# X-ray Spectra and Multiwavelength Machine Learning Classification for Likely
Counterparts to Fermi 3FGL Unassociated Sources

Stephen Kerby Department of Astronomy and Astrophysics
Pennsylvania State University University Park, PA 16802, USA  Amanpreet Kaur
Department of Astronomy and Astrophysics
Pennsylvania State University University Park, PA 16802, USA  Abraham D.
Falcone Department of Astronomy and Astrophysics
Pennsylvania State University University Park, PA 16802, USA  Michael C. Stroh
Center for Interdisciplinary Exploration and Research in Astrophysics (CIERA),
Northwestern University, Evanston, IL 60201, USA  Elizabeth C. Ferrara  NASA
Goddard Space Flight Center, Greenbelt, MD 20771, USA  Department of
Astronomy, University of Maryland College Park, MD 20742, USA  Jamie A. Kennea
Department of Astronomy and Astrophysics
Pennsylvania State University University Park, PA 16802, USA  Joseph Colosimo
Department of Astronomy and Astrophysics
Pennsylvania State University University Park, PA 16802, USA

(Received Sep 09 2020 ; Revised Dec 10 2020 ; Accepted Jan 9, 2021)

###### Abstract

We conduct X-ray spectral fits on 184 likely counterparts to Fermi-LAT 3FGL
unassociated sources. Characterization and classification of these sources
allows for more complete population studies of the high-energy sky. Most of
these X-ray spectra are well fit by an absorbed power law model, as expected
for a population dominated by blazars and pulsars. A small subset of 7 X-ray
sources have spectra unlike the power law expected from a blazar or pulsar and
may be linked to coincident stars or background emission. We develop a
multiwavelength machine learning classifier to categorize unassociated sources
into pulsars and blazars using gamma- and X-ray observations. Training a
random forest procedure with known pulsars and blazars, we achieve a cross-
validated classification accuracy of $98.6\%$. Applying the random forest
routine to the unassociated sources returned 126 likely blazar candidates
(defined as $P_{bzr}\geq 90\%$) and 5 likely pulsar candidates ($P_{bzr}\leq
10\%$). Our new X-ray spectral analysis does not drastically alter the random
forest classifications of these sources compared to previous works, but it
builds a more robust classification scheme and highlights the importance of
X-ray spectral fitting. Our procedure can be further expanded with UV, visual,
or radio spectral parameters or by measuring flux variability.

catalogs — surveys

††journal: AJ

##  1 Introduction

The bulk of the $3033$ sources in the Fermi Gamma-ray Space Telescope - Large
Area Telescope (Fermi-LAT) 3FGL catalog fall into one of two classes:
extragalactic blazars or nearby pulsars (Acero et al., 2015). A small number
of other identified sources include supernova remnants, X-ray binaries, and
starburst galaxies (Ferrara et al., 2015). However, $1010$ 3FGL sources are
“unassociated”, without a confident astrophysical identification with a known
pinpointed source, or with several competing astronomical explanations within
the gamma-ray detection area. Based on the dominance of blazars and pulsars
among identified 3FGL sources, many of these unassociated sources are likely
blazars and pulsars. Capturing these unassociated and heretofore unclassified
sources would create a more complete all-sky sample of various classes of
objects, particularly blazars.

Categorizing the blazars and pulsars in the 3FGL unassociated list is an
important step towards confident population studies of both classes. The
Fermi-LAT unassociated sources might include blazars that are lower luminosity
or higher redshift than their more easily detected and identified cousins in
the established 3FGL blazar catalog (Ferrara et al., 2015). Therefore,
pursuing the classification of the 3FGL unassociated sources will help build a
more complete population study, which will aid in verification and analysis of
the blazar sequence (e.g., Fossati et al., 1998; Ghisellini et al., 2017) as a
theoretical unifying scheme for blazars.

In a similar way, identification and classification of 3FGL unassociated
sources can also lead to new pulsar candidates, a population which has
included new accreting pulsars in the 3FGL catalog (e.g. Wu et al., 2018; Li
et al., 2018). Furthermore, in-depth analysis might show that an object suits
neither blazar nor pulsar classification. Such sources might represent new
gamma-ray binaries, or possibly more exotic objects (Saz Parkinson et al.,
2016). The 3FGL unassociated list could contain several such objects, but
classification of the blazars and pulsars that probably make up most of the
unassociated sample is the first step in identifying any unique sources.

While gamma-ray observations from Fermi-LAT are the foundation for the 3FGL
catalog, matching gamma-ray sources with X-ray counterparts and extending
analysis to lower energy photons is a vital step in deeper analysis of the
3FGL sources. Recent work (Kaur et al., 2019) developed a machine learning
(ML) approach to sort 217 high-S/N unassociated 3FGL sources into blazars and
pulsars. The 217 unassociated sources were analyzed by combining the gamma-ray
flux and photon index of each object with a coarse estimate for the X-ray flux
using probable counterpart X-ray excesses from the Neil Gehrels Swift
Observatory (aka Swift) (Gehrels et al., 2004). For simplicity, the X-ray
fluxes used in that work assumed an X-ray photon index of $\Gamma_{X}=2$
rather than conducting full spectral fits. Training an ML routine with known
pulsar and blazar samples, the authors identified $173$ likely blazars with
$P_{bzr}>90\%$ ($134$ with $P_{bzr}>99\%$) and $13$ likely pulsars with
$P_{bzr}<10\%$ ($7$ with $P_{bzr}<1\%$). $31$ sources from the 3FGL
unassociated list defied categorization and were labeled ’ambiguous’.

The unassociated sources examined in (Kaur et al., 2019) each had only one
high-S/N X-ray excess in their gamma-ray confidence ellipse, and the majority
of gamma-ray sources are expected to have counterpart X-ray sources. This is
especially true of blazars, which make up the majority of the catalog. In Kaur
et al. (2019) and this work we only examine gamma-ray sources with a single
high-S/N (S/N $>$ 4) X-ray excess in the confidence region, and for the
purposes of classification, we make the initial assumption that this source is
the counterpart. There may be some rare cases in which this X-ray source and
the unassociated gamma-ray source do not correspond to one another, but since
there is no other strong X-ray source in the Fermi error ellipse, this X-ray
source is the most likely counterpart.

In this work, we expand upon ML investigations by conducting detailed X-ray
spectral analysis of 184 possible X-ray counterparts of the unassociated 3FGL
sources. We obtain fully fitted X-ray fluxes and photon flux power law indices
using an absorbed power-law model. Our training and validation sample was
drawn from known lists of Fermi-LAT blazars and pulsars which had data for all
six gamma- and X-ray parameters used in our ML process (Ackermann et al.,
2015; Abdo et al., 2013). Kaur et al. (2019) used a list of 217 unassociated
sources for the test sample. 56 of those 217 objects later were associated
with an astronomical object, so those were removed from consideration in this
paper. Since then new observations have added 26 solitary X-ray excesses with
high $S/N>4$, leading to our initial list of 187 unassociated sources with one
possible X-ray counterpart within the $95\%$ confidence region of the Fermi-
LAT unassociated source. We found three members of the 187 that were spurious
contaminants from optically bright coincident stars creating optical loading
in the Swift-XRT detector, and we excluded these three excesses from all
analysis.

The goal of this work is to conduct X-ray spectral analysis for some possible
counterparts to Fermi-LAT unassociated gamma-ray sources and to begin to build
a multiwavelength classification routine. To this end we update the machine
learning approach in Kaur et al. (2019) to include full X-ray spectral fits
for high-S/N X-ray excesses spatially coincident with the 3FGL unassociated
sources. In section 2, we discuss observations, spectral fitting, and ML
processes used in our analysis, plus we describe the training, test, and
research samples. Next, in section 3 we tabulate and plot parameters for the
various samples and we describe our fitting and classification results. In
section 4 we discuss the classifications in comparison to previous works and
summarize our findings. Tables of spectral fits and ML classification results
are also included.

##  2 Observations and Analysis

###  2.1 Swift-XRT Observations of 3FGL Unassociated Sources

Our sample is based on a collection that initially contained 187 Swift X-ray
counterparts to Fermi-LAT 3FGL sources with high detection Signal-to-Noise
(S/N) ratio ($S/N\geq 4$) and only a single X-ray excess in the $95\%$
confidence region of the 3FGL source. As three of these counterparts (3FGL
J0858.0$-$4843, J1050.6$-$6112, and J1801.5$-$7825) were near bright
($m_{V}<8$) stars, the X-rays from those excesses are almost certainly
spurious products of optical
loading111https://www.swift.ac.uk/analysis/xrt/optical_loading.php in the
Swift-XRT (X-ray Telescope) (Burrows et al., 2005) detector. We excluded these
false detections from all further analysis. Particularly bright stars in the
field of view cause optical loading in the Swift-XRT as optical photons
contaminate the detector and introduce spurious signals. Optical loading is
less likely for dimmer coincident stars.

We used the NASA HEASARC keyword interface to download all Swift-XRT
observations within 8’ of the centroid position of each 3FGL counterpart. Two
more sources (3FGL J1216.6-0557 and 3FGL J0535.7-0617c) were matched with
Swift-XRT observations only upon expanding the HEASARC search radius to 10’.
These sources are positioned close to the edge of the Swift-XRT field of view
in their respective observations.

In total we collected over $500$ individual Swift-XRT observations, capturing
all $184$ members of the unassociated list. All observations used the photon
counting (PC) mode of the Swift-XRT, enabling two-dimensional imaging across
the 23 arcminute XRT field-of-view. Usable Swift-XRT exposure times for most
objects was around 4 ks, but ranged from 1 ks to over 60 ks.

We cleaned and processed each level 1 event file using `xrtpipeline` v.0.13.5
from the HEASOFT software222https://heasarc.gsfc.nasa.gov/docs/software.html,
then merged with other observations of that particular object using `xselect`
v.2.4g and `ximage` v.4.5.1 to create a single summed event list for each
source plus a summed exposure map and ancillary response file using
`xrtmkarf`. For each unassociated 3FGL object, we produced spectra for source
and background regions using `xselect`. The source region was circular with
radius 20 arcseconds, and the background region was annular with inner and
outer radii of 50 and 150 arcseconds. Both regions were centered on the
coordinates of the examined X-ray excess.

If the count rate in the source region of any excess exceeded 0.5 counts per
second, we would draw a new annular source region with an inner radius
depending on the count rate to avoid photon pile-up and saturation on the
detector. The possible X-ray counterparts to 3FGL unassociated sources are
faint enough that none caused pile-ups to warrant annular source regions.
Adding X-ray variability to our spectral and photometric analysis of the X-ray
excesses would certainly be an interesting probe into variability timescales
of blazars and pulsars, but unfortunately most of the sources examined here do
not have X-ray observations spanning a wide enough time range to maintain a
large training, test, or research sample.

Finally, we examined the total number of counts in the source region to
determine whether to use $\chi^{2}$ initial spectral fitting. The Cash
statistic is a useful fitting statistic for spectra with few counts,
particularly in cases for which there are not sufficient counts to group for a
$\chi^{2}$ fit (Cash, 1976). When an excess has enough photons in its source
region, we binned our data with 20 counts per bin to enable an initial
$\chi^{2}$ fit and also prepared a spectrum file for eventual Cash fitting.
For an excess with only a few dozen detected X-ray photons, this approach
would result in only one or two bins, and $\chi^{2}$ fitting would produce
unreliable non-Gaussian distributed fits. The Cash statistic does not require
any such binning, and therefore is often used in fitting faint X-ray spectra
with few counts. While the Cash statistic cannot be directly considered as a
measure of goodness of fit like $\chi^{2}$, a Cash statistic similar to the
degrees of freedom is a rough indicator of a reasonable fit.

###  2.2 Detailed X-ray Spectral Fitting

While the machine learning analysis of the X-ray counterparts to 3FGL
unassociated sources in Kaur et al. (2019) assumed an X-ray photon index
$\Gamma_{X}=2$ to calculate X-ray flux, our archival Swift-XRT observations
facilitated a full spectral fitting. We used `Xspec` v.12.10.1f (Arnaud, 1996)
to fit each spectrum. The fitting model $tbabs\times cflux\times powerlaw$
included three nested functions: `tbabs`, `cflux`, and `powerlaw`. `cflux`
calculated the total unabsorbed flux between 0.3 and 10 keV and `tbabs`
modeled line-of-sight hydrogen extinction using galactic values from the `nH`
lookup function described in Wilms et al. (2000).

The galactic line-of-sight extinction is fixed at the catalog value for each
spectrum analyzed. `powerlaw` is a simple power law. Uncertainties on the
fitted photon index and X-ray flux were jointly measured using the iterative
`steppar` routine; this routine occasionally encountered numerical errors
finding the error of photon indices close to zero. For these spectra we report
the symmetric error generated by `fit`. Spectra with high photon counts were
initially fit using $\chi^{2}$ as the optimization statistic to create first
guesses for Cash statistic fitting.

Comparing the new fitted fluxes to fluxes calculated assuming $\Gamma_{X}=2$
provides a useful sanity check for the X-ray spectral fitting routine. Objects
with fitted $\Gamma_{X}$ close to $2$ should show fitted X-ray fluxes close to
the previous X-ray fluxes that assumed $\Gamma_{X}=2$. Large revisions in
X-ray flux should be reserved for objects with $\Gamma_{X}\neq 2$. Figure 1
shows this reassuring close correspondence for spectra with fitted photon
indices near $\Gamma_{X}=2$, while spectra with fitted indices departing from
$2$ show significantly corrected fluxes. All of the successful spectral fits
are displayed in Table 3.

Of the eleven X-ray excesses whose fully fitted X-ray fluxes differed by more
than an order of magnitude from the X-ray flux assuming $\Gamma_{X}=2$, seven
corresponded to the individually analyze unusual sources described in table 1.
Of the remaining four, one with a fitted index of $\Gamma_{X}=4.4$ saw a drop
in X-ray flux by a factor of ten compared to $\Gamma_{X}=2$ fits. This gamma-
ray source (3FGL J1837.3-2403) has Swift exposures of such duration that at
least four X-ray excesses are expected to spuriously appear in the gamma-ray
confidence region, suggesting that this X-ray source is background. The
remaining three X-ray excesses with radically different fitted fluxes are
three of the five excesses where spectral fitting failed to converge,
described above. Figure 1 excludes only the five excesses with unconverged
X-ray fitting.

After fitting with a power-law model, seven excesses showed unusually high or
low photon indices compared to the photon index of $\Gamma_{X}\sim 2$ expected
for pulsars and blazars. We conducted further analysis on these excesses
listed in Table 1, and they were excluded from the main ML process since they
warranted individual investigation to check for coincident stars or catalog
objects that might explain their spectra. Six of these seven excesses are
located within a few arcseconds of dim catalogued stars in the galactic plane,
possible targets for future observations to verify whether the X-ray radiation
is linked to the star. A quantitative estimate of optical loading for Swift-
XRT showed that none of the seven spectra can have count rates heavily
impacted by optical loading from the nearby star, although one of the seven
could have a minor contribution.

Figure 1: Fully fitted X-ray flux vs X-ray flux assuming $\Gamma_{X}=2$. Points are colored based on fitted X-ray photon index. Black triangles are excesses identified as having extreme X-ray photon indices in table 1. This figure excludes the five excesses for which full X-ray fitting failed to converge. Table 1: Seven of the possible X-ray counterparts to unassociated 3FGL sources with extreme X-ray photon indices, $\Gamma_{X}<-1$ or $\Gamma_{X}>5$, were excluded from the main ML classification effort. We describe the spectrum of the excess and list any notable objects close to the X-ray excess via SIMBAD and NASA Extragalactic Database coordinate searches. Possible stellar counterparts include spectral type and apparent magnitude from SIMBAD if available. 3FGL Source | $\Gamma_{X}$ | Notes
---|---|---
3FGL J0748.8$-$2208 | 6.29 | very few counts, 2” from TYC 5993-3722-1 ($m_{V}=12.4$)
3FGL J0905.6$-$4917 | 7.84 | diffuse in XRT image, listed as ‘confused’ in 4FGL, 3” from 2MASS J09053033-4918382 (M4, $m_{J}=9.5$)
3FGL J1329.8$-$6109 | 6.22 | peaked spectrum, 10” from HD117110 (G0V, $m_{V}=9.2$)
3FGL J1624.1$-$4700 | 7.42 | peaked spectrum, 1” from CD-46 10711 (K1IV rotationally variable star, $m_{V}=11.0$)
3FGL J1710.6$-$4317 | 6.32 | peaked spectrum at 0.9 keV
3FGL J1921.6+1934 | 6.19 | flat spectrum, 2” from HD231222 ($m_{V}=10.8$)
3FGL J2035.8+4902 | 5.82 | peaked spectrum at 0.8 keV, 5” from V* V2552 Cyg (Eclipsing binary, $m_{V}=10.8$)

###  2.3 Machine Learning

While multi-wavelength spectral analysis enables comprehensive study of
individual unassociated gamma-ray sources, the observations and interpretation
of hundreds of such objects would pose an onerous time burden on human
scientists. Fortunately, recent developments in machine learning (ML)
techniques have resulted in numerous applications of ML classification schemes
to Fermi-LAT unassociated source catalogs (Hassan et al., 2012; Saz Parkinson
et al., 2016; McFadden et al., 2017). These developments are part of a wave of
ML techniques promulgating into survey analysis.

In this work, we use a random forest (RF) classifier, an aggregate of many
individual decision tree (DT) realizations, to classify sources into blazars
and pulsars, following a procedure described in Breiman (2001). Our approach
here is nearly identical to that in Kaur et al. (2019), which achieved
$\approx 95\%$ accuracy with a decision tree method and $\sim 99\%$ accuracy
with a random forest approach.

A detailed description of the statistics and theory of decision trees and
random forests can be found in Breiman (2001). In brief, decision tree
classifiers are non-parametric supervised and trained machine learning
methods. DT classifiers discriminate objects between classes by branching
classes one by one at decision nodes, each node judging a single parameter of
an object via an inequality. A tree is optimized using the Gini impurity
index, representing the probability of a randomly selected source from the
dataset being incorrectly labeled at one decision node.

The random forest approach compounds the DT method described above, generating
a forest of decision trees and classifying test objects based on the average
of multiple decision trees (Breiman, 2001). An RF algorithm constructs
numerous decision trees by randomly creating subsamples of the training
dataset. The overall forest also returns the relative importance of the
parameters of the training dataset.

Once the forest is fully trained, a new observation is assigned a
classification probability based on the average of the classifications of each
tree in the forest. Overall, the use of many decision trees in the RF routine
creates a more robust analysis of test objects and prevents overfitting in a
single tree from biasing results. The DT and RF methods used in this paper
utilize the `sklearn` package available in Python.

###  2.4 Training and Test Samples

To train the RF classifier, we gathered a sample of 831 known sources,
including 772 known blazars and 59 known pulsars. The sample was derived from
the 3FGL catalog which provided gamma-ray properties and second Swift X-ray
Point Source Catalog (2SXPS, Evans et al., 2020) which provided X-ray
parameters. In addition, the X-ray properties of known pulsars were obtained
from the literature search of various studies; (e.g., Marelli, 2012; Saz
Parkinson et al., 2016; Wu et al., 2018; Zyuzin et al., 2018). The parameters
for each source included333In this work, $\log{x}$ always refers to the
logarithm in base 10 of $x$.:

  * •

X-ray photon index $\Gamma_{X}$

  * •

Gamma-ray photon index $\Gamma_{\gamma}$

  * •

The logarithm of gamma-ray flux $\log{F_{\gamma}}$

  * •

The logarithm of X-ray to gamma-ray flux ratio $\log{F_{X}/F_{\gamma}}$

  * •

The significance of the curvature in the gamma-ray spectrum (henceforth simply
curvature)

  * •

The gamma-ray variability index

All the X-ray parameters were determined through our analysis as explained
earlier, while the gamma ray parameters were extracted from the 3FGL catalog.
The complete details of the obtained gamma-ray data and the methods are
provided in Kaur et al. (2019).

Because there are many more known blazars than known pulsars in the training
and test samples, we used Synthetic Minority Over-sampling Technique (SMOTE)
(Chawla et al., 2002) to generate synthetic members of the underrepresented
class (pulsars) with a k-nearest neighbors approach. Previous classification
efforts have shown that seriously unbalanced training datasets can lead to
trained RF classifiers that are biased against the underrepresented class
(Last et al., 2017). The result of the SMOTE expansion is a catalog of known
blazars and pulsars, plus artificial pulsars generated with the same
distribution in parameter-space as the real pulsars, producing a catalog with
a balanced number of 772 blazars and 772 pulsars. Expansion of the pulsar
catalog via SMOTE is executed before the catalog is split into training and
validation samples.

To optimize RF parameters such as number of individual trees and maximum tree
depth, we utilized GridSearchCV in sklearn v.0.20.3. We found that 1000
decision trees splitting to a maximum depth of 15 nodes with at least one
source in each leaf were required to effectively train the classifier. The
reported blazar probability for each source is the fraction of the 1000 trees
in which the object was classified as a blazar.

In this paper, we utilized a cross-validation method, cross_val_predict from
sklearn v.0.20.3 by dividing our total sample into 10 folds and then used each
fold (one at a time) as a test sample For the validation step, a RF classifier
is generated using a training subsample. Members of the corresponding test
subsample are then classified as a pulsar or a blazar and the generated
classification is compared to the actual class label. In this way, an accuracy
score for the entire RF tree is generated. This process is repeated ten time,
so ten random forest classifiers are trained and each is validated in turn;
the overall accuracy of the RF classifier is the average of the validation
accuracy of the ten folded iterations. The overall RF accuracy obtained in
this way was 98.5%.

In Kaur et al. (2019), the authors separated 100 sources from the complete set
of blazars and pulsars for a test sample to calculate the accuracy of the
classifier trained on the rest of the data set. This unitary test sample leads
to an accuracy based on only that one test sample. In this way, the reported
RF validation accuracy in that paper measures the same reliability as in this
paper, but with a more restricted approach to selecting a test sample.

Table 2: RF feature importance $\Gamma_{X}$ | $\Gamma_{\gamma}$ | Curvature | Variability index | $\log{F_{\gamma}}$ | $\log{F_{X}/F_{\gamma}}$
---|---|---|---|---|---
0.033 | 0.097 | 0.41 | 0.13 | 0.08 | 0.25

###  2.5 Unassociated Sample

We conducted an initial investigation of our sample by combining gamma-ray
properties from the 3FGL catalog for the 184 unassociated sources with the
X-ray properties derived from the new spectral fits. Comparing the photometric
and spectroscopic properties of the unassociated sample with those of the
known pulsars and blazars, the unassociated sources tend to have lower gamma-
ray fluxes than both the known blazars and pulsars. The mean X-ray fluxes of
the unassociated counterparts fall between the mean fluxes of the known
blazars and known pulsars.

The histograms in Figure 2 show that the unassociated sources most readily
overlap with the known blazar sample, suggesting that the majority of the
unassociated sources should be blazars, consistent with the membership of
known 3FGL sources. Interestingly, the histogram for X-ray photon index (the
plot in the upper-left corner of Figure 2) shows two distinct peaks in the
known 3FGL blazar distribution, with the unassociated source distribution
overlapping primarily with the higher/softer peak.

Figure 2: A full pairs plot of the known blazars (red), known pulsars (blue)
and the unassociated sources (green). Histograms in the diagonal plots are
smoothed and normalized to different scales for each class. The six parameters
are the X-ray photon index, the gamma-ray photon index, the gamma-ray
curvature index, the logarithm of the variability index, the logarithm of the
gamma-ray flux in $\rm{erg/s/cm^{2}}$, and the logarithm of the ratio of X-ray
to gamma-ray flux

Our model incorporating hydrogen-extincted power law spectra returned
unusually high or low $\Gamma_{X}$ for seven sources, as described above and
listed in Table 1. Given that some of these excesses also coincided with
catalogued stars, we view these sources as dubious and do not include these
seven sources in the ML classification.

##  3 Results

Our X-ray spectral fits and RF classification results for the entire 3FGL
unassociated catalog is available at CDS via anonymous ftp to
cdsarc.u-strasbg.fr (130.79.128.5) or via http://cdsarc.u-strasbg.fr/viz-
bin/qcat?J/AJ

The vast majority of examined X-ray excesses obtained well-defined spectral
fits, reported in Table 3. Most of the fits to the X-ray spectra have photon
indices between $0$ and $4$, a similar range to the lists of known pulsars and
blazars used to train the RF routine (as in Figure 2). This supports our first
assumption that most of the unassociated sources are pulsars or blazars. These
fits represent a large collection of X-ray parameters for likely counterparts
to previously unassociated 3FGL gamma-ray sources. Five excesses had very few
X-ray photons after summing up the Swift-XRT observations. With so few
photons, the `xspec` fitting routine could not return useful spectral fits.
For these spectra, we assumed $\Gamma_{X}=2$ to calculate the flux.

The importances of the different parameters in the RF classifier indicate the
features of the gamma- and X-ray spectra that are the strongest predictors of
blazar or pulsar identification. The two most important features are gamma-ray
spectral curvature and $\log{F_{X}/F_{\gamma}}$, shown in Table 2. Figure 1
shows that full X-ray fitting with both flux and photon index as free
parameters did not alter X-ray flux by more than an order of magnitude for the
vast majority of spectra. Some excesses did see a change in X-ray flux by
around a half an order of magnitude; these spectra also showed the largest
alterations in fitted photon index from $\Gamma_{X}\sim 2$. By this measure,
fully fitting X-ray spectra instead of assuming $\Gamma_{X}=2$ can correct
reported X-ray flux by up to an order of magnitude and obtain photon index as
a fitted parameter.

Applying the optimized RF classifier to the 177 fitted unassociated sources
and X-ray excesses (ignoring the seven troublesome spectra discussed above),
we use cross-validated blazar probabilities to categorize the unassociated
sources. In this way, we use each of the ten subfolds used to validate the RF
accuracy and average the blazar probability of each source from each fold. We
identified 5 likely pulsars ($P_{bar}\leq 10\%$) and 126 likely blazars
($P_{bar}\geq 90\%$), with 46 sources remaining ambiguous. The results from
this classification are reported in Table 4.

Figure 3 compares the blazar probability for the RF classification in this
work to the same in Kaur et al. (2019) for the $161$ sources analyzed in both
works, color-coding the points by the fully fitted X-ray photon index. While
the validation accuracy of the new RF classifier is not significantly
different from the approach in Kaur et al. (2019), we have refined the blazar
and pulsar catalogs and introduced new spectral information to all sources by
fully fitting for photon index and X-ray flux. These alterations suggest that
the new $P_{bzr}$ values are more reliable than previous versions,
facilitating the direct comparison in Figure 3.

In general, the addition of fully-fitted X-ray indices and fluxes to the ML
training and test data sets did not alter the RF classifications for most of
the 3FGL unassociated sources, and there is no pattern linking severe
alterations in flux or photon index to previous estimates with drastically
changed blazar probabilities. The most significant change in classifications
were 13 sources previously classified as likely blazars and 3 previously
classified as likely pulsars but here were labeled as ambiguous. Additionally,
7 ambiguous classifications in that previous work were here labeled as likely
blazars. 138 of the 161 shared sources were sorted into the same category in
both approaches.

We did not see any systematic relation that could be attributed as arising
from a one-to-one relationship between altered X-ray photon index and changes
in $P_{bzr}$. While many sources were classified with a similar blazar
probability in this work and in (Kaur et al., 2019), some were classified as
blazars or pulsars with greater confidence with more comprehensive spectral
fits. Figure 3 shows that changes in blazar probability from (Kaur et al.,
2019) to this work occur independently of divergence from coarse estimated
spectral parameters. However, there are three overarching trends diverging
from a one-to-one correspondence in the blazar probabilities shown in figure
3. Some likely blazars became ambiguous (or vice versa), and there is a
general shift among previously more ambiguous sources towards higher blazar
probabilites. That only ambiguous sources saw large systematic shifts towards
higher blazar probabilities is reassurance that the addition of more
comprehensive X-ray spectral fits adds valuable information for discriminating
pulsars and blazars.

The locations of the X-ray counterparts in galactic coordinates are shown in
Figure 4. While the counterparts classified as likely blazars and ambiguous
are scattered roughly uniformly across the sky, the excesses with photon
indices very different from expected for pulsars or blazars are almost
entirely restricted to the galactic plane, which could be an indication that
the X-rays of these excesses may be of galactic origin. If all the unusual
excesses originate in similar astronomical objects, a catalog of gamma- and
X-ray parameters of such objects could be used to add additional class to an
RF scheme. As we do not know of a unified explanation for those excesses and
as there is no previous catalogue of known similar objects it is not feasible
to include sources of this unusual character as a class in the RF classifier
method.

Figure 3: Blazar probability in this work vs in Kaur et al. (2019) for
excesses analyzed in both works. Dotted red and blue vertical and horizontal
lines show $<10\%$ (likely pulsar) and $>90\%$ (likely blazar) categorization
bounds respectively. Points are color-coded by fully fitted X-ray photon
indices with the same scale as figure 1 Figure 4: Galactic coordinates for the
184 X-ray counterparts examined in this work. Points include likely blazars,
likely pulsars, and ambiguous counterparts, as well as the seven sources with
unusual spectral fits. The galactic plane is approximately within the purple
boundaries, and the galactic center is at $(l,b)=(0,0)$.

##  4 Discussion and Conclusions

In this work, we conducted full X-ray spectral analysis of counterparts to
3FGL unassociated sources, obtaining fluxes and photon indices for most of the
X-ray sources examined. The vast majority of the X-ray sources linked to the
3FGL catalog unassociated sources were ably fit by our model, and represent a
significant survey of dim excesses in the X-ray sky. Comprehensive X-ray
spectral fits and the exclusion of unsuitable spectra together increase
confidence in the output of the ML classification.

After training the RF classifier, the feature importances show in Table 2 show
that $log(F_{X}/F_{G})$ was heavily weighted compared to most other parameters
in the ML process, indicating that the ratio is an important discriminator for
discerning blazars from pulsars. With previous estimates for X-ray flux
differing from fully-fit fluxes by a factor of two or more, full X-ray
spectral analysis is an important contribution to ML classification of pulsars
and blazars and to cataloging unassociated source X-ray parameters.

Finally, we compared our classification results to those obtained in previous
work and found that introducing fully fit X-ray parameters achieves the same
RF classification accuracy ($98.5\%$ in this work) as in Kaur et al. (2019)
($99\%$) where the X-ray flux was obtained by assuming $\Gamma_{X}=2$. The new
approach with full X-ray fitting essentially matches the validation accuracy
of the fixed photon index approach in Kaur et al. (2019), fulfilling our goal
of meeting the old validation accuracy as a target. 138 of the 161 sources
examine in both investigations were classified similarly. Including full X-ray
spectral parameters shifts some sources into or out of the “likely blazar”
category and shifts previously ambiguous sources to higher blazar probability.

Figure 3 shows a general increase in blazar probability for previously
ambiguous sources, while likely pulsars did not see an increase in blazar
probability. Given that many of the ambiguous sources are probably blazars,
this trend suggests that the addition of X-ray spectral fits adds valuable
information for discerning pulsars from blazars even if the changes are not
dramatic enough to shift sources to 100% blazar probability.

In conducting full X-ray fitting, we discovered several clearly spurious X-ray
sources. The elimination of these excesses from consideration is an important
step in ensuring a reliable classification method, as those excesses would
otherwise remain in consideration for classification as blazars or pulsars. By
identifying possible stellar or instrumental origins for seven sources with
X-ray photon indices $\Gamma_{X}<-1$ or $\Gamma_{X}>5$, we showed that X-ray
fitting can also sift out excesses that should not be immediately classified
into blazars or pulsars via ML, further increasing the reliability of our
results.

Besides a few sources identified as optical loading contaminants due to nearby
bright stars, we selected seven spectra with X-ray photon indices
significantly divergent from typical theoretical predictions of pulsars and
blazars for further investigation. Because these sources are largely contained
within the galactic plane, it is likely that they originate within the Milky
Way. That six of the seven have a star within a few arcseconds suggests that
some of these excesses may be linked to stellar phenomena and may be
interesting targets for future investigations. Alternatively, the nearby stars
in these seven cases may have been simple coincidences that are unrelated to
the X-ray or gamma-ray source. In one of the seven cases, the stars may be
contributing some optical loading in the Swift-XRT CCD, but for this
$m_{V}=9.2$ star, the optical loading contribution is expected to be a small
fraction relative to the detected X-ray count rate.

Though the requirements for X-ray fits create more stringent observational
requirements in a multiwavelength ML routine compared to using only gamma-ray
observations, incorporating diverse observations increases the comprehensive
capabilities of the ML routine. Assuming $\Gamma_{X}=2$ to obtain X-ray flux
admitted to the ML analysis spectra that now are revealed to be poorly
described by a blazar or pulsar model. These spectra might not actually be
pulsars and blazars, but in assuming an X-ray photon index they might be
incorrectly categorized. These unusual sources can be examined individually
instead of analyzed automatically, allowing for more specialized future
studies.

Even more comprehensive classification could be achieved by including X-ray
variability measurements or expanding analysis into the ultraviolet bands
using the UVOT telescope onboard Swift (Roming et al., 2005). Analysis of
lower-energy photons together with X-ray and gamma-ray photons could also
constrain the location of the synchrotron peak in each spectrum, greatly
increasing the detail in the characterization of the source. It will be
particularly interesting to apply a growing understanding of pulsar and blazar
classification to the results of surveys using new and upcoming space
telescopes such as eROSITA. Whether the characteristics of bright pulsars and
blazars are similar to those of dimmer pulsars and blazars should become
evident as wide-field surveys extend to dimmer magnitudes.

Eventually, after the easier to find blazars and pulsars have been identified,
it may become prudent to expand ML classification beyond a binary choice of
pulsars and blazars. To that end, switching from a RF routine to a more
detailed approach such as guided clustering would dramatically increase the
flexibility of the automated system at the cost of more strenuous supervision
requirements.

## Acknowledgements

We are grateful to the reviewer for their insightful and detailed commentary,
which was vital in improving and focusing this work.

This research has made use of data and/or software provided by the High Energy
Astrophysics Science Archive Research Center (HEASARC), which is a service of
the Astrophysics Science Division at NASA/GSFC. The authors gratefully
acknowledge the support of NASA grants 80NSSC17K0752 and 80NSSC18K1730.

Michael C. Stroh is partially supported by the Heising-Simons Foundation under
grant 2018-0911.

## Catalog

The machine-readable tables corresponding to Tables 3 and 4 are available at
CDS via anonymous ftp to cdsarc.u-strasbg.fr (130.79.128.5) or via
http://cdsarc.u-strasbg.fr/viz-bin/qcat?J/AJ.

Additional thanks is due to the CDS and VizieR teams for facilitating this web
service.

## References

  * Abdo et al. (2013) Abdo, A. A., Ajello, M., Allafort, A., et al. 2013, ApJS, 208, 17, doi: 10.1088/0067-0049/208/2/17
  * Acero et al. (2015) Acero, F., Ackermann, M., Ajello, M., et al. 2015, Astrophysical Journal, Supplement Series, 218, doi: 10.1088/0067-0049/218/2/23
  * Ackermann et al. (2015) Ackermann, M., Ajello, M., Atwood, W. B., et al. 2015, Astrophysical Journal, 810, doi: 10.1088/0004-637X/810/1/14
  * Arnaud (1996) Arnaud, K. A. 1996, ASP Conference Series, 101
  * Breiman (2001) Breiman, L. 2001, 45, 5
  * Burrows et al. (2005) Burrows, D. N., Hill, J. E., Nousek, J. A., et al. 2005, Space Sci. Rev., 120, 165, doi: 10.1007/s11214-005-5097-2
  * Cash (1976) Cash, W. 1976, Astronomy & Astrophysics, 52, 307
  * Chawla et al. (2002) Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. 2002, Journal of Artificial Intelligence Research, 16, 321
  * Evans et al. (2020) Evans, P. A., Page, K. L., Osborne, J. P., et al. 2020, The Astrophysical Journal Supplement Series, 247, 54, doi: 10.3847/1538-4365/ab7db9
  * Ferrara et al. (2015) Ferrara, E. C., Mirabal, N. R., & Fermi-LAT Collaboration. 2015, in American Astronomical Society Meeting Abstracts, Vol. 225, American Astronomical Society Meeting Abstracts #225, 336.02
  * Fossati et al. (1998) Fossati, G., Maraschi, L., Celotti, A., Comastri, A., & Ghisellini, G. 1998, MNRAS, 299, 433, doi: 10.1046/j.1365-8711.1998.01828.x
  * Gehrels et al. (2004) Gehrels, N., Chincarini, G., Giommi, P., et al. 2004, ApJ, 611, 1005, doi: 10.1086/422091
  * Ghisellini et al. (2017) Ghisellini, G., Righi, C., Costamante, L., & Tavecchio, F. 2017, doi: 10.1093/mnras/stx806
  * Hassan et al. (2012) Hassan, T., Mirabal, N., Contreras, J. L., & Oya, I. 2012, Monthly Notices of the Royal Astronomical Society, 428, 220, doi: 10.1093/mnras/sts022
  * Kaur et al. (2019) Kaur, A., Falcone, A. D., Stroh, M. D., Kennea, J. A., & Ferrara, E. C. 2019, doi: 10.3847/1538-4357/ab4ceb
  * Last et al. (2017) Last, F., Douzas, G., & Bacao, F. 2017, arXiv e-prints, arXiv:1711.00837.  https://arxiv.org/abs/1711.00837
  * Li et al. (2018) Li, K.-L., Hou, X., Strader, J., et al. 2018, ApJ, 863, 194, doi: 10.3847/1538-4357/aad243
  * Marelli (2012) Marelli, M. 2012, arXiv e-prints, arXiv:1205.1748.  https://arxiv.org/abs/1205.1748
  * McFadden et al. (2017) McFadden, R., Karastergiou, A., & Roberts, S. 2017, Proceedings of the International Astronomical Union, 13, 372–373, doi: 10.1017/S1743921317009000
  * Roming et al. (2005) Roming, P. W. A., Kennedy, T. E., Mason, K. O., et al. 2005, Space Sci. Rev., 120, 95, doi: 10.1007/s11214-005-5095-4
  * Saz Parkinson et al. (2016) Saz Parkinson, P. M., Xu, H., Yu, P. L. H., et al. 2016, ApJ, 820, 8, doi: 10.3847/0004-637X/820/1/8
  * Wilms et al. (2000) Wilms, J., Allen, A., & Mccray, R. 2000, Astrophysical Journal, 542, 914
  * Wu et al. (2018) Wu, J., Clark, C. J., Pletsch, H. J., et al. 2018, ApJ, 854, 99, doi: 10.3847/1538-4357/aaa411
  * Zyuzin et al. (2018) Zyuzin, D. A., Karpova, A. V., & Shibanov, Y. A. 2018, MNRAS, 476, 2177, doi: 10.1093/mnras/sty359

Table 3: X-ray photon indices and fluxes ($0.3-10.0\>\rm{keV}$) for the 184 X-ray excesses matched with 3FGL unassociated sources used in this paper, including the five spectra unable to be fit by Xspec (marked with dashes) and the seven excluded spectra (boldface). Certain spectra were unable to have asymmetric errors for $\Gamma_{X}$; these results use the default symmetric error and are marked with an asterisk (*). Available at CDS via anonymous ftp to cdsarc.u-strasbg.fr (130.79.128.5) or via http://cdsarc.u-strasbg.fr/viz-bin/qcat?J/AJ Swift X-ray excess | 3FGL gamma-ray source | $\Gamma_{X}$ | $\log_{10}F_{X}$ | Cstat | DOF
---|---|---|---|---|---
SwF3 | 3FGL |  | $\rm{erg/s/cm^{2}}$ |  |
J000805.3+145018 | J0008.3+1456 | $1.60^{0.15}_{0.14}$ | $-11.17^{+0.06}_{-0.05}$ | 82.17 | 89
J003159.9+093615 | J0031.6+0938 | $2.11^{0.51}_{0.52}$ | $-12.41^{+0.17}_{-0.13}$ | 7.73 | 15
J004859.5+422348 | J0049.0+4224 | $2.24^{0.38}_{0.39}$ | $-12.19^{+0.09}_{-0.08}$ | 33.91 | 34
J012152.5$-$391544 | J0121.8$-$3917 | $1.81^{0.08}_{0.08}$ | $-11.45^{+0.03}_{-0.03}$ | 174.26 | 198
J013255.1+593213 | J0133.3+5930 | $2.16^{0.24}_{0.23}$ | $-11.85^{+0.06}_{-0.06}$ | 51.59 | 57
J015624.4$-$242003 | J0156.5$-$2423 | $2.30^{0.14}_{0.14}$ | $-11.90^{+0.04}_{-0.04}$ | 79.58 | 93
J015852.4+010126 | J0158.6+0102 | $1.82^{0.82}_{0.73}$ | $-12.54^{+0.16}_{-0.17}$ | 4.09 | 6
J020020.9$-$410933 | J0200.3$-$4108 | $2.66^{0.24}_{0.24}$ | $-12.19^{+0.06}_{-0.06}$ | 34.45 | 44
J021210.6+532139 | J0212.1+5320 | $1.06^{0.14}_{0.14}$ | $-11.82^{+0.05}_{-0.05}$ | 73.84 | 110
J023854.1+255405 | J0239.0+2555 | $2.17^{0.15}_{0.14}$ | $-11.83^{+0.04}_{-0.04}$ | 79.87 | 93
J024454.9+475117 | J0244.4+4745 | $2.33^{0.50}_{0.50}$ | $-12.68^{+0.10}_{-0.11}$ | 14.22 | 14
J025111.4$-$183115 | J0251.1$-$1829 | $2.01^{0.16}_{0.15}$ | $-12.20^{+0.05}_{-0.05}$ | 68.54 | 82
J025857.4+055243 | J0258.9+0552 | $1.97^{0.30}_{0.29}$ | $-12.22^{+0.08}_{-0.08}$ | 23.23 | 31
J034050.0$-$242259 | J0340.4$-$2423 | $2.27^{0.30}_{0.30}$ | $-12.50^{+0.09}_{-0.09}$ | 18.24 | 27
J034158.1+314851 | J0342.3+3148c | $1.22^{0.16}_{0.16}$ | $-11.59^{+0.05}_{-0.05}$ | 90 | 97
J034819.8+603506 | J0348.4+6039 | $1.42^{0.36}_{0.36}$ | $-11.09^{+0.09}_{-0.09}$ | 28.53 | 25
J035051.2$-$281632 | J0351.0$-$2816 | $1.94^{0.06}_{0.06}$ | $-11.49^{+0.02}_{-0.02}$ | 235.76 | 239
J035939.3+764627 | J0359.7+7649 | $3.10^{0.80}_{0.79}$ | $-12.13^{+0.21}_{-0.18}$ | 3.20 | 7
J041433.2$-$084213 | J0414.9$-$0840 | $2.12^{0.38}_{0.39}$ | $-12.66^{+0.13}_{-0.10}$ | 21.33 | 25
J042011.0$-$601504 | J0420.4$-$6013 | $2.15^{0.10}_{0.10}$ | $-11.69^{+0.03}_{-0.03}$ | 117.35 | 146
J042749.8$-$670434* | J0427.9$-$6704 | $-0.15^{0.19}_{0.19}$ | $-11.78^{+0.06}_{-0.06}$ | 93.03 | 101
J042958.7$-$305931 | J0430.1$-$3103 | $2.67^{0.24}_{0.24}$ | $-12.19^{+0.06}_{-0.06}$ | 41.66 | 42
J043836.9$-$732919 | J0437.7$-$7330 | $2.08^{0.29}_{0.29}$ | $-12.39^{+0.08}_{-0.09}$ | 36.58 | 26
J044722.5$-$253937 | J0447.1$-$2540 | $4.02^{0.93}_{0.75}$ | $-12.80^{+0.15}_{-0.16}$ | 4.52 | 8
J045149.6+572140 | J0451.7+5722 | $2.82^{0.57}_{0.50}$ | $-12.42^{+0.18}_{-0.15}$ | 17.11 | 14
J050650.1+032359 | J0506.9+0321 | $2.61^{0.14}_{0.13}$ | $-12.24^{+0.03}_{-0.03}$ | 96.36 | 117
J051641.4+101243 | J0516.6+1012 | $2.32^{0.35}_{0.34}$ | $-12.40^{+0.08}_{-0.08}$ | 25.04 | 28
J052939.5+382321 | J0529.2+3822 | $1.75^{0.46}_{0.45}$ | $-12.19^{+0.09}_{-0.09}$ | 27.12 | 24
J053357.3$-$375754 | J0533.8$-$3754 | $2.50^{0.61}_{0.58}$ | $-12.39^{+0.13}_{-0.15}$ | 3.40 | 8
J053559.3$-$061624 | J0535.7$-$0617c | $2.58^{0.32}_{0.32}$ | $-11.95^{+0.07}_{-0.07}$ | 28.41 | 34
J055940.6+304232 | J0559.8+3042 | $1.92^{0.81}_{0.83}$ | $-12.36^{+0.22}_{-0.12}$ | 6.97 | 14
J070421.7$-$482645 | J0704.3$-$4828 | $2.49^{0.15}_{0.14}$ | $-12.01^{+0.04}_{-0.04}$ | 78.40 | 99
J071046.2$-$102942 | J0711.1$-$1037 | $1.04^{0.34}_{0.34}$ | $-11.73^{+0.10}_{-0.09}$ | 33.45 | 39
J072547.5$-$054830 | J0725.7$-$0550 | $2.35^{0.16}_{0.16}$ | $-11.63^{+0.04}_{-0.04}$ | 91.01 | 105
J074626.10$-$022551 | J0746.4$-$0225 | $2.41^{0.17}_{0.17}$ | $-11.87^{+0.04}_{-0.05}$ | 76.14 | 79
J074724.8$-$492633 | J0747.5$-$4927 | $2.46^{0.22}_{0.21}$ | $-11.97^{+0.05}_{-0.05}$ | 55.18 | 56
J074903.8$-$221015 | J0748.8$-$2208 | $6.29^{1.86}_{1.76}$ | $-10.59^{+0.80}_{-0.77}$ | 4 | 4
J080215.8$-$094214 | J0802.3$-$0941 | $2.44^{0.24}_{0.23}$ | $-12.20^{+0.06}_{-0.06}$ | 32.19 | 43
J081338.1$-$035717 | J0813.5$-$0356 | $1.80^{0.12}_{0.12}$ | $-11.49^{+0.04}_{-0.04}$ | 142.84 | 125
J082623.6$-$505742 | J0826.3$-$5056 | $1.35^{0.32}_{0.31}$ | $-12.27^{+0.09}_{-0.09}$ | 26.19 | 33
J082628.2$-$640415 | J0826.3$-$6400 | $2.09^{0.05}_{0.05}$ | $-10.75^{+0.01}_{-0.01}$ | 312.05 | 327
J083843.4$-$282701 | J0838.8$-$2829 | $1.75^{0.04}_{0.04}$ | $-11.25^{+0.01}_{-0.01}$ | 374.75 | 464
J084831.8$-$694108 | J0847.2$-$6936 | $1.90^{0.21}_{0.21}$ | $-12.18^{+0.06}_{-0.06}$ | 67.42 | 66
J085505.8$-$481517 | J0855.4$-$4818 | $--\pm--$ | $--^{--}_{--}$ | $-$ | $-$
J090530.4$-$491840 | J0905.6$-$4917 | $7.84^{0.51}_{0.50}$ | $-8.99^{+0.23}_{-0.23}$ | 40.38 | 38
J091926.1$-$220043 | J0919.5$-$2200 | $2.05^{0.20}_{0.20}$ | $-12.49^{+0.06}_{-0.06}$ | 48.26 | 52
J092818.4$-$525659 | J0928.3$-$5255 | $2.98^{0.63}_{0.57}$ | $-11.47^{+0.33}_{-0.24}$ | 25.91 | 19
J093444.6+090355 | J0935.2+0903 | $2.40^{0.79}_{0.81}$ | $-12.34^{+0.22}_{-0.17}$ | 7.13 | 6
J093754.6$-$143349 | J0937.9$-$1435 | $2.42^{0.36}_{0.35}$ | $-12.53^{+0.08}_{-0.09}$ | 16.12 | 24
J095249.5+071329 | J0952.8+0711 | $1.98^{0.24}_{0.23}$ | $-12.16^{+0.08}_{-0.08}$ | 38.43 | 37
J101545.9$-$602938 | J1016.5$-$6034 | $3.08^{0.23}_{0.21}$ | $-11.54^{+0.09}_{-0.08}$ | 146.34 | 104
J102432.6$-$454428 | J1024.4$-$4545 | $2.35^{0.12}_{0.11}$ | $-11.51^{+0.03}_{-0.03}$ | 112.07 | 145
J103332.4$-$503526 | J1033.4$-$5035 | $2.28^{0.18}_{0.17}$ | $-11.75^{+0.04}_{-0.04}$ | 80.52 | 88
J103755.1$-$242546 | J1038.0$-$2425 | $1.24^{0.26}_{0.27}$ | $-12.24^{+0.11}_{-0.11}$ | 34.05 | 31
J103831.1$-$581346 | J1039.1$-$5809 | $4.28^{0.66}_{0.59}$ | $-11.38^{+0.36}_{-0.32}$ | 42.52 | 25
J104939.4+154839 | J1049.7+1548 | $2.62^{0.17}_{0.17}$ | $-12.27^{+0.04}_{-0.04}$ | 68.58 | 73
J105224.5+081409 | J1052.0+0816 | $1.90^{0.15}_{0.14}$ | $-11.62^{+0.05}_{-0.05}$ | 96.27 | 90
J110025.5$-$205333 | J1100.2$-$2044 | $1.01^{0.66}_{0.79}$ | $-12.02^{+0.41}_{-0.30}$ | 14.86 | 11
J110224.1$-$773339 | J1104.3$-$7736c | $2.47^{0.09}_{0.10}$ | $-11.77^{+0.02}_{-0.02}$ | 192.57 | 145
J111601.8$-$484222 | J1116.7$-$4854 | $2.18^{0.41}_{0.41}$ | $-12.58^{+0.08}_{-0.08}$ | 29.45 | 32
J111715.2$-$533815 | J1117.2$-$5338 | $1.97^{0.31}_{0.32}$ | $-12.08^{+0.08}_{-0.08}$ | 28.25 | 35
J111956.10$-$264322 | J1119.8$-$2647 | $1.98^{0.32}_{0.32}$ | $-12.35^{+0.10}_{-0.09}$ | 37.97 | 27
J111958.9$-$220456 | J1119.9$-$2204 | $1.89^{0.18}_{0.17}$ | $-13.10^{+0.06}_{-0.06}$ | 78.43 | 82
J112042.4+071313 | J1120.6+0713 | $1.76^{0.26}_{0.26}$ | $-12.54^{+0.09}_{-0.09}$ | 36.75 | 29
J112504.2$-$580539 | J1125.1$-$5803 | $2.47^{0.26}_{0.25}$ | $-11.66^{+0.07}_{-0.07}$ | 40.45 | 52
J112624.8$-$500806 | J1126.8$-$5001 | $1.67^{0.06}_{0.07}$ | $-11.84^{+0.02}_{-0.02}$ | 248.78 | 282
J113032.7$-$780107 | J1130.7$-$7800 | $1.80^{0.05}_{0.05}$ | $-10.80^{+0.02}_{-0.02}$ | 312.78 | 326
J113209.3$-$473853 | J1132.0$-$4736 | $1.68^{0.07}_{0.07}$ | $-11.17^{+0.02}_{-0.02}$ | 268.25 | 287
J114600.8$-$063851 | J1146.1$-$0640 | $2.12^{0.22}_{0.22}$ | $-12.01^{+0.07}_{-0.06}$ | 49.95 | 51
J114911.10+280719 | J1149.1+2815 | $4.05^{0.77}_{0.62}$ | $-12.47^{+0.16}_{-0.14}$ | 13.54 | 10
J115514.5$-$111125 | J1155.3$-$1112 | $1.78^{0.38}_{0.38}$ | $-12.28^{+0.15}_{-0.13}$ | 27.06 | 26
J120055.1$-$143039 | J1200.9$-$1432 | $1.76^{0.26}_{0.26}$ | $-12.09^{+0.09}_{-0.08}$ | 39.89 | 37
J121553.0$-$060940* | J1216.6$-$0557 | $1.50^{2.12}_{2.12}$ | $-12.37^{+16.37}_{-\infty}$ | 0 | 0
J122014.4$-$245948 | J1220.0$-$2502 | $1.93^{0.11}_{0.11}$ | $-11.54^{+0.03}_{-0.03}$ | 114.51 | 151
J122019.8$-$371414 | J1220.1$-$3715 | $2.05^{0.17}_{0.17}$ | $-11.78^{+0.05}_{-0.05}$ | 78.36 | 72
J122127.4$-$062845 | J1221.5$-$0632 | $1.89^{0.17}_{0.16}$ | $-12.54^{+0.06}_{-0.06}$ | 58.62 | 73
J122257.0+121438 | J1223.2+1215 | $1.89^{0.31}_{0.30}$ | $-13.09^{+0.10}_{-0.10}$ | 31.83 | 31
J122536.7$-$344723 | J1225.4$-$3448 | $2.14^{0.10}_{0.10}$ | $-11.52^{+0.03}_{-0.03}$ | 116.66 | 154
J123140.3+482148 | J1231.6+4825 | $1.97^{0.62}_{0.64}$ | $-12.38^{+0.25}_{-0.16}$ | 16.77 | 13
J123204.2+165527 | J1232.3+1701 | $--\pm--$ | $--^{--}_{--}$ | $-$ | $-$
J123235.9$-$372055 | J1232.5$-$3720 | $2.40^{0.35}_{0.33}$ | $-12.40^{+0.09}_{-0.09}$ | 11.07 | 20
J123447.7$-$043253 | J1234.7$-$0437 | $2.13^{0.39}_{0.38}$ | $-12.58^{+0.12}_{-0.12}$ | 19.29 | 16
J123726.6$-$705140 | J1236.6$-$7050 | $1.67^{0.43}_{0.43}$ | $-12.20^{+0.13}_{-0.11}$ | 21.67 | 20
J124021.3$-$714858 | J1240.3$-$7149 | $1.87^{0.05}_{0.05}$ | $-10.81^{+0.01}_{-0.01}$ | 310.77 | 370
J124919.5$-$280833 | J1249.1$-$2808 | $2.08^{0.07}_{0.07}$ | $-11.46^{+0.02}_{-0.02}$ | 189.35 | 236
J124919.7$-$054540 | J1249.5$-$0546 | $2.91^{0.31}_{0.30}$ | $-12.52^{+0.07}_{-0.08}$ | 20.84 | 31
J125058.4$-$494444 | J1251.0$-$4943 | $2.32^{0.45}_{0.42}$ | $-12.52^{+0.09}_{-0.10}$ | 22.31 | 18
J125821.5+212351 | J1258.4+2123 | $2.33^{0.21}_{0.21}$ | $-12.22^{+0.06}_{-0.06}$ | 43.69 | 48
J130059.5$-$814809 | J1259.3$-$8151 | $1.29^{0.46}_{0.46}$ | $-12.31^{+0.17}_{-0.15}$ | 19.12 | 15
J130128.9+333711 | J1301.5+3333 | $1.59^{0.40}_{0.40}$ | $-12.68^{+0.16}_{-0.15}$ | 17.50 | 16
J130832.0+034406 | J1309.0+0347 | $1.59^{0.28}_{0.27}$ | $-12.13^{+0.11}_{-0.11}$ | 27.09 | 30
J131140.3$-$623313* | J1311.8$-$6230 | $1.02^{1.51}_{1.51}$ | $-12.85^{+0.40}_{-0.34}$ | 1.84 | 2
J131552.8$-$073304 | J1315.7$-$0732 | $2.37^{0.10}_{0.10}$ | $-11.64^{+0.03}_{-0.03}$ | 118.98 | 144
J132928.6$-$053135 | J1329.1$-$0536 | $1.83^{0.13}_{0.13}$ | $-11.60^{+0.05}_{-0.05}$ | 92.98 | 98
J132939.6$-$610735 | J1329.8$-$6109 | $6.22^{0.54}_{0.59}$ | $-10.83^{+0.23}_{-0.26}$ | 26.73 | 22
J140514.7$-$611822 | J1405.4$-$6119 | $-0.12^{0.37}_{0.38}$ | $-12.10^{+0.05}_{-0.05}$ | 129.53 | 128
J141045.2+740504 | J1410.9+7406 | $2.59^{0.53}_{0.53}$ | $-12.79^{+0.12}_{-0.11}$ | 7.23 | 14
J141133.3$-$072256 | J1411.4$-$0724 | $2.24^{0.26}_{0.25}$ | $-12.25^{+0.07}_{-0.07}$ | 30.32 | 35
J142035.9$-$243021 | J1421.0$-$2431 | $4.79^{0.74}_{0.66}$ | $-12.06^{+0.16}_{-0.16}$ | 16.31 | 13
J144544.5$-$593200 | J1445.7$-$5925 | $3.17^{1.07}_{0.96}$ | $-11.20^{+0.63}_{-0.44}$ | 5 | 5
J151150.10+662450 | J1512.3+6622 | $1.74^{0.12}_{0.12}$ | $-11.70^{+0.05}_{-0.04}$ | 126.57 | 109
J151256.6$-$564027* | J1512.8$-$5639 | $-0.35^{0.47}_{0.47}$ | $-11.70^{+0.12}_{-0.12}$ | 34.73 | 28
J151319.0$-$372015 | J1513.3$-$3719 | $2.81^{0.28}_{0.28}$ | $-12.42^{+0.07}_{-0.07}$ | 40.28 | 34
J151649.8+263635 | J1517.0+2637 | $2.20^{0.43}_{0.41}$ | $-12.59^{+0.10}_{-0.11}$ | 16.68 | 16
J152603.0$-$083146 | J1525.8$-$0834 | $--\pm--$ | $--^{--}_{--}$ | $-$ | $-$
J152818.2$-$290256 | J1528.1$-$2904 | $1.72^{0.24}_{0.24}$ | $-12.10^{+0.08}_{-0.08}$ | 36.96 | 47
J154150.1+141441 | J1541.6+1414 | $2.58^{0.44}_{0.37}$ | $-12.53^{+0.10}_{-0.10}$ | 18.66 | 18
J154343.6$-$255607 | J1544.1$-$2555 | $3.10^{0.54}_{0.54}$ | $-12.34^{+0.12}_{-0.11}$ | 10.24 | 15
J154459.2$-$664147 | J1545.0$-$6641 | $2.13^{0.06}_{0.06}$ | $-11.01^{+0.02}_{-0.02}$ | 269.72 | 300
J154946.4$-$304502 | J1549.9$-$3044 | $2.24^{0.16}_{0.16}$ | $-11.80^{+0.04}_{-0.04}$ | 66.66 | 88
J161543.2$-$444921 | J1615.6$-$4450 | $4.36^{0.42}_{0.44}$ | $-11.42^{+0.15}_{-0.16}$ | 44.98 | 23
J162432.2$-$465756 | J1624.1$-$4700 | $7.42^{0.42}_{0.42}$ | $-9.17^{+0.17}_{-0.18}$ | 32.20 | 40
J162437.8$-$423144 | J1624.8$-$4233 | $3.08^{0.65}_{0.53}$ | $-12.09^{+0.22}_{-0.18}$ | 27.59 | 14
J162607.8$-$242736 | J1626.2$-$2428c | $0.87^{0.56}_{0.56}$ | $-12.34^{+0.17}_{-0.16}$ | 8.14 | 12
J162743.0+322102 | J1627.8+3217 | $2.38^{0.20}_{0.19}$ | $-12.47^{+0.05}_{-0.06}$ | 51.73 | 53
J165338.2$-$015837 | J1653.6$-$0158 | $1.29^{0.28}_{0.28}$ | $-12.73^{+0.10}_{-0.10}$ | 38.62 | 37
J170409.6+123423 | J1704.1+1234 | $2.02^{0.10}_{0.09}$ | $-11.50^{+0.03}_{-0.03}$ | 137.89 | 163
J170433.9$-$052840 | J1704.4$-$0528 | $1.91^{0.09}_{0.09}$ | $-11.43^{+0.03}_{-0.03}$ | 144.39 | 197
J170521.6$-$413436 | 1710.6$-$4128c | $--\pm--$ | $--^{--}_{--}$ | $-$ | $-$
J171106.10$-$432415 | J1710.6$-$4317 | $6.32^{0.42}_{0.43}$ | $-10.26^{+0.16}_{-0.18}$ | 40.58 | 39
J172142.1$-$392204 | J1721.8$-$3919 | $1.08^{0.24}_{0.25}$ | $-11.78^{+0.06}_{-0.06}$ | 63.43 | 65
J172858.2+604359 | J1729.0+6049 | $4.79^{1.55}_{1.42}$ | $-11.72^{+0.57}_{-0.45}$ | 16.32 | 7
J173250.5+591233 | J1732.7+5914 | $2.14^{0.30}_{0.29}$ | $-12.36^{+0.08}_{-0.08}$ | 20.59 | 28
J173508.1$-$292955 | J1734.7$-$2930 | $1.94^{0.41}_{0.40}$ | $-11.91^{+0.09}_{-0.09}$ | 23.55 | 23
J174511.10$-$225455 | J1744.7$-$2252 | $0.88^{0.14}_{0.15}$ | $-11.74^{+0.05}_{-0.05}$ | 111.59 | 129
J175316.4$-$444822 | J1753.6$-$4447 | $2.03^{0.40}_{0.41}$ | $-12.58^{+0.10}_{-0.10}$ | 13.72 | 19
J175359.7$-$292909 | J1754.0$-$2930 | $0.83^{0.22}_{0.22}$ | $-11.80^{+0.07}_{-0.07}$ | 71.97 | 74
J180351.7+252607 | J1804.1+2532 | $1.20^{0.35}_{0.34}$ | $-12.39^{+0.13}_{-0.12}$ | 23.03 | 22
J180425.0$-$085002 | J1804.5$-$0850 | $1.48^{0.33}_{0.32}$ | $-12.08^{+0.09}_{-0.09}$ | 23.34 | 31
J181307.6$-$684713 | J1813.6$-$6845 | $2.04^{0.46}_{0.46}$ | $-12.95^{+0.13}_{-0.12}$ | 25.63 | 20
J181720.4$-$303257 | J1817.3$-$3033 | $1.86^{0.13}_{0.13}$ | $-11.80^{+0.04}_{-0.04}$ | 108.11 | 129
J182914.0+272901 | J1829.2+2731 | $1.25^{0.19}_{0.19}$ | $-12.10^{+0.07}_{-0.07}$ | 50.73 | 65
J182915.5+323432 | J1829.2+3229 | $2.46^{0.83}_{0.73}$ | $-12.02^{+0.22}_{-0.18}$ | 4.32 | 6
J183659.5$-$240027 | J1837.3$-$2403 | $1.17^{0.93}_{1.04}$ | $-13.28^{+0.31}_{-0.27}$ | 5.42 | 7
J184433.1$-$034627* | J1844.3$-$0344 | $0.62^{0.64}_{0.64}$ | $-12.64^{+0.12}_{-0.11}$ | 31.22 | 29
J184833.10+323249 | J1848.6+3232 | $0.47^{0.62}_{0.65}$ | $-12.45^{+0.25}_{-0.24}$ | 10.01 | 8
J185520.0+075138 | J1855.6+0753 | $0.84^{0.40}_{0.40}$ | $-12.12^{+0.11}_{-0.10}$ | 43.66 | 26
J185606.6$-$122147 | J1856.1$-$1217 | $2.06^{0.26}_{0.25}$ | $-11.78^{+0.05}_{-0.06}$ | 45.45 | 52
J190444.5$-$070743 | J1904.7$-$0708 | $2.21^{0.35}_{0.34}$ | $-12.23^{+0.08}_{-0.09}$ | 25.28 | 26
J192113.10+194004 | J1921.6+1934 | $6.19^{0.51}_{0.49}$ | $-10.13^{+0.22}_{-0.22}$ | 41.87 | 30
J192242.1$-$745354 | J1923.2$-$7452 | $2.25^{0.09}_{0.09}$ | $-11.44^{+0.03}_{-0.03}$ | 167.01 | 174
J193420.1+600138 | J1934.2+6002 | $2.51^{0.27}_{0.27}$ | $-12.11^{+0.06}_{-0.06}$ | 33.01 | 45
J194633.6$-$540235 | J1946.4$-$5403 | $1.69^{0.24}_{0.24}$ | $-12.68^{+0.09}_{-0.09}$ | 39.86 | 39
J195149.7+690719 | J1951.3+6909 | $2.65^{0.47}_{0.45}$ | $-12.33^{+0.09}_{-0.09}$ | 16.30 | 19
J195800.3+243803 | J1958.1+2436 | $2.22^{0.31}_{0.31}$ | $-11.43^{+0.09}_{-0.07}$ | 33.81 | 48
J200635.7+015222 | J2006.6+0150 | $1.82^{0.32}_{0.32}$ | $-12.40^{+0.11}_{-0.10}$ | 28.78 | 22
J201020.3$-$212434 | J2010.0$-$2120 | $1.50^{0.50}_{0.52}$ | $-12.14^{+0.21}_{-0.18}$ | 14.71 | 11
J201525.3$-$143204 | J2015.3$-$1431 | $3.15^{0.35}_{0.34}$ | $-12.28^{+0.08}_{-0.08}$ | 19.68 | 28
J203027.9$-$143919 | J2030.5$-$1439 | $1.51^{0.32}_{0.30}$ | $-12.19^{+0.12}_{-0.12}$ | 21.77 | 31
J203450.9$-$420038 | J2034.6$-$4202 | $2.59^{0.12}_{0.12}$ | $-11.84^{+0.03}_{-0.03}$ | 108.50 | 121
J203556.9+490038 | J2035.8+4902 | $5.82^{0.61}_{0.63}$ | $-10.70^{+0.24}_{-0.26}$ | 21.96 | 21
J203935.8+123001 | J2039.7+1237 | $--\pm--$ | $--^{--}_{--}$ | $-$ | $-$
J204351.5+103407 | J2044.0+1035 | $2.51^{0.33}_{0.33}$ | $-12.34^{+0.08}_{-0.08}$ | 21.71 | 28
J204806.3$-$312011 | J2047.9$-$3119 | $2.21^{0.28}_{0.28}$ | $-12.23^{+0.07}_{-0.07}$ | 28.19 | 38
J205350.8+292312 | J2053.9+2922 | $2.24^{0.09}_{0.09}$ | $-10.93^{+0.02}_{-0.02}$ | 191.26 | 194
J205357.9+690517 | J2054.3+6907 | $0.54^{0.31}_{0.32}$ | $-12.66^{+0.11}_{-0.12}$ | 38.95 | 32
J205950.4+202905 | J2059.9+2029 | $2.22^{0.35}_{0.34}$ | $-12.28^{+0.08}_{-0.09}$ | 26.63 | 23
J210940.0+043958 | J2110.0+0442 | $1.99^{0.25}_{0.24}$ | $-11.94^{+0.07}_{-0.07}$ | 36.05 | 39
J211522.2+121801 | J2115.2+1215 | $3.18^{0.47}_{0.45}$ | $-12.38^{+0.12}_{-0.11}$ | 8.35 | 18
J212051.6$-$125300 | J2120.4$-$1256 | $0.66^{0.39}_{0.41}$ | $-12.57^{+0.16}_{-0.16}$ | 7.79 | 17
J212601.5+583148 | J2125.8+5832 | $1.81^{0.36}_{0.35}$ | $-12.08^{+0.08}_{-0.08}$ | 35.73 | 26
J212729.3$-$600102 | J2127.5$-$6001 | $1.24^{0.15}_{0.13}$ | $-11.48^{+0.05}_{-0.06}$ | 131.95 | 94
J214247.5+195811 | J2142.7+1957 | $2.38^{0.22}_{0.22}$ | $-11.83^{+0.05}_{-0.05}$ | 64.31 | 60
J214429.5$-$563849 | J2144.6$-$5640 | $2.04^{0.25}_{0.24}$ | $-12.27^{+0.08}_{-0.08}$ | 35.85 | 38
J215046.5$-$174956 | J2150.5$-$1754 | $3.50^{1.06}_{0.75}$ | $-12.38^{+0.19}_{-0.16}$ | 13.51 | 9
J215122.10+415634 | J2151.6+4154 | $2.58^{0.24}_{0.23}$ | $-11.61^{+0.06}_{-0.06}$ | 42.19 | 63
J220941.4$-$045108 | J2209.8$-$0450 | $2.46^{0.49}_{0.51}$ | $-12.53^{+0.10}_{-0.11}$ | 12.92 | 13
J222911.2+225456 | J2229.1+2255 | $2.14^{0.07}_{0.07}$ | $-11.24^{+0.02}_{-0.02}$ | 191.20 | 215
J224437.0+250344 | J2244.6+2503 | $2.87^{0.36}_{0.34}$ | $-12.42^{+0.08}_{-0.09}$ | 39.92 | 24
J224710.1$-$000512* | J2247.2$-$0004 | $0.30^{0.60}_{0.60}$ | $-12.57^{+0.33}_{-0.30}$ | 13.32 | 8
J225003.5$-$594520 | J2249.3$-$5943 | $1.71^{0.41}_{0.42}$ | $-12.40^{+0.17}_{-0.14}$ | 13.36 | 18
J225032.7+174918 | J2250.3+1747 | $1.26^{0.42}_{0.43}$ | $-12.64^{+0.18}_{-0.17}$ | 14.05 | 13
J225045.7+330514 | J2250.6+3308 | $1.63^{0.65}_{0.68}$ | $-12.42^{+0.28}_{-0.19}$ | 10.01 | 12
J230012.4+405223 | J2300.0+4053 | $1.99^{0.09}_{0.09}$ | $-11.20^{+0.03}_{-0.03}$ | 135.26 | 181
J230351.7+555617 | J2303.7+5555 | $1.84^{0.17}_{0.17}$ | $-11.53^{+0.04}_{-0.04}$ | 77.52 | 99
J230848.5+542612 | J2309.0+5428 | $2.54^{0.27}_{0.26}$ | $-12.19^{+0.07}_{-0.07}$ | 47.02 | 46
J232127.1+511117 | J2321.3+5113 | $2.55^{0.40}_{0.40}$ | $-12.22^{+0.09}_{-0.09}$ | 27.99 | 24
J232137.1$-$161926 | J2321.6$-$1619 | $2.45^{0.12}_{0.12}$ | $-11.61^{+0.03}_{-0.03}$ | 101.96 | 119
J232653.3$-$412713 | J2327.2$-$4130 | $1.97^{0.41}_{0.39}$ | $-12.48^{+0.12}_{-0.12}$ | 19.61 | 16
J232938.7+610111 | J2329.8+6102 | $2.62^{0.18}_{0.18}$ | $-11.28^{+0.06}_{-0.06}$ | 94.22 | 120
J233626.4$-$842649 | J2337.2$-$8425 | $2^{0.24}_{0.24}$ | $-12.12^{+0.07}_{-0.07}$ | 35.91 | 47
J235115.9$-$760017 | J2351.9$-$7601 | $1.99^{0.21}_{0.20}$ | $-12.03^{+0.06}_{-0.06}$ | 56.64 | 51
J235824.10+382857 | J2358.5+3827 | $2.15^{0.15}_{0.15}$ | $-11.68^{+0.04}_{-0.04}$ | 86 | 96
J235836.8$-$180717 | J2358.6$-$1809 | $2.57^{0.10}_{0.10}$ | $-11.55^{+0.03}_{-0.03}$ | 125.82 | 135
Table 4: RF classification results for the unassociated sources and paired excesses. We used the grid-search optimized RF parameters to determine the blazar probabilities. Available at CDS via anonymous ftp to cdsarc.u-strasbg.fr (130.79.128.5) or via http://cdsarc.u-strasbg.fr/viz-bin/qcat?J/AJ Swift X-ray excess | 3FGL gamma-ray source | X-ray excess RA | X-ray excess Decl | Blazar Prob. | Categorization
---|---|---|---|---|---
SwF3 | 3FGL | (J2000) | (J2000) | $P_{bzr}$ |
J000805.3+145018 | J0008.3+1456 | 00 08 05.27 | +14 50 18.5 | 0.976 | blazar
J003159.9+093615 | J0031.6+0938 | 00 31 59.89 | +09 36 15.9 | 0.979 | blazar
J004859.5+422348 | J0049.0+4224 | 00 48 59.45 | +42 23 48.8 | 0.990 | blazar
J012152.5$-$391544 | J0121.8$-$3917 | 01 21 52.53 |  $-$39 15 44.7 | 0.943 | blazar
J013255.1+593213 | J0133.3+5930 | 01 32 55.11 | +59 32 13.3 | 0.999 | blazar
J015624.4$-$242003 | J0156.5$-$2423 | 01 56 24.44 |  $-$24 20 03.4 | 1.000 | blazar
J015852.4+010126 | J0158.6+0102 | 01 58 52.44 | +01 01 26.9 | 0.988 | blazar
J020020.9$-$410933 | J0200.3$-$4108 | 02 00 20.90 |  $-$41 09 33.9 | 1.000 | blazar
J021210.6+532139 | J0212.1+5320 | 02 12 10.55 | +53 21 39.6 | 0.081 | pulsar
J023854.1+255405 | J0239.0+2555 | 02 38 54.11 | +25 54 05.6 | 0.991 | blazar
J024454.9+475117 | J0244.4+4745 | 02 44 54.93 | +47 51 17.5 | 0.817 |
J025111.4$-$183115 | J0251.1$-$1829 | 02 51 11.39 |  $-$18 31 15.1 | 0.966 | blazar
J025857.4+055243 | J0258.9+0552 | 02 58 57.44 | +05 52 43.7 | 0.996 | blazar
J034050.0$-$242259 | J0340.4$-$2423 | 03 40 50.02 |  $-$24 22 59.2 | 0.995 | blazar
J034158.1+314851 | J0342.3+3148c | 03 41 58.10 | +31 48 51.7 | 0.841 |
J034819.8+603506 | J0348.4+6039 | 03 48 19.77 | +60 35 06.6 | 0.986 | blazar
J035051.2$-$281632 | J0351.0$-$2816 | 03 50 51.24 |  $-$28 16 32.6 | 0.998 | blazar
J035939.3+764627 | J0359.7+7649 | 03 59 39.29 | +76 46 27.5 | 0.999 | blazar
J041433.2$-$084213 | J0414.9$-$0840 | 04 14 33.20 |  $-$08 42 13.2 | 0.997 | blazar
J042011.0$-$601504 | J0420.4$-$6013 | 04 20 11.02 |  $-$60 15 04.8 | 0.998 | blazar
J042749.8$-$670434 | J0427.9$-$6704 | 04 27 49.76 |  $-$67 04 34.8 | 0.772 |
J042958.7$-$305931 | J0430.1$-$3103 | 04 29 58.74 |  $-$30 59 31.7 | 0.989 | blazar
J043836.9$-$732919 | J0437.7$-$7330 | 04 38 36.85 |  $-$73 29 19.9 | 0.994 | blazar
J044722.5$-$253937 | J0447.1$-$2540 | 04 47 22.52 |  $-$25 39 37.4 | 0.998 | blazar
J045149.6+572140 | J0451.7+5722 | 04 51 49.56 | +57 21 40.6 | 0.993 | blazar
J050650.1+032359 | J0506.9+0321 | 05 06 50.08 | +03 23 59.6 | 0.999 | blazar
J051641.4+101243 | J0516.6+1012 | 05 16 41.44 | +10 12 43.4 | 0.984 | blazar
J052939.5+382321 | J0529.2+3822 | 05 29 39.54 | +38 23 21.4 | 0.777 |
J053357.3$-$375754 | J0533.8$-$3754 | 05 33 57.33 |  $-$37 57 54.6 | 0.920 | blazar
J053559.3$-$061624 | J0535.7$-$0617c | 05 35 59.28 |  $-$06 16 24.1 | 0.999 | blazar
J055940.6+304232 | J0559.8+3042 | 05 59 40.58 | +30 42 32.7 | 0.985 | blazar
J070421.7$-$482645 | J0704.3$-$4828 | 07 04 21.74 |  $-$48 26 45.3 | 0.990 | blazar
J071046.2$-$102942 | J0711.1$-$1037 | 07 10 46.18 |  $-$10 29 42.2 | 0.437 |
J072547.5$-$054830 | J0725.7$-$0550 | 07 25 47.51 |  $-$05 48 30.3 | 0.997 | blazar
J074626.10$-$022551 | J0746.4$-$0225 | 07 46 26.98 |  $-$02 25 51.8 | 0.965 | blazar
J074724.8$-$492633 | J0747.5$-$4927 | 07 47 24.78 |  $-$49 26 33.9 | 0.999 | blazar
J080215.8$-$094214 | J0802.3$-$0941 | 08 02 15.78 |  $-$09 42 14.0 | 0.998 | blazar
J081338.1$-$035717 | J0813.5$-$0356 | 08 13 38.10 |  $-$03 57 17.1 | 0.968 | blazar
J082623.6$-$505742 | J0826.3$-$5056 | 08 26 23.55 |  $-$50 57 42.5 | 0.806 |
J082628.2$-$640415 | J0826.3$-$6400 | 08 26 28.16 |  $-$64 04 15.5 | 0.941 | blazar
J083843.4$-$282701 | J0838.8$-$2829 | 08 38 43.37 |  $-$28 27 01.6 | 0.719 |
J084831.8$-$694108 | J0847.2$-$6936 | 08 48 31.82 |  $-$69 41 08.9 | 0.994 | blazar
J085505.8$-$481517 | J0855.4$-$4818 | 08 55 05.78 |  $-$48 15 17.9 | 0.130 |
J091926.1$-$220043 | J0919.5$-$2200 | 09 19 26.09 |  $-$22 00 43.6 | 0.995 | blazar
J092818.4$-$525659 | J0928.3$-$5255 | 09 28 18.40 |  $-$52 56 59.7 | 0.980 | blazar
J093444.6+090355 | J0935.2+0903 | 09 34 44.60 | +09 03 55.8 | 0.807 |
J093754.6$-$143349 | J0937.9$-$1435 | 09 37 54.55 |  $-$14 33 49.1 | 0.999 | blazar
J095249.5+071329 | J0952.8+0711 | 09 52 49.52 | +07 13 29.6 | 0.996 | blazar
J101545.9$-$602938 | J1016.5$-$6034 | 10 15 45.85 |  $-$60 29 38.1 | 0.213 |
J102432.6$-$454428 | J1024.4$-$4545 | 10 24 32.56 |  $-$45 44 28.5 | 1.000 | blazar
J103332.4$-$503526 | J1033.4$-$5035 | 10 33 32.38 |  $-$50 35 26.7 | 1.000 | blazar
J103755.1$-$242546 | J1038.0$-$2425 | 10 37 55.09 |  $-$24 25 46.0 | 0.981 | blazar
J103831.1$-$581346 | J1039.1$-$5809 | 10 38 31.12 |  $-$58 13 46.6 | 0.497 |
J104939.4+154839 | J1049.7+1548 | 10 49 39.44 | +15 48 39.0 | 0.973 | blazar
J105224.5+081409 | J1052.0+0816 | 10 52 24.46 | +08 14 09.5 | 0.994 | blazar
J110025.5$-$205333 | J1100.2$-$2044 | 11 00 25.46 |  $-$20 53 33.1 | 0.906 | blazar
J110224.1$-$773339 | J1104.3$-$7736c | 11 02 24.13 |  $-$77 33 39.9 | 0.916 | blazar
J111601.8$-$484222 | J1116.7$-$4854 | 11 16 01.82 |  $-$48 42 22.6 | 0.966 | blazar
J111715.2$-$533815 | J1117.2$-$5338 | 11 17 15.15 |  $-$53 38 15.3 | 0.997 | blazar
J111956.10$-$264322 | J1119.8$-$2647 | 11 19 56.96 |  $-$26 43 22.2 | 0.995 | blazar
J111958.9$-$220456 | J1119.9$-$2204 | 11 19 58.93 |  $-$22 04 56.6 | 0.032 | pulsar
J112042.4+071313 | J1120.6+0713 | 11 20 42.35 | +07 13 13.1 | 0.337 |
J112504.2$-$580539 | J1125.1$-$5803 | 11 25 04.21 |  $-$58 05 39.6 | 1.000 | blazar
J112624.8$-$500806 | J1126.8$-$5001 | 11 26 24.82 |  $-$50 08 06.8 | 0.991 | blazar
J113032.7$-$780107 | J1130.7$-$7800 | 11 30 32.65 |  $-$78 01 07.5 | 0.902 | blazar
J113209.3$-$473853 | J1132.0$-$4736 | 11 32 09.31 |  $-$47 38 53.8 | 0.981 | blazar
J114600.8$-$063851 | J1146.1$-$0640 | 11 46 00.77 |  $-$06 38 51.0 | 0.998 | blazar
J114911.10+280719 | J1149.1+2815 | 11 49 11.98 | +28 07 19.9 | 0.985 | blazar
J115514.5$-$111125 | J1155.3$-$1112 | 11 55 14.54 |  $-$11 11 25.3 | 0.896 |
J120055.1$-$143039 | J1200.9$-$1432 | 12 00 55.07 |  $-$14 30 39.0 | 0.992 | blazar
J121553.0$-$060940 | J1216.6$-$0557 | 12 15 53.04 |  $-$06 09 40.6 | 0.988 | blazar
J122014.4$-$245948 | J1220.0$-$2502 | 12 20 14.43 |  $-$24 59 48.0 | 0.994 | blazar
J122019.8$-$371414 | J1220.1$-$3715 | 12 20 19.82 |  $-$37 14 14.1 | 0.999 | blazar
J122127.4$-$062845 | J1221.5$-$0632 | 12 21 27.38 |  $-$06 28 45.9 | 0.989 | blazar
J122257.0+121438 | J1223.2+1215 | 12 22 57.04 | +12 14 38.5 | 0.986 | blazar
J122536.7$-$344723 | J1225.4$-$3448 | 12 25 36.66 |  $-$34 47 23.8 | 0.999 | blazar
J123140.3+482148 | J1231.6+4825 | 12 31 40.28 | +48 21 48.9 | 0.993 | blazar
J123204.2+165527 | J1232.3+1701 | 12 32 04.22 | +16 55 27.7 | 0.776 |
J123235.9$-$372055 | J1232.5$-$3720 | 12 32 35.87 |  $-$37 20 55.8 | 0.992 | blazar
J123447.7$-$043253 | J1234.7$-$0437 | 12 34 47.69 |  $-$04 32 53.9 | 0.976 | blazar
J123726.6$-$705140 | J1236.6$-$7050 | 12 37 26.61 |  $-$70 51 40.1 | 0.996 | blazar
J124021.3$-$714858 | J1240.3$-$7149 | 12 40 21.34 |  $-$71 48 58.3 | 0.973 | blazar
J124919.5$-$280833 | J1249.1$-$2808 | 12 49 19.46 |  $-$28 08 33.5 | 0.999 | blazar
J124919.7$-$054540 | J1249.5$-$0546 | 12 49 19.69 |  $-$05 45 40.2 | 1.000 | blazar
J125058.4$-$494444 | J1251.0$-$4943 | 12 50 58.43 |  $-$49 44 44.3 | 0.998 | blazar
J125821.5+212351 | J1258.4+2123 | 12 58 21.49 | +21 23 51.7 | 0.865 |
J130059.5$-$814809 | J1259.3$-$8151 | 13 00 59.45 |  $-$81 48 09.6 | 0.992 | blazar
J130128.9+333711 | J1301.5+3333 | 13 01 28.85 | +33 37 11.0 | 0.793 |
J130832.0+034406 | J1309.0+0347 | 13 08 32.00 | +03 44 06.9 | 0.779 |
J131140.3$-$623313 | J1311.8$-$6230 | 13 11 40.25 |  $-$62 33 13.9 | 0.577 |
J131552.8$-$073304 | J1315.7$-$0732 | 13 15 52.84 |  $-$07 33 04.0 | 0.998 | blazar
J132928.6$-$053135 | J1329.1$-$0536 | 13 29 28.56 |  $-$05 31 35.4 | 0.995 | blazar
J140514.7$-$611822 | J1405.4$-$6119 | 14 05 14.67 |  $-$61 18 22.7 | 0.113 |
J141045.2+740504 | J1410.9+7406 | 14 10 45.24 | +74 05 04.8 | 0.849 |
J141133.3$-$072256 | J1411.4$-$0724 | 14 11 33.30 |  $-$07 22 56.3 | 0.996 | blazar
J142035.9$-$243021 | J1421.0$-$2431 | 14 20 35.85 |  $-$24 30 21.9 | 0.901 | blazar
J144544.5$-$593200 | J1445.7$-$5925 | 14 45 44.49 |  $-$59 32 00.2 | 0.996 | blazar
J151150.10+662450 | J1512.3+6622 | 15 11 50.95 | +66 24 50.1 | 0.988 | blazar
J151256.6$-$564027 | J1512.8$-$5639 | 15 12 56.57 |  $-$56 40 27.3 | 0.865 |
J151319.0$-$372015 | J1513.3$-$3719 | 15 13 19.03 |  $-$37 20 15.0 | 0.977 | blazar
J151649.8+263635 | J1517.0+2637 | 15 16 49.82 | +26 36 35.2 | 0.995 | blazar
J152603.0$-$083146 | J1525.8$-$0834 | 15 26 03.03 |  $-$08 31 46.2 | 0.785 |
J152818.2$-$290256 | J1528.1$-$2904 | 15 28 18.18 |  $-$29 02 56.8 | 0.981 | blazar
J154150.1+141441 | J1541.6+1414 | 15 41 50.12 | +14 14 41.2 | 0.983 | blazar
J154343.6$-$255607 | J1544.1$-$2555 | 15 43 43.60 |  $-$25 56 07.6 | 0.804 |
J154459.2$-$664147 | J1545.0$-$6641 | 15 44 59.17 |  $-$66 41 47.8 | 0.991 | blazar
J154946.4$-$304502 | J1549.9$-$3044 | 15 49 46.41 |  $-$30 45 02.3 | 0.883 |
J161543.2$-$444921 | J1615.6$-$4450 | 16 15 43.18 |  $-$44 49 21.1 | 0.966 | blazar
J162437.8$-$423144 | J1624.8$-$4233 | 16 24 37.81 |  $-$42 31 44.3 | 0.986 | blazar
J162607.8$-$242736 | J1626.2$-$2428c | 16 26 07.79 |  $-$24 27 36.2 | 0.330 |
J162743.0+322102 | J1627.8+3217 | 16 27 43.00 | +32 21 02.4 | 0.776 |
J165338.2$-$015837 | J1653.6$-$0158 | 16 53 38.15 |  $-$01 58 37.0 | 0.045 | pulsar
J170409.6+123423 | J1704.1+1234 | 17 04 09.60 | +12 34 23.0 | 0.977 | blazar
J170433.9$-$052840 | J1704.4$-$0528 | 17 04 33.91 |  $-$05 28 40.7 | 0.986 | blazar
J170521.6$-$413436 | J1705.5$-$4128c | 17 05 21.56 |  $-$41 34 36.9 | 0.749 |
J172142.1$-$392204 | J1721.8$-$3919 | 17 21 42.13 |  $-$39 22 04.7 | 0.929 | blazar
J172858.2+604359 | J1729.0+6049 | 17 28 58.17 | +60 43 59.9 | 0.995 | blazar
J173250.5+591233 | J1732.7+5914 | 17 32 50.51 | +59 12 33.8 | 0.999 | blazar
J173508.1$-$292955 | J1734.7$-$2930 | 17 35 08.07 |  $-$29 29 55.4 | 0.496 |
J174511.10$-$225455 | J1744.7$-$2252 | 17 45 11.98 |  $-$22 54 55.7 | 0.177 |
J175316.4$-$444822 | J1753.6$-$4447 | 17 53 16.43 |  $-$44 48 22.0 | 0.668 |
J175359.7$-$292909 | J1754.0$-$2930 | 17 53 59.69 |  $-$29 29 09.0 | 0.165 |
J180351.7+252607 | J1804.1+2532 | 18 03 51.72 | +25 26 07.0 | 0.780 |
J180425.0$-$085002 | J1804.5$-$0850 | 18 04 25.02 |  $-$08 50 02.5 | 0.966 | blazar
J181307.6$-$684713 | J1813.6$-$6845 | 18 13 07.62 |  $-$68 47 13.2 | 0.909 | blazar
J181720.4$-$303257 | J1817.3$-$3033 | 18 17 20.35 |  $-$30 32 57.8 | 0.985 | blazar
J182914.0+272901 | J1829.2+2731 | 18 29 14.00 | +27 29 01.8 | 0.713 |
J182915.5+323432 | J1829.2+3229 | 18 29 15.50 | +32 34 32.4 | 0.851 |
J183659.5$-$240027 | J1837.3$-$2403 | 18 36 59.46 |  $-$24 00 27.9 | 0.455 |
J184433.1$-$034627 | J1844.3$-$0344 | 18 44 33.14 |  $-$03 46 27.4 | 0.020 | pulsar
J184833.10+323249 | J1848.6+3232 | 18 48 33.96 | +32 32 49.6 | 0.503 |
J185520.0+075138 | J1855.6+0753 | 18 55 20.02 | +07 51 38.6 | 0.747 |
J185606.6$-$122147 | J1856.1$-$1217 | 18 56 06.60 |  $-$12 21 47.7 | 0.894 |
J190444.5$-$070743 | J1904.7$-$0708 | 19 04 44.53 |  $-$07 07 43.1 | 0.968 | blazar
J192242.1$-$745354 | J1923.2$-$7452 | 19 22 42.10 |  $-$74 53 54.9 | 0.980 | blazar
J193420.1+600138 | J1934.2+6002 | 19 34 20.08 | +60 01 38.2 | 0.999 | blazar
J194633.6$-$540235 | J1946.4$-$5403 | 19 46 33.57 |  $-$54 02 35.1 | 0.042 | pulsar
J195149.7+690719 | J1951.3+6909 | 19 51 49.66 | +69 07 19.2 | 0.971 | blazar
J195800.3+243803 | J1958.1+2436 | 19 58 00.28 | +24 38 03.8 | 0.997 | blazar
J200635.7+015222 | J2006.6+0150 | 20 06 35.73 | +01 52 22.4 | 0.861 |
J201020.3$-$212434 | J2010.0$-$2120 | 20 10 20.25 |  $-$21 24 34.2 | 0.737 |
J201525.3$-$143204 | J2015.3$-$1431 | 20 15 25.25 |  $-$14 32 04.7 | 0.999 | blazar
J203027.9$-$143919 | J2030.5$-$1439 | 20 30 27.92 |  $-$14 39 19.0 | 0.997 | blazar
J203450.9$-$420038 | J2034.6$-$4202 | 20 34 50.88 |  $-$42 00 38.1 | 0.988 | blazar
J203935.8+123001 | J2039.7+1237 | 20 39 35.76 | +12 30 01.8 | 0.719 |
J204351.5+103407 | J2044.0+1035 | 20 43 51.54 | +10 34 07.7 | 0.921 | blazar
J204806.3$-$312011 | J2047.9$-$3119 | 20 48 06.25 |  $-$31 20 11.5 | 0.901 | blazar
J205350.8+292312 | J2053.9+2922 | 20 53 50.77 | +29 23 12.4 | 0.914 | blazar
J205357.9+690517 | J2054.3+6907 | 20 53 57.94 | +69 05 17.7 | 0.787 |
J205950.4+202905 | J2059.9+2029 | 20 59 50.44 | +20 29 05.0 | 0.984 | blazar
J210940.0+043958 | J2110.0+0442 | 21 09 40.04 | +04 39 58.3 | 1.000 | blazar
J211522.2+121801 | J2115.2+1215 | 21 15 22.17 | +12 18 01.7 | 1.000 | blazar
J212051.6$-$125300 | J2120.4$-$1256 | 21 20 51.55 |  $-$12 53 00.4 | 0.851 |
J212601.5+583148 | J2125.8+5832 | 21 26 01.49 | +58 31 48.3 | 0.630 |
J212729.3$-$600102 | J2127.5$-$6001 | 21 27 29.30 |  $-$60 01 02.1 | 0.970 | blazar
J214247.5+195811 | J2142.7+1957 | 21 42 47.47 | +19 58 11.9 | 1.000 | blazar
J214429.5$-$563849 | J2144.6$-$5640 | 21 44 29.48 |  $-$56 38 49.7 | 0.918 | blazar
J215046.5$-$174956 | J2150.5$-$1754 | 21 50 46.46 |  $-$17 49 56.0 | 0.913 | blazar
J215122.10+415634 | J2151.6+4154 | 21 51 22.99 | +41 56 34.5 | 0.999 | blazar
J220941.4$-$045108 | J2209.8$-$0450 | 22 09 41.35 |  $-$04 51 08.2 | 0.933 | blazar
J222911.2+225456 | J2229.1+2255 | 22 29 11.17 | +22 54 56.1 | 0.998 | blazar
J224437.0+250344 | J2244.6+2503 | 22 44 37.00 | +25 03 44.3 | 0.930 | blazar
J224710.1$-$000512 | J2247.2$-$0004 | 22 47 10.11 |  $-$00 05 12.1 | 0.488 |
J225003.5$-$594520 | J2249.3$-$5943 | 22 50 03.45 |  $-$59 45 20.4 | 0.993 | blazar
J225032.7+174918 | J2250.3+1747 | 22 50 32.71 | +17 49 18.4 | 0.980 | blazar
J225045.7+330514 | J2250.6+3308 | 22 50 45.65 | +33 05 14.8 | 0.647 |
J230012.4+405223 | J2300.0+4053 | 23 00 12.36 | +40 52 23.3 | 0.992 | blazar
J230351.7+555617 | J2303.7+5555 | 23 03 51.70 | +55 56 17.9 | 0.968 | blazar
J230848.5+542612 | J2309.0+5428 | 23 08 48.49 | +54 26 12.0 | 1.000 | blazar
J232127.1+511117 | J2321.3+5113 | 23 21 27.12 | +51 11 17.8 | 1.000 | blazar
J232137.1$-$161926 | J2321.6$-$1619 | 23 21 37.05 |  $-$16 19 26.2 | 0.999 | blazar
J232653.3$-$412713 | J2327.2$-$4130 | 23 26 53.32 |  $-$41 27 13.4 | 0.993 | blazar
J232938.7+610111 | J2329.8+6102 | 23 29 38.70 | +61 01 11.6 | 1.000 | blazar
J233626.4$-$842649 | J2337.2$-$8425 | 23 36 26.35 |  $-$84 26 49.5 | 0.989 | blazar
J235115.9$-$760017 | J2351.9$-$7601 | 23 51 15.89 |  $-$76 00 17.6 | 0.994 | blazar
J235824.10+382857 | J2358.5+3827 | 23 58 24.95 | +38 28 57.2 | 1.000 | blazar
J235836.8$-$180717 | J2358.6$-$1809 | 23 58 36.75 |  $-$18 07 17.9 | 1.000 | blazar