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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.

 

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.

 

Phase separation of biomolecules into condensates has emerged as a mechanism for intracellular organization and affects many intracellular processes, including reaction pathways through the clustering of enzymes and pathway intermediates. Precise and rapid spatiotemporal control of reactions by condensates requires tuning of their sizes. However, the physical processes that govern the distribution of condensate sizes remain unclear. Here we show that both native and synthetic condensates display an exponential size distribution, which is captured by Monte Carlo simulations of fast nucleation followed by coalescence. In contrast, pathological aggregates exhibit a power-law size distribution. These distinct behaviours reflect the relative importance of nucleation and coalescence kinetics. We demonstrate this by utilizing a combination of synthetic and native condensates to probe the underlying physical mechanisms determining condensate size. The appearance of exponential distributions for abrupt nucleation versus power-law distributions under continuous nucleation may reflect a general principle that determines condensate size distributions.

 

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 present the Texas Euclid Survey for Lyα(TESLA), a spectroscopic survey in the 10 deg²of the Euclid North Ecliptic Pole (NEP) field. Using TESLA, we study how the physical properties of Lyαemitters (LAEs) correlate with Lyαemission to understand the escape of Lyαemission from galaxies at redshifts of 2–3.5. We present an analysis of 43 LAEs performed in the NEP field using early data from the TESLA survey. We use Subaru Hyper Suprime-Cam imaging in thegrizybands, Spitzer/IRAC channels 1 and 2 from the Hawaii 20 deg²(H20) survey, and spectra acquired by the Visible Integral-Field Replicable Unit Spectrograph (VIRUS) on the Hobby–Eberly Telescope. We perform spectral energy distribution (SED) fitting to compute the galaxy properties of 43 LAEs, and study correlations between stellar mass, star formation rate (SFR), and dust to the Lyαrest-frame equivalent width (W_Lyα). We uncover marginal (1σsignificance) correlations between stellar mass andW_Lyα, and SFR andW_Lyα, with a Spearman correlation coefficient of −0.${34}_{-.14}^{+.17}$and −0.${37}_{-.14}^{+.16}$, respectively. We show that theW_Lyαdistribution of the 43 LAEs is consistent with being drawn from an exponential distribution with an e-folding scale ofW₀= 150 Å. Once complete the TESLA survey will enable the study of ≳50,000 LAEs to explore more correlations between galaxy properties andW_Lyα. The large sample size will allow the construction of a predictive model forW_Lyαas a function of SED-derived galaxy properties, which could be used to improve Lyα-based constraints on reionization.

 

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 present James Webb Space Telescope (JWST) imaging of NGC 7469 with the Near-Infrared Camera and the Mid-InfraRed Instrument. NGC 7469 is a nearby, z = 0.01627, luminous infrared galaxy that hosts both a Seyfert Type-1.5 nucleus and a circumnuclear starburst ring with a radius of ∼0.5 kpc. The new near-infrared (NIR) JWST imaging reveals 66 star-forming regions, 37 of which were not detected by Hubble Space Telescope (HST) observations. Twenty-eight of the 37 sources have very red NIR colors that indicate obscurations up to A v ∼ 7 and a contribution of at least 25% from hot dust emission to the 4.4 μ m band. Their NIR colors are also consistent with young (<5 Myr) stellar populations and more than half of them are coincident with the mid-infrared (MIR) emission peaks. These younger, dusty star-forming regions account for ∼6% and ∼17% of the total 1.5 and 4.4 μ m luminosity of the starburst ring, respectively. Thanks to JWST, we find a significant number of young dusty sources that were previously unseen due to dust extinction. The newly identified 28 young sources are a significant increase compared to the number of HST-detected young sources (4–5). This makes the total percentage of the young population rise from ∼15% to 48%. These results illustrate the effectiveness of JWST in identifying and characterizing previously hidden star formation in the densest star-forming environments around active galactic nuclei (AGN).