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The recent Chandra-JWST discovery of a quasar in thez≈ 10.1 galaxy UHZ1 reveals that accreting supermassive black holes were already in place 470 million years after the Big Bang. The Chandra X-ray source detected in UHZ1 is a Compton-thick quasar with a bolometric luminosity ofL_bol∼ 5 × 10⁴⁵erg s⁻¹, which corresponds to an estimated black hole (BH) mass of ∼4 × 10⁷M_⊙, assuming accretion at the Eddington rate. JWST NIRCAM and NIRSpec data yield a stellar mass estimate for UHZ1 comparable to its BH mass. These characteristics are in excellent agreement with prior theoretical predictions for a unique class of transient, high-redshift objects, overmassive black hole galaxies (OBGs) by Natarajan et al., that harbor a heavy initial black hole seed that likely formed from the direct collapse of the gas. Given the excellent agreement between the observed multiwavelength properties of UHZ1 and theoretical model template predictions, we suggest that UHZ1 is the first detected OBG candidate. Our assertion rests on multiple lines of concordant evidence between model predictions and the following observed properties of UHZ1: its X-ray detection and the estimated ratio of the X-ray flux to the IR flux, which is consistent with theoretical expectations for a heavy initial BH seed; its high measured redshift ofz≈ 10.1, as predicted for the transient OBG stage (9 <z< 12); the amplitude and shape of the detected JWST spectral energy distribution (SED) between 1 and 5μm, which is in very good agreement with simulated template SEDs for OBGs; and the extended JWST morphology of UHZ1, which is suggestive of a recent merge and is also expected for the formation of transient OBGs. As the first OBG candidate, UHZ1 provides compelling evidence for the formation of heavy initial seeds from direct collapse in the early Universe.

 

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 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 There exist hitherto unexplained fluctuations in the cosmic infrared background on arcminute scales and larger. These have been shown to cross-correlate with the cosmic X-ray background, leading several authors to attribute the excess to a high-redshift growing black hole population. In order to investigate potential sources that could explain this excess, in this paper, we develop a new framework to compute the power spectrum of undetected sources that do not have constant flux as a function of halo mass. In this formulation, we combine a semi-analytic model for black hole growth and their simulated spectra from hydrodynamical simulations. Revisiting the possible contribution of a high-redshift black hole population, we find that too much black hole growth is required at early epochs for z > 6 accretion to explain these fluctuations. Examining a population of accreting black holes at more moderate redshifts, z ∼ 2–3, we find that such models produce a poor fit to the observed fluctuations while simultaneously overproducing the local black hole mass density. Additionally, we rule out the hypothesis of a missing Galactic foreground of warm dust that produces coherent fluctuations in the X-ray via reflection of Galactic X-ray binary emission. Although we firmly rule out accreting massive black holes as the source of these missing fluctuations, additional studies will be required to determine their origin.