row
and saved the row to disk.

We then sorted the resulting rows by score, with the highest score first, and
examined animations of WISE images at the coordinates for each row. We used
WiseView (Caselden et al. 2018), which is an interactive online image blinking
tool designed explicitly for human verification of motion candidates in WISE
imagery. When the row pertained to a moving object, we would record the row as
a true positive. When unsure, we recorded the row as unknown. After examining
candidates sorted simply by score, we various strategies, such as restricting
rows by W1-W2 color, proper motion, W2 magnitude, position in the sky, or a
combination of these constraints.

Eventually, our sorting returned so few true positives per false positive, and
we stopped reviewing results. We then added all of the true positives to the
moving object class of the training set, removed rows that we had marked as
unknown from the training set entirely, and added all remaining rows to the
non-moving object class of the training set. Adding true positives to the
moving objects class gave future classifiers more examples, thus making the
classifiers more accurate. By adding false positives to the non-moving object
class, we add examples that are manually vetted and represent particularly
hard examples that challenged our classifier. Both properties make these
examples valuable additions to the otherwise randomly sampled non-moving
class.

We repeated this process many times: training a classifier, vetting the
results, and expanding the training set. Periodically, we purified our
training set by running a classifier over the training set itself and re-
inspecting highly scored members of the non-moving class and lowly scored
members of the moving class. Doing so identified erroneous entries that
arrived in our training set through the initial random sample that created the
non-moving class, the cross-match that created the moving class, and human
process errors such as copy-pasting coordinates into the wrong list.

After some iterations, new classifiers began scoring proper motion stars
higher than their predecessors. Our initial moving object training set
consisted more of fainter and higher proper motion objects, like brown and
white dwarfs, than lower motion main sequence stars. As we added more moving
objects to our training set, they inevitably included more objects outside of
the brown dwarf and white dwarf set. The classifiers we trained on the updated
training set then became more sensitive to proper motion stars of all kinds.
This created a feedback loop, the effects of which are apparent in Figure 1
that shows distributions of the initial and final moving object training sets.
The initial training set is redder in W1 - W2 colors than the final training
set. This is an effect of updating the training set for each successive
classifier and accepting main sequence stars.

Proper motion main sequence stars—despite overwhelming the human verifier—are
not true contaminants in our search. Since they are so well recovered by Gaia
and so plentiful, careful accounting was not warranted. To save time for the
human verifier, we cross-matched all CatWISE2020 rows that scored higher than
0.5 against a subset of Gaia DR1 with total proper motion $>$ 100 mas/yr and a
separation of $<$ 6 arcseconds. The proper motion and angular separation
constraints were added to reduce false matches. Over results from one
classifier, this process automatically labeled approximately 15000 rows. After
incorporating these automatic labels into our training set and training
another classifier, this process automatically labelled approximately 24000
rows. On the third repetition, we changed our cross-match criteria to require
total proper motion $>$ 150 mas/yr and separation $<$ 5 arcseconds. The third
repetition resulted in about 13000 automatic labels. These automatic labels,
like all manual labels, were subjected to our periodic purification processes
that aimed to eliminate bad matches.

In early iterations, each new classifier raised many previously unseen moving
objects. With each iteration, however, the number of new moving objects
decreased. We continued iterating until, ultimately, there were too many false
positives per true positive to continue. Once the yield of new sources slowed
significantly, we halted the training of other classifiers with this
methodology and stopped verifying results from our existing classifiers. Of
the motion candidates selected by our classifiers, we eliminated those for
which there existed a Gaia DR1 astrometric solution. This left us with
$\sim$6000 candidates, both known and unknown, for further analysis.

##  3 Analyzing Candidates

###  3.1 Other Databases

As stated earlier, CatWISE2020 only contains two magnitudes (W1 and W2). This
meant that we only had access to one color (W1$-$W2) when analyzing our
candidates. With only one color, it is difficult to characterize objects.
Thus, in order to get more colors, we gathered additional infrared data from
Two Micron All-Sky Survey (2MASS: Skrutskie et al. 2006), UK Infra-Red
Telescope (UKIRT) Hemisphere Survey (UHS: Dye et al. 2018) and Visible and
Infrared Survey Telescope for Astronomy Hemisphere Survey (VHS: (McMahon et
al. 2013)).

Figure 2: J-W2 vs. J-Ks color-color diagram. The orange points are discoveries
announced in this paper. For comparison, other proper motion discoveries from
Figure 7 of Kirkpatrick et al. (2016) are plotted as blue points. The green
arrows denote color limits. Objects labeled in purple are discussed in the
text. Note that the UHS discoveries are not plotted on this diagram because
they have no Ks data.  Figure 3: W1-W2 vs. J-W2 color-color diagram. The
orange points are discoveries announced in this paper. For comparison, other
proper motion discoveries from Figure 8 of Kirkpatrick et al. (2016) are
plotted as blue points. The green arrows denote color limits. Objects labeled
in purple are discussed in the text.

Using the CatWISE2020 proper motion values, we propagated the position of each
source in time to predict the position at the epoch of the database we were
querying. Using a cone search of 5 arcseconds around the predicted position,
we collected the J (1.25 $\mu$m), H (1.65 $\mu$m), and Ks (2.2 $\mu$m)
magnitudes. (UHS only has J-band coverage.) We used multiple databases to
maximize our chances of finding ancillary data, as not all data sets cover the
entire sky or probe deeply enough to detect our objects.

After we matched our candidate list with the various infrared databases, we
searched for mismatched candidates. Due to the difference in epochs and
uncertainty in the proper motion measurements, our predicted location was not
always accurate. 2MASS, with its large epoch difference from CatWISE2020
(15-18 yr), gave us the most spurious matches. We used WiseView to verify or
refute matches.

###  3.2 Color-Color Diagrams

The reference color-color diagrams we used are Figures 7 and 8 in Kirkpatrick
et al. (2016), which plot J-W2 vs. J-Ks and W1-W2 vs. J-W2 respectively. These
plots contain all identified proper motion objects in the AllWISE1
(Kirkpatrick et al. 2014) and AllWISE2 (Kirkpatrick et al. 2016) motion
surveys with magnitudes fainter than the nominal WISE W1 saturation limit of
8.1 mag. F, G, K, and M stars compose the dense locus while L and T dwarfs lie
on the outer sections. For a complete description, please consult Kirkpatrick
et al. (2016).

Using TopCAT (Taylor 2005), we made a plot of our CatWISE2020 candidates with
the same colors as the reference diagrams from Kirkpatrick et al. (2016). We
compared our CatWISE2020 color-color plots side by side with the reference
diagrams. As described further in section 4, we then extracted candidates that
fell outside the loci of the F, G, K, or M stars determined in Figures 7 and 8
of Kirkpatrick et al. (2016) and inputted them into a sublist. These are
objects that have the highest probability of being cool brown dwarfs or other
rare objects.

Next, to check the novelty of the objects recovered, we cross checked our
entire sublist with SIMBAD (Wenger et al. 2000) and other more recent or
unpublished data sets (20 parsec census list from Kirkpatrick et al. 2021,
Backyard Worlds candidate list (Kuchner et al. 2017), etc.) that have not yet
been ingested in SIMBAD. Any previously discovered objects were removed from
our sublist.

Finally, we created finder charts of the new discoveries using a publicly
available PYTHON program111Access Link: Finder Charts These charts show the
WISE images in all four bands and their counterparts (when there was coverage)
in the three bands of 2MASS, three bands of VHS, three bands of the Digitized
Sky Survey (DSS), 1 band of UHS, 5 bands of the Pan-Starrs (Chambers et al.
2016) survey, and 5 bands of the Dark Energy Survey (DES; Abbott et al. 2018).
First, we used the charts to further verify or refute matches. Second, we used
the charts to ensure our candidates had photometric properties across a wider
wavelength range that were consistent with the classifications derived from
their J-W2, J-Ks and W1-W2 colors.

Note that we only looked for interesting objects that had ancillary data at J,
H, or Ks bands. Any object lacking UHS/VHS/2MASS data, or lacking a detection
despite imaging in those data sets, will be part of a future study.

##  4 Interesting objects found

Our entire search yielded 16 new objects. All of the new discoveries are
plotted on two color-color diagrams – J-W2 vs. J-Ks (Figure 2) and W1-W2 vs.
J-W2 (Figure 3). Outlying objects identified on these plots are discussed
further below. The coordinates and photometry of our 16 discoveries are given
in Table 1. Additionally, when available, Gaia photometry and parallaxes of
our 16 discoveries are given in Table 2.

###  4.1 CWISE J215859.63-085441.9 and CWISE J235713.17-630827.3

CWISEJ215859.63$-$085441.9 and CWISEJ235713.17$-$630827.3 are two nearby M
dwarfs that are missing from our census of the solar neighborhood. After
comparing the J-W2 colors of our objects to the data in Table 5 of Pecaut &
Mamajek (2013)222See also:
http://www.pas.rochester.edu/~emamajek/EEM_dwarf_UBVIJHK_colors_Teff.txt, we
find that their colors most closely match those of M4 and M5 dwarfs
respectively.

Both objects were detected by Gaia EDR3 (Gaia Collaboration et al. 2016) but
had no reported parallaxes. This may suggest that these objects are a part of
multiple systems, confounding the short time baseline astrometry currently
available to Gaia EDR3.

We collected Transiting Exoplanet Survey Satellite (TESS; Ricker et al. 2015)
time series photometry on CWISE J235713.17$-$630827.3 to explore this
hypothesis. We note that the location of CWISE J215859.63$-$085441.9 is
currently not observed by TESS (at the time of this writing, September, 2021).
The CWISE J235713.17$-$630827.3 light curve is shown in Figure 4. The full
light curve is shown in the top panel, and the Lomb-Scargle periodogram is
shown in the bottom panel. The leftmost column is the Sector 1 30-min light
curve. The middle column shows the same plots but for the Sector 1 2-min light
curve. Finally, the rightmost column follows the same convention as the other
two, but for the Sector 28 10-min light curve.

Figure 4: Time series photometry of CWISE J235713.17$-$630827.3. The first row
illustrates the entire light curve in three different sectors. The second row
illustrates the periodogram for each of the light curves above.

There are two high powered peaks in each periodogram with periods of P1 =
0.3028 +/- 0.0004 d and P2 = 0.2646 +/- 0.0003 d. The implied periods are
consistent over the two year baseline. The cause for the variation is likely
to be star spots rotating into and out of view. There are two possible
explanations for the double period. First, the object may be experiencing
differential rotation like our Sun, leading to detect two distinct periods.
Another explanation is the object is a system comprised of two stars with two
slightly different rotation periods. Above we surmised that Gaia reported no
parallax because the object may be a multiple system. The second explanation
above for the TESS double period provides some credence to this statement. The
objects seen by TESS are likely to be similar in magnitude because both
variations are detected in a single light curve.

Since no Gaia parallax is available, we estimate the distance as follows. With
a J-W2 vs. MJ relation for M dwarfs constructed using data from Pecaut &
Mamajek (2013), we estimate the distance of CWISE J235713.17-630827.3 if it is
an equal magnitude double to be 27.5 parsecs. (Note, if single, the distance
estimate would be $\sim$ 23 pc.) In addition, we found two sources in Gaia
EDR3 which may correspond to the two components of our proposed physical pair.
The separation between sources is $0{\farcs}24$. Using the GBP\- GRP vs MG
relation found in Table 4 of Kiman et al. (2019), we estimate a MG value of
11.86 mag and spectral type of M5 for both Gaia sources. Then, by using the
apparent G magnitude, we estimate distances of $\sim$33 and $\sim$37 pc for
the two components of the binary.

Since CWISE J215859.63-085441.9 lacks a Gaia parallax, we theorize that it is
also a binary like CWISE J235713.17-630827.3. Again, assuming an equal
magnitude pair and using the J-W2 vs. MJ relation for M dwarfs constructed
using data from Pecaut & Mamajek (2013), the distance estimate for CWISE
J215859.63-085441.9 is $\sim$20.5 pc. (Note, if single, the distance estimate
would be 14.5 pc.) We found only one source in Gaia EDR3. Using the GBP\- GRP
vs. MG relation found in Table 4 of Kiman et al. (2019), we estimate a MG
value of 12.92 mag and a spectral type of M6. Then, by using the apparent G
magnitude we estimate a distance of $\sim$29 pc

Despite their proximity to the Sun, these two M dwarfs were not discovered
until now due to their small proper motions of $\mu_{tot}=135{\pm}36$ mas/yr
for CWISE J215859.63$-$085441.9 and $\mu_{tot}=53{\pm}5$ mas/yr for CWISE
J235713.17-630827.3, as measured by CatWISE. Finder charts of the two M dwarfs
(Figure 5) show the slow proper motion. Using the CatWISE2020 proper motion
data and the distance estimate we calculated above, we estimated the
tangential velocity of CWISE J215859.63$-$085441.9 to be 6.1 km/s. Again,
assuming an equal magnitude pair, the tangential velocity estimate for CWISE
J235713.17-630827.3 is 4.5 km/s.

Figure 5: Finder charts for CWISE J2158$-$0854 and CWISE J2357$-$6308\. The
finder chart shows a $>$30yr baseline of time. DSS2 R, 2MASS J, WISE W1, and
WISE W2 images are shown. The objects have small but clear proper motions.
Each cutout is 2 arcminutes on a side with north up and east to the left.

The two M dwarfs were initially flagged as outliers when we plotted them using
their reported VHS magnitudes. However, these magnitudes differ markedly from
the 2MASS magnitudes. For example, CWISE J215859.63$-$085441.9 has VHS
magnitudes of JMKO=$11.868{\pm}0.001$ and KsVHS=$11.205{\pm}0.001$ mag,
whereas, it has 2MASS magnitudes of J2MASS=$11.215{\pm}0.026$ and
Ks2MASS=$10.470{\pm}0.025$ mag. The differences between the VHS and 2MASS
magnitudes are $\Delta$J=0.653 and $\Delta$Ks=0.735 mag. This difference is
larger than what we can attribute to the difference in filter sets. We believe
the 2MASS data are credible and the VHS data problematic for the following
reasons: (1) We know that 2MASS magnitudes are valid between $-4<Ks<16$ mag,
333See:
https://irsa.ipac.caltech.edu/data/2MASS/docs/releases/allsky/doc/sec2_2b.html
and our source falls within this range. (2) VHS data has a bright limit (11.5
to 12 mag) where sources saturated and where the reported magnitudes cannot be
properly measured (González-Fernández et al. 2018). Our objects are brighter
than this limit. Thus, the difference in magnitudes is caused by saturation in
the VHS data. Using the 2MASS magnitudes, the colors of both objects are
consistent with M dwarfs and they are no longer color- color outliers.

###  4.2 CWISE J123041.80+380140.9

CWISE J123041.80+380140.9 is a high proper motion object
($\mu_{tot}=764{\pm}115$ mas/yr). We used the W1-W2 color of 0.879$\pm$0.117
mag and the color vs. spectral type relation found in Table 13 of Kirkpatrick
et al. (2021) to estimate a spectral type of $\sim$T2. Based on Figure 3, the
$J-$W2 vs. W1-W2 location is consistent with an early T dwarf.

The finder chart (Figure 6) of our object illustrates its faintness. The
object is not detected by 2MASS, but it is faintly detected in UHS and more
clearly detected in WISE.

Figure 6: Finder charts for CWISE J1230+3801 and CWISE J1848$-$8757\. The
finder charts illustrate the faintness of the objects. The UHS J, WISE W1, and
WISE W2 images are shown for CWISE J1230+3801. The VHS J, WISE W1, and WISE W2
images are shown for CWISE J1848-8757. Each cutout is 2 arcminutes on a side
with north up and east to the left.

From the same paper and table, we used the absolute magnitude vs. color
relation to derive an absolute W2 value of 12.3 mag, which implies a distance
of $\sim$74 pc, using the apparent W2 magnitude. Using the CatWISE2020 proper
motion and distance estimate, we calculate a tangential velocity of $\sim$234
km/s. We theorize that the large velocity of this object is likely caused by
dynamical heating, which implies an old age. Furthermore, old age often
implies low metallicity. Oddly, however, the object falls on the normal T
dwarf track, so future spectroscopic observations are needed to reveal its
nature.

###  4.3 CWISE J184842.02-875747.9

CWISE J184842.02-875747.9444CWISE J184901.34-875753.4 lies 11.2 arcseconds to
the southeast. Despite the CatWISE2020 catalog measuring no significant proper
proper motion, it appears to be moving in the WiseView blink. However this
proper motion is not aligned with CWISE J184842.02-875747.9. falls along the L
dwarf track as seen in Figure 2 and Figure 3. A finder chart is shown in
Figure 6. We used the W1-W2 color of 0.659$\pm$0.026 mag and the color vs.
spectral type relation found in Table 13 of Kirkpatrick et al. (2021) to
estimate a spectral classification of $\sim$L7.5. From the same paper and
table, we used the absolute magnitude vs. color relation to derive an absolute
W2 value of 12.2 mag, which implies a distance of $\sim$29 pc, using the
apparent W2 magnitude. A color of J-W2=$4.23{\pm}0.13$ mag is surprisingly red
for an L dwarf. The only L dwarfs that are this red on Figure 14 of
Kirkpatrick et al. (2021) are young objects. In addition, the only L dwarfs
that rival this J-W2 color in Figure 8 of Faherty et al. (2016) are young
objects. Using the W2YNG vs. Spectral Type relation found in Table 19 of
Faherty et al. (2016) we derived a absolute W2 value of 10.8 mag, which
implies a distance of$\sim$ 54 pc, using the apparent magnitude. If the object
is determined to be young, the true distance should be closer to this value
than the 29 pc distance value estimated above. Using the CatWISE2020 proper
motion ($\mu_{tot}=158{\pm}15$ mas/yr) and the distance estimate if the object
is young, we calculate a tangential velocity of $\sim$35 km/s which would not
run counter to the young classification. We therefore propose this is likely a
candidate young L dwarf. However, using the CatWISE 2020 proper motion data,
we find no clear membership in any young nearby moving group from Banyan
$\Sigma$ (Gagné et al. 2018), and it is determined to be a field object with
99 % probability. Future spectroscopic observations of CWISE
J184842.02-875747.9 would help determine whether or not it is young via low-
gravity spectral features (Allers & Liu 2013).

###  4.4 Additional objects discovered

The rest of the 16 objects are discussed further below:

Figure 7: Finder charts for CWISE J0400$-$5111 and CWISE J1840+4107. For CWISE
J0400-5111 these are three-color images made from DES g/i/y, VHS J/H/Ks and
WISE W1/W2/W3. For WISE J1840$+$4107 the images are PSO g/i/y, UHS J and WISE
W1/W2/W3. (Only one band was available for UHS.) For CWISE J0400-5111, the red
color in the optical (DES), the blue color in the near infrared (VHS) and the
green color in the mid infrared (WISE) parallel the spectral energy
distribution of a T dwarf. CWISE J1840+4107 has less information than CWISE
J0400$-$5111 but is faint in the optical, detected at J, and brighter and
green in the WISE bands, again characteristic of a T dwarf.

  * •

CWISE J044109.35+254854.4 falls in an unusual spot in Figure 2 and Figure 3 at
colors of J-W2>5.8 mag, J-K>3.7 mag and W1-W2=$1.031{\pm}0.016$ mag. Objects
that live in this portion of the diagram are either extremely red L dwarfs or
reddened objects (cf. figure 7 and 8 of Kirkpatrick et al. 2016). The
coordinates place the object in a region of nebulosity toward the Taurus
Molecular Cloud. Thus, we surmise the object is behind the cloud and is being
reddened by it. CWISE J044109.35+254854.4 has a motion significance of 21
$\sigma$ ($\mu_{tot}=160{\pm}7.5$ mas/yr). In addition, this source appears
point-like and exhibits clear motion between the 1998-epoch 2MASS Ks image and
the 2010-epoch AllWISE W1/W2 images, further confirming the proper motion
measured by CatWISE2020. A spectrum of this object is needed to measure a
spectral type, measure the amount of extinction, estimate a distance, and
confirm if the distance is sufficient to place the object behind the cloud.

  * •

CWISE J030009.79+413051.2 is likely a late T dwarf. This object was not
detected by 2MASS and was only found in UHS and WISE. We used the J-W2 color
of 3.22$\pm$0.18 mag and the color vs. spectral type relation found in Table
13 of Kirkpatrick et al. (2021) to estimate a spectral classification of T8.
As shown in Figure 3, this object falls along the normal late-T dwarf track.
From the same paper and table, we used the absolute magnitude vs. color
relation to derive an absolute W2 value of 13.0 mag, which implies a distance
of $\sim$40 pc, using the apparent W2 magnitude.

  * •

CWISE J040052.80-511142.5 and CWISE J184048.17+410727.4 have a motion
significance of only 1.3$\sigma$ and 1.0 $\sigma$ respectively in CatWISE 2020
(see Table 1) giving us little confidence that the objects are nearby.
However, when querying NOIRLab Source Catalog, we find that CWISE
J040052.80-511142.5 has a motion significance of 4.3 $\sigma$
($\mu_{tot}=98{\pm}23$ mas/yr). The location of CWISE J184048.17+410727.4 is
unfortunately not covered by the NOIRLab Source Catalog. However in Figure 7
we see that the overall colors from the optical to the mid infrared of both
objects correspond to T dwarfs. Using the methodology above, we estimated
spectral types of T7.5 for CWISE J040052.80-511142.5 and T8.5 for CWISE
J184048.17+410727.4, which correspond to 31 and 27 pc, respectively. To
confirm these classifications, spectra are needed.

  * •

CWISE J103716.55-750257.7 and CWISE J125924.86-552531.8 are two likely L
dwarfs. We used the W1-W2 color of 0.23$\pm$0.07 mag and the color vs.
spectral type relation found in Table 13 of Kirkpatrick et al. (2021) to
estimate a spectral classification of L2.5 for CWISE J103716.55-750257.7. From
the same paper and table, we used the absolute magnitude vs. color relation to
derive an absolute W2 value of 12.0 mag, which implies a distance of $\sim$37
pc, using the apparent W2 magnitude. Using the methodology above, CWISE
J125924.86-552531.8 has a spectral type of L4 and a distance of $\sim$36 pc.

  * •

CWISE J141545.74-330545.0 and CWISE J210159.84-783846.8 have measurements in
all three colors found in Figure 2 and Figure 3. Their locations in these
figures when compared to Figure 7 and 8 of Kirkpatrick et al. (2016) are
consistent with L subdwarfs or early T dwarfs. Spectra are needed for both
objects to determine their nature and to estimate distances

  * •

CWISE J120657.58-311221.1 also has measurements in all three colors found in
Figure 2 and Figure 3. Its location is consistent with an M subdwarf. Using
its Gaia EDR3 parallax, we calculate a distance of $\sim$156 parsecs. Then,
using the Gaia EDR3 proper motion data (($\mu_{tot}=534{\pm}23$ mas/yr) and
distance, we calculate a very high tangential velocity of $\sim$394 km/s.

  * •

All of the following objects are outliers, but not for astrophysical reasons.
CWISE J144053.88-121712.3 lies near another object leading to contaminated
photometry. CWISE J052703.28-113056.7 and CWISE J015330.24-520046.6 are close
comoving doubles. WISE has combined photometry from both sources while VHS
reports photometry from both, which leads to inaccurate J-W2 colors.

##  5 Conclusion

We have presented 16 new dwarfs identified through supervised learning
methods. Specifically, we highlight four interesting objects: 2 nearby M
dwarfs (CWISE J215859.63$-$085441.9, CWISE J235713.17$-$630827.3), a high
proper motion early T dwarf (CWISE J123041.80+380140.9) and likely a young L
dwarf (CWISE J184842.02-875747.9).

Spectroscopy will help to reveal the nature of these objects. For the M
dwarfs, this will be used to get more definitive spectral classifications and
distance estimates. We also await future Gaia data to confirm the binary
hypothesis. For the high proper motion T dwarf, a spectrum would confirm the
spectral type and metallicity. Finally, a spectrum of the suspected young L
dwarf would be used to search for features of low gravity, which is a direct
indicator of youth.

There are a few limitations to our search method described in § 2. First,
given we are searching for nearby objects through their significant proper
motions, we are not sensitive to nearby objects with small, insignificant
proper motions. Second, some of our objects are not detected by 2MASS. Of
these, some lack deeper UHS or VHS data, while others are undetected in their
UHS or VHS images. Without detections in these data, we are not able to use
our color-color diagrams to pick out interesting objects. Additional analysis
must also be done on the objects in the candidate list with UHS and VHS
imaging but no detection in their respective images. This means that UHS and
VHS failed to detect the objects at the sensitivities of those surveys. This
is relevant because the elusive Y dwarfs (Kirkpatrick et al. 2012) may be
tucked away in this exclusive list of objects.

This paper represents just one way to search through the CatWISE2020 data. The
entire CatWISE2020 and Backyard Worlds team is employing a myriad of methods
for analyzing WISE data and this will culminate in new insights about the
luminosity and mass functions (Kirkpatrick et al. in prep). Our approach is
just one machine learning method applied to WISE catalog data. Other machine
learning methods have used different training sets to explore new areas of
parameter space or they have used WISE imaging data (Caselden in prep). There
are other approaches that do not use machine learning such as using catalog
selection (Meisner et al. 2020) directly from IRSA or human vetting of motion
candidates (Kuchner et al. 2017). All of these methods will help researchers
fully explore the almost 1.8 billion sources in the CatWISE2020 catalog and
the epochal coadds from the unWISE collection, and undoubtedly aid in
uncovering even more interesting objects.

Our research illustrates that, despite our best efforts over the last decade,
there are still discoveries near the Sun to be made. In addition, our census
of the faintest stellar and sub-stellar objects is still incomplete. To finish
the census, astronomers (and the public!) are encouraged to continue exploring
CatWISE2020 and exploit its capabilities.

##  6 acknowledgements

CatWISE is funded by NASA under Proposal No. 16-ADAP16-0077 issued through the
Astrophysics Data Analysis Program, and uses data from the NASA-funded WISE
and NEOWISE projects. This research has made use of the NASA/IPAC Infrared
Science Archive, which is funded by the National Aeronautics and Space
Administration and operated by the California Institute of Technology. The
Backyard Worlds: Planet 9 team would like to thank the many Zooniverse
volunteers who have participated in this project, from providing feedback
during the beta review stage to classifying flipbooks to contributing to the
discussions on TALK. We would also like to thank the Zooniverse web
development team for their work creating and maintaining the Zooniverse
platform and the Project Builder tools. F.M. acknowledges support from grant
80NSSC20K0452 under the NASA Astrophysics Data Analysis Program. This research
was supported by NASA grant 2017-ADAP17-0067. This material is based upon work
supported by the National Science Foundation under Grant No. 2007068, 2009136,
and 2009177. This research has made use of the SIMBAD database, operated at
CDS, Strasbourg, France. This publication makes use of data products from the
Two Micron All Sky Survey, which is a joint project of the University of
Massachusetts and the Infrared Processing and Analysis Center/California
Institute of Technology, funded by the National Aeronautics and Space
Administration and the National Science Foundation. This publication makes use
of data products from the Near-Earth Object Wide-field Infrared Survey
Explorer (NEOWISE), which is a joint project of the Jet Propulsion
Laboratory/California Institute of Technology and the University of Arizona.
NEOWISE is funded by the National Aeronautics and Space Administration. Our
research used data from the UHS collaboration and the VISTA Hemisphere Survey
(VHS). We want to thank the Student Astrophysics
Society555https://www.studentastrophysicssociety.com for providing the
resources that enabled the pairing of high school and undergraduate students
with practicing astronomers and advanced citizen scientists. We thank the
anonymous referee for insightful suggestions that helped improve the
manuscript.

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Table 1: Data for New Discoveries CatWISE Designation | J2000 RA | J2000 Dec | $\mu_{\alpha}$ | $\mu_{\delta}$ | W1aaThese are the W1 and W2 magnitudes measured while taking into account proper motion from the CatWISE2020 Catalog(listed as w1mpro_pm and w2mpro_pm) | W2aaThese are the W1 and W2 magnitudes measured while taking into account proper motion from the CatWISE2020 Catalog(listed as w1mpro_pm and w2mpro_pm) | $J$ | $H$ | $K$ |  $JHK$ Ref. | Classification | BYW CodiscoversccReference code for discover: A = Caselden, B = Bickle and C = Goodman. These discoverers independently discovered objects in this paper using tools provided and developed for the Backyard Worlds citizen science project.
---|---|---|---|---|---|---|---|---|---|---|---|---
| (deg) | (deg) | (asec yr-1) | (asec yr-1) | (mag) | (mag) | (mag) | (mag) | (mag) |  |  |
(1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) | (9) | (10) | (11) | (12) | (13)
CWISE J015330.24-520046.6 | 28.3760178 |  $-$52.0129465 | $0.12459{\pm}0.0075$ | $0.04784{\pm}0.0065$ | $14.202{\pm}0.013$ | $14.050{\pm}0.013$ | $15.783{\pm}0.005$ |  $\cdots$ | $15.076{\pm}0.009$ | VHS | bbCannot be classified. See Section 4.4 |
CWISE J030009.79+413051.2 | 45.0408097 | 41.5142428 | $-0.26243{\pm}0.0857$ | $0.04321{\pm}0.0838$ | $19.173{\pm}0.343$ | $15.984{\pm}0.065$ | $19.199{\pm}0.165$ |  $\cdots$ |  $\cdots$ | UHSddNote that UHS is the only UKIRT survey our targets fell into. | T dwarf |
CWISE J040052.80-511142.5 | 60.2200208 |  $-$51.1951632 | $0.03525{\pm}0.0435$ | $-0.06973{\pm}0.0443$ | $18.274{\pm}.098$ | $15.828{\pm}0.037$ | $18.547{\pm}0.036$ | $18.863{\pm}0.232$ | $18.778{\pm}0.243$ | VHS | T dwarf | B
CWISE J044109.35+254854.4 | 70.2889956 | 25.8151209 | $0.14188{\pm}0.0053$ | $-0.08250{\pm}0.0053$ | $13.181{\pm}0.013$ | $12.150{\pm}0.010$ |  $>$17.987 | $16.324{\pm}0.022$ | $14.274{\pm}0.074$ | 2MASS | reddened star |
CWISE J052703.28-113056.7 | 81.7636813 |  $-$11.5157684 | $0.01655{\pm}0.0185$ | $-0.23600{\pm}0.0188$ | $14.687{\pm}0.027$ | $14.569{\pm}0.027$ | $16.320{\pm}0.011$ |  $\cdots$ | $15.636{\pm}0.024$ | VHS | bbCannot be classified. See Section 4.4 |
CWISE J103716.55-750257.7 | 159.3189815 |  $-$75.0493751 | $-0.19359{\pm}0.0115$ | $0.02816{\pm}0.0119$ | $15.118{\pm}0.063$ | $14.884{\pm}0.056$ | $16.948{\pm}0.020$ |  $\cdots$ | $15.967{\pm}0.041$ | VHS | L dwarf |
CWISE J120657.58-311221.1 | 181.7399330 |  $-$31.2058758 | $-0.40234{\pm}0.0204$ | $0.40390{\pm}0.0233$ | $15.766{\pm}0.022$ | $15.616{\pm}0.046$ | $16.542{\pm}0.027$ |  $\cdots$ | $16.343{\pm}0.055$ | VHS | M subdwarf |
CWISE J123041.80+380140.9 | 187.6741736 | 38.0280398 | $0.14901{\pm}0.0743$ | $-0.74981{\pm}0.0880$ | $17.559{\pm}0.069$ | $16.680{\pm}0.095$ | $19.117{\pm}0.163$ |  $\cdots$ |  $\cdots$ | UHS | high proper motion T2 dwarf |
CWISE J125924.86$-$552531.8 | 194.8535993 |  $-$55.4255028 | $-0.21910{\pm}0.0126$ | $0.00887{\pm}0.0144$ | $15.126{\pm}0.017$ | $14.826{\pm}0.023$ | $16.992{\pm}0.022$ |  $\cdots$ | $15.515{\pm}0.022$ | VHS | L dwarf |
CWISE J141545.74-330545.0 | 213.9405948 |  $-$33.0958410 | $-0.17852{\pm}0.0115$ | $-0.21219{\pm}0.0133$ | $14.941{\pm}0.017$ | $14.672{\pm}0.025$ | $16.634{\pm}0.018$ |  $\cdots$ | $15.839{\pm}0.032$ | VHS | L subdwarf or early T dwarf, A |
CWISE J144053.88-121712.3 | 220.2245053 |  $-$12.2867517 | $-0.09991{\pm}0.0136$ | $-0.23436{\pm}0.0157$ | $15.063{\pm}0.019$ | $14.906{\pm}0.030$ | $17.004{\pm}0.0165$ | $16.443{\pm}0.029$ | $16.146{\pm}0.036$ | VHS | bbCannot be classified. See Section 4.4 |
CWISE J184048.17+410727.4 | 280.2007202 | 41.1242809 | $-0.00784{\pm}0.0438$ | $-0.06299{\pm}0.0479$ | $18.938{\pm}0.199$ | $15.829{\pm}0.036$ | $19.243{\pm}0.164$ |  $\cdots$ |  $\cdots$ | UHS | T dwarf |
CWISE J184842.02-875747.9 | 282.1750944 |  $-$87.9633209 | $-0.01739{\pm}0.0106$ | $-0.15779{\pm}0.0107$ | $15.124{\pm}0.019$ | $14.465{\pm}0.017$ | $18.699{\pm}0.124$ |  $\cdots$ | $16.382{\pm}0.059$ | VHS | young L dwarf? | A,C
CWISE J210159.84-783846.8 | 315.4993413 |  $-$78.6463487 | $0.27545{\pm}0.0317$ | $-0.18208{\pm}0.0301$ | $16.489{\pm}0.031$ | $16.254{\pm}0.063$ | $18.324{\pm}0.079$ |  $\cdots$ | $17.451{\pm}0.176$ | VHS | L subdwarf or early T dwarf |
CWISE J215859.63$-$085441.9 | 329.7484963 |  $-$8.9116413 | $0.0748{\pm}0.0044$ | $-0.11231{\pm}0.0040$ | $10.303{\pm}0.013$ | $10.060{\pm}0.089$ | $11.215{\pm}0.026$ | $10.708{\pm}0.023$ | $10.470{\pm}0.025$ | 2MASS | nearby M dwarf |
CWISE J235713.17$-$630827.3 | 359.3048900 |  $-$63.1409326 | $-0.03229{\pm}0.0038$ | $0.04222{\pm}0.0034$ | $9.798{\pm}0.012$ | $9.573{\pm}0.007$ | $10.813{\pm}0.023$ | $10.272{\pm}0.022$ | $9.997{\pm}0.023$ | 2MASS | nearby M dwarf |
Table 2: Gaia EDR3 data on new discoveries CatWISE Designation | Gaia RA | Gaia Dec | $G$ | $G_{BP}$ | $G_{RP}$ | Plx
---|---|---|---|---|---|---
| (deg) | (deg) | (mag) | (mag) | (mag) | (mas)
(1) | (2) | (3) | (4) | (5) | (6) | (7)
CWISE J015330.24-520046.6 | 28.375773 |  $-$52.012694 | $19.037842{\pm}0.003477$ | $20.801437{\pm}0.166603$ | $17.803684{\pm}0.014892$ | $4.7697{\pm}0.1993$
CWISE J052703.28-113056.7 | 81.763843 |  $-$11.516011 | $19.325026{\pm}0.003703$ | $20.786207{\pm}0.072447$ | $18.060532{\pm}0.015927$ | $3.2509{\pm}0.2729$
CWISE J120657.58-311221.1 | 181.739803 |  $-$31.205797 | $19.547705{\pm}0.003853$ | $20.997263{\pm}0.114532$ | $18.291862{\pm}0.020912$ | $6.4006{\pm}0.3416$
CWISE J215859.63$-$085441.9 | 329.748553 |  $-$8.911683 | $14.468976{\pm}0.015447$ | $15.793684{\pm}0.003651$ | $12.920640{\pm}0.003923$ |  $\cdots$
CWISE J235713.17$-$630827.3 aaThis CatWISE2020 object is split into two components in Gaia EDR3 | 359.304944 |  $-$63.140875 | $14.482848{\pm}0.020162$ | $16.019608{\pm}0.006164$ | $12.760140{\pm}0.004215$ |  $\cdots$
CWISE J235713.17$-$630827.3 aaThis CatWISE2020 object is split into two components in Gaia EDR3 | 359.304881 |  $-$63.140937 | $14.717904{\pm}0.004546$ | $16.005678{\pm}0.007803$ | $12.754520{\pm}0.004263$ |  $\cdots$