et al. 2012; Xu et al. 2016). Both type I and type II AGNs often exhibit redshifted Fe emission, with the neutral Fe line typically being strongest, except in some highly ionized AGNs where the He-like and H-like Fe lines can rival the neutral Fe line in strength. Fe line emission from AGNs typically has equivalent widths <100 eV, but they can be higher in Compton-thick AGNs (Page et al. 2004; Iwasawa et al. 2012; Ricci et al. 2014). The Fe lines in X-ray binaries also tend to have equivalent widths ≲100 eV, so the strength of Fe line emission can also help discriminate between CVs and LMXBs (Hirano et al. 1987; Nagase 1989).

Seven of the tier 1 sources (NNR 4, 6, 9, 12, 18, 19, and 25) fulfill at least one of the three criteria listed above and are most likely CVs. NNR 4 meets all three criteria, and there is strong evidence that it is an IP, a CV in which the WD magnetic field is strong enough ($B \approx 10^{6}-^{7}$ G) to truncate the accretion disk and channel the accreting material onto the magnetic poles. The X-ray spectrum of NNR 4 shows low absorption ($N_H < 4 \times 10^{21}$ cm$^{-2}$), indicating that it is a Galactic source residing at a distance of ≲2 kpc. The joint fitting of the *Chandra* and *NuSTAR* spectra provides evidence for partial-covering absorption, which is frequently observed in IPs as some of the X-rays produced in the accretion column pass through the accretion curtain on their way to the observer (de Martino et al. 2004; Bernardini et al. 2012). The near-IR counterpart of NNR 4 is variable and displays emission lines often produced in the accretion streams of IPs (Rahoui et al. 2014). Furthermore, this source also exhibits Fe line emission centered at $6.65_{-0.06}^{+0.10}$ keV with a high equivalent width ($0.9_{-0.1}^{+0.2}$ keV) and a 7150 s period detected by *Chandra*, both of which are typical for IPs (Scaringi et al. 2010). NNR 4 exhibits flux variations on month-year timescales, which is more typical for nonmagnetic CVs and polars than IPs (Ramsay et al. 2004). But the flux only varies by a factor of <2, so the case for this source being an IP remains strong. Assuming a distance of 2 kpc, the unabsorbed 3–10 keV luminosity of NNR 4 is $2 \times 10^{32}$ erg s$^{-1}$, which is within the luminosity range of IPs (Muno et al. 2004 and references therein).

Sources NNR 6, 9, and 12 all have strong Fe emission centered between 6.4 and 6.8 keV and equivalent widths of 1.3 ± 0.4, 0.4 ± 0.2, and 1.2 ± 0.4 keV, respectively. This strongly indicates that these sources are CVs, since both AGNs and X-ray binaries tend to have much weaker Fe emission, and the Fe emission from AGNs is likely to be redshifted. These large equivalent widths are likely due to multiple Fe lines being blended because of *NuSTAR*'s low energy resolution. NNR 6 and 9 are best fit by thermal models with high plasma temperatures ($kT > 15$ keV), which are more typical of magnetic than nonmagnetic CVs (Landi et al. 2009; Xu et al. 2016). The lack of flux variations for NNR 6 and 9 suggests that they are most likely IPs. In addition, NNR 6 has a low-mass (late GIII) stellar counterpart (Rahoui et al. 2014), lending further support to a CV origin for this source. The nature of NNR 12 is less certain, because its softer spectrum ($kT = 6_{-1}^{+3}$ keV for an *apec* model) is typical for both nonmagnetic and magnetic CVs. NNR 12 is likely located at a distance >10 kpc given its high $N_H$, so its 3–10 keV luminosity is likely ≳2 × $10^{33}$ erg s$^{-1}$; this high luminosity, coupled with the lack of flux variability, suggests that this source is also probably an IP.

Another likely IP candidate is NNR 13. This source displays one of the hardest spectra of all of the *NuSTAR* Norma sources, with $kT > 21$ keV or $\Gamma = 1.0 \pm 0.5$. Its very hard spectrum and constant flux over long timescales is typical of IPs.

The nature of NNR 18 is discussed in B14; our spectral analysis yields consistent results, finding a high $N_H$ of $1.9_{-0.6}^{+0.9} \times 10^{23}$ cm$^{-2}$ and $\Gamma = 2.6_{-0.8}^{+1.0}$. Assuming a distance of >10 kpc based on the high $N_H$ value, NNR 18 has an unabsorbed 3–10 keV luminosity ≳5 × $10^{33}$ erg s$^{-1}$. NNR 18 has an early MIII counterpart and exhibits mild X-ray variability on short timescales in *Chandra* observations. As discussed by B14, these properties are consistent with those of an IP or LMXB. Another possibility is that this source is a hard-spectrum symbiotic binary (SB) hosting a WD or a symbiotic X-ray binary (SyXB) hosting a NS (Luna et al. 2013). The compact objects in SBs and SyXBs accrete material from the wind of a red giant companion, which is typically of spectral type M or K (Morihana et al. 2016). Hard-spectrum SBs and SyXBs display X-ray luminosities between $10^{32}$ and $10^{34}$ erg s$^{-1}$ (Masetti et al. 2002; Smith et al. 2008; Nespoli et al. 2010) and variability on short and long timescales (Luna & Sokoloski 2007; Corbet et al. 2008). An IP origin is favored for NNR 18 based on its low levels of variability, while its estimated luminosity and the M giant spectral type of its counterpart favor an SB or SyXB origin.

NNR 19 and 25 show low absorption in their X-ray spectra, indicating that they are Galactic sources and probably located at a distance of a few kpc. Both sources are transients that were not detected in NARCS, but they were detected in follow-up *Chandra* observations taken 3 and 34 days after the *NuSTAR* observations, respectively. The flux of NNR 25 increased by a factor of ≳4 in the couple of years between the NARCS and *NuSTAR* observations and remained high for at least 34 days. NNR 19 was detected at a consistent flux level in multiple *NuSTAR* observations that spanned ≈100 days. About 250 days before it was first detected by *NuSTAR*, the 90% confidence upper limit for its 3–10 keV photon flux was $2 \times 10^{-6}$ cm$^{-2}$ s$^{-1}$ (a factor of 4 below its peak flux), and about 40 days after it was detected by *NuSTAR*, its flux fell below $4 \times 10^{-6}$ cm$^{-2}$ s$^{-1}$. Thus, we find that the flux of NNR 19 increased by a factor of ≳4 and remained high for a period between 100 and 400 days. In addition, NNR 19 was detected in the archival *Chandra* ObsID 7591, demonstrating that this transient experienced an outburst in 2007 during which its flux was a factor of ≳7 higher than the upper limit measured by NARCS in 2011. The spectra of NNR 19 and 25 have $kT = 11_{-4}^{+18}$ keV ($\Gamma = 1.7_{-0.4}^{+0.3}$) and $kT > 6$ keV ($\Gamma = 1.8 \pm 0.7$), respectively. The temporal and spectral properties of NNR 19 and 25 most closely resemble those of polars, CVs with magnetic fields so strong ($B > 10^{7}$ G) that the WD magnetosphere inhibits the formation of an accretion disk. Thus, compared to other CVs, polar X-ray emission is very sensitive to changes in the mass transfer rate, and polars exhibit flux variations of factors ≳4 as they transition between low and high accretion states on ~month-year timescales (Ramsay et al. 2004; Worpel et al. 2016), very similar to the behavior of NNR 19 and 25. No IR counterparts in the VVV survey are found for NNR 19 or 25 within the 90% positional uncertainty determined from *Chandra*. While the variability and spectra of NNR 19 and 25 would also be consistent with hard-spectrum SBs or SyXBs, the lack of a counterpart with $K_s < 18$ mag rules out the possibility that these sources have red giant companions, which should be visible out to ≳10 kpc. In contrast, it is possible for main-sequence K- or M-type stars located at distances of a few kpc to fall below the VVV survey sensitivity.
The nine remaining tier 1 sources (NNR 11, 16, 17, 20, 22, 23, 26, 27, and 28) are well fit either by thermal models with $kT = 4–30$ keV or power-law models with $\Gamma \approx 2$, consistent with the spectra of CVs, SBs, SyXBs, LMXBs, or AGNs. The uncertainties in the spectral parameters for many of these sources are quite large, since they are among the faintest in our survey. All of these sources have high absorption that is equal to or in excess of the ISM column density through the Galaxy, and they lack IR counterparts, so it is difficult to determine whether they are Galactic or extragalactic. The lack of counterparts does rule out the possibility that these sources are SBs or SyXBs, since their red giant companions should be visible through most of the galaxy given the sensitivity of the VVV survey ($K_s < 18$ mag). Based on the $\log N–\log S$ distribution of AGNs measured in the COSMOS survey (Cappelluti et al. 2009) and accounting for Galactic absorption, conversion from the 2–10 keV to the 3–10 keV band, and the sensitivity curve of the *NuSTAR* Norma survey (see Section 6.2), we estimate that about five AGNs are present in this survey. Therefore, roughly half of the remaining tier 1 sources may be AGNs. The other half are probably CVs, since quiescent LMXBs are expected to be relatively rare (Tanaka 1996). Additional *NuSTAR* or *XMM* observations are required to distinguish between the possible CV or AGN origin of these nine sources by measuring the strength of Fe line emission and better constraining their spectral hardness. The 3–10 keV fluxes of NNR 11, 20, and 28 vary by factors of >5 between the NARCS and *NuSTAR* observations. Such long-term variability is common for AGNs, polars, and nonmagnetic CVs (Orio et al. 2001; Markowitz & Edelson 2004; Ramsay et al. 2004; Baskill et al. 2005), so it does not help us discriminate between Galactic and extragalactic sources; however, it at least excludes an IP origin for these three sources.

The 10 tier 2 sources included in our catalog do not have enough *NuSTAR* counts to meaningfully constrain their spectral properties, but their distribution in the *Chandra* quantile diagram is very similar to the distribution of the 17 tier 1 sources described in this subsection: two are foreground sources, while the rest are heavily absorbed and have $\Gamma < 2$. Seven of these tier 2 sources (NNR 29, 30, 31, 34, 35, 36, and 38) have reliable IR counterparts, three of which (NNR 29, 30, and 36) have been spectrally identified as low-mass stars (Rahoui et al. 2014). These seven sources are likely to be a mixture of CVs, SBs, and SyXBs like the majority of identified tier 1 sources. NNR 29 and 36 display such low absorption that they are likely located within a few kpc and thus have $3–10$ keV luminosities $\lesssim 3 \times 10^{31}$ erg s$^{-1}$, so they could also be active binaries given their low luminosity (Strassmeier et al. 1993). An AGN origin cannot be ruled out for NNR 34 and 35, which have VVV counterparts but are not detected by *WISE*. NNR 32, 33, and 37 lack IR counterparts, are heavily absorbed, and could be AGNs or Galactic sources. Based on the $\log N–\log S$ derived for AGNs and CVs in the Norma region by F14, a 1:2 ratio of AGNs to CVs/ABs is expected in the 2–10 keV flux range of these tier 2 sources ($4 \times 10^{-14} < f_X < 1 \times 10^{-13}$ erg cm$^{-2}$ s$^{-1}$). Such a ratio is plausible among tier 2 sources given the current constraints we can place on their physical nature. However, it is odd that none of the sources that may be AGNs are detected by *WISE* with $W1 – W2 \ge 0.8$, since the majority of AGNs discovered in the *NuSTAR* serendipitous survey have these properties (Lansbury et al. 2017). The fact that our AGN candidates either lack IR

counterparts or have only VVV but not *WISE* counterparts indicates that, if they truly are AGNs, they are likely to have low luminosities ($L_X \lesssim 10^{43}$ erg s$^{-1}$).

### 6.1.6. On the Search for Low-luminosity HMXBs

As discussed in Section 1, one of reasons the Norma arm was targeted by *Chandra* and *NuSTAR* was to search for low-luminosity HMXBs with fluxes below the sensitivity limits of previous surveys. A key criterion for identifying an HMXB candidate is to find a source with a high-mass stellar counterpart, which should be visible in the infrared through the whole Galaxy in the VVV survey. Of the Norma sources detected by *NuSTAR*, only two, NNR 7 and 14, have high-mass stellar counterparts, and their broadband X-ray spectra indicate that they are CWBs (see Section 6.1.1). However, there are three *Chandra* sources, NARCS 239, 1168, and 1326, with high-mass stellar counterparts (Rahoui et al. 2014) that were not detected by the *NuSTAR* Norma survey. The 2–10 keV *Chandra* fluxes of these sources ($f_X \approx 7–8 \times 10^{-14}$ erg cm$^{-2}$ s$^{-1}$; F14) are comparable to the sensitivity limit of the *NuSTAR* survey (see Section 6.2), and thus it is not surprising that they are not detected. In the *Chandra* band, these three sources have harder spectra than NNR 7 and 14 and reside in a region of quantile space consistent with $\Gamma \approx 2$ power-law spectra (F14), which are typical of accreting HMXBs.

Future spectroscopic observations of the infrared counterparts of these *Chandra* sources will confirm whether these systems are HMXBs and help to estimate better distances to these sources. Once we determine how many of these HMXB candidates, if any, are truly HMXBs, the constraints provided by the Norma survey on the faint end of the HMXB luminosity function will be presented in a future paper. By extrapolating the measured slope of the HMXB luminosity function above $10^{34}$ erg s$^{-1}$ to lower luminosities, F14 predicted that at least a few HMXBs would be detected in the Norma region with $f_X > 7 \times 10^{-14}$ erg cm$^{-2}$ s$^{-1}$. Thus, if the three HMXB candidates are confirmed, the number of HMXBs in the Norma region would be consistent with a continuation of the HMXB luminosity function slope to lower luminosities, but if none of these sources prove to be HMXBs, it would imply that the HMXB luminosity function flattens substantially at $L_X < 10^{34}$ erg s$^{-1}$.

## 6.2. Survey Sensitivity

To compute the sky coverage for the *NuSTAR* Norma survey, we used the same method employed for NARCS, which is taken from Georgakakis et al. (2008). For a given detection probability threshold, $P_{thresh}$, we determined the minimum number of total counts required for a detection ($C_{lim}$) at each position in the image, such that $P(\ge C_{lim}) = P_{thresh}$. To this end, we made background maps in the 3–10 keV and 10–20 keV bands by removing the counts within 60" (90") radius circular regions centered on the point (extended) source positions listed in Table 5 and then filling in these regions by randomly distributing the expected background counts determined from the local background. Using these background maps, we calculated the mean expected background counts ($\langle C_{bkg} \rangle$) in circular regions centered on each pixel with radii equal to the 15%, 22%, and 30% PSF enclosures, which are the cell sizes we used for source detection (see Section 4). The probability that the observed counts will exceed $C_{lim}$ within a
<figure><img src="image_12.png" /><figcaption>**Figure 10.** Sky coverage of the NuSTAR Norma region survey for different energy bands and PSF enclosure fractions. The curves in this plot use a photon flux to energy flux conversion factor based the spectral modeling of tier 1 sources.</figcaption></figure>

particular region is

$$P(\ge C_{\text{lim}}) = \gamma(C_{\text{lim}}, \langle C_{\text{bkg}} \rangle), \quad (2)$$

where $\gamma(a, x)$ is the lower incomplete $\gamma$ function, defined as

$$\gamma(a, x) = \frac{1}{\Gamma(a)} \int_{0}^{x} e^{-t} t^{a-1} dt. \quad (3)$$

Calculating $C_{\text{lim}}$ requires setting $P(\ge C_{\text{lim}}) = P_{\text{thresh}}$ and inverting Equation (2) numerically.

Then, we computed the probability of detecting a source of a given flux $f_x$ at each pixel, given by

$$P_{f_x}(\ge C_{\text{lim}}) = \gamma(C_{\text{lim}}, C_{\text{src}}) \quad (4)$$

and $C_{\text{src}} = f_x t_{\text{exp}} A_{\text{src}} \epsilon + \langle C_{\text{bkg}} \rangle$, where $t_{\text{exp}}$, $A_{\text{src}}$, and $\epsilon$ are the exposure time, mean effective area, and unabsorbed energy flux to observed photon flux conversion factor, respectively. For $\epsilon$, we used the mean ratio of photon flux to energy flux measured for tier 1 sources in a given energy band. To estimate the effective area at each pixel location, we made vignetting-corrected exposure maps. By comparing the ratio of the vignetting-corrected exposure over the uncorrected exposures to the effective areas of tier 1 sources, we derived a linear relation to convert the exposure ratio at a given location to the effective area for the average source spectrum. These relations were derived using vignetting corrections evaluated at 8 keV for the 3–10 keV band and at 10 keV for the 10–20 keV band; they were also calibrated for the three different cell sizes.

There are a few possible sources of systematic error in our calculation of the sky coverage curves. Different $\epsilon$ values and effective area to exposure ratio relations were derived based on nonparametric and modeling-derived fluxes to account for systematic errors associated with flux calculation methods; the sky coverage curves derived using these different fluxes vary by about 0.1 dex. Another potential source of systematic error arises from our use of the average source spectrum as representative of the NuSTAR Norma sources. Based on the spread of spectral properties they exhibit, a systematic error of roughly 0.1 dex on the sky coverage could result from the choice of a representative source spectrum. Finally, the calculated sky coverage includes all the observations shown

in Figures 1 and 2, but, as can be seen, a significant wedge of stray light and some residual ghost rays are present, especially in the 3–10 keV band. This contamination effectively reduces our sky coverage, because even though there were about 20 clusters of pixels in these regions that exceeded our detection threshold, we ascribed most of them to artifacts associated with stray light and ghost rays rather than true sources. The only exceptions we included in our final source list (NNR 2, 6, 10, 16, and 18) were either very bright sources, had bright ($\gtrsim 10^{-5}$ photons cm$^{-2}$ s$^{-1}$) Chandra counterparts, or had a clear point-like morphology. Since the contaminated areas only make up about 2% of the total survey area, their inclusion in our sky coverage does not significantly impact our logN–logS results.

The sky coverage was calculated as the sum of probabilities in Equation (4) over all pixels multiplied by the solid angle per pixel. We repeated this calculation for a range of fluxes to produce a sensitivity curve for each of the three detection cell sizes in both the 3–10 keV and the 10–20 keV bands. Figure 10 shows the sky coverage for different energy bands and cell sizes. We used the sensitivity curves for the 22% PSF enclosures to calculate the logN–logS distribution and sensitivity limits of our survey in the 3–10 keV and 10–20 keV bands, because all but one of the 26 (17) tier 1 sources that were detected in the 3–10 keV (10–20 keV) band were detected in the 22% PSF trial maps, and the 22% PSF sky coverage represents a rough average of the different PSF fraction curves. The deep field of the NuSTAR Norma survey has an area of about 0.04 deg$^2$ and sensitivity limits of $4 \times 10^{-14}$ erg cm$^{-2}$ s$^{-1}$ ($5 \times 10^{-14}$ erg cm$^{-2}$ s$^{-1}$) in the observed (unabsorbed) 3–10 keV band and $4 \times 10^{-14}$ erg cm$^{-2}$ s$^{-1}$ in the 10–20 keV band. The shallow survey has an area of $\sim 1$ deg$^2$ with sensitivity limits of $1 \times 10^{-13}$ erg cm$^{-2}$ s$^{-1}$ ($1.5 \times 10^{-13}$ erg cm$^{-2}$ s$^{-1}$) in the observed (unabsorbed) 3–10 keV band and $1.5 \times 10^{-13}$ erg cm$^{-2}$ s$^{-1}$ in the 10–20 keV band.

### 6.3. The logN–logS Distribution

Since many of the NuSTAR Norma sources have fluxes approaching our sensitivity limits, when calculating the number-count distribution for our survey, it is important to consider the effect of Poisson fluctuations of the source and background counts on the measured source flux. Thus, rather than assigning a single flux value to each source, we determine its flux probability distribution by computing the source count distribution from Equation (A21) in Weisskopf et al. (2007) and converting counts to energy fluxes. The number count distribution is then equal to the sum of the flux probability distributions of individual sources divided by the sensitivity curve calculated in Section 6.2.

We computed logN–logS distributions in the 3–10 keV and 10–20 keV bands for both observed and unabsorbed fluxes. In order to check for systematic errors, we performed these calculations using both the modeling-derived and nonparametric fluxes. When constructing the distribution in a given energy band, we only included the sources that exceed the detection threshold in that particular energy band. In addition, in order to compare the NuSTAR number-count distribution with that derived from NARCS, we excluded extended sources. For the sources that are blended in the NuSTAR observations but resolved with Chandra, we estimated the NuSTAR fluxes of the individual sources by assuming that the ratio of the fluxes (see comments in Table 9) of the two sources is the same in NuSTAR as it is in the Chandra
<figure><img src="image_13.png" /><figcaption>**Figure 11.** The logN–logS distributions shown in black include all tier 1 sources exceeding the detection threshold in a given energy band. The gray band shows the $1\sigma$ errors on the logN–logS distribution. The logN–logS distributions shown in blue exclude NNR 2, 4, and 5, which were specifically targeted by *NuSTAR*. The green dotted (magenta dashed) lines show the NARCS logN–logS converted from unabsorbed 2–10 keV into the given bands assuming power-law spectral models with $\Gamma = 3, 2, \text{ and } 1$ (thermal models with $kT = 10, 20, \text{ and } 50$ keV). When converting the NARCS distribution into the observed 3–10 or 10–20 keV bands, a column density of $N_H = 10^{23} \text{ cm}^{-2}$ is used, the mean of measured $N_H$ values for the *NuSTAR* sources; varying $N_H$ between 0.7 and $2.0 \times 10^{23} \text{ cm}^{-2}$ does not significantly change the conversion factor. Top: the logN–logS distribution in the 3–10 keV band, calculated using observed fluxes derived from spectral fitting, as well as nonparametric fluxes calculated from aperture photometry using 30" and 40" radius regions. Middle: the logN–logS distribution in the 3–10 keV band, calculated using unabsorbed fluxes derived from spectral fitting. The blue band shows the $1\sigma$ errors on the distribution shown in blue. Bottom: same as top, except the logN–logS distributions are shown as a function of observed 10–20 keV flux.</figcaption></figure>

2–10 keV band. Thus, NNR 8 is excluded from the sample of sources used in the number-count distribution; the fluxes of the point sources at the center of the extended sources, NNR 3 and 21, are estimated to be 30% and 20% of the total, respectively; and the fraction of NNR 7's flux attributed to NARCS 1278 and 1279 is 30% and 70%, respectively.

We calculated the statistical errors of the logN–logS distribution using the bootstrapping method. We accounted for the errors associated with our sample size, as well as the distribution of fluxes within that sample, by generating new samples of sources from our original list used to calculate the logN–logS distribution. For each new sample, we drew the sample size ($N_{\text{sample}}$) from a Poisson distribution with a mean equal to the original sample size and then randomly selected $N_{\text{sample}}$ sources from the original list. We generated 10,000 new samples and calculated the resulting logN–logS distribution for each of them. Then, we used the simulated distributions to determine the $1\sigma$ upper and lower confidence bounds of the measured logN–logS distribution. The $1\sigma$ statistical errors are comparable in size to the systematic errors associated with the sensitivity curves, which are discussed in Section 6.2.

In addition to the possible sources of systematic error already discussed, there is a simplification in our logN–logS calculation that could bias our measurements. As described in Section 4.2, we require that a source exceed the detection threshold in two or three trial maps in order to be included in our source list, but our logN–logS calculation does not explicitly account for this criterion. This criterion helps to screen out spurious detections that may occur in a given trial map but are unlikely to correlate across different energy bands or aperture sizes. However, it is an easy criterion for a real source to pass, since it is very likely for a real source exceeding the threshold in one trial map to exceed it in at least another trial map with the same energy band but a different PSF enclosure fraction. Thus, we do not expect our detection criterion to substantially alter the sensitivity curves or, in turn, the logN–logS distribution. To gauge the magnitude of the possible bias due to our choice of sensitivity curve, we produced different versions of the logN–logS distribution in the 3–10 keV and 10–20 keV bands. We adopted sensitivity curves for different PSF enclosures and tested the effect of limiting the source subsample to tier 1 sources exceeding the threshold in the trial map with the same energy band and PSF enclosure as the sensitivity curve, rather than just the same energy band. The resulting variations in our logN–logS results are smaller than the statistical uncertainties.

Figure 11 shows the resulting logN–logS distributions for the *NuSTAR* Norma region. Magenta and green lines show the logN–logS distribution measured by NARCS converted from the unabsorbed 2–10 keV band to the *NuSTAR* bands assuming different spectral models: thermal models with $kT = 10, 20, \text{ and } 50$ keV and power-law models with $\Gamma = 1, 2, \text{ and } 3$. When converting to observed energy fluxes, a typical $N_H$ value of $10^{23} \text{ cm}^{-2}$ is used. The logN–logS distributions shown in black include all tier 1 sources that exceed the detection threshold in a given energy band, while the blue distributions exclude the sources that were specifically targeted by *NuSTAR* and detected (NNR 2, 4, and 5), which could unnaturally inflate the logN–logS distribution. As shown in the top panel, there is little difference between the NARCS distributions converted using different spectral models into the observed 3–10 keV band, which is not surprising given its large amount of overlap with the *Chandra* 2–10 keV band. Regardless of how the source energy fluxes are calculated, the *NuSTAR* distribution is consistent with the NARCS distribution at $1\sigma$ confidence, exhibiting a similar slope of
$\alpha \approx -1.24$. The NuSTAR distribution only deviates significantly from the NARCS distribution at low fluxes. This discrepancy may be due to the Eddington bias or variance in the spatial density of sources, given that the sources with the lowest fluxes are only detected in the deep HESS field, which is only 100 arcmin$^2$ in size.

The middle panel of Figure 11 shows the logN–logS distribution calculated using the unabsorbed 3–10 keV fluxes from spectral fitting. Although this distribution is still largely consistent with the NARCS distribution at $1\sigma$ confidence when the sources specifically targeted by NuSTAR are removed (shown in blue), the NuSTAR distribution is slightly higher than the Chandra distribution above $>3 \times 10^{-13} \text{ erg cm}^{-2} \text{ s}^{-1}$. The fact that this excess is seen using the unabsorbed 3–10 keV fluxes but not the observed 3–10 keV fluxes suggests that, for some sources, we measure $N_H$ values that are too high and thus overcorrect for absorption.

The bottom panel of Figure 11 shows the logN–logS distributions calculated using modeling-derived and nonparametric fluxes in the observed 10–20 keV band. Since there is very little difference between the observed and unabsorbed 10–20 keV fluxes, the logN–logS distribution in the unabsorbed 10–20 keV band is not shown. Although the 10–20 keV NuSTAR distributions deviate from a simple power law due to the small number of sources (16) detected in this hard X-ray band, overall the slope is still consistent with the NARCS slope. The normalizations of the different NARCS distributions extrapolated into the 10–20 keV band are distinct depending on the spectral model assumed; for the NuSTAR and NARCS normalizations to be consistent, the average spectrum of the Norma sources must either have $kT = 10–20$ keV or $\Gamma = 2$. This average spectrum is indeed consistent with the individual spectral fits of most of the NuSTAR sources and their locations in the NuSTAR quantile space.

## 6.4. Comparison to the GC NuSTAR Population

Comparing the logN–logS distributions of sources in the Norma region and the $1^\circ \times 0^\circ$6 GC region surveyed by NuSTAR, the number density of the NuSTAR sources is $\approx 2$ times higher in the GC (Hong et al. 2016). This is to be expected, since the stellar density in the vicinity of the GC is higher than the stellar density along the line of sight of the Norma region. The power-law slope of the number-count distribution is also steeper in the GC ($\alpha \approx -1.4$; Hong et al. 2016), which is consistent with the trend that is seen for Chandra sources in the GC and the field in the 0.5–8 keV band (Muno et al. 2009). In order for the normalizations of the GC NuSTAR and Chandra number-count distributions to be consistent, the typical spectrum of the GC sources must either have $kT = 20–50$ keV or $\Gamma \approx 1.5$, which is harder than the typical spectrum of Norma sources.

Hong et al. (2016) argue that 40%–60% of the NuSTAR GC sources are magnetic CVs, primarily IPs, given their very hard X-ray spectra ($\Gamma \lesssim 1.5$) and the presence of strong Fe emission. All but two of the Norma CV candidates have softer spectra ($\Gamma > 1.5$, $kT \lesssim 20$ eV). The spectral differences between the NuSTAR populations in the Norma and GC regions are mirrored in the differences between the Galactic ridge X-ray emission (GRXE; Revnivtsev et al. 2006a, 2006b, 2009) and the central hard X-ray emission (CHXE) discovered by NuSTAR in the GC (Perez et al. 2015). The lower temperatures of the Norma CV candidates are consistent with the thermal spectra of the GRXE, whose hot component has a temperature of $kT \approx 15$ keV (Türler et al. 2010; Yuasa et al. 2012), while the high temperatures of the GC CVs

resemble the $kT > 25$ keV emission observed in the inner few pc of the Galaxy (Perez et al. 2015; Hong et al. 2016).

However, it is unclear why the X-ray populations in the GC and the disk are different. Under the assumption that most of the sources contributing to the CHXE and GRXE are IPs, the differences in their typical X-ray temperatures have been attributed to differences in their WD masses, with WDs in the GC CVs having masses $\gtrsim 0.8 M_\odot$ (Perez et al. 2015; Hong et al. 2016) and those in the disk CVs having masses $\approx 0.6 M_\odot$ (Krivonos et al. 2007; Türler et al. 2010; Yuasa et al. 2012). However, the mean WD mass among all CVs has been measured to be $0.83 \pm 0.23 M_\odot$ (Zorotovic et al. 2011), and the X-ray inferred masses of the confirmed field IPs are consistent with this higher value of $\approx 0.8 M_\odot$ (Hailey et al. 2016). The discrepancy between the measured WD masses for the field CVs and the lower masses inferred from the temperature of the GRXE suggests that it may be incorrect to assume that the GRXE is dominated by IPs (Hailey et al. 2016). Thus, it may be similarly incorrect to attribute the temperature differences between the NuSTAR CV candidates in the GC and Norma regions to differences in their WD masses.

In fact, as discussed in Section 6.1.5, a significant fraction of the Norma CV candidates may not be IPs but rather a mixture of polars, nonmagnetic CVs, hard-spectrum SBs, and SyXBs. These types of sources have softer spectra than IPs, and thus the difference in the average temperatures of Norma and GC sources may be explained by variations in the relative fractions of different types of CVs and SBs. It is unclear what physical processes would drive variations in the relative fractions of different types of compact object binaries in these two Galactic regions, but investigating these issues further will first require confirming the true nature of the CV candidates.

The clearest ways of distinguishing different types of CVs and SBs is by measuring the relative flux ratios of their Fe emission lines (Xu et al. 2016) or measuring both their spin and orbital periods (Scaringi et al. 2010), but since most of the Norma CV candidates are quite faint, it will be difficult to obtain X-ray spectra or light curves with enough photons to make such measurements with current telescopes. Monitoring the long-term X-ray and infrared variability of the CV candidates and determining the spectral types of their counterparts more accurately in order to estimate distances and luminosities will help to identify the nature of these sources.

## 7. Conclusions

1. We have detected 28 hard X-ray sources in a square-degree region in the direction of the Norma spiral arm surveyed by NuSTAR that are designated as tier 1 sources. Twenty-three of these sources were previously detected in NARCS observations; one was a well-studied BH transient (4U 1630–472), and four were newly discovered transients that we followed up and localized with Chandra. Out of 28 sources, 16 are detected above 10 keV. In addition, we found 10 NARCS sources with $2–10$ keV fluxes $>6 \times 10^{-6} \text{ cm}^{-2} \text{ s}^{-1}$ that did not exceed our formal detection threshold for NuSTAR but displayed significant X-ray emission ($S/N > 3$) in at least one of three energy bands; these are designated as tier 2 sources. We have provided photometric information for these sources in our catalog but do not include them in our calculation of the logN–logS distribution, since they do not meet our detection thresholds.
<table><caption>**Table 11**
Classification of NNR Sources</caption><thead><tr><th>Source No.</th><th>Classification</th></tr></thead><tbody><tr><td></td><td>Tier 1—Confirmed</td></tr><tr><td>1</td><td>BH LMXB</td></tr><tr><td>2</td><td>NS HMXB</td></tr><tr><td>3</td><td>pulsar/PWN</td></tr><tr><td></td><td>Tier 1—Candidate</td></tr><tr><td>4</td><td>CV (IP)</td></tr><tr><td>5</td><td>bow shock PWN</td></tr><tr><td>6</td><td>CV (IP)</td></tr><tr><td>7</td><td>CWB</td></tr><tr><td>8</td><td>young PWN</td></tr><tr><td>9</td><td>CV (IP)</td></tr><tr><td>10</td><td>magnetar or AB</td></tr><tr><td>11</td><td>CV (polar or nonmagnetic) or AGN</td></tr><tr><td>12</td><td>CV (IP)</td></tr><tr><td>13</td><td>CV (IP)</td></tr><tr><td>14</td><td>CWB</td></tr><tr><td>15</td><td>BH LMXB</td></tr><tr><td>16</td><td>CV or AGN</td></tr><tr><td>17</td><td>CV or AGN</td></tr><tr><td>18</td><td>CV (IP), SB, SyXB</td></tr><tr><td>19</td><td>CV (polar)</td></tr><tr><td>20</td><td>CV (polar or nonmagnetic) or AGN</td></tr><tr><td>21</td><td>PWN/SNR</td></tr><tr><td>22</td><td>CV or AGN</td></tr><tr><td>23</td><td>CV or AGN</td></tr><tr><td>25</td><td>CV (polar)</td></tr><tr><td>26</td><td>CV or AGN</td></tr><tr><td>27</td><td>CV or AGN</td></tr><tr><td>28</td><td>CV (polar or nonmagnetic) or AGN</td></tr><tr><td></td><td>Tier 2—Tentative</td></tr><tr><td>29</td><td>Galactic</td></tr><tr><td>30</td><td>Galactic</td></tr><tr><td>31</td><td>Galactic</td></tr><tr><td>32</td><td>Galactic or AGN</td></tr><tr><td>33</td><td>Galactic or AGN</td></tr><tr><td>34</td><td>Galactic or AGN</td></tr><tr><td>35</td><td>Galactic or AGN</td></tr><tr><td>36</td><td>Galactic</td></tr><tr><td>37</td><td>Galactic or AGN</td></tr><tr><td>38</td><td>Galactic</td></tr></tbody></table>

**Notes.** Classifications of NNR sources are discussed in Section 6.1. The classifications of NNR 1, 2, and 3 are robust, while all other classifications of tier 1 sources should be considered candidate identifications (see Section 6.1 for details). For candidate CVs, we provide in parentheses the most likely CV type when possible. For tier 2 sources, we provide only tentative classifications of the sources as Galactic or extragalactic AGNs.

2. The logN-logS distribution of the *NuSTAR* sources in the 3–10 keV band is consistent with the distribution of the 2–10 keV *Chandra* sources in the Norma region.

3. The *NuSTAR* logN-logS distribution in the 10–20 keV band is consistent with the 2–10 keV *Chandra* distribution if the average spectrum of the *NuSTAR* sources can be described by a power-law model with $\Gamma = 2$ or a single-temperature *apsec* model with a plasma temperature between 10 and 20 keV. The broadband (3–40 keV) energy quantiles of the *NuSTAR* sources show that the majority of sources have photon indices of $\Gamma = 2–3$ for a power-law model or $kT = 5–30$ keV for a bremsstrahlung model, which are consistent with the spectral

parameters required for good agreement between the 10–20 keV and 2–10 keV logN-logS distributions.

4. We fit the joint *Chandra* and *NuSTAR* spectra of all sources with >100 counts in the 3–40 keV band but find that >300 *NuSTAR* counts are required to provide meaningful constraints on the spectral model parameters. We find good agreement between the spectral parameters from our fits and the location of the sources in the quantile diagrams.

5. Four of the sources detected in the *NuSTAR* Norma arm region survey are previously well-studied sources: NNR 1 is the BH LMXB 4U 1630–472, NNR 2 is the supergiant HMXB IGR J1639–4643, NNR 3 is the PWN and luminous TeV source HESS J1640–465, and NNR 24 is the magnetar SGR J1627–41. Based on the X-ray variability, spectral fits, and infrared counterpart information for each source, we determine the most likely nature of the fainter sources in our survey, which are summarized in Table 11. NNR 5, 8, and 21 are PWN candidates, NNR 7 and 14 are likely CWBs, NNR 10 is a possible magnetar, and NNR 15 is a quiescent BH LMXB candidate. The other sources are primarily CV candidates, a mixture of IPs, polaris, nonmagnetic CVs, and SBs. We estimate that five background AGNs are present among the tier 1 *NuSTAR* sources.

6. Compared to the *NuSTAR* sources that are detected in the GC region, the sources in the Norma region have softer spectra on average. Even if we restrict the comparison to the CV candidates in these two regions, the Norma CVs exhibit lower plasma temperatures than those in the GC. The $kT \approx 15$ keV temperatures of the Norma CV candidates resemble the hot component of the GRXE spectrum.

7. If most of the Norma CV candidates are IPs, then their plasma temperatures indicate that the WDs in these systems have masses of $\approx 0.6 M_{\odot}$, which are lower than the WD masses of $\gtrsim 0.8 M_{\odot}$ estimated for the GC IPs. However, we argue that it is more likely that the fraction of IPs relative to polaris, nonmagnetic CVs, and SBs is lower among the Norma CV candidates than in the GC region. Since IPs have the hardest X-ray spectra of all of these types of sources, a lower fraction of IPs in the Norma region would result in lower plasma temperatures for the average source.

8. In order to understand the nature of the hard X-ray sources in the Norma region and why they differ from the hard X-ray sources in the GC region, it is necessary to continue monitoring the X-ray variability of the Norma CV candidates, better characterize the variability and spectral types of their infrared counterparts, and obtain higher quality spectra, especially at Fe line energies, for the brighter sources. Follow-up multiwavelength observations of the candidate PWNs, CWBs, and quiescent BH binary would be useful in furthering our understanding of compact stellar remnants and the evolution of massive stars.

We thank the referee for feedback that helped improve the clarity of the work presented in this paper. This work made use of data from the *NuSTAR* mission, a project led by the California Institute of Technology, managed by the Jet Propulsion Laboratory, and funded by the National Aeronautics and Space Administration. We thank the *NuSTAR* operations, software, and calibration teams for support with the execution and analysis of these observations. This research has made use of the *NuSTAR* Data Analysis Software (NuSTARDAS), jointly developed by the ASI Science Data Center
<figure><img src="image_14.png" /><figcaption>**Figure 12.** *Chandra* and *NuSTAR* spectra with residuals of the best-fitting models for sources NNR 10–27. See the Appendix for more details.</figcaption></figure>

(ASDC, Italy) and the California Institute of Technology (USA). We also made use of observations taken by the *Chandra X-ray Observatory* and of software provided by the *Chandra X-ray Center (CXC)* in the application packages *CIAO* and *Sherpa*. This work also made use of data products from observations made with

ESO telescopes at the La Silla or Paranal Observatories under ESO program ID 179.B-2002. In addition, F.M.F. acknowledges support from the National Science Foundation Graduate Research Fellowship and thanks G. K. Keating for helpful conversations on some of the statistical measures and figures in the paper. JAT
<figure><img src="image_15.png" /><figcaption>Figure 12. (Continued.)</figcaption></figure>

acknowledges support from Chandra grants GO4-15138X and GO5-16152X. FEB acknowledges support from CONICYT-Chile grants Basal-CATA PFB-06/2007, FONDECYT Regular

1141218, “EMBIGGEN” Anillo ACT1101, and the Ministry of Economy, Development, and Tourism’s Millennium Science Initiative through grant IC120009, awarded to the Millennium
<figure><img src="image_16.png" /><figcaption>Figure 12. (Continued.)</figcaption></figure>

Institute of Astrophysics (MAS). JCS acknowledges support from FONDECYT 3140310. RK acknowledges support from the Russian Science Foundation (grant 14-12-01315). DB thanks the French Space Agency (CNES) for financial support.

Appendix

In this appendix, we present the *Chandra* and *NuSTAR* spectra of sources NNR 10–27 and residuals for the best-fitting spectral models listed in Table 9. Our spectral analysis is
described in Section 5.7. The spectra of NNR 1, 2, and 3 are shown in King et al. (2014), B16, and G14, respectively, while the spectra of sources NNR 4–9 are shown in Figure 8. In Figure 12, *Chandra* data is shown in black, *NuSTAR* FPMA data is shown in red, and FPMB data is shown in blue. For NNR 19, black points show the *Chandra* spectrum from ObsID 7591, while orange points show the *Chandra* spectrum from ObsID 16170. For NNR 21, black points denote the *Chandra* spectrum for the point source and extended emission combined, while orange points display the point source contribution alone.

## References

An, H., Kaspi, V. M., Tomsick, J. A., et al. 2012, ApJ, 757, 68

Archibald, R. F., Gotthelf, E. V., Ferdman, R. D., et al. 2016, ApJL, 819, L16

Arnaud, K. A. 1996, in ASP Conf. Ser. 101, Astronomical Data Analysis Software and Systems V, ed. G. H. Jacoby & J. Barnes (San Francisco, CA: ASP), 17

Barlow, E. J., Knigge, C., Bird, A. J., et al. 2006, MNRAS, 372, 224

Barret, D., McClintock, J. E., & Grindlay, J. E. 1996, ApJ, 473, 963

Baskill, D. S., Wheatley, P. J., & Osborne, J. P. 2005, MNRAS, 357, 626

Berghofer, T. W., Schmitt, J. H. M. M., Danner, R., & Cassinelli, J. P. 1997, A&A, 322, 167

Bernardini, F., de Martino, D., Falanga, M., et al. 2012, A&A, 542, A22

Bhalerao, V. 2012, PhD thesis, Caltech

Bocchino, F., & Bykov, A. M. 2001, A&A, 376, 248

Bodaghee, A., Tomsick, J. A., Fornasini, F. M., et al. 2016, ApJ, 823, 146

Bodaghee, A., Tomsick, J. A., Krivonos, R., et al. 2014, ApJ, 791, 68

Bodaghee, A., Tomsick, J. A., Rodriguez, J., & James, J. B. 2012, ApJ, 744, 108

Bodaghee, A., Walter, R., Zurita Heras, J. A., et al. 2006, A&A, 447, 1027

Brogan, C. L., Frail, D. A., Goss, W. M., & Troland, T. H. 2000, ApJ, 537, 875

Bronfman, L., Alvarez, H., Cohen, R. S., & Thaddeus, P. 1989, ApJS, 71, 481

Buccheri, R., Bennett, K., Bignami, G. F., et al. 1983, A&A, 128, 245

Cappelluti, N., Brusa, M., Hasinger, G., et al. 2009, A&A, 497, 635

Casares, J., Charles, P. A., & Naylor, T. 1992, Natur, 355, 614

Cash, W. 1979, ApJ, 228, 939

Choi, C.-S., Dotani, T., & Agrawal, P. C. 1999, ApJ, 525, 399

Civano, F., Hickox, R. C., Puccetti, S., et al. 2015, ApJ, 808, 185

Combi, J. A., Albacete Colombo, J. F., Romero, G. E., & Benaglia, P. 2006, ApJL, 653, L41

Corbet, R. H. D., Sokoloski, J. L., Mukai, K., Markwardt, C. B., & Tueller, J. 2008, ApJ, 675, 1424

Corcoran, M. F. 1996, RMxAC, 5, 54

Corcoran, M. F. 2005, AJ, 129, 2018

Cutri, R. M., Wright, E. L., Conrow, T., et al. 2013, yCat, 2328

Dame, T. M., Hartmann, D., & Thaddeus, P. 2001, ApJ, 547, 792

Davies, B., de La Fuente, D., Najarro, F., et al. 2012, MNRAS, 419, 1860

de Martino, D., Matt, G., Belloni, T., et al. 2004, NuPhS, 132, 693

Dempsey, R. C., Linsky, J. L., Fleming, T. A., & Schmitt, J. H. M. M. 1993, ApJS, 86, 599

Dieters, S. W., Belloni, T., Kuulkers, E., et al. 2000, ApJ, 538, 307

Elshamouty, K., Heinke, C., & Chouinard, R. 2016, MNRAS, 463, 78

Eracleous, M., Halpern, J., & Patterson, J. 1991, ApJ, 382, 290

Esposito, P., Israel, G. L., Zane, S., et al. 2008, MNRAS, 390, L34

Esposito, P., Tiengo, A., Mereghetti, S., et al. 2009, ApJL, 690, L105

Ezuka, H., & Ishida, M. 1999, ApJS, 120, 277

Fornasini, F. M., Tomsick, J. A., Bodaghee, A., et al. 2014, ApJ, 796, 105

Franciosini, E., Pallavicini, R., & Tagliaferri, G. 2001, A&A, 375, 196

Freeman, P. E., Kashyap, V., Rosner, R., & Lamb, D. Q. 2002, ApJS, 138, 185

Gagné, M., Oksala, M. E., Cohen, D. H., et al. 2005, ApJ, 628, 986

Gehrels, N. 1986, ApJ, 303, 336

Georgakisakis, A., Nandra, K., Laird, E. S., Aird, J., & Trichas, M. 2008, MNRAS, 388, 1205

Gotthelf, E. V. 2003, ApJ, 591, 361

Gotthelf, E. V., Tomsick, J. A., Halpern, J. P., et al. 2014, ApJ, 788, 155

Hailey, C. J., Mori, K., Perez, K., et al. 2016, ApJ, 826, 160

Hamaguchi, K., Corcoran, M. F., Gull, T., et al. 2007, ApJ, 663, 522

Harrison, F. A., Craig, W. W., Christensen, F. E., et al. 2013, ApJ, 770, 103

Hellier, C., Mukai, K., & Beardmore, A. P. 1997, MNRAS, 292, 397

Hirano, T., Hayakawa, S., Nagase, F., Masai, K., & Mitsuda, K. 1987, PASJ, 39, 619

Hong, J., Mori, K., Hailey, C. J., et al. 2016, ApJ, 825, 132

Hong, J., Schlegel, E. M., & Grindlay, J. E. 2004, ApJ, 614, 508

Hong, J., van den Berg, M., Schlegel, E. M., et al. 2005, ApJ, 635, 907

House, L. L. 1969, ApJS, 18, 21

Hurley, K., Kouveliotou, C., Woods, P., et al. 1999, ApJL, 519, L143

Iwasawa, K., Mainieri, V., Brusa, M., et al. 2012, A&A, 537, A86

Jakobsen, S. J. 2013, Master's thesis, Copenhagen Univ., Niels Bohr Institute

Jakobsen, S. J., Tomsick, J. A., Watson, D., Gotthelf, E. V., & Kaspi, V. M. 2014, ApJ, 787, 129

Jones, C., & Dickey, J. M. 2012, ApJ, 753, 62

Kalberla, P. M. W., Burton, W. B., Hartmann, D., et al. 2005, A&A, 440, 775

Kargaltsev, O., & Pavlov, G. G. 2008, in AIP Conf. Ser. 983, 40 Years of Pulsars: Millisecond Pulsars, Magnetars and More, ed. C. Bassa et al. (Melville, NY: AIP), 171

King, A. L., Walton, D. J., Miller, J. M., et al. 2014, ApJL, 784, L2

Klein-Wolt, M., Homan, J., & van der Klis, M. 2004, NuPhS, 132, 381

Koglin, J. E., An, H., Barrière, N., et al. 2011, Proc. SPIE, 8147, 81470J

Krivonos, R., Revnivtsev, M., Churazov, E., et al. 2007, A&A, 463, 957

Kuchar, T. A., & Clark, F. O. 1997, ApJ, 488, 224

Kuulkers, E., Parmar, A. N., Kitamoto, S., Cominsky, L. R., & Sood, R. K. 1997, MNRAS, 291, 81

Landi, R., Bassani, L., Dean, A. J., et al. 2009, MNRAS, 392, 630

Lansbury, G. B., Stern, D., Aird, J., et al. 2017, ApJ, 836, 99

Lemiere, A., Slane, P., Gaensler, B. M., & Murray, S. 2009, ApJ, 706, 1269

Luna, G. J. M., & Sokoloski, J. L. 2007, ApJ, 671, 741

Luna, G. J. M., Sokoloski, J. L., Mukai, K., & Nelson, T. 2013, A&A, 559, A6

Madsen, K. K., Harrison, F. A., Markwardt, C. B., et al. 2015, ApJS, 220, 8

Makino, F., Wagner, R. M., Starrfield, S., et al. 1989, IAUC, 4786

Markowitz, A., & Edelson, R. 2004, ApJ, 617, 939

Masetti, N., Dal Fiume, D., Cusumano, G., et al. 2002, A&A, 382, 104

Mateos, S., Alonso-Herrero, A., Carrera, F. J., et al. 2012, MNRAS, 426, 3271

Maughan, B. J., Giles, P. A., Randall, S. W., Jones, C., & Forman, W. R. 2012, MNRAS, 421, 1583

Mercer, E. P., Clemens, D. P., Meade, M. R., et al. 2005, ApJ, 635, 560

Mikles, V. J., Eikenberry, S. S., Muno, M. P., Bandyopadhyay, R. M., & Patel, S. 2006, ApJ, 651, 408

Minniti, D., Lucas, P. W., Emerson, J. P., et al. 2010, New Astronomy, 15, 433

Mori, K., Gotthelf, E. V., Dufour, F., et al. 2014, ApJ, 793, 88

Mori, K., Hailey, C. J., Krivonos, R., et al. 2015, ApJ, 814, 94

Morihana, K., Tsujimoto, M., Dubath, P., et al. 2016, PASJ, 68, 57

Mukai, K., & Shiokawa, K. 1993, ApJ, 418, 863

Muno, M. P., Arabadjis, J. S., Baganoff, F. K., et al. 2004, ApJ, 613, 1179

Muno, M. P., Bauer, F. E., Baganoff, F. K., et al. 2009, ApJS, 181, 110

Nagase, F. 1989, PASJ, 41, 1

Nespoli, E., Fabregat, J., & Mennickent, R. E. 2010, A&A, 516, A94

Orio, M., Covington, J., & Ögelman, H. 2001, A&A, 373, 542

Page, K. L., O'Brien, P. T., Reeves, J. N., & Turner, M. J. L. 2004, MNRAS, 347, 316

Pandey, J. C., & Singh, K. P. 2012, MNRAS, 419, 1219

Perez, K., Hailey, C. J., Bauer, F. E., et al. 2015, Natur, 520, 646

Petit, V., Owocki, S. P., Wade, G. A., et al. 2013, MNRAS, 429, 398

Pittard, J. M., Stevens, I. R., Corcoran, M. F., & Ishibashi, K. 1998, MNRAS, 299, L5

Portegies Zwart, S. F., Pooley, D., & Lewin, W. H. G. 2002, ApJ, 574, 762

Possenti, A., Cerutti, R., Colpi, M., & Mereghetti, S. 2002, A&A, 387, 993

Rahoui, F., Tomsick, J. A., Fornasini, F. M., Bodaghee, A., & Bauer, F. E. 2014, A&A, 568, A54

Ramsay, G., Cropper, M., Wu, K., et al. 2004, MNRAS, 350, 1373

Rana, V., Loh, A., Corbel, S., et al. 2016, ApJ, 821, 103

Revnivtsev, M., Molkov, S., & Sazonov, S. 2006a, MNRAS, 373, L11

Revnivtsev, M., Sazonov, S., Churazov, E., et al. 2009, Natur, 458, 1142

Revnivtsev, M., Sazonov, S., Gilfanov, M., Churazov, E., & Sunyaev, R. 2006b, A&A, 452, 169

Ricci, C., Ueda, Y., Paltani, S., et al. 2014, MNRAS, 441, 3622

Sana, H., Rauw, G., Nazé, Y., Gosset, E., & Vreux, J.-M. 2006, MNRAS, 372, 661

Sarma, A. P., Goss, W. M., Green, A. J., & Frail, D. A. 1997, ApJ, 483, 335

Sazonov, S., Krivonos, R., Revnivtsev, M., Churazov, E., & Sunyaev, R. 2008, A&A, 482, 517

Scaringi, S., Bird, A. J., Norton, A. J., et al. 2010, MNRAS, 401, 2207

Schulz, N. S., Canizares, C., Huenemoerder, D., & Tibbets, K. 2003, ApJ, 595, 365

Schulz, N. S., Canizares, C. R., Huenemoerder, D., & Lee, J. C. 2000, ApJL, 545, L135

Seifina, E., Titarchuk, L., & Shaposhnikov, N. 2014, ApJ, 789, 57
Shahbaz, T., Bandyopadhyay, R., Charles, P. A., & Naylor, T. 1996, MNRAS, 282, 977

Smith, R. K., Mushotzky, R., Mukai, K., et al. 2008, PASJ, 60, S43

Stern, D., Assef, R. J., Benford, D. J., et al. 2012, ApJ, 753, 30

Still, M., & Mukai, K. 2001, ApJL, 562, L71

Strassmeier, K. G., Hall, D. S., Fekel, F. C., & Scheck, M. 1993, A&AS, 100, 173

Szkody, P., Nishikida, K., Erb, D., et al. 2002, AJ, 123, 413

Tanaka, Y. 1996, in Röntgenstrahlung from the Universe, ed. H. U. Zimmermann, J. Trümper, & H. Yorke (Munich: MPE), 85

Tomsick, J. A., Gotthelf, E. V., Rahoui, F., et al. 2014, ApJ, 785, 4

Tomsick, J. A., & Kaaret, P. 2000, ApJ, 537, 448

Torrejón, J. M., Schulz, N. S., Nowak, M. A., & Kallman, T. R. 2010, ApJ, 715, 947

Townsley, L. K., Broos, P. S., Corcoran, M. F., et al. 2011, ApJS, 194, 1

Tozzi, P., Gilli, R., Mainieri, V., et al. 2006, A&A, 451, 457

Traulsen, I., Reinsch, K., Schwarz, R., et al. 2010, A&A, 516, A76

Türler, M., Chernyakova, M., Courvoisier, T. J.-L., et al. 2010, A&A, 512, A49

Vallée, J. P. 2008, ApJ, 681, 303

van der Klis, M. 1989, in NATO ASI Series C, Vol. 262, Timing Neutron Stars, ed. H. Ögelman & E. P. J. van den Heuvel (New York: Kluwer Academic/Plenum Publishers), 27

Verner, D. A., Ferland, G. J., Korista, K. T., & Yakovlev, D. G. 1996, ApJ, 465, 487

Viotti, R. F., Antonelli, L. A., Rossi, C., & Rebecchi, S. 2004, A&A, 420, 527

Weisskopf, M. C., Wu, K., Trimble, V., et al. 2007, ApJ, 657, 1026

Wik, D. R., Hornstrup, A., Molendi, S., et al. 2014, ApJ, 792, 48

Wilms, J., Allen, A., & McCray, R. 2000, ApJ, 542, 914

Wilson, T. L., Mezger, P. G., Gardner, F. F., & Milne, D. K. 1970, A&A, 6, 364

Woods, P. M., Kouveliotou, C., van Paradijs, J., et al. 1999, ApJL, 519, L139

Worpel, H., Schwope, A. D., Granzer, T., et al. 2016, A&A, 592, A114

Wright, E. L., Eisenhardt, P. R. M., Mainzer, A. K., et al. 2010, AJ, 140, 1868

Xu, X.-j., Wang, Q. D., & Li, X.-D. 2016, ApJ, 818, 136

Yuasa, T., Makishima, K., & Nakazawa, K. 2012, ApJ, 753, 129

Zhekov, S. A., & Skinner, S. L. 2000, ApJ, 538, 808

Zorotovic, M., Schreiber, M. R., & Gänsicke, B. T. 2011, A&A, 536, A42