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The COSMOS field has been extensively observed by most major telescopes, including Chandra, HST, and Subaru. HST imaging boasts very high spatial resolution and is used extensively in morphological studies of distant galaxies. Subaru provides lower spatial resolution imaging than HST but a substantially wider field of view with greater sensitivity. Both telescopes provide near-infrared imaging of COSMOS. Successful morphological fitting of Subaru data would allow us to measure morphologies of over 10⁴known active galactic nucleus (AGN) hosts, accessible through Subaru wide-field surveys, currently not covered by HST. The morphological parameters indicate the types of galaxies that host AGNs. For 4016 AGNs between 0.03 <z< 6.5, we study the morphology of their galaxy hosts using GALFIT, fitting components representing the AGN and host galaxy simultaneously using thei-band imaging from both HST and Subaru. Comparing the fits for the differing telescope spatial resolutions and image signal-to-noise ratios, we identify parameter regimes for which there is strong disagreement between distributions of fitted parameters for HST and Subaru. In particular, the Sérsic index values strongly disagree between the two sets of data, including sources at lower redshifts. In contrast, the measured magnitude and radius parameters show reasonable agreement. Additionally, large variations in the Sérsic index have little effect on the${\chi }_{\nu }^2$of each fit, whereas variations in other parameters have a more significant effect. These results indicate that the Sérsic index distributions of high-redshift galaxies that host AGNs imaged at ground-based spatial resolution are not reliable indicators of galaxy type and should be interpreted with caution.

 

We use the Galaxy Morphology Posterior Estimation Network (GaMPEN) to estimate morphological parameters and associated uncertainties for ∼8 million galaxies in the Hyper Suprime-Cam Wide survey withz≤ 0.75 andm≤ 23. GaMPEN is a machine-learning framework that estimates Bayesian posteriors for a galaxy’s bulge-to-total light ratio (L_B/L_T), effective radius (R_e), and flux (F). By first training on simulations of galaxies and then applying transfer learning using real data, we trained GaMPEN with <1% of our data set. This two-step process will be critical for applying machine-learning algorithms to future large imaging surveys, such as the Rubin-Legacy Survey of Space and Time, the Nancy Grace Roman Space Telescope, and Euclid. By comparing our results to those obtained using light profile fitting, we demonstrate that GaMPEN’s predicted posterior distributions are well calibrated (≲5% deviation) and accurate. This represents a significant improvement over light profile fitting algorithms, which underestimate uncertainties by as much as ∼60%. For an overlapping subsample, we also compare the derived morphological parameters with values in two external catalogs and find that the results agree within the limits of uncertainties predicted by GaMPEN. This step also permits us to define an empirical relationship between the Sérsic index andL_B/L_Tthat can be used to convert between these two parameters. The catalog presented here represents a significant improvement in size (∼10×), depth (∼4 mag), and uncertainty quantification over previous state-of-the-art bulge+disk decomposition catalogs. With this work, we also release GaMPEN’s source code and trained models, which can be adapted to other data sets.

 

Spectral energy distributions (SEDs) from X-ray to far-infrared (FIR) wavelengths are presented for a sample of 1246 X-ray-luminous active galactic nuclei (AGNs;L_{0.5–10 keV}> 10⁴³erg s⁻¹), withz_spec< 1.2, selected from Stripe 82X, COSMOS, and GOODS-N/S. The rest-frame SEDs show a wide spread (∼2.5 dex) in the relative strengths of broad continuum features at X-ray, ultraviolet (UV), mid-infrared (MIR), and FIR wavelengths. A linear correlation (log–log slope of 0.7 ± 0.04) is found betweenL_MIRandL_X. There is significant scatter in the relation between theL_UVandL_Xowing to heavy obscuration; however, the most luminous and unobscured AGNs show a linear correlation (log–log slope of 0.8 ± 0.06) in the relation above this scatter. The relation betweenL_FIRandL_Xis predominantly flat, but with decreasing dispersion atL_X> 10⁴⁴erg s⁻¹. The ratio between the “galaxy-subtracted” bolometric luminosity and the intrinsicL_Xincreases from a factor of ∼10 to 70 from logL_bol/(erg s⁻¹) = 44.5 to 46.5. Characteristic SED shapes have been determined by grouping AGNs based on relative strengths of the UV and MIR emission. The averageL_1μmis constant for the majority of these SED shapes, while AGNs with the strongest UV and MIR emission have elevatedL_1μm, consistent with the AGN emission dominating their SEDs at optical and near-infrared wavelengths. A strong correlation is found between the SED shape and both theL_XandL_bol, such thatL_bol/L_X= 20.4 ± 1.8, independent of the SED shape. This is consistent with an evolutionary scenario of increasingL_bolwith decreasing obscuration as the AGN blows away circumnuclear gas.

 

We present a machine-learning framework to accurately characterize the morphologies of active galactic nucleus (AGN) host galaxies withinz< 1. We first use PSFGAN to decouple host galaxy light from the central point source, then we invoke the Galaxy Morphology Network (GaMorNet) to estimate whether the host galaxy is disk-dominated, bulge-dominated, or indeterminate. Using optical images from five bands of the HSC Wide Survey, we build models independently in three redshift bins: low (0 <z< 0.25), mid (0.25 <z< 0.5), and high (0.5 <z< 1.0). By first training on a large number of simulated galaxies, then fine-tuning using far fewer classified real galaxies, our framework predicts the actual morphology for ∼60%–70% of the host galaxies from test sets, with a classification precision of ∼80%–95%, depending on the redshift bin. Specifically, our models achieve a disk precision of 96%/82%/79% and bulge precision of 90%/90%/80% (for the three redshift bins) at thresholds corresponding to indeterminate fractions of 30%/43%/42%. The classification precision of our models has a noticeable dependency on host galaxy radius and magnitude. No strong dependency is observed on contrast ratio. Comparing classifications of real AGNs, our models agree well with traditional 2D fitting with GALFIT. The PSFGAN+GaMorNetframework does not depend on the choice of fitting functions or galaxy-related input parameters, runs orders of magnitude faster than GALFIT, and is easily generalizable via transfer learning, making it an ideal tool for studying AGN host galaxy morphology in forthcoming large imaging surveys.

 

We aim to determine the intrinsic far-Infrared (far-IR) emission of X-ray-luminous quasars over cosmic time. Using a 16 deg2 region of the Stripe 82 field surveyed by XMM-Newton and Herschel Space Observatory, we identify 2905 X-ray luminous (LX > 1042 erg/s) active galactic nuclei (AGN) in the range z ≈ 0–3. The IR is necessary to constrain host galaxy properties such as star formation rate (SFR) and gas mass. However, only 10 per cent of our AGN are detected both in the X-ray and IR. Because 90 per cent of the sample is undetected in the far-IR by Herschel, we explore the mean IR emission of these undetected sources by stacking their Herschel/SPIRE images in bins of X-ray luminosity and redshift. We create stacked spectral energy distributions from the optical to the far-IR, and estimate the median SFR, dust mass, stellar mass, and infrared luminosity using a fitting routine. We find that the stacked sources on average have similar SFR/Lbol ratios as IR detected sources. The majority of our sources fall on or above the main sequence line suggesting that X-ray selection alone does not predict the location of a galaxy on the main sequence. We also find that the gas depletion time scales of our AGN are similar to those of dusty star forming galaxies. This suggests that X-ray selected AGN host high star formation and that there are no signs of declining star formation.

 

Abstract We present multiwavelength high-spatial resolution (∼0.″1, 70 pc) observations of UGC 4211 at z = 0.03474, a late-stage major galaxy merger at the closest nuclear separation yet found in near-IR imaging (0.″32, ∼230 pc projected separation). Using Hubble Space Telescope/Space Telescope Imaging Spectrograph, Very Large Telescope/MUSE+AO, Keck/OSIRIS+AO spectroscopy, and the Atacama Large Millimeter/submillimeter Array (ALMA) observations, we show that the spatial distribution, optical and near-infrared emission lines, and millimeter continuum emission are all consistent with both nuclei being powered by accreting supermassive black holes (SMBHs). Our data, combined with common black hole mass prescriptions, suggest that both SMBHs have similar masses, log M BH / M ⊙ ∼ 8.1 (south) and log M BH / M ⊙ ∼ 8.3 (north), respectively. The projected separation of 230 pc (∼6× the black hole sphere of influence) represents the closest-separation dual active galactic nuclei (AGN) studied to date with multiwavelength resolved spectroscopy and shows the potential of nuclear (<50 pc) continuum observations with ALMA to discover hidden growing SMBH pairs. While the exact occurrence rate of close-separation dual AGN is not yet known, it may be surprisingly high, given that UGC 4211 was found within a small, volume-limited sample of nearby hard X-ray detected AGN. Observations of dual SMBH binaries in the subkiloparsec regime at the final stages of dynamical friction provide important constraints for future gravitational wave observatories. 

The corona is an integral component of active galactic nuclei (AGNs) which produces the bulk of the X-ray emission above 1-2 keV. However, many of its physical properties and the mechanisms powering this emission remain a mystery. In particular, the temperature of the coronal plasma has been difficult to constrain for large samples of AGNs, as constraints require high-quality broadband X-ray spectral coverage extending above 10 keV in order to measure the high-energy cutoff, which provides constraints on the combination of coronal optical depth and temperature. We present constraints on the coronal temperature for a large sample of Seyfert 1 AGNs selected from the Swift/BAT survey using high-quality hard X-ray data from the NuSTAR observatory combined with simultaneous soft X-ray data from Swift/XRT or XMM-Newton. When applying a physically motivated, nonrelativistic disk-reflection model to the X-ray spectra, we find a mean coronal temperature kT e = 84 ± 9 keV. We find no significant correlation between the coronal cutoff energy and accretion parameters such as the Eddington ratio and black hole mass. We also do not find a statistically significant correlation between the X-ray photon index, Γ, and Eddington ratio. This calls into question the use of such relations to infer properties of supermassive black hole systems. 

Abstract We constrain the intrinsic Eddington ratio ( λ Edd ) distribution function for local active galactic nuclei (AGN) in bins of low and high obscuration [ log ( N H / cm − 2 ) ≤ 22 and 22 < log ( N H / cm − 2 ) < 25 ], using the Swift Burst Alert Telescope 70 month/BASS DR2 survey. We interpret the fraction of obscured AGN in terms of circumnuclear geometry and temporal evolution. Specifically, at low Eddington ratios ( log λ Edd < −2), obscured AGN outnumber unobscured ones by a factor of ∼4, reflecting the covering factor of the circumnuclear material (0.8, or a torus opening angle of ∼34°). At high Eddington ratios ( log λ Edd > −1), the trend is reversed, with <30% of AGN having log ( N H / cm − 2 ) > 22 , which we suggest is mainly due to the small fraction of time spent in a highly obscured state. Considering the Eddington ratio distribution function of narrow-line and broad-line AGN from our prior work, we see a qualitatively similar picture. To disentangle temporal and geometric effects at high λ Edd , we explore plausible clearing scenarios such that the time-weighted covering factors agree with the observed population ratio. We find that the low fraction of obscured AGN at high λ Edd is primarily due to the fact that the covering factor drops very rapidly, with more than half the time spent with <10% covering factor. We also find that nearly all obscured AGN at high- λ Edd exhibit some broad lines. We suggest that this is because the height of the depleted torus falls below the height of the broad-line region, making the latter visible from all lines of sight. 

We introduce a novel machine-learning framework for estimating the Bayesian posteriors of morphological parameters for arbitrarily large numbers of galaxies. The Galaxy Morphology Posterior Estimation Network (GaMPEN) estimates values and uncertainties for a galaxy’s bulge-to-total-light ratio (L_B/L_T), effective radius (R_e), and flux (F). To estimate posteriors, GaMPEN uses the Monte Carlo Dropout technique and incorporates the full covariance matrix between the output parameters in its loss function. GaMPEN also uses a spatial transformer network (STN) to automatically crop input galaxy frames to an optimal size before determining their morphology. This will allow it to be applied to new data without prior knowledge of galaxy size. Training and testing GaMPEN on galaxies simulated to matchz< 0.25 galaxies in Hyper Suprime-Cam Wideg-band images, we demonstrate that GaMPEN achieves typical errors of 0.1 inL_B/L_T, 0.″17 (∼7%) inR_e, and 6.3 × 10⁴nJy (∼1%) inF. GaMPEN's predicted uncertainties are well calibrated and accurate (<5% deviation)—for regions of the parameter space with high residuals, GaMPEN correctly predicts correspondingly large uncertainties. We also demonstrate that we can apply categorical labels (i.e., classifications such ashighly bulge dominated) to predictions in regions with high residuals and verify that those labels are ≳97% accurate. To the best of our knowledge, GaMPEN is the first machine-learning framework for determining joint posterior distributions of multiple morphological parameters and is also the first application of an STN to optical imaging in astronomy.

 

Abstract We determine the low-redshift X-ray luminosity function, active black hole mass function (BHMF), and Eddington ratio distribution function (ERDF) for both unobscured (Type 1) and obscured (Type 2) active galactic nuclei (AGNs), using the unprecedented spectroscopic completeness of the BAT AGN Spectroscopic Survey (BASS) data release 2. In addition to a straightforward 1/ V max approach, we also compute the intrinsic distributions, accounting for sample truncation by employing a forward-modeling approach to recover the observed BHMF and ERDF. As previous BHMFs and ERDFs have been robustly determined only for samples of bright, broad-line (Type 1) AGNs and/or quasars, ours are the first directly observationally constrained BHMF and ERDF of Type 2 AGNs. We find that after accounting for all observational biases, the intrinsic ERDF of Type 2 AGNs is significantly more skewed toward lower Eddington ratios than the intrinsic ERDF of Type 1 AGNs. This result supports the radiation-regulated unification scenario, in which radiation pressure dictates the geometry of the dusty obscuring structure around an AGN. Calculating the ERDFs in two separate mass bins, we verify that the derived shape is consistent, validating the assumption that the ERDF (shape) is mass-independent. We report the local AGN duty cycle as a function of mass and Eddington ratio, by comparing the BASS active BHMF with the local mass function for all supermassive black holes. We also present the log N − log S of the Swift/BAT 70 month sources.