PEARLS: Discovery of Point-source Features within Galaxies in the North Ecliptic Pole Time Domain Field Rafael Ortiz III1 , Rogier A. Windhorst1 , Seth H. Cohen1 , Steven P. Willner2 , Rolf A. Jansen1 , Timothy Carleton1 , Patrick S. Kamieneski1 , Michael J. Rutkowski3 , Brent M. Smith1 , Jake Summers1 , Cheng Cheng4 , Dan Coe5 , Christopher J. Conselice6 , Jose M. Diego7 , Simon P. Driver8 , Jordan C. J. D’Silva8,9 , Brenda L. Frye10 , Hansung B. Gim11 , Norman A. Grogin12 , Heidi B. Hammel13 , Nimish P. Hathi14 , Benne W. Holwerda15 , Minhee Hyun16 , Myungshin Im17 , William C. Keel18 , Anton M. Koekemoer12 , Juno Li8,9 , Madeline A. Marshall9,19 , Tyler J. McCabe1 , Noah J. McLeod1 , Stefanie N. Milam20 , Rosalia O’Brien1 , Nor Pirzkal12 , Aaron S. G. Robotham8 , Russell E. Ryan, Jr.12 , Christopher N. A. Willmer10 , Haojing Yan21 , Min S. Yun22 , and Adi Zitrin23 1 School of Earth and Space Exploration, Arizona State University, Tempe, AZ 85287-1404, USA; rortizii@asu.edu 2 Center for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA 3 Department of Physics and Astronomy, Minnesota State University, Mankato, MN 56001, USA 4 Chinese Academy of Sciences South America Center for Astronomy, National Astronomical Observatories, CAS, Beijing 100101, Peopleʼs Republic of China 5 AURA for the European Space Agency (ESA), Space Telescope Science Institute, 3700 San Martin Drive, Baltimore, MD 21210, USA 6 Jodrell Bank Centre for Astrophysics, Alan Turing Building, University of Manchester, Oxford Road, Manchester, M13 9PL, UK 7 Instituto de Física de Cantabria (CSIC-UC), Avda. Los Castros s/n. 39005 Santander, Spain 8 International Centre for Radio Astronomy Research (ICRAR) and the International Space Centre (ISC), The University of Western Australia, M468, 35 Stirling Highway, Crawley, WA 6009, Australia 9 ARC Centre of Excellence for All Sky Astrophysics in 3 Dimensions (ASTRO 3D), Australia 10 Steward Observatory, University of Arizona, 933 North Cherry Avenue, Tucson, AZ 85721-0009, USA 11 Department of Physics, Montana State University, Bozeman, MT 59717, USA 12 Space Telescope Science Institute, 3700 San Martin Drive, Baltimore, MD 21210, USA 13 Association of Universities for Research in Astronomy, Washington, DC 20004, USA 14 Space Telescope Science Institute, 3700 San Martin Drive, Baltimore, MD 21218, USA 15 Department of Physics, University of Louisville, Natural Science Building 102, Louisville, KY 40292, USA 16 Korea Astronomy and Space Science Institute, Yuseong-gu, Daejeon 34055, Republic of Korea 17 SNU Astronomy Research Center, Department of Physics & Astronomy, Seoul 08826, Republic of Korea 18 Department of Physics and Astronomy, University of Alabama, Box 870324, Tuscaloosa, AL 35404, USA 19 National Research Council of Canada, Herzberg Astronomy & Astrophysics Research Centre, 5071 West Saanich Road, Victoria, BC V9E 2E7, Canada 20 Astrochemistry Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA 21 Department of Physics and Astronomy, University of Missouri, Columbia, MO 65211, USA 22 Department of Astronomy, University of Massachusetts, Amherst, MA 01003, USA 23 Department of Physics, Ben-Gurion University of the Negev, P.O. Box 653, Be’er-Sheva 84105, Israel Received 2024 April 16; revised 2024 July 17; accepted 2024 August 7; published 2024 October 16 Abstract The first public 0.9–4.4 μm NIRCam images of the North Ecliptic Pole Time Domain Field uncovered galaxies displaying point-source features in their cores as seen in the longer-wavelength filters. We visually identified a sample of 66 galaxies (∼1 galaxy arcmin–2) with pointlike cores and have modeled their two-dimensional light profiles with GalFit, identifying 16 galactic nuclei with measurable point-source components. GalFit suggests that the visual sample is a mix of both compact stellar bulge and point-source galaxy cores. This core classification is complemented by spectral energy distribution modeling to infer the sample’s active galactic nucleus (AGN) and host-galaxy parameters. For galaxies with measurable point-source components, the median fractional AGN contribution to their 0.1–30.0 μm flux is 0.44, and 14/16 are color-classified AGN. We conclude that near-infrared point-source galaxy cores are signatures of AGN. In addition, we define an automated sample-selection criterion to identify these point-source features. This criterion can be used in other extant and future NIRCam images to streamline the search for galaxies with unresolved IR-luminous AGN. The James Webb Space Telescope’s superb angular resolution and sensitivity at infrared wavelengths are resurrecting the morphological identification of AGN. Unified Astronomy Thesaurus concepts: Active galactic nuclei (16) Materials only available in the online version of record: figure set, machine-readable tables 1. Introduction More than 80 yr ago, C. K. Seyfert (1943) drew attention to six “extragalactic nebulae” with broad emission lines and “exceedingly luminous stellar or semistellar” nuclei. Galaxies of this type became known as “Seyfert galaxies” (E. M. Burbi- dge et al. 1963), and they are now recognized (e.g., D. E. Ost- erbrock 1993) as members of the low-luminosity end of the population of active galactic nuclei (AGN). AGN are now thought to consist of an accretion disk around a supermassive black hole (SMBH) within a wider, optically thick dust torus (e.g., R. R. J. Antonucci & J. S. Miller 1985). This central engine can emit enormous luminosities and can even outshine the entire host galaxy (e.g., P. Padovani et al. 2017). Modern studies of AGN probe the relationship with The Astrophysical Journal, 974:258 (14pp), 2024 October 20 https://doi.org/10.3847/1538-4357/ad6d5e © 2024. The Author(s). Published by the American Astronomical Society. Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. 1 https://orcid.org/0000-0002-6150-833X https://orcid.org/0000-0002-6150-833X https://orcid.org/0000-0002-6150-833X https://orcid.org/0000-0001-8156-6281 https://orcid.org/0000-0001-8156-6281 https://orcid.org/0000-0001-8156-6281 https://orcid.org/0000-0003-3329-1337 https://orcid.org/0000-0003-3329-1337 https://orcid.org/0000-0003-3329-1337 https://orcid.org/0000-0002-9895-5758 https://orcid.org/0000-0002-9895-5758 https://orcid.org/0000-0002-9895-5758 https://orcid.org/0000-0003-1268-5230 https://orcid.org/0000-0003-1268-5230 https://orcid.org/0000-0003-1268-5230 https://orcid.org/0000-0001-6650-2853 https://orcid.org/0000-0001-6650-2853 https://orcid.org/0000-0001-6650-2853 https://orcid.org/0000-0001-9394-6732 https://orcid.org/0000-0001-9394-6732 https://orcid.org/0000-0001-9394-6732 https://orcid.org/0000-0001-7016-5220 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http://creativecommons.org/licenses/by/4.0/ their host galaxies and intergalactic environments along with properties of the central SMBHs (e.g., F. Bollati et al. 2023; J. H. Costa-Souza et al. 2024; V. M. Sampaio et al. 2023). AGN emit energy across the entire electromagnetic spec- trum, and identification and classification of AGN are based on X-ray (e.g., M. Elvis et al. 1978), UV–visible (e.g., C. K. Seyfert 1943), infrared (e.g., D. Stern et al. 2005), and radio (e.g., B. L. Fanaroff & J. M. Riley 1974) observations. AGN are most often identified via their spectral energy distributions (SEDs; e.g., J. Lyu et al. 2024; Q. Li et al. 2024; G. Yang et al. 2023), colors (e.g., D. Stern et al. 2005; Y.-H. Hwang et al. 2021; I. Juodzbalis et al. 2023; L. J. Furtak et al. 2024), spectroscopy (e.g., C. J. Burke et al. 2024; M. Mehdipour et al. 2024), variability (e.g., E. Pouliasis et al. 2019; R. O’Brien et al. 2024), and radio (e.g., M. Hyun et al. 2023) or X-ray emission (e.g., A. Masini et al. 2020; X. Zhao et al. 2021). However, heavily obscured AGN whose intrinsic UV–visible signatures are hidden by dust may be missed (e.g., E. Glikman et al. 2012; R. C. Hickox & D. M. Alexander 2018), and at infrared wavelengths, where the dust extinction is lower, wide-field surveys can only be done from space. Until now, the low angular resolution of infrared space observatories and the “big data” era of advanced instrumentation and vast surveys (such as the Sloan Digital Sky Survey; D. G. York et al. 2000) has caused identification of AGN through visual morphologies to have fallen out of favor. Recent work indicates that a significant population of redder AGN exists. Their UV–visible SEDs suggest intermediate levels of extinction, AV∼ 1–3 mag (e.g., B. J. Wilkes et al. 2002; G. T. Richards et al. 2003; J. R. Trump et al. 2013; S. L. Cales & M. S. Brotherton 2015; J. Wang et al. 2019). These objects exhibit hybrid properties, blurring the lines between canonical AGN classifications, and may represent an important evolutionary phase. For instance, modeling suggests that the AGN torus geometry evolves as material accretes onto the black hole (e.g., K. Ohsuga & M. Umemura 2001; A. K. Mandal et al. 2018), with early dust-free to intermediate- stage reddened phases necessarily preceding the well-studied mature dust-obscured AGN. Disentangling the nature of these moderately reddened AGN exhibiting a blend of obscured and unobscured traits has proved challenging but vitally important for refining our understanding of the complex coevolution between SMBHs and their host galaxies over cosmic time. Detailed multiwavelength modeling is critical to robustly characterize the properties of these objects. The James Webb Space Telescope (JWST), with its unprecedented resolution in the infrared (e.g., G. Yang et al. 2023; Q. Li et al. 2024; R. L. Davies et al. 2024), offers the chance to revisit the morphological selection and analysis of AGN. This paper is an initial exploration of the possibilities via the characteristic diffraction spikes of JWST’s point-spread function (PSF) at the longer near-infrared wavelengths that suggest pointlike galaxy cores. The North Ecliptic Pole (NEP) Time Domain Field (TDF; R. A. Jansen & R. A. Windhorst 2018) is located within JWSTʼs continuous viewing zone, making it a prime target for AGN variability and other time domain science. Because the ultimate goal will be to compare morphological selection with other methods, we used the now-public JWST NEP TDF images (R. A. Windhorst et al. 2023) to visually search for galaxies with pointlike features in their center, i.e., potential AGN, following the core morphology and Seyfert class relationship (e.g., R. A. Windhorst & S. H. Cohen 2010; M. J. Rutkowski et al. 2013). The morphologically selected sample was then analyzed in order to infer physical parameters and AGN presence. Section 2 describes the observations and data, along with the construction of a morphologically selected AGN catalog and a means to automate the selection of galaxies with point-source nuclei. Section 3 discusses the results of fitting two-dimensional light profiles and various galaxy SEDs to the sample, and Section 4 gives a summary. All magnitudes are in AB units (J. B. Oke & J. E. Gunn 1983). Where relevant, we adopt a flat ΛCDM cosmology with H0= 68 km s−1Mpc−1, ΩM= 0.32, and ΩΛ= 0.68 (Planck Collaboration et al. 2016, 2020). 2. Data and Cataloging 2.1. Observations The JWST NEP TDF is centered at (R.A., decl.)J2000= (17:22:47.896, +65:49:21.54) (R. A. Jansen & R. A. Windho- rst 2018). The TDF was observed with JWST as part of the Prime Extragalactic Areas for Reionization and Lensing Science (PEARLS) GTO program (R. A. Windhorst et al. 2023) in eight NIRCam filters: F090W, F115W, F150W, F200W, F277W, F356W, F410M, and F444W. The observations consisted of four orthogonal spokes (see Figure 1 of R. O’Brien et al. 2024) observed between 2022 August 25 and 2023 May 30. The NIRCam image quality is diffraction-limited at wavelengths 1.3 μm (J. Rigby et al. 2023; R. A. Windhorst et al. 2023) with point-source FWHM values ranging from 60 to 160mas at wavelengths of 1.3–4.8 μm. The data were retrieved from MAST and postprocessed by the PEARLS team using their custom pipeline to mitigate 1/f noise, identify and subtract wisps in the NIRCam/SW filters, mask snowball artifacts, and flatten the background across readout amplifier boundaries. The individual postprocessed images were rectified and aligned to Gaia/DR3. Full mosaics of the field were created for each filter with a 0 030 pixel scale. The 5σ point-source limit is typically between 28.0 and 29.1 mag, depending on the filter, and 29.0 mag in F200W specifically. A. S. G. Robotham et al. (2023), R. A. Windhorst et al. (2023), and R. A. Jansen et al. (2024a, in preparation) give more details of the data reduction, calibration, and postprocessing. While the primary focus of this work is the ability to identify and analyze likely AGN with JWST/ NIRCam photometry, we incorporate ancillary Hubble Space Telescope (HST) observations of the NEP TDF with the Wide Field Camera 3 (WFC3/UVIS) in the F275W filter (λc; 0.272 μm) and the Advanced Camera for Surveys (ACS/WFC) in the F435W and F606W filters (λc; 0.433 and 0.592 μm, respectively) with a total area of ;194 arcmin2 (ACS/WFC). Observations from HST GO 15278 were taken between 2017 October 1 and 2019 February 9, and those from GO 16252+16793 (TREASUREHUNT) were taken between 2020 September 25 and 2022 October 31. Both programs used four-orbit continuous viewing zone (CVZ) visits to reach 2σ limiting depths of mAB; 28.0, 28.6, and 29.5 mag in F275W, F435W, and F606W, respectively. R. O’Brien et al. (2024, Section 2.1) and R. A. Jansen et al. (2024b, in preparation) give further details of the HST observations. For an initial comparison with longer-wavelength observa- tions in the NEP TDF, we used the Very Large Array (VLA) 2 The Astrophysical Journal, 974:258 (14pp), 2024 October 20 Ortiz III et al. 3 GHz radio observations detailed by M. Hyun et al. (2023, their Appendix A). 2.2. Catalogs and Samples We used SExtractor (E. Bertin & S. Arnouts 1996) to generate catalogs. The detection threshold required 9 contiguous pixels 1.5σ above the background. We used 32 deblending subthresholds with the 0.06 minimum contrast necessary for object deblending. We ran SExtractor on all eight filters in dual-image mode using F444W as the detection filter to produce position-matched catalogs of the entire JWST NEP TDF. Magnitudes are MAG_AUTO except where indicated otherwise. We position-matched objects in both R.A. and decl. between the HST (R. O’Brien et al. 2024) and VLA (M. Hyun et al. 2023) catalogs using 0 1 and 0 5 separations, respectively, to identify counterparts to the samples used in our analysis. We used both the EAZY24 (G. B. Brammer et al. 2008) and CIGALE25 (M. Boquien et al. 2019) SED-fitting codes to compute photometric redshifts for our samples, as discussed in Section 3. We used the CIGALE-computed photometric redshifts for nominal redshifts in this work and for sample construction because the CIGALE fitting incorporates multiple components, including AGN, whereas EAZY was tuned for single-template fitting. Table 1 catalogs our sample. 2.3. Visual Sample Selection We visually inspected an eight-filter color-composite of all NIRCam observations within the JWST NEP TDF to identify resolved galaxies with unresolved pointlike features in their cores. The color-composite was constructed according to the Trilogy26 prescription (D. Coe et al. 2012). The entire JWST NEP TDF was visually inspected for galaxies that displayed the characteristic diffraction spikes or PSF effects from compact, luminous galaxy cores. This identified 66 galaxies,27 which we refer to as “central point-source galaxies” (CPGs) hereafter. Table 1 lists the CPGs, and Figure 1 shows their images and highlights the qualitative criteria, diversity, and similarities of the CPGs. Several objects within the CPG sample display obvious pointlike features in their cores (i.e., IDs 1, 14, 28, 48), whereas the vast majority show less distinct features of the characteristic PSF from JWST. The NIRCam images of the NEP TDF sampled an area of 65.4 arcmin2, and hence our sample of 66 CPGs corresponds to ∼1 galaxy arcmin–2 to mF444W 22 ABmag. This is compar- able to the Wide-field Infrared Survey Explorer (WISE; W1– W2) color-selected AGN density (e.g., R. J. Assef et al. 2013, their Table 1), ∼0.5 AGN arcmin–2 to mW2 20.5 ABmag. 2.4. Automated Sample Selection While our initial sample was obtained through qualitative visual selection, a quantitative method is needed to identify objects similar to CPGs, especially for large surveys. The concentration index (e.g., C. J. Conselice 2003) is one measure, but its basic ratio finds sources that are peaky but not necessarily pointlike. Instead, we used SExtractor to measure F444W magnitudes in circular apertures at the object cores. Aperture radii were 6, 8, and 10 pixels, corresponding to radii of 0 18, 0 24, and 0 30, respectively. We adopted these apertures to probe the surface brightness near the cores of the CPG sample and identify objects that are morphologically similar to stars out to a certain radius. We used the smallest aperture to measure the average surface brightness within an inner area of ∼0.1 arcsec2 (SBINNER) and the two other apertures to measure the average surface brightness within an outer annulus (SBOUTER), which has an area equal to that within the inner circular aperture. Multiple aperture radii for SBINNER and SBOUTER were tested, and by experiment, the ones adopted provided the most effective way to probe point-source features in galaxies. Figure 2 plots SBOUTER against SBINNER. Stars are identified as straddling the NIRCam diffraction limit with 150mas� FWHM� 170mas with mF444W� 28mag (R. A. Windhorst et al. 2023). CPGs near the stars in Figure 2 are point-source CPGs (PS-CPGs), characterized and identified in Section 3.1 as having point-source cores. These objects show that there exists a parameter space where we can probe point sources within galaxies, barring contamination from other stars. The longer wavelengths of the near-infrared regime can probe both optically obscured and unobscured AGN (e.g., R. J. Assef et al. 2013) and both star-forming galaxies and weak AGN (e.g., D. Kim et al. 2019) via redder colors in the 3.5–3.6 μm and 4.4–4.5μm bands. Thus, JWST’s aperture photometry—in contrast to shorter- wavelength data—can be more effective at probing morphological signatures of AGN at these wavelengths. Obscured AGN in the UV–visible are likely showing up in these images because of the much smaller dust extinction at these wavelengths, and we carried out complementary SED analysis (Section 3) to clarify whether AGN are the most probable explanation for the pointlike features in our infrared CPGs. The above classification procedure provides a robust star–CPG separation and a CPG separation from brighter galaxies that automatically recovers our CPG sample and finds objects similar to it. Particularly, this classification procedure can probe the presence of a point source within resolved galaxies. Identifying galaxies that straddle the morphology of stars (such as the orange squares in Figure 2 representing PS-CPGs) without contamination would allow a search for galaxies with pointlike cores in any JWST/NIRCam image. The automated method does not pick up all of the weak Seyferts; some will require higher signal-to-noise data to be identified by this procedure. 2.5. Control Sample Understanding how CPGs differ from other galaxies requires a comprehensive control sample. To create one, we find the four most similar galaxies in the JWST NEP TDF with respect to brightness and CIGALE-computed photometric redshift down to ABmag� 22, the limiting magnitude for CPG selection. The resulting control sample has 225 galaxies, slightly fewer than 4× the CPG sample size. These are all the galaxies we could find with a similar mF444W and photometric redshift distribution. The control sample was also analyzed with the same two- dimensional light profile and SED analysis as the CPG sample, providing robust number statistics and similar galaxy demo- graphics to assess the reliability of the classifications. The first spoke of the NEP TDF, observed in 2022 August, had a scheduling interruption due to a guide star failure. The 24 https://github.com/gbrammer/eazy-photoz 25 https://cigale.lam.fr/ 26 https://www.stsci.edu/~dcoe/trilogy 27 We verified that no objects in the sample are stars by applying an FWHM and magnitude cut similar to that used by R. A. Windhorst et al. (2023) and requiring zphot ≠ 0. 3 The Astrophysical Journal, 974:258 (14pp), 2024 October 20 Ortiz III et al. https://github.com/gbrammer/eazy-photoz https://cigale.lam.fr/ https://www.stsci.edu/~dcoe/trilogy Figure 1. RGB image cutouts for the 66 CPGs sorted by zphot from CIGALE. All image cutouts are 6″ square. The top left corner of each image gives the image ID from Table 1, and zphot and fAGN are in the bottom left. The RGB scaling is R = 0.3·F356W + 0.8·F410M + 1.0·F444W, G = 1·F200W + 0.7·F277W + 0.5·F356W, B = 1·F090W + 1·F115W + 1·F150W + 1·F200W + 1·F277W + 0.5·F356W + 0.5·F410M + 0.5·F444W. 4 The Astrophysical Journal, 974:258 (14pp), 2024 October 20 Ortiz III et al. second half of that spoke was observed 10 days later, resulting in two sets of diffraction spikes rotated by 10°. The PSF structure with 12 diffraction spikes could not be modeled for morphological parameters with affordable computational efficiency, and that left only 132 control galaxies that could be so modeled. All other parameters were determined for the full control sample of 225 galaxies. 3. Results and Discussion 3.1. Two-dimensional Light Modeling We used the two-dimensional fitting algorithm GalFit (C. Y. Peng et al. 2002) to model the 4.4 μm light profiles of the CPGs. We modeled all galaxies with both a double-Sérsic model and a Sérsic component plus a PSF component. Comparison of the two models determines whether CPGs contain true point sources or a compact stellar bulge. Many galaxies in both the CPG and control samples cannot be completely modeled with only two components, but this modeling approach is adequate to characterize the apparent pointlike features. This structural analysis is primarily sensitive to AGN that have similar or higher luminosities to that of a central stellar bulge. The Sérsic profile is a robust representation of a variety of galaxy types because of its flexibility in profile characteristics. The Sérsic profile is parameterized as ( ) ( )⎜ ⎟ ⎧ ⎨⎩ ⎡ ⎣ ⎢ ⎛ ⎝ ⎞ ⎠ ⎤ ⎦ ⎥ ⎫ ⎬⎭ I R I b R R exp 1 , 1e n m e n1 = - - where Ie is the surface brightness at the half-light radius Re, n is the Sérsic index, bn is a derived parameter ensuring proper integration at Re (and is the value at which the gamma probability distribution function integrates to 0.5 with a shape parameter of 2n), and Rm is the two-dimensional modified radius where the profile is being evaluated (e.g., A. S. G. Rob- otham et al. 2017). For our purposes, the Sérsic profile is assumed to generally model the nucleus and extended stellar disk of a galaxy in a double-Sérsic component fit. For two- component fits with a Sérsic component and a PSF component, the Sérsic profile generally models the bulk of the extended galaxy, while the PSF component models an unresolved point source in the galaxy nucleus. Thus, a GalFit analysis classifies the core type of the CPGs. For both our CPG and control sample, we used a simulated 4.4 μm PSF from WebbPSF (M. D. Perrin et al. 2012) based on 300× 300 pixel (9″× 9″) image stamps of each galaxy. We masked out neighboring objects through a SExtractor- generated segmentation map and estimated the sky background using the uncertainty image from the JWST pipeline. A note at the end of Table 2 gives the constraints on the GalFit parameters. Figure 3 visualizes the GalFit output for six CPGs. Figure 4 compares the goodness-of-fit results for the single- and double-Sérsic fits. Most CPGs prefer the double-Sérsic fit, but 16 are best fit with the single-Sérsic component plus a PSF component. This is not the case for the control-sample galaxies. Many galaxies in both samples are equally well fit by both models with a preference for a double-Sérsic fit in borderline cases. The preference is consistent with the double Sérsic having more free parameters. Both the CPG and control sample demonstrate a similar bulge core frequency. Hereafter, we refer to CPGs with a point-source core classification from this two- Figure 2. F444W central surface brightness vs. the surface brightness in a surrounding annulus. The central surface brightness was measured in a circular aperture with a radius of 0 18, and the outer annulus was measured between circular apertures of 0 24 and 0 30. Navy blue circles and orange squares represent CPGs and PS-CPGs (see Section 3.1), and pink stars represent stars in the field (R. A. Windhorst et al. 2023). Only objects with mF444W < 22 AB mag, the limiting magnitude of the CPG sample, are plotted. The gray-scale bar (right) maps the galaxy total F444W magnitudes (MAG_AUTO), with brighter galaxies plotted as darker. 5 The Astrophysical Journal, 974:258 (14pp), 2024 October 20 Ortiz III et al. component GalFit procedure as “PS-CPGs,” borderline cases as “undetermined CPGs” (U-CPGs), and bulge core classified CPGs as “bulge CPGs.” This distinction is particularly useful when using SED techniques to infer the physical properties of the CPGs with likely point-source features in their cores. A number of CPGs are not completely fit with two components. There are definitely galaxies within our CPG sample that were visually selected simply for having ultraluminous, compact nuclei, thus having steep Sérsic indices and showing faint features of the PSF. Consequently, these bulge CPGs are likely galaxies not hosting an observable AGN at our observed wavelengths and are more suggestive of being a compact stellar bulge or nuclear starburst (e.g., V. A. Bruce et al. 2016). It is reasonable to expect that the CPG sample would show a mix of true point-source and stellar-bulge cores. More comprehensive GalFit modeling would clarify the point-source presence in galaxies that are not well fit with only two components (i.e., U-CPGs). Nevertheless, Figure 4 suggests that the CPG sample does indeed have point sources within galactic nuclei. Additional constraints from SED fitting are needed to clarify whether an AGN is responsible for these features. Figure 3. GalFit image output for six CPGs. For each object, there are six subpanels; the top three subpanels visualize the Sérsic+Sérsic fit, and the bottom three visualize the Sérsic+PSF fit. Each row shows the image (left), model (middle), and residual image (right). Green text identifies the CPG ID, the two-component fit, and the goodness-of-fit statistic, 2cn , for the fit. The color map indicates negative flux in black and positive flux in white. Figure 4. Histogram of the goodness-of-fit statistic ( 2cn) between the GalFit procedure that models both the CPG and control sample with a Sérsic+Sérsic two-component fit and a Sérsic+PSF two-component fit. 2 2c cD ºn n[Sérsic +Sérsic]– 2cn[Sérsic+PSF], indicating a preference for a bulge core (negative values) or a point-source core (positive values). The hatched histogram identifies CPGs, with distinction being made between general CPGs (navy) and PS-CPGs (orange) to further classify and characterize the CPG core type. The navy histogram contains hatched and double-hatched bins, identifying bulge cores and undetermined CPG core types, respectively. The gray histogram represents the control sample and is normalized to the CPG sample size. Figure 5. χ2 of EAZY fits with and without an AGN. Colored markers represent CPGs, with squares identifying PS-CPGs and circles representing the remaining CPGs. Colors indicate each CPG's best-fit host/AGN ratio as indicated in the color bar. Gray plus signs represent control-sample galaxies. The diagonal line indicates equality, i.e., an undetermined classification. CPG IDs 1 and 29 are not plotted because their fits failed to converge owing to the core’s extreme brightness. 6 The Astrophysical Journal, 974:258 (14pp), 2024 October 20 Ortiz III et al. 3.2. Seyfert Template Fitting with EAZY In order to estimate AGN fractions and derive photometric redshifts, we used EAZY to fit the photometry for each of the CPG and control-sample galaxies. For this single-component template fitting procedure, we ran EAZY twice: one run allowing a single- component fit from the 12 EAZY tweak_fsps_QSF_12_v3 templates and a second run fitting the AGN-ATLAS SEDs. The former templates are galaxy templates used for stellar population synthesis, and the latter templates (M. J. I. Brown et al. 2019) are blends of AGN and host-galaxy SEDs covering 0.09–30.0μm. The host/AGN ratios available are powers of 2 from 0.5 to 64 normalized at 0.6 μm. As shown in Figure 5, 70% of the CPGs prefer an AGN- ATLAS template over a standard EAZY template. Generally, the fits with the highest AGN contributions to the total SED are still fairly well fit with an EAZY template, i.e., with no AGN at all. Even when an AGN is indicated, for most galaxies in the sample, the host galaxy outshines the AGN emission. The fitting does not prove that our sample galaxies are Seyferts. Offering more SED templates is likely just adding more free parameters and resulting in better fits as demonstrated by the control sample, which shows a similar distribution to the CPGs in Figure 5. In fact, 86% of the control-sample fits prefer an AGN-ATLAS template. Nevertheless, the fitting is consistent with the hypothesis that the morphological selection is finding AGN. 3.3. SED Parameter Estimation with CIGALE To infer the galaxies’ physical properties, we used CIGALE, an SED-fitting code relying on the energy balance between the ultraviolet and infrared. The fits are good, as expected with so many free parameters, with 94% of the fits having  1.02cn . This paper includes a figure set of all 66 CIGALE fits for our CPG sample, and Figure 6 shows three examples. A CIGALE output parameter of immediate interest is fAGN, the fractional contribution of AGN emission to the bolometric luminosity. We tuned CIGALE to compute fAGN based on AGN emission from 0.1 to 30 μm (following R. J. Assef et al. 2013, their Equation 1) and the NIRCam photometry. Most interestingly, the PS-CPG median fAGN= 0.44± 0.12, while the control sample’s fAGN= 0.24± 0.09. The formal statistical separation is only at the 1σ level, though our result suggests that the PS-CPG sample has measurable AGN emission in the infrared. fAGN can be better constrained with additional photometry in future work, particularly in the submillimeter (see, e.g., L. Ciesla et al. 2015). CIGALE also provides estimated stellar masses and star formation rates (SFRs), which provide context for the CPGs. Typical galaxies fall on the star formation main sequence, which depends on both redshift and stellar mass (J. S. Speagle et al. 2014, Equation (28)). As Figure 7 shows, the majority of our sample lies near the star formation main sequence, and ∼31% lies at or above the starburst boundary given by G. Rodighiero et al. (2011). An independent SFR estimate comes from radio observations Figure 6. Example CIGALE model fits for three CPG SEDs. Fit residuals are shown below each SED panel. The CPG ID, χ2, photometric redshift, and fAGN are indicated in each panel. The legend identifies the model components included in the SED fitting. (The complete figure set (66 images) is available in the online article.) Figure 7. SFRs from CIGALE relative to the respective star formation main sequence. The y-axis shows ( ) (SFR log SFR log SFR10 10 MSD º - ), with SFRMS defined by J. S. Speagle et al. (2014, their Equation (28)). The horizontal dotted line and shaded region mark the main sequence and the ±0.2 dex range. The upper cyan, the middle green, and the lower magenta dashed lines mark 0.6 dex above the main sequence, commonly taken as the starburst boundary (e.g., G. Rodighiero et al. 2011); the lower boundary to the main sequence; and the upper boundary to the quiescent region (e.g., A. Renzini & Y.-j. Peng 2015), respectively. Circles and squares identify CPGs and PS-CPGs, respectively, and markers with crosses indicate sources with VLA 3 GHz matches. 7 The Astrophysical Journal, 974:258 (14pp), 2024 October 20 Ortiz III et al. https://doi.org/10.3847/1538-4357/ad6d5e (e.g., J. J. Condon 1992; F. S. Tabatabaei et al. 2017). Positions for 24 CPGs (identified in Table 1) match sources in the VLA 3GHz catalog (M. Hyun et al. 2023). Generally, massive and starburst CPGs are the ones having radio detections. This could be attributed to either the synchrotron radiation produced from remnants of Type II supernovae (e.g., R. A. Chevalier 1982), i.e., star formation; radio emission from the AGN (e.g., K. I. Kellerm- ann 1987); or both. S. P. Willner et al. (2023, their Figure 11) similarly showed that few radio sources in the TDF are in the starburst range. Figure 8 compares the inferred physical properties for the CPG and control samples. In short, the CPGs are massive galaxies with lower SFRs compared to the control sample. The PS-CPGs tend to have slightly lower inferred stellar masses and higher SFRs than other CPGs. D. J. Rosario et al. (2013) showed that AGN are more likely to be hosted by a star-forming galaxy owing to the need for cold gas to fuel both AGN and star formation, though X-ray AGN can be present in similar mass and redshift nonactive galaxies. While the CPG and control samples show a similar distribution of fAGN, the PS-CPGs dominate the upper quartile of CPG fAGN values with 〈fAGN〉= 0.47. L. Ciesla et al. (2015) emphasized that the small fAGN values from CIGALE are not well constrained absent submillimeter photometry; therefore, the values of fAGN 0.2 may be overestimated. Therefore, our CIGALE inferences do not necessarily preclude an AGN presence for the rest of our CPGs, though they do suggest unique AGN activity in our PS-CPGs. Finally, CIGALE identified a visual selection bias toward massive galaxies in the CPG sample because only bright galaxies could be examined for morphology. Often it was the luminous core that qualified a galaxy for our visual sample. Regardless, PS-CPGs’ active star formation, normal stellar mass, and frequent radio-loudness all suggest that an AGN giving rise to a near-infrared point source is present. 3.4. (mF356W−mF444W) Colors Another standard AGN selection method is red (mF356W−mF444W) color (e.g., D. Stern et al. 2005). The NIRCam F356W and F444W filters are close to the WISE W1 Figure 8. CIGALE-inferred physical parameters from SED fitting 0.2–4.4 μm observations. All panels are histograms of the CPG sample in navy against the control sample in gray. PS-CPGs are plotted in orange and included in the CPG distribution. From top left, the panels show the inferred stellar mass, SFRs, photometric redshifts, and fAGN. Figure 9. (mF356W,AB − mF444W,AB) color vs. CIGALE-computed photometric redshift. Navy blue circles and orange squares identify CPGs and PS-CPGs, respectively. Plus signs represent galaxies in the control sample. The green and magenta lines show the colors of a galaxy that formed at z = 5 and has AV = 0 and AV = 1, respectively (D. Calzetti et al. 2000 extinction law). The galaxy model is a G. Bruzual & S. Charlot (2003) stellar population with a Salpeter initial mass function, solar metallicity, and τ = 0.5 Gyr exponentially declining star formation history. The horizontal dotted line at 0.16 shows the (mF356W,Vega − mF444W,Vega) = 0.8 AGN color-selection criterion in AB mag, replicating the (W1–W2) AGN colors (R. J. Assef et al. 2013). 8 The Astrophysical Journal, 974:258 (14pp), 2024 October 20 Ortiz III et al. and W2 filters (E. L. Wright et al. 2010) and can replicate the (W1−W2)> 0.80 (Vega magnitudes) AGN color selection (e.g., D. Stern et al. 2012). There are 19 CPGs with (mF356W,Vega−mF444W,Vega)> 0.80 as shown in Figure 9. (Table 1 gives the CPG colors in AB mag.) Nearly all remaining CPGs and control-sample galaxies have integrated colors consistent with a pure stellar population that formed early and evolved passively. These colors do not preclude a modest AGN or star formation contribution, especially for higher-redshift CPGs. However, PS-CPGs demonstrate a strong preference for colors consistent with an AGN (i.e., (mF356W−mF444W)> 0.16 mag), further suggesting that the near-infrared point-source feature comes from a visible AGN. This is most likely because dust obscuration decreases at the longer wavelengths (D. Kim et al. 2019), resulting in redder colors because an AGN is shining through the galaxy. This is consistent with the point-source feature often dominating the reddest wavelengths of our observations (Figure 1). 4. Summary and Future Prospects The superb angular resolution of JWST/NIRCam reveals galaxies (“CPGs”) with pointlike cores at redder wavelengths. Based on GalFit analysis, 16/66 CPGs have a point-source core. This is a much higher frequency of point-source cores than in a control sample. Best-fit SEDs for the CPGs in the JWST NEP TDF suggest that the CPGs are massive and luminous, and in most cases, the extended galaxy outshines the unresolved core. The SEDs are well characterized with AGN components, though the CPG sample is not AGN-dominated. However, the core classifica- tion from GalFit suggests that at least 14 PS-CPGs host IR- luminous AGN for the following reasons: 1. 14/16 are classified as AGN via their mF356W−mF444W colors; 2. GalFit prefers a point-source nucleus over a compact stellar bulge in the nucleus; 3. CIGALE finds 〈fAGN〉= 0.47, higher than for the control sample; and 4. all display the point-source signatures in their images that motivated this work (Figure 1). Photometry at additional wavelengths and spectroscopy would aid in classifying these objects, confirming AGN presence, constraining host-galaxy parameters, and character- izing the mechanisms driving the pointlike galaxy cores for the entire CPG sample. More robust GalFit analysis could clarify which are either PS-CPGs or bulge CPGs (e.g., CPG ID 48 was classified as a bulge CPG despite displaying an obvious point-source signature). Additionally, cross-correlation of our CPGs with X-ray detections will confirm their AGN nature and measure the nuclear luminosity and absorption. This work has identified AGN only at 4.4 μm. Future work could extend this morphological classification to shorter-wavelength JWST data to improve the angular resolution and make it more broadly applicable—despite the larger effects from dust at the shorter wavelengths—and to test at what wavelength an automated morphological approach would break down. The initial visual sample selection revives the classical approach to AGN identification via a bright, starlike nucleus. This simple selection method is possible because of JWST’s superb angular resolution at long wavelengths, where extinc- tion is much lower than in visible light. Taking advantage of the near-infrared surface brightness and morphology of our CPG sample made it possible to quantify and automate the morphological approach. Future simulations and modeling can refine this procedure to constrain where the PS-CPG separation from brighter galaxies and stars can confidently exist. Even as a prototype, this automated sample selection of brighter galaxies with point-source features streamlines the search for and analysis of AGN in current and future JWST/NIRCam imaging. Acknowledgments R.O. III dedicates this work to the glory of God who makes all things possible. We thank the anonymous referee for the helpful recommendations that significantly improved the results of this work. R.O. III acknowledges support from an undergraduate Arizona NASA Space Grant, cooperative agreement 80NSSC20M0041. R.A.W., S.H.C., and R.A.J. acknowledge support from NASA JWST Interdisciplinary Scientist grants NAG5-12460, NNX14AN10G, and 80NSSC18K0200 from GSFC. This work is based on observations made with the NASA/ESA/CSA James Webb Space Telescope. The data were obtained from the Mikulski Archive for Space Telescopes (MAST) at the Space Telescope Science Institute, which is operated by the Association of Universities for Research in Astronomy, Inc., under NASA contract NAS 5-03127 for JWST. These observations are associated with JWST programs 1176, 2736, and 2738. A.Z. acknowledges support by grant No. 2020750 from the United States–Israel Binational Science Foundation (BSF) and grant No. 2109066 from the United States National Science Foundation (NSF); by the Ministry of Science & Technology, Israel; and by the Israel Science Foundation grant No. 864/23. H.B.H. and S.N.M. also acknowledge support from NASA JWST Interdisciplinary Scientist grant 21-SMDSS21-0013. All the JWST data used in this paper can be found on MAST at doi:10.17909/jtd6-af15. Facilities: JWST (JWST/NIRCam), HST, VLA. Software: AstroPy (Astropy Collaboration et al. 2013, 2018, 2022); CIGALE (M. Boquien et al. 2019); SExtractor (E. Bertin & S. Arnouts 1996); EAZY (G. B. Brammer et al. 2008); GalFit (C. Y. Peng et al. 2002); WebbPSF (M. D. Perrin et al. 2012). Appendix Table 1 is a catalog of morphologically identified CPGs in the JWST NEP TDF. Table 2 is a catalog of GalFit output parameters for two-component fitting of our visual sample. 9 The Astrophysical Journal, 974:258 (14pp), 2024 October 20 Ortiz III et al. https://doi.org/10.17909/jtd6-af15 Table 1 Catalog of Morphologically Identified CPGs in the JWST NEP TDF ID R.A. Decl. F275W F435W F606W F090W F115W F150W F200W F277W F356W F410M F444W z fAGN Color H23 J2000 J2000 ––––––––––––––––––––––––––––––––––––––––––––––––––AB mag–––––––––––––––––––––––––––––––––––––––––––––––––– AB mag (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15) (16) (17) (18) 1 260.671752 +65.7116883 L 19.99 18.75 L L L L 16.69 17.07 17.05 17.32 0.35 0.13 −0.25 194 2 260.756207 +65.7131767 27.05 24.95 24.54 23.11 22.16 21.61 21.44 20.92 20.72 20.46 20.37 1.37 0.5 0.35 314 3 260.694024 +65.7169286 L 24.02 22.12 20.74 20.14 19.63 19.27 19.15 19.66 19.78 19.78 0.36 0.07 −0.12 L 4 260.751425 +65.735378 23.24 21.03 19.96 19.21 18.9 18.71 18.61 19.13 19.5 19.63 19.74 0.11 0.09 −0.24 308 5 260.690857 +65.7380851 25.43 25.29 25.12 22.9 21.76 21.22 20.8 20.29 20.15 20.06 20.08 1.3 0.22 0.07 218 6 260.70125 +65.7761802 25.7 25.88 24.91 22.79 22.02 21.52 21.14 20.59 20.26 19.99 19.91 1.0 0.83 0.35 232 7 260.697887 +65.7779331 25.88 26.0 25.52 24.22 22.84 21.98 21.31 20.89 20.57 20.34 20.2 1.66 0.33 0.37 L 8 260.659521 +65.7845551 25.76 26.86 23.03 20.74 20.18 19.78 19.41 19.18 19.19 19.32 19.55 0.81 0.21 −0.36 L 9 260.695583 +65.7843522 24.9 25.23 24.2 23.31 22.8 22.43 21.92 21.55 21.22 21.04 20.88 0.95 0.57 0.34 223 10 260.746297 +65.7842212 L 26.62 26.31 25.07 23.94 22.74 22.13 21.58 21.11 20.94 20.82 2.04 0.51 0.29 300 11 260.719022 +65.7862604 27.13 25.89 24.95 22.53 21.59 21.15 20.81 20.48 20.34 20.28 20.36 1.17 0.35 −0.02 L 12 260.773037 +65.7856466 25.74 24.97 23.01 21.27 20.79 20.45 20.14 19.98 20.16 20.52 20.6 0.51 0.07 −0.44 L 13 260.641043 +65.787557 25.94 L 25.79 23.04 22.02 21.45 21.02 20.65 20.45 20.46 20.52 1.16 0.34 −0.07 L 14 260.53697 +65.7952973 23.06 20.8 20.8 20.97 20.8 20.73 20.53 20.58 20.27 19.95 19.78 2.02 0.84 0.49 L 15 260.733993 +65.7985085 L 21.49 20.1 19.04 18.64 18.29 18.06 18.17 18.59 18.66 18.56 0.3 0.13 0.03 283 16 260.758634 +65.800809 L L L 18.11 17.84 17.67 17.52 17.94 18.06 17.95 18.11 0.22 0.1 −0.05 319 17 260.639149 +65.799387 24.94 24.6 22.76 20.0 19.53 19.21 18.87 18.67 18.55 18.66 18.79 1.0 0.21 −0.24 141 18 260.507214 +65.799489 L 27.3 24.45 22.3 21.64 21.13 20.64 20.31 20.43 20.58 20.73 0.76 0.2 −0.3 L 19 260.684968 +65.7998337 27.55 26.5 24.42 22.16 21.56 21.09 20.69 20.42 20.52 20.74 20.92 0.8 0.13 −0.4 L 20 260.722957 +65.8043552 23.72 23.04 20.84 19.41 18.95 18.56 18.3 18.28 18.71 18.93 18.99 0.46 0.03 −0.28 L 21 260.473516 +65.8030315 26.88 26.22 23.28 21.35 20.85 20.5 20.19 19.98 20.22 20.46 20.6 0.63 0.08 −0.38 L 22 260.536763 +65.8052802 26.52 26.09 24.88 22.7 21.89 21.33 20.9 20.59 20.46 20.56 20.6 0.96 0.29 −0.14 64 23 260.456228 +65.8076758 L 26.97 24.91 22.62 21.55 21.08 20.7 20.35 20.26 20.24 20.29 1.12 0.34 −0.03 L 24 260.524706 +65.8099822 26.19 26.23 24.75 22.34 21.23 20.74 20.36 20.0 19.89 19.82 19.89 1.2 0.33 0.0 L 25 260.483404 +65.8118176 L 25.87 24.21 23.52 22.87 22.22 21.37 20.82 21.15 21.03 20.88 1.2 0.31 0.27 L 26 260.486571 +65.8162335 24.16 23.89 22.01 20.15 19.64 19.24 18.91 18.71 18.99 19.34 19.39 0.59 0.05 −0.4 L 27 260.531751 +65.8156273 25.67 25.37 24.46 22.6 21.85 21.12 20.57 20.01 19.76 19.72 19.79 1.08 0.32 −0.03 63 28 260.896667 +65.8174626 L 23.96 22.34 20.54 19.84 19.54 19.11 18.67 18.49 18.33 18.22 0.61 0.71 0.27 497 29 260.905043 +65.8175627 L 22.77 20.92 L L L L 18.72 19.31 19.45 19.5 0.36 0.04 −0.19 L 30 260.81488 +65.821168 L 25.38 26.26 24.78 23.62 22.99 22.6 22.18 22.04 21.96 21.87 1.58 0.32 0.17 L 31 260.851755 +65.8249769 24.8 24.36 21.99 20.05 19.6 19.27 18.96 18.74 18.9 19.16 19.31 0.72 0.1 −0.41 L 32 260.898138 +65.8218193 L 27.19 26.36 23.92 22.53 21.99 21.69 21.33 21.25 21.14 21.1 1.47 0.36 0.15 L 33 260.423208 +65.8239109 26.69 25.69 24.67 22.44 21.63 21.18 20.82 20.44 20.33 20.25 20.32 1.07 0.37 0.01 L 34 260.768573 +65.8261999 28.36 26.89 25.35 23.12 21.65 21.05 20.73 20.37 20.18 20.08 20.06 1.51 0.38 0.12 L 35 260.538451 +65.8275573 L 24.48 23.78 22.05 21.51 21.19 20.85 20.53 20.42 20.44 20.49 0.96 0.29 −0.07 L 36 260.942294 +65.8276848 28.26 26.06 24.22 21.87 21.19 20.73 20.34 20.03 20.09 20.21 20.39 0.86 0.19 −0.3 L 37 260.924987 +65.8298372 25.49 23.17 21.07 19.69 19.22 18.86 18.62 18.74 19.31 19.45 19.49 0.34 0.02 −0.18 L 38 260.919468 +65.8313406 27.59 23.49 21.48 20.38 20.03 19.72 19.52 19.56 19.92 19.88 19.75 0.33 0.25 0.17 528 39 260.96505 +65.8346423 22.1 22.11 21.87 20.87 20.34 20.03 19.68 19.46 19.6 19.72 19.74 0.92 0.23 −0.14 L 40 260.661331 +65.832686 25.39 27.82 26.32 23.97 22.52 21.86 21.42 21.01 20.81 20.67 20.68 1.53 0.39 0.13 L 41 260.886193 +65.8338751 26.57 24.65 23.77 22.27 21.59 21.08 20.64 20.28 20.32 20.4 20.55 0.79 0.11 −0.23 L 42 260.498867 +65.8337582 23.85 23.2 23.06 22.56 22.07 21.71 21.42 21.13 21.0 20.86 20.71 1.33 0.44 0.29 L 43 260.66199 +65.8414 L 24.6 23.69 21.45 20.29 19.84 19.51 19.14 18.98 18.86 18.95 1.42 0.36 0.03 L 44 260.673587 +65.837855 26.21 25.48 24.68 22.42 21.72 21.2 20.8 20.5 20.36 20.49 20.58 0.99 0.15 −0.22 L 45 260.894292 +65.8416492 22.68 20.75 19.61 18.77 18.35 18.06 17.87 18.37 18.64 18.73 18.85 0.11 0.07 −0.21 492 46 260.679864 +65.8446726 L 23.57 22.84 21.2 20.56 20.11 19.69 19.36 19.25 19.34 19.46 0.89 0.18 −0.21 205 10 T h e A stro ph y sica l Jo u rn a l, 974:258 (14pp), 2024 O ctober 20 O rtiz III et al. Table 1 (Continued) ID R.A. Decl. F275W F435W F606W F090W F115W F150W F200W F277W F356W F410M F444W z fAGN Color H23 J2000 J2000 ––––––––––––––––––––––––––––––––––––––––––––––––––AB mag–––––––––––––––––––––––––––––––––––––––––––––––––– AB mag (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15) (16) (17) (18) 47 260.74563 +65.8423048 L 24.82 23.48 21.31 20.67 20.19 19.69 19.31 19.2 19.26 19.44 0.85 0.2 −0.24 299 48 260.63956 +65.8449596 20.66 20.08 19.97 20.17 19.76 19.42 19.25 18.9 18.51 18.25 18.15 0.37 0.8 0.36 142 49 260.626602 +65.8521721 24.96 26.08 24.25 21.51 20.75 20.23 19.82 19.52 19.39 19.52 19.61 1.03 0.24 −0.22 124 50 260.756515 +65.849021 26.22 25.48 24.08 21.63 21.0 20.64 20.34 20.1 19.98 20.08 20.16 1.02 0.24 −0.18 L 51 260.692874 +65.8619061 23.94 21.71 20.01 18.89 18.41 17.98 17.64 17.74 18.15 18.2 18.24 0.32 0.08 −0.09 222 52 260.624274 +65.8678777 24.78 23.49 22.43 20.75 20.16 19.66 19.15 18.79 18.56 18.59 18.69 0.84 0.33 −0.13 121 53 260.681038 +65.8692821 L 24.39 23.35 22.22 21.58 21.01 20.43 19.94 19.5 19.19 18.97 1.88 0.72 0.53 207 54 260.701071 +65.876123 L 25.98 23.78 21.86 21.33 20.93 20.62 20.39 20.61 20.94 21.05 0.56 0.07 −0.44 L 55 260.73803 +65.9028757 L 27.06 26.17 24.04 22.76 22.16 21.71 21.23 20.97 20.73 20.51 1.36 0.66 0.46 L 56 260.708784 +65.876954 26.69 L 25.62 24.01 22.44 21.85 21.55 21.26 21.15 21.1 21.02 1.79 0.36 0.13 L 57 260.651265 +65.8935901 27.95 27.81 25.81 23.5 22.6 22.07 21.68 21.37 21.24 21.27 21.33 1.05 0.34 −0.09 L 58 260.668029 +65.8778179 24.97 27.77 25.18 23.65 22.46 21.76 21.16 20.62 20.29 20.15 20.03 1.43 0.4 0.26 187 59 260.665718 +65.8772567 25.42 27.52 26.35 24.78 23.24 22.31 21.92 21.59 21.42 21.32 21.25 1.91 0.38 0.17 L 60 260.690274 +65.8996399 L L L 22.37 21.4 20.99 20.67 20.29 20.24 20.23 20.28 1.37 0.3 −0.04 L 61 260.668589 +65.8807234 25.36 23.86 23.31 21.78 21.16 20.74 20.43 20.04 19.88 19.83 19.87 0.95 0.28 0.01 L 62 260.709682 +65.8805701 25.76 25.99 25.45 24.22 23.03 22.44 21.95 21.43 21.23 21.05 21.04 1.5 0.33 0.19 L 63 260.62903 +65.8741014 L 25.8 25.72 24.68 23.15 22.42 21.91 21.4 21.17 21.0 21.03 1.77 0.32 0.14 L 64 260.649498 +65.8709509 26.83 24.95 24.46 22.85 21.98 21.72 21.58 21.39 21.21 21.02 20.95 1.2 0.44 0.26 L 65 260.739964 +65.9251014 25.13 24.63 23.57 21.89 21.15 20.62 20.04 19.59 19.68 19.93 19.99 0.77 0.11 −0.31 292 66 260.672783 +65.9111158 L L L 24.73 23.24 22.45 21.93 21.51 21.38 21.29 21.2 1.7 0.37 0.18 L Note. Columns show ID number, J2000 positions, AB mag in three HST filters and eight JWST/NIRCam filters, photometric redshift (z) as measured by CIGALE, fAGN from CIGALE, (mF356W − mF444W) color, and counterpart ID in the M. Hyun et al. (2023; H23) VLA 3 GHz source catalog. The CIGALE fits incorporated a stellar continuum component from G. Bruzual & S. Charlot (2003) models, a dust component from D. A. Dale et al. (2014) templates, a D. Calzetti et al. (2000) dust attenuation law, nebular emission templates from A. K. Inoue (2011), and a clumpy two-phase torus model from M. Stalevski et al. (2016). (This table is available in machine-readable form in the online article.) 11 T h e A stro ph y sica l Jo u rn a l, 974:258 (14pp), 2024 O ctober 20 O rtiz III et al. https://doi.org/10.3847/1538-4357/ad6d5e Table 2 Catalog of GalFit Output Parameters for Two-component Fitting of Our Visual Sample Sérsic + Sérsic - Sérsic + PSF - ID mag Re n mag Re n 2cn mag Re n mag 2cn Core Type Sérsic 1 Sérsic 2 Sérsic 1 PSF 1 (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) 1 16.52 4.00 6.00 16.52 25.00 6.00 109.26 17.68 30.00 2.21 20.68 185.59 Bulge 2 21.02 4.00 1.11 21.02 4.00 6.00 1.57 20.41 4.00 2.25 22.80 1.51 Point source 3 20.34 5.66 3.05 20.34 16.28 0.83 2.06 19.79 10.94 1.96 22.79 3.12 Bulge 4 20.97 4.00 3.95 20.97 16.05 3.65 1.93 19.68 11.88 4.01 22.68 2.22 Undetermined 5 20.80 6.20 0.74 20.80 7.63 4.74 1.87 20.15 5.25 2.56 23.15 5.82 Bulge 6 20.46 4.00 2.29 20.46 4.00 1.95 3.65 19.91 4.00 1.87 22.79 3.45 Point source 7 20.83 4.00 6.00 20.83 4.00 0.99 8.06 20.49 4.00 1.31 21.59 5.32 Point source 8 20.19 23.88 0.77 20.19 4.00 1.04 1.72 19.53 11.04 3.07 22.53 4.77 Bulge 9 21.21 4.00 1.59 21.21 4.00 6.00 3.01 21.07 4.00 1.47 22.72 2.68 Point source 10 21.36 4.00 1.29 21.36 4.00 4.71 1.53 20.83 4.00 1.86 23.33 1.31 Point source 11 20.92 4.00 2.66 20.92 4.41 6.00 1.86 20.36 4.00 4.08 23.36 2.07 Undetermined 12 21.77 16.92 0.70 21.77 4.05 1.76 1.47 20.59 6.71 2.81 23.59 2.28 Bulge 13 21.33 12.39 4.04 21.33 4.00 1.25 1.12 20.54 4.42 2.86 23.54 1.51 Undetermined 14 20.49 4.00 6.00 20.49 4.00 0.89 9.08 20.34 4.00 1.09 20.72 5.27 Point source 15 18.88 24.11 1.12 18.88 4.00 0.70 20.71 18.61 18.97 2.26 21.45 25.07 Bulge 16 19.00 6.61 0.70 19.00 18.44 2.37 12.37 18.36 7.21 3.43 21.36 93.77 Bulge 17 18.86 15.75 3.64 18.86 4.00 2.06 3.99 18.78 10.15 4.45 21.78 13.56 Bulge 18 22.53 4.00 0.70 22.53 10.02 1.75 1.84 20.69 8.37 2.08 23.69 2.27 Undetermined 19 21.17 7.42 1.23 21.17 4.00 6.00 1.09 20.91 6.57 1.73 23.91 1.16 Undetermined 20 19.64 12.88 5.65 19.64 25.00 6.00 16.59 19.16 17.39 5.44 22.16 35.62 Bulge 21 23.06 4.00 0.85 23.06 8.98 2.14 1.14 20.59 8.68 1.85 23.59 1.18 Undetermined 22 21.53 23.52 6.00 21.53 4.25 2.54 1.75 20.62 5.11 3.05 23.62 2.23 Undetermined 23 21.55 15.25 1.29 21.55 4.00 2.74 2.18 20.31 5.72 2.63 23.31 2.91 Bulge 24 20.87 14.47 6.00 20.87 5.66 2.08 1.61 19.92 5.94 2.69 22.92 2.96 Bulge 25 21.38 4.00 6.00 21.38 4.00 6.00 4.58 21.13 4.00 6.00 22.10 2.67 Point source 26 21.42 28.29 0.70 21.42 11.88 3.56 18.16 19.53 14.50 2.52 22.53 19.77 Bulge 27 20.23 8.61 0.70 20.23 7.46 0.70 8.42 19.85 8.16 0.70 22.85 11.20 Bulge 28 18.83 4.00 3.51 18.83 4.00 6.00 55.99 18.53 4.00 4.61 19.94 47.26 Point source 29 20.02 25.01 5.32 20.02 9.24 2.84 1.98 19.61 10.83 3.04 22.61 5.51 Bulge 30 22.37 30.00 6.00 22.37 4.00 6.00 3.58 21.87 4.00 6.00 24.33 1.81 Point source 31 21.87 8.48 6.00 21.87 16.45 4.41 3.45 19.40 13.62 3.15 22.40 8.98 Bulge 32 21.82 4.00 6.00 21.82 4.00 1.08 1.35 21.23 4.00 1.51 22.89 1.38 Undetermined 33 21.23 4.00 6.00 21.23 4.00 1.71 4.20 20.34 4.00 2.89 23.14 4.74 Bulge 34 20.62 8.30 1.37 20.62 4.00 1.96 1.87 20.05 6.54 1.56 23.05 2.26 Undetermined 35 21.58 4.00 6.00 21.58 4.00 6.00 2.21 20.43 4.00 6.00 23.36 1.86 Point source 36 20.90 4.09 2.18 20.90 12.93 0.70 1.38 20.40 7.94 1.62 23.40 1.98 Bulge 37 21.20 15.53 0.70 21.20 8.04 4.29 1.95 19.50 9.69 2.38 22.50 2.95 Bulge 38 20.23 6.44 6.00 20.23 4.00 6.00 3.18 19.67 5.48 6.00 22.41 3.01 Point source 39 20.12 4.00 4.67 20.12 4.00 6.00 7.25 19.77 4.00 4.33 22.60 6.88 Point source 40 21.17 5.26 1.88 21.17 7.63 3.53 1.06 20.69 6.11 2.07 23.69 1.21 Undetermined 41 20.89 5.45 1.44 20.89 25.00 6.00 5.83 20.77 4.66 2.20 23.77 8.68 Bulge 42 21.48 4.00 3.76 21.48 4.00 6.00 1.62 20.68 4.00 3.95 23.20 1.37 Point source 43 19.18 30.00 3.03 19.18 4.00 0.79 4.60 19.00 10.64 5.95 22.00 18.97 Bulge 44 21.43 16.03 0.70 21.43 4.00 2.40 1.23 20.53 8.94 2.26 23.49 1.75 Bulge 45 18.94 10.41 4.57 18.94 25.00 0.70 9.74 18.97 12.67 2.71 21.97 14.97 Bulge 46 20.18 5.41 2.09 20.18 25.00 6.00 17.06 19.74 7.16 2.65 22.74 18.40 Bulge 47 20.02 4.00 0.72 20.02 25.00 0.70 4.56 19.51 8.61 3.02 22.51 15.58 Bulge 48 20.29 25.12 0.70 20.29 4.00 6.00 13.22 18.51 9.80 3.64 19.44 17.46 Bulge 49 19.97 30.00 6.00 19.97 7.14 2.63 3.54 19.72 9.36 3.44 22.72 6.11 Bulge 50 20.31 6.99 2.59 20.31 5.79 3.55 1.26 20.16 7.40 2.16 23.16 1.53 Undetermined 51 20.23 5.27 0.87 20.23 25.00 2.28 31.77 18.41 19.72 2.32 21.41 60.68 Bulge 52 19.69 7.67 5.57 19.69 15.23 0.70 41.44 18.87 13.86 0.72 21.04 45.58 Bulge 53 21.26 25.59 0.70 21.26 4.00 6.00 36.44 19.29 9.68 2.85 20.20 48.88 Bulge 54 21.49 4.00 2.27 21.49 4.55 4.61 0.87 21.01 4.32 2.54 24.01 1.01 Undetermined 55 21.34 4.00 2.87 21.34 4.00 4.28 2.68 20.81 4.00 1.68 21.84 1.83 Point source 56 21.93 6.44 3.60 21.93 4.00 2.13 1.04 21.02 4.37 2.05 24.02 1.08 Undetermined 57 22.03 6.92 6.00 22.03 4.00 1.39 1.12 21.36 4.00 3.01 24.36 1.34 Undetermined 58 20.68 8.08 0.70 20.68 6.17 2.97 2.23 20.07 7.39 1.06 23.07 3.29 Bulge 59 21.93 4.00 6.00 21.93 4.00 0.84 1.95 21.26 4.00 2.87 23.71 2.58 Bulge 60 20.36 5.24 3.39 20.36 11.66 0.70 1.23 20.27 6.74 2.34 23.27 1.38 Undetermined 12 The Astrophysical Journal, 974:258 (14pp), 2024 October 20 Ortiz III et al. ORCID iDs Rafael Ortiz III https://orcid.org/0000-0002-6150-833X Rogier A. Windhorst https://orcid.org/0000-0001-8156-6281 Seth H. Cohen https://orcid.org/0000-0003-3329-1337 Steven P. Willner https://orcid.org/0000-0002-9895-5758 Rolf A. Jansen https://orcid.org/0000-0003-1268-5230 Timothy Carleton https://orcid.org/0000-0001-6650-2853 Patrick S. Kamieneski https://orcid.org/0000-0001-9394-6732 Michael J. Rutkowski https://orcid.org/0000-0001-7016-5220 Brent M. Smith https://orcid.org/0000-0002-0648-1699 Jake Summers https://orcid.org/0000-0002-7265-7920 Cheng Cheng https://orcid.org/0000-0003-0202-0534 Dan Coe https://orcid.org/0000-0001-7410-7669 Christopher J. Conselice https://orcid.org/0000-0003-1949- 7638 Jose M. Diego https://orcid.org/0000-0001-9065-3926 Simon P. Driver https://orcid.org/0000-0001-9491-7327 Jordan C. J. D’Silva https://orcid.org/0000-0002-9816-1931 Brenda L. Frye https://orcid.org/0000-0003-1625-8009 Hansung B. Gim https://orcid.org/0000-0003-1436-7658 Norman A. Grogin https://orcid.org/0000-0001-9440-8872 Heidi B. Hammel https://orcid.org/0000-0001-8751-3463 Nimish P. Hathi https://orcid.org/0000-0001-6145-5090 Benne W. Holwerda https://orcid.org/0000-0002-4884-6756 Minhee Hyun https://orcid.org/0000-0003-4738-4251 Myungshin Im https://orcid.org/0000-0002-8537-6714 William C. Keel https://orcid.org/0000-0002-6131-9539 Anton M. Koekemoer https://orcid.org/0000-0002-6610-2048 Juno Li https://orcid.org/0000-0002-8184-5229 Madeline A. Marshall https://orcid.org/0000-0001-6434-7845 Tyler J. McCabe https://orcid.org/0000-0002-5506-3880 Noah J. McLeod https://orcid.org/0009-0000-5821-4325 Stefanie N. Milam https://orcid.org/0000-0001-7694-4129 Rosalia O’Brien https://orcid.org/0000-0003-3351-0878 Nor Pirzkal https://orcid.org/0000-0003-3382-5941 Aaron S. G. Robotham https://orcid.org/0000-0003- 0429-3579 Russell E. Ryan, Jr. https://orcid.org/0000-0003-0894-1588 Christopher N. A. Willmer https://orcid.org/0000-0001-9262- 9997 Haojing Yan https://orcid.org/0000-0001-7592-7714 Min S. Yun https://orcid.org/0000-0001-7095-7543 Adi Zitrin https://orcid.org/0000-0002-0350-4488 References Antonucci, R. R. J., & Miller, J. S. 1985, ApJ, 297, 621 Assef, R. J., Stern, D., Kochanek, C. S., et al. 2013, ApJ, 772, 26 Astropy Collaboration, Price-Whelan, A. M., Lim, P. L., et al. 2022, ApJ, 935, 167 Astropy Collaboration, Price-Whelan, A. M., Sipőcz, B. M., et al. 2018, AJ, 156, 123 Astropy Collaboration, Robitaille, T. P., Tollerud, E. J., et al. 2013, A&A, 558, A33 Bertin, E., & Arnouts, S. 1996, A&AS, 117, 393 Bollati, F., Lupi, A., Dotti, M., & Haardt, F. 2023, arXiv:2311.07576 Boquien, M., Burgarella, D., Roehlly, Y., et al. 2019, A&A, 622, A103 Brammer, G. B., van Dokkum, P. G., & Coppi, P. 2008, ApJ, 686, 1503 Brown, M. J. I., Duncan, K. J., Landt, H., et al. 2019, MNRAS, 489, 3351 Bruce, V. A., Dunlop, J. 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S., et al. 2018, MNRAS, 475, 5330 Table 2 (Continued) Sérsic + Sérsic - Sérsic + PSF - ID mag Re n mag Re n 2cn mag Re n mag 2cn Core Type Sérsic 1 Sérsic 2 Sérsic 1 PSF 1 (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) 61 20.44 15.60 0.70 20.44 4.94 1.31 7.62 19.91 10.78 1.59 22.54 14.69 Bulge 62 21.81 4.00 6.00 21.81 4.00 0.89 1.06 21.03 4.00 1.58 23.98 1.27 Undetermined 63 21.80 9.66 6.00 21.80 4.00 2.85 1.49 21.06 4.51 3.42 24.06 1.60 Undetermined 64 21.93 4.00 6.00 21.93 4.00 2.15 4.23 21.08 4.00 2.32 22.84 3.95 Point source 65 20.17 11.32 1.78 20.17 12.22 1.73 2.94 20.02 12.41 1.28 23.02 3.55 Bulge 66 21.24 4.00 1.74 21.24 4.00 6.00 1.40 21.25 4.00 1.60 23.95 1.35 Point source Note. Columns show ID number, AB mag, half-light radius, Sérsic index, and 2cn for respective components in two-component fits with GalFit. The galaxy core type classification is in the final column, which is determined by which fit—double-Sérsic or Sérsic + PSF—has 2cn closest to 1 . Constraints on the GalFit parameters are as follows for each two-component fit: Sérsic+Sérsic: mag ±3 of MAG_AUTOF444W (via SExtractor), 0.7 � n � 6.0, Re � 4 pixels (=0 12). Sérsic +PSF: magSérsic ±3 of MAG_AUTOF444W (via SExtractor),   31 3 mag mag Sersic PSF , 0.7 � n � 6.0, Re � 4 pixels (=0 12). (This table is available in machine-readable form in the online article.) 13 The Astrophysical Journal, 974:258 (14pp), 2024 October 20 Ortiz III et al. https://orcid.org/0000-0002-6150-833X https://orcid.org/0000-0002-6150-833X https://orcid.org/0000-0002-6150-833X https://orcid.org/0000-0002-6150-833X https://orcid.org/0000-0002-6150-833X https://orcid.org/0000-0002-6150-833X https://orcid.org/0000-0002-6150-833X https://orcid.org/0000-0002-6150-833X https://orcid.org/0000-0001-8156-6281 https://orcid.org/0000-0001-8156-6281 https://orcid.org/0000-0001-8156-6281 https://orcid.org/0000-0001-8156-6281 https://orcid.org/0000-0001-8156-6281 https://orcid.org/0000-0001-8156-6281 https://orcid.org/0000-0001-8156-6281 https://orcid.org/0000-0001-8156-6281 https://orcid.org/0000-0003-3329-1337 https://orcid.org/0000-0003-3329-1337 https://orcid.org/0000-0003-3329-1337 https://orcid.org/0000-0003-3329-1337 https://orcid.org/0000-0003-3329-1337 https://orcid.org/0000-0003-3329-1337 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Introduction 2. Data and Cataloging 2.1. Observations 2.2. Catalogs and Samples 2.3. Visual Sample Selection 2.4. Automated Sample Selection 2.5. Control Sample 3. Results and Discussion 3.1. Two-dimensional Light Modeling 3.2. Seyfert Template Fitting with EAZY 3.3. SED Parameter Estimation with CIGALE 3.4.(mF356W - mF444W) Colors 4. Summary and Future Prospects Appendix References