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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.

 

We present new JWST NIRSpec integral field spectroscopy (IFS) data for the luminous infrared galaxy NGC 7469, a nearby (70.6 Mpc) active galaxy with a Seyfert 1.5 nucleus that drives a highly ionized gas outflow and a prominent nuclear star-forming ring. Using the superb sensitivity and high spatial resolution of the JWST instrument NIRSpec IFS, we investigate the role of the Seyfert nucleus in the excitation and dynamics of the circumnuclear gas. Our analysis focuses on the [Feii], H₂, and hydrogen recombination lines that trace the radiation/shocked-excited molecular and ionized interstellar medium around the active galactic nucleus (AGN). We investigate gas excitation through H₂/Brγand [Feii]/Paβemission line ratios and find that photoionization by the AGN dominates within the central 300 pc of the galaxy except in a small region that shows signatures of shock-heated gas; these shock-heated regions are likely associated with a compact radio jet. In addition, the velocity field and velocity dispersion maps reveal complex gas kinematics. Rotation is the dominant feature, but we also identify noncircular motions consistent with gas inflows as traced by the velocity residuals and the spiral pattern in the Paαvelocity dispersion map. The inflow is 2 orders of magnitude higher than the AGN accretion rate. The compact nuclear radio jet has enough power to drive the highly ionized outflow. This scenario suggests that the inflow and outflow are in a self-regulating feeding–feedback process, with a contribution from the radio jet helping to drive the outflow.

 

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.

 

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 The 2 mm Mapping Obscuration to Reionization with ALMA (MORA) Survey was designed to detect high-redshift ( z ≳ 4), massive, dusty star-forming galaxies (DSFGs). Here we present two likely high-redshift sources, identified in the survey, whose physical characteristics are consistent with a class of optical/near-infrared (OIR)-invisible DSFGs found elsewhere in the literature. We first perform a rigorous analysis of all available photometric data to fit spectral energy distributions and estimate redshifts before deriving physical properties based on our findings. Our results suggest the two galaxies, called MORA-5 and MORA-9, represent two extremes of the “OIR-dark” class of DSFGs. MORA-5 ( z phot = 4.3 − 1.3 + 1.5 ) is a significantly more active starburst with a star formation rate (SFR) of 830 − 190 + 340 M ⊙ yr −1 compared to MORA-9 ( z phot = 4.3 − 1.0 + 1.3 ), whose SFR is a modest 200 − 60 + 250 M ⊙ yr −1 . Based on the stellar masses ( M ⋆ ≈ 10 10−11 M ⊙ ), space density ( n ∼ (5 ± 2) × 10 −6 Mpc −3 , which incorporates two other spectroscopically confirmed OIR-dark DSFGs in the MORA sample at z = 4.6 and z = 5.9), and gas depletion timescales (<1 Gyr) of these sources, we find evidence supporting the theory that OIR-dark DSFGs are the progenitors of recently discovered 3 < z < 4 massive quiescent galaxies. 

ABSTRACT We present optical and near-infrared imaging covering a ∼1.53 deg2 region in the Super-Cluster Assisted Shear Survey (SuperCLASS) field, which aims to make the first robust weak lensing measurement at radio wavelengths. We derive photometric redshifts for ≈176 000 sources down to $i^\prime _{\rm AB}\sim 24$ and present photometric redshifts for 1.4 GHz expanded Multi-Element Radio Linked Interferometer Network (e-MERLIN) and Karl G. Jansky Very Large Array (VLA) detected radio sources found in the central 0.26 deg2. We compile an initial catalogue of 149 radio sources brighter than S1.4 > 75 μJy and find their photometric redshifts span 0 < zphot < 4 with radio luminosities between 1021 and 1025 W Hz−1, with medians of $\langle z \rangle \, =0.55$ and $\langle L_{1.4}\rangle \, =1.9\times 10^{23}$ W Hz−1, respectively. We find 95 per cent of the μJy radio source sample (141/149) have spectral energy distributions (SEDs) best fit by star-forming templates while 5 per cent (8/149) are better fit by active galactic nuclei (AGN). Spectral indices are calculated for sources with radio observations from the VLA and Giant Metrewave Radio Telescope (GMRT) at 325 MHz, with an average spectral slope of α = 0.59 ± 0.04. Using the full photometric redshift catalogue, we construct a density map at the redshift of the known galaxy clusters, z = 0.20 ± 0.08. Four of the five clusters are prominently detected at $\gt 7\, \sigma$ in the density map and we confirm the photometric redshifts are consistent with previously measured spectra from a few galaxies at the cluster centres. 

We present the survey design, implementation, and outlook for COSMOS-Web, a 255 hr treasury program conducted by the James Webb Space Telescope in its first cycle of observations. COSMOS-Web is a contiguous 0.54 deg²NIRCam imaging survey in four filters (F115W, F150W, F277W, and F444W) that will reach 5σpoint-source depths ranging ∼27.5–28.2 mag. In parallel, we will obtain 0.19 deg²of MIRI imaging in one filter (F770W) reaching 5σpoint-source depths of ∼25.3–26.0 mag. COSMOS-Web will build on the rich heritage of multiwavelength observations and data products available in the COSMOS field. The design of COSMOS-Web is motivated by three primary science goals: (1) to discover thousands of galaxies in the Epoch of Reionization (6 ≲z≲ 11) and map reionization’s spatial distribution, environments, and drivers on scales sufficiently large to mitigate cosmic variance, (2) to identify hundreds of rare quiescent galaxies atz> 4 and place constraints on the formation of the universe’s most-massive galaxies (M_⋆> 10¹⁰M_⊙), and (3) directly measure the evolution of the stellar-mass-to-halo-mass relation using weak gravitational lensing out toz∼ 2.5 and measure its variance with galaxies’ star formation histories and morphologies. In addition, we anticipate COSMOS-Web’s legacy value to reach far beyond these scientific goals, touching many other areas of astrophysics, such as the identification of the first direct collapse black hole candidates, ultracool subdwarf stars in the Galactic halo, and possibly the identification ofz> 10 pair-instability supernovae. In this paper we provide an overview of the survey’s key measurements, specifications, goals, and prospects for new discovery.

 

ABSTRACT The SuperCLuster Assisted Shear Survey (SuperCLASS) is a legacy programme using the e-MERLIN interferometric array. The aim is to observe the sky at L-band (1.4 GHz) to a r.m.s. of $7\, \mu {\rm Jy}\,$beam−1 over an area of $\sim 1\, {\rm deg}^2$ centred on the Abell 981 supercluster. The main scientific objectives of the project are: (i) to detect the effects of weak lensing in the radio in preparation for similar measurements with the Square Kilometre Array (SKA); (ii) an extinction free census of star formation and AGN activity out to z ∼ 1. In this paper we give an overview of the project including the science goals and multiwavelength coverage before presenting the first data release. We have analysed around 400 h of e-MERLIN data allowing us to create a Data Release 1 (DR1) mosaic of $\sim 0.26\, {\rm deg}^2$ to the full depth. These observations have been supplemented with complementary radio observations from the Karl G. Jansky Very Large Array (VLA) and optical/near infrared observations taken with the Subaru, Canada-France-Hawaii, and Spitzer Telescopes. The main data product is a catalogue of 887 sources detected by the VLA, of which 395 are detected by e-MERLIN and 197 of these are resolved. We have investigated the size, flux, and spectral index properties of these sources finding them compatible with previous studies. Preliminary photometric redshifts, and an assessment of galaxy shapes measured in the radio data, combined with a radio-optical cross-correlation technique probing cosmic shear in a supercluster environment, are presented in companion papers.