Variable Active Galactic Nuclei in the Galaxy Evolution Explorer Time Domain Survey

The GALEX TDS is a data set collected in the near-UV (NUV) band of the GALEX satellite. This NUV channel covered a range of 1750 –2800 Å. GALEX TDS consists of seven 11 diameter circular field pointings within six of the Pan-STARRS1 Medium Deep Survey (PS1 MDS) fields, being the XMMLSS, CDFS, COSMOS, GROTH, ELAISN1, and VVDS22H fields (Gezari et al. 2013; Hung et al. 2016). Taken over approximately 3 yr with a cadence of roughly 2 days, the GALEX TDS generated a catalog particularly useful for searches for variable AGNs. Figure 1 displays an image of the galaxy with IAU designation J100207.04+030327.6 for the GALEX images as well as the Legacy Survey.

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For aperture photometry, a reference catalog was needed to identify galaxies within the imaging data to build the parent sample. In addition, we selected a star catalog in order to characterize the field around each galaxy.

We used the NASA Sloan Atlas (NSA) as our parent sample. The NSA combines SDSS and GALEX images to construct a list of roughly 641, 500 galaxies (Blanton & Moustakas 2009; Maller et al. 2009; Zhu et al. 2010). The NSA covers galaxies observed up to SDSS DR13 and provides various photometric and spectroscopically derived quantities, including stellar masses.

The NSA catalog's most recent iteration extends to a redshift of z = 0.15. With a decl. angle range in −24°–85°, the NSA does not provide ample coverage for the southern fields (XMMLSS and CDFS) of the GALEX TDS, which will be discussed in a future publication using a separate catalog to generate the parent sample of galaxies.

We used the LAMOST catalog, which consists of roughly 5.7 million sources, to develop a list of stars surrounding our galaxy targets (Luo et al. 2015). Bai et al. (2018) crossmatched stars observed by GALEX and acquired UV aperture photometry. About 2.2 million stars of the LAMOST catalog were detected by GALEX; thus LAMOST provides a check to the stars among the GALEX galaxies. This catalog is our stellar sample for the star selection process described in Section 3.1.1.

Finally, we obtained multiwavelength photometry for modeling the spectral energy distributions (SEDs) of our sources. We use our GALEX photometry (Section 3.1.1) with data from SDSS DR16 (Ahumada et al. 2020), the United Kingdom Infrared Telescope (UKIRT; Lawrence et al. 2007), Wide-field Infrared Survey Explorer (WISE; Wright et al. 2010), unblurred and unofficial coadds of the WISE imaging (unWISE; Lang 2014), and the Two Micron All-Sky Survey (Skrutskie et al. 2006).

Here we describe the construction of light curves of stars and galaxies within the GALEX TDS using aperture photometry.

Gezari et al. (2013) stated that as most galaxies are unresolved by the GALEX point-spread function (PSF) and space-based observations are not seeing-dependent, aperture photometry was a sufficiently accurate method for determining variability. Two apertures were applied to each object to measure the signal. A circular aperture with 9'' radius—the FWHM of the GALEX PSF—was centered over the object's position to determine the total signal. A secondary annulus, with inner radius 27'' (3x the FWHM) and outer radius 54'' (6 x the FWHM) was used to measure the local background surrounding each object. This was repeated for each object and each epoch that the pointing was observed.

Typical photometric uncertainties were calculated using Poissonian errors. Poisson error has been shown to underestimate the uncertainties in GALEX photometry by a factor of ∼2 (Trammell et al. 2007; Gezari et al. 2013). To compensate for this, we estimated uncertainties empirically following the technique described in Gezari et al. (2013). This is discussed in further detail below and demonstrated in Figure 2.

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3.1.1. Aperture Photometry

3.1.2. Variable Galaxy Selection

Galaxies were selected as highly variable from calculations of the standard deviation of the object's light curve. We confirmed their variability by calculating the object's fractional variance, such that

$$\begin{eqnarray}&&{F}_{\mathrm{var}}=\displaystyle \frac{\sqrt{{\sigma }_{\mathrm{var}}^{2}-{\delta }^{2}}}{\left\langle f\right\rangle },\end{eqnarray} \tag{ 1 }$$

where ${\sigma }_{\mathrm{var}}^{2}$ is the variance of the flux signal, δ² is the mean square of the uncertainty of the fluxes, and $\left\langle f\right\rangle$ is the mean flux for all observations (Edelson et al. 1990; Rodriguez-Pascual et al. 1997; Vaughan et al. 2003). Figure 3 provides an example light curve of a highly variable galaxy and a nearby star. In this figure, the magnitude of the galaxy varies in value by 0.30 mag, more than twice that of the star. This comparison shows how nonvarying stars set the reference for varying galaxies.

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After calculating the standard deviation of each object's light curve, the objects were binned by their mean magnitude values, in bins of 0.5 mag. In each bin, galaxies were selected as highly variable if their standard deviation value was greater than twice the deviation above the bin's mean value. The highly variable galaxies can be seen compared to the less variable galaxies in Figure 4.

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This process was repeated for the values of fractional variation from the object's light curves. This resulted in recovering 23 of the variable galaxies identified through high standard deviation values with no new objects. The 25 galaxies not selected still demonstrated comparable F_var to the rest of the population, as shown in Table 1. Objects that were selected based on their standard deviation but not fractional variation were lost in the more populated higher magnitude bins. Of these objects, 16 had NUV magnitudes greater than 21 mag. As F_var has dependence on the uncertainty of the photometry, dimmer objects would be more likely to have higher fractional variance values. For those bins, our threshold value is higher, so we collect a less complete sample.

Table 1. Sample Properties

NSA ID	R.A.	Decl.	$\mathrm{log}{M}_{* }$	z	NUV Mag	σ	Fvar	$\mathrm{log}{M}_{\mathrm{BH}}$
	(deg)	(deg)	(M⊙)					(M⊙)
1382	150.8071007	0.9166644	11.13	0.0966	21.15	0.097	0.0035	⋯
28585	334.1202944	−1.2454316	10.41	0.1162	21.26	0.0974	0.0034	⋯
28616	333.9262375	−0.6027033	10.89	0.0991	19.41	0.1321	0.0066	⋯
28762	333.3791149	0.8221411	10.77	0.0982	21.03	0.1016	0.0039	⋯
28810	334.0740128	0.1650286	11.24	0.1394	21.45	0.0982	0.0032	${8.29}_{-2.41}^{+0.90}$
58753	149.0006157	1.7933316	10.87	0.0981	21.68	0.1333	0.0051	⋯
58912	148.5312013	2.2277921	10.79	0.0698	21.53	0.1094	0.0038	⋯
58917	148.5755379	2.7412974	9.39	0.0845	19.58	0.0724	0.0033	⋯
58935	148.8960094	2.7014311	11.0	0.1309	21.16	0.1136	0.0045	${7.09}_{-0.63}^{+0.40}$
63970	148.821601	1.7666729	10.81	0.1003	21.28	0.115	0.0045	⋯
63979	148.8004955	1.7608818	10.19	0.0349	19.25	0.0679	0.0032	⋯
64049	149.1378657	2.7037899	10.83	0.1283	21.36	0.0958	0.0032	⋯
64050	149.0904962	2.640145	10.91	0.1299	21.61	0.1271	0.0048	⋯
64059	148.5897165	1.9534373	10.49	0.1	21.46	0.1371	0.0055	⋯
64129	149.7027968	2.8786719	10.53	0.0783	20.56	0.1063	0.0046	${5.75}_{-0.02}^{+0.02}$
64145	150.0656545	2.9292917	10.59	0.103	20.54	0.1228	0.0055	⋯
64234	149.951175	1.0951285	10.38	0.0323	19.02	0.0691	0.0034	⋯
64258	149.4748494	1.9582107	10.74	0.1084	21.75	0.1137	0.0038	⋯
64266	149.9034921	2.9396754	10.68	0.0805	20.95	0.3258	0.0153	⋯
64272	149.7438977	2.2497568	10.89	0.1329	20.57	0.2974	0.0143	${7.87}_{-0.05}^{+0.05}$
64286	150.5293341	3.0577013	10.67	0.0234	18.25	0.1106	0.0059	${7.35}_{-0.03}^{+0.03}$
83185	150.0678076	3.3380463	10.63	0.1033	21.45	0.0968	0.0031	⋯
83201	149.8727195	3.1837508	11.02	0.1244	18.82	0.2395	0.0127	${7.18}_{-0.03}^{+0.03}$
83204	149.8440779	3.1908699	10.9	0.1246	21.7	0.1145	0.0039	⋯
83262	149.2909323	2.5633208	10.8	0.1263	21.2	0.1033	0.0039	${6.22}_{-0.45}^{+0.55}$
97153	241.366954	54.9073605	10.35	0.0624	21.87	0.1179	0.0039	⋯
97156	241.3026668	54.9307658	10.26	0.0642	21.83	0.1118	0.0036	⋯
97781	241.9093182	53.7136762	10.39	0.0661	21.87	0.1084	0.0033	⋯
205160	334.1350066	0.4734242	10.52	0.1396	21.44	0.0981	0.0032	⋯
208465	212.9559958	52.8167127	11.61	0.0765	19.71	0.105	0.005	${6.28}_{-1.17}^{+0.54}$
208625	213.7651102	52.0761692	10.85	0.0732	21.42	0.1388	0.0056	⋯
208662	213.9684008	52.0524492	10.98	0.1122	19.87	0.076	0.0034	${5.93}_{-1.39}^{+0.45}$
208702	214.8480654	52.0831737	9.61	0.0448	21.31	0.1093	0.0041	⋯
217051	333.8808418	1.0297436	10.77	0.0614	21.04	0.0989	0.0038	⋯
259478	212.194614	53.2740888	10.84	0.0784	19.15	0.0726	0.0035	⋯
259789	214.6720162	52.871023	10.76	0.1063	21.05	0.0969	0.0036	⋯
259790	214.7703074	53.047393	11.38	0.0818	21.35	0.1091	0.004	⋯
259880	212.9356623	54.1584797	10.65	0.0762	21.46	0.1292	0.0051	⋯
259895	212.1853626	53.8578921	10.11	0.0834	20.45	0.1349	0.0062	${6.50}_{-0.02}^{+0.02}$
259919	211.9709118	53.6142803	10.52	0.0781	20.67	0.107	0.0046	⋯
260221	214.2461774	52.7304623	10.73	0.0742	21.43	0.1039	0.0036	⋯
260241	214.0785351	53.8415265	10.51	0.0743	21.03	0.1083	0.0043	⋯
583470	332.4441817	−0.6396369	10.08	0.0889	20.82	0.2173	0.0101	${6.37}_{-0.01}^{+0.01}$
613397	243.2130022	54.4802705	11.54	0.1274	21.28	0.1146	0.0044	${5.92}_{-1.41}^{+0.57}$
631480	214.2188319	52.5807769	7.74	0.0442	21.49	0.0988	0.0032	⋯
631492	214.3279483	52.5141623	6.89	0.0119	21.44	0.1192	0.0045	⋯
631637	215.6606315	54.2344583	8.19	0.0206	19.7	0.075	0.0034	⋯
649438	149.3216589	2.1478328	9.8	0.0862	20.03	0.1011	0.0047	⋯

Note. Table displaying the characteristic quantities for the selected variable population. No BH mass estimates were given for those spectrums with Hα absorption features, poor quality spectrums, or those lacking any broadline Hα to be fit. Equation (2) has an intrinsic ∼0.4 dex uncertainty (Baldassare et al. 2015; Burke et al. 2021). The quoted uncertainties are derived from the bootstrapping method.

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The high standard deviation population of galaxies well represented the distribution of mass and mean magnitude within the parent population, as shown in the histograms of Figure 5. These 48 objects comprised our final group of variable AGN candidates.

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After selecting variable galaxies, we used SDSS DR7 observations to study their spectroscopic properties. All spectral lines were fitted with a sequence of 1D Gaussian models following Reines et al. (2013). Prior to fitting, the local continuum around each peak was modeled by a linear line, which was subtracted from the spectrum. Flux densities were corrected from reddening using the dust maps of Schlegel et al. (1998). This method is outlined as such:

• 1.  
  First we fit the [S ii] doublet with a single Gaussian for each line.
• 2.  
  We fit the Hα + [N ii] complex following the steps below.

  • (a)  
    The [S ii] fit sets the template for the narrow lines in the Hα + [N ii] complex. The narrow lines for this complex were fit by fixing the centers of the [N ii] lines to the values of 6584 Å and 6548 Å. In addition, the flux ratio of the 6584 Å to the 6548 Å [N ii] line was held to 2.96. This was achieved by recursively adjusting the amplitudes of the fitting of the [N ii] lines as the widths of these peaks were held constant to those of the [S ii] doublet. The Hα width was allowed to increase up to 25% of the line width value.
  • (b)  
    The broad component was then included with the model of the narrow lines and the entire complex was fitted once more. This new fitting is used if the broad component's FWHM is greater than 500 km s⁻¹ and the reduced χ² of this model is at least 20% better than that of narrow line fitting.
• 3.  
  The Hβ line was fit once more using a narrow line fit first, adjusting the width up to an additional 25% of template widths. It was then fitted with the addition of the broad component.
• 4.  
  The Hβ, [O iii], and [O i] lines were also fit using Gaussian models.

An example spectrum is shown in Figure 6. This methodology takes special care of the Hα + [N ii] complex as the broad Hα associated with gas in close proximity to the BH can become harder to detect at lower BH mass. As mentioned in Reines et al. (2013), the [S ii] doublet has previously been used to closely model the narrow lines of the Hα + [N ii] complex (Ho et al. 1997; Greene & Ho 2004). The Hβ, [O iii], and [O i] fits are included to apply optical spectroscopic diagnostics as further discussed in Section 4.

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We estimated BH masses using the luminosity (L_Hα ) and FWHM of the broad Hα component (Greene & Ho 2005; Reines et al. 2013; Burke et al. 2021). Specifically, we used the results from Burke et al. (2021), where:

$$\begin{eqnarray}\begin{array}{rcl}\mathrm{log}\left(\displaystyle \frac{{M}_{\mathrm{BH}}}{{M}_{\odot }}\right) & = & 6.57+0.47\mathrm{log}\left(\displaystyle \frac{{L}_{{\rm{H}}\alpha }}{{10}^{42}\,{{\rm{erg}}\,{\rm{s}}}^{-1}}\right)\\ & & +2.06\mathrm{log}\left(\displaystyle \frac{{{\rm{FWHM}}}_{{\rm{H}}\alpha }}{{10}^{3}\,{{\rm{km}}\,{\rm{s}}}^{-1}}\right).\end{array}\end{eqnarray} \tag{ 2 }$$

Our BH mass estimates are presented in Table 1 with uncertainties collected from bootstrapping over 100 iterations of the Hα broad component fitting.

We discuss the origins of the broad Hα emission in Section 4.1.

We then used our multiwavelength photometry to model SEDs for each object in our sample. Using the AGN and galaxy templates of Assef et al. (2010) and Kirkpatrick et al. (2015), which were constructed empirically based on the NOAO Deep-wide Field Survey's Boötes field, the iterative method of nonnegative linear combinations constructs the SEDs model over a 300 Å (0.03 μm) to 300,000 Å (30.0 μm) wavelength range.

Templates for AGN, elliptical (ELL), star-forming (SFG), and irregular (IRR) galaxies are considered in the decomposition of the SEDs. As 15 μm emission can mainly be linked to AGN contributions (Lambrides et al. 2020), we used the fractional AGN contribution of the total SED at 15 μm (f_AGN) as a threshold for selection. We follow Carroll et al. (2021) in adopting f_AGN ≥ 0.7 as a quality cut for AGN identification. For objects with f_AGN ≥ 0.7, we identified them as SED AGNs.

We use our emission line fluxes to plot objects on the Baldwin, Phillips & Terlevich diagram (BPT; Baldwin et al. 1981; see Figure 7). Classification lines were taken from Kewley et al. (2001, 2006) and Kauffmann et al. (2003).

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The classification lines of Kewley et al. (2001) separate regions of AGNs from starburst galaxies. Kauffmann et al. (2003) added an additional classification to the standard log([O iii]/Hβ) versus the log([N ii]/Hα) BPT diagram to distinguish pure star formation objects from those that are composite HII-AGN types. This classification line sets the lower limit of composite galaxies such that objects located between this and the Kewley (Ke01) line of Figure 7 are defined to be composite galaxies. Lastly, Kewley et al. (2006) presented an additional categorization to separate Seyfert (Edelson et al. 1990; Dong et al. 2012) from LINER (low-ionization nuclear emission line region) galaxies. In separating these two types of galactic nuclei, Kewley et al. (2006) divides the AGN region of two of the optical diagrams, where Seyfert galaxies lie above the line and LINERs below. Table 2 provides our classifications for each spectroscopic diagnostic diagram.

In Figure 7, it is shown that some points positioned near classification lines in the original BPT cross into new classifications with alternate optical spectroscopic diagnostics. This is especially apparent in a fraction of AGNs and starburst galaxies near the Ke01 line in comparing the [N ii] to [S ii] and [O i] diagnostics. These cases will be discussed in more detail below. From the original BPT diagram (left panel of Figure 7), eight galaxies were selected as AGNs and five as composite galaxies.

All but one of the BPT AGNs are also classified as AGNs on the [S ii] and [O i] diagrams. These sources also tend to have high AGN contributions to their SED models. The five BPT composite galaxies are all classified as starburst galaxies in the [S ii] diagram. One composite galaxy—NSA 259478—is a Seyfert in the [O i] diagnostic. None of the five BPT composite galaxies are cross-listed as an SED AGN as all have extremely low or no AGN contribution to their SED models.

We also estimated BH masses for objects with broad Hα emission. We found 12 of the variable galaxies to have a broad Hα component (25%), with BH masses ranging from 5 × 10⁵–2 × 10⁸ (see Table 1). We note that broad emission lines were observed in galaxies with stellar masses >10¹⁰ M_⊙. One possibility is that broad emission from lower-mass BHs is generally too faint to detect in SDSS observations, and could be detected in deeper, higher-resolution spectroscopy (Baldassare et al. 2015). Alternatively, broad emission line production could be inefficient for BHs below ∼10⁵ M_⊙ (Chakravorty et al. 2013).

We also explore the IR properties of this sample. The WISE AllWISE catalog (Wright et al. 2010) was used to collect the IR photometry in 3.4, 4.6, and 12 μm (W1, W2, and W3, respectively) magnitudes for our sample. Photometry was available for 46/48 of our variable galaxies. Figure 8 shows a WISE color–color diagram for these 46 galaxies, where the color of each point is set by the stellar mass of the galaxy. AGN selection criteria are taken from Stern et al. (2012) and Jarrett et al. (2011).

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Objects above the threshold of W1 − W2 > 0.80 met the Stern et al. (2012) criterion for IR AGNs. This value was set based on the high reliability and completeness of its WISE AGN selection in the COSMOS field. The criteria of Jarrett et al. (2011) sets an interior box where within it, the selected IR AGN would lie. This box, originally defined in Lacy et al. (2004), was set through evaluating the fraction of a mid-IR AGN in the Spitzer Space Telescope First Look Survey. In examining Figure 8, only two objects appear to be picked as AGNs by the Stern criteria and one by the Jarrett criteria, as shown in the middle columns of Table 2.

NSA 28616 is selected by both IR criteria and NSA 631637 is only selected by the criteria of Jarrett et al. (2011). Interestingly, the vast majority of our highly variable sources are not selected by WISE color–color diagnostics. NSA 28616 is also selected as a BPT AGN based on log([O iii]/Hβ) versus log([N ii]/Hα). In addition, NSA 28616 has significant AGN contribution to its overall SED (f_AGN = 0.90). NSA 631637 did not have SDSS spectroscopy. NSA 631637 is a dwarf galaxy, having a mass of 10^8.19 M_⊙ with a large AGN contribution (f_AGN = 0.93) to its SED. Both WISE AGNs are categorized as an SED AGN (see Section 4.3.).

We constructed SEDs for each galaxy and modeled them following the methodology of Carroll et al. (2021). Carroll et al. (2021) coadds these low-resolution templates using a nonnegative linear least squares approach to construct SEDs. A simple χ² minimization is used to determine the normalization of each template needed to construct the full model for each object. In the last columns of Table 2, percent AGN (%_AGN) describes what percent of realizations contained any AGN contribution during the fitting process, with f_AGN giving the fraction contribution of the AGN template at 15 μm. We parameterize nuclear obscuration with color excess (E(B − V)_AGN) applied only to the AGN component. The latter two columns were then used to identify objects as SED AGNs based on our selection criteria, as well as classifying obscured AGNs. We defined obscured AGNs as sources with E(B − V); values greater than 0.15. Signatures of nuclear obscuration are visible in the SED decomposition as attenuation of the AGN component at shorter wavelengths.

We crossmatch the spectroscopic and IR analysis with the AGN fractions. Of our 48 variable galaxies, 12 objects do not require any AGN contribution. NSA 64266, 259880, and 649438 present interesting cases: They have 0.0 for both their fractional AGN contribution and excess color values, but significant uncertainties on this E(B − V) number. This demonstrates that small changes in their photometry cause large variations in their overall SED. We therefore lack the confidence to further consider the SEDs of these sources.

Examples of SED models are given in Figure 9. These three models give a representation of subpopulations developed by this analysis. The top panel shows the SED of NSA 28616. With a high AGN fraction (f_AGN = 0.90) and narrow lines placing it in the AGN region of the BPT diagram, this is a secure UV-variable AGN. The SED model of NSA 58917 has an AGN fraction of 17.3% and only 3.1% of iterations contain an AGN template. This dwarf galaxy's SED contains a high contribution of star-forming emission from the SFG and IRR templates. A BPT composite galaxy SED is given in the final panel for NSA 64234. All three of these examples are also obscured AGNs with E(B − V) > 0.15.

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Using a cut of f_AGN ≥ 0.70, we identified 28 objects as SED AGNs (see Figure 10). Six of our eight BPT AGNs are classified as SED AGNs. Four of the SED AGNs were classified as starburst galaxies on the standard BPT diagram, with two of these objects having conflicting classifications. NSA 63970 falls in the Seyfert regime of [S ii]/Hα versus [O iii]/Hβ, and NSA 583470 lies in the Seyfert regime of the [O i]/Hα versus [O iii]/Hβ plot. These are interesting case studies; further study of host galaxy properties may yield information on their contrasting optical spectroscopic classifications. The rest of the objects in this subsample either lacked emission line spectra or did not have available SDSS spectroscopy.

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Of the 28 SED AGNs, 11 were identified as highly obscured, with E(B − V)_AGN ≥ 0.15 (see Table 1). Of these 11 obscured AGNs, two have also been identified as dwarf galaxies. NSA 28616 and 64129 were identified as BPT AGNs as well, each with E_B−V = 0.15.

Of the eight BPT AGNs (NSA 28616, 64059, 64129, 64272, 64286, 83201, 83204, and 259895), all but 64129 and 83204 had AGN contribution in every iteration of the model fitting (i.e., %_AGN = 100). Only NSA 64059 and 64286 did not have a significant AGN fraction to their SEDs (f_AGN < 0.70). We also note that NSA 64059 and 83204 were categorized as starburst galaxies in the [O i]/Hα versus [O iii]/Hβ plot of Figure 7.

Figure 11 depicts how our sample of 48 variable AGNs are cross-categorized using the different selection criteria. The bottom panel of Figure 11 focuses on the six select variable dwarf galaxies and their categorizations from our SED and IR color selection methods.

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Optical, IR, and X-ray surveys identify AGNs with different BH and host galaxy properties. Each spectrum and selection method has its own benefits and selection biases. This emphasizes the importance of comparing selection techniques across wavelength regimes to distinguish the processes at play within each galaxy.

Selection and categorization of AGNs through their optical emission uses particular spectroscopic signatures to identify candidates. Searching for AGNs based on photoionized emission line ratios and broad Hα emission are directly tied to the quality of the observed spectrum for each object. As one of the most prominent optical signatures of AGN activity, broad Hα becomes harder to detect for low-luminosity and low-mass galaxies (Greene & Ho 2004; Reines et al. 2013). Host galaxy properties also determine whether narrow line signatures are detectable. This is particularly relevant for low-mass galaxies (see Section 5.2).

In contrast, IR color–color diagnostics are sensitive to the obscuring material surrounding the AGN. As this wavelength regime can be dominated by dust, heated by star formation within the galaxy, IR selection techniques are heavily influenced by both processes.

The decomposition of an SED into component parts allows us to estimate the contribution of different physical processes to the total energy budget of the galaxy. BH accretion is taken into account in the AGN template, while ongoing star formation and starbursts are represented in the SFG and IRR templates, and finally with older stellar populations characterized in the ELL template.

Using a multiwavelength approach, we have compiled diagnostic techniques from the UV to IR, viewing AGN activity from its nuclear accretion and obscuring torus. Demonstrated by Figure 11, some candidates would only be characterized as AGNs by one technique. Here we have demonstrated the importance of a multiwavelength approach when investigating active galaxies. Follow-up studies will incorporate X-ray data to focus on the structure of the accretion disks (e.g., Greene & Ho 2007; Desroches et al. 2009).

Six galaxies of the selected variable population have stellar masses less than 10¹⁰ M_⊙ (NSA 58917, 208702, 631480, 631492, 631637, and 649438). The AGN fraction at 15 μm splits this group into either virtually none (f_AGN < 20%, NSA 58917, 208702, and 649438) or significant (f_AGN > 90%, NSA 631480, 631492, 631637) AGN contribution. Only NSA 589117 and 208702 have emission line spectra for testing optical spectroscopic diagnostics: NSA 589117 was categorized as a starburst and NSA 208702 was categorized as a composite galaxy in the standard BPT diagram. The lack of SDSS spectra for the remaining four low-mass galaxies and the results for NSA 589117 and 208702 demonstrate that spectroscopic selection would have missed these sources, whereas our use of UV variability included them in our population.

12.5% of our candidates are dwarf galaxies; 30.68% of the parent sample of 1819 sources are dwarf galaxies, of which we selected 1.1% as active dwarf candidates. Reines et al. (2013) uses an earlier version of the NSA, v0_1_2, to initially collect 44,594 galaxies with stellar masses M_* < 3*10⁹ M_⊙, which represents 30.72% of the entire catalog. From this, their optical selection identifies 0.5% as active candidates.

Cann et al. (2019) discussed the pitfalls of the BPT diagram classifications, particularly in confirming AGNs who host IMBHs. Through their models, they conclude that these standard optical diagnostics do not adequately apply to BHs with masses lower than ∼10⁴ M_⊙. For higher-mass IMBHs, further theoretical investigation would ideally determine the uncertainties within these diagnostics. Optically, lower-mass AGNs have weaker line emission ratios than their more massive counterparts.

It has been shown that AGNs in dwarf galaxies are not reliably selected using the WISE color diagnostics. Hainline et al. (2016) found that a majority of dwarf galaxies (that have masses in range of 10⁷–10⁹ M_⊙) in the AGN and composite regions of the BPT diagram were below the AGN selection criteria on the WISE diagram. IR color selection for dwarf galaxies is limited as the host galaxy can dominate the IR over the AGN and that high star formation rates can heat dust enough to produce W1 − W2 values within the AGN range (Hainline et al. 2016). With these biases against low-mass galaxies, WISE color selection is not an individually reliable diagnostic to identify AGNs.

We crossmatched our 48 variable galaxies with the SDSS DR7 broadline AGN catalog (Liu et al. 2019). We find that 7 of the 48 variable galaxies are listed in that catalog. Six of these (NSA 64129, 64145, 64272, 64286, 83201, 28616) are selected as BPT AGNs and one (NSA 65145) is not.

Additionally, crossmatching with the SDSS Quasar Catalog DR16Q (Lyke et al. 2020) reveals that only NSA 583470 is listed in this catalog. This object was placed in the starburst region of the BPT diagram but meets our criteria for an SED AGN without obscuration. Using [O i]/Hα versus [O iii]/Hβ, this object was categorized as a Seyfert AGN.

Figure 12 is the set of light curves for all 48 high standard deviation galaxies of the population selected by the way of Section 3.1.2. Error bars are set by the equation of the fit line of Figure 2.

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View figure set (48 panels)

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