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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 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.