Search for: All records

Photo-z algorithms that utilize SED template fitting have matured, and are widely adopted for use on high-redshift near-infrared data that provides a unique window into the early universe. Alternative photo-z methods have been developed, largely within the context of low-redshift optical surveys. Machine learning based approaches have gained footing in this regime, including those that utilize raw pixel information instead of aperture photometry. However, the efficacy of image-based algorithms on high-redshift, near-infrared data remains underexplored. Here, we test the performance of Detection, Instance Segmentation and Classification with Deep Learning (DeepDISC) on photometric redshift estimation with NIRCam images from the JWST Advanced Deep Extragalactic Survey (JADES) program. DeepDISC is designed to produce probabilistic photometric redshift estimates directly from images, after detecting and deblending sources in a scene. Using NIRCam-only images and a compiled catalog of spectroscopic redshifts, we show that DeepDISC produces reliable photo-zs and uncertainties comparable to those estimated from template fitting using HST+JWST filters; DeepDISC even outperforms template fitting (lower scatter/fewer outliers) when the input photometric filters are matched. Compared with template fitting, DeepDISC does not require measured photometry from images, and can produce a catalog of 94000 photo-zs in ~4 minutes on a single NVIDIA A40 GPU. While current spectroscopic training samples are small and incomplete in color-magnitude space, this work demonstrates the potential of DeepDISC for increasingly larger image volumes and spectroscopic samples from ongoing and future programs. We discuss the impact of the training data on applications to broader samples and produce a catalog of photo-zs for all JADES DR2 photometric sources in the GOOD-S field, with quality flags indicating caveats. 

Electrochemical C–N coupling via the coreduction of CO₂and nitrogenous species (N₂/NO_x) presents a sustainable route to synthesize value‐added C–N compounds under mild conditions. However, competing pathways and mismatched intermediate kinetics hinder the selective formation of products like urea, amines, and amides. Recent advances reveal that rational modulation of the electrochemical microenvironment can effectively steer reaction pathways and stabilize coupling‐relevant intermediates. This review systematically summarizes how microenvironment engineering, originally developed for CO₂and NO_xreduction reactions, can be leveraged to enhance C–N coupling efficiency and selectivity. The key strategies are categorized into 1) catalyst‐centered design (e.g., ligand coordination, defect engineering, and morphology control), 2) ionic and electrolyte modifications (e.g., cation/pH effects), and 3) dynamic approaches such as pulsed electrolysis. These methods shape local fields, surface coverage, and mass transport properties, ultimately directing reactants toward cross‐coupling over competing routes. By drawing parallels with well‐established CO₂RR/NO_xRR systems and showcasing emerging examples in C–N coupling, the central role of microenvironment control is highlighted. Finally, a perspectives on strategies to further improve activity, selectivity, and atom economy in future C–N coupling systems are offered. 

Abstract We study the black hole mass–host galaxy stellar mass relation,M_BH–M_*, for a sample of 706z ≲ 1.5 andi ≲ 24 optically variable active galactic nuclei (AGNs) in three Dark Energy Survey (DES) Deep Fields: C3, X3, E2, which partially cover Chandra Deep Field-South, XMM Large Scale Structure survey, and European Large Area ISO Survey, respectively. The parent sample was identified by optical variability from the DES supernova survey program imaging. Using publicly available spectra and photometric catalogs, we consolidate their spectroscopic redshifts, estimate their black hole masses using broad line widths and luminosities, and obtain improved stellar masses using spectral energy distribution fitting from X-ray to mid-infrared wavelengths. Our results confirm previous work from Hyper-Suprime Camera imaging that variability searches with deep, high-precision photometry can reliably identify AGNs in low-mass galaxies up toz ∼ 1. However, we find that the hosted black holes are more massive than predicted by the local AGN relation, fixing host galaxy stellar mass. Instead,z ∼ 0.1–1.5 variability-selected AGNs lie in between theM_BH–M_*relation for local inactive early-type galaxies and local active galaxies. This result agrees with most previous studies of theM_BH–M_*relation for AGNs at similar redshifts, regardless of the selection technique. We demonstrate that studies of variability-selected AGN provide critical insights into the low-mass end of theM_BH–M_*relation, shedding light on the occupation fraction of that provides constraints on early black hole seeding mechanisms and self-regulated feedback processes during their growth and coevolution with their hosts. 

Abstract Subparsec binary supermassive black holes (BSBHs) should be common from galaxy mergers, yet direct evidence has been elusive. We present Hubble Space Telescope (HST)/WFC3IR F160W imaging for a sample of eight candidate subparsec BSBHs at redshiftsz ∼ 0.1–0.5, as well as cross-comparison with a sample of ordinary quasars with archival HST/WFC3 IR F160W images. These eight candidate subparsec BSBHs were identified from multiepoch spectroscopic surveys of quasars (including both typical quasars and those with single-peaked velocity-offset broad lines), whose broad Hβlines are significantly offset (by ≳ a few hundred kilometers per second) from the systemic redshifts. We directly test the prediction that the host galaxies of BSBHs would have a higher fraction of disturbed morphologies and younger stellar bulges from recent interactions than those of control quasars. After careful subtraction of the central quasar light, our candidate BSBH hosts show a statistically undifferentiated distribution of host asymmetry, indicative of a similar fraction of recent mergers. While a significantly larger sample is needed to place this result on a much firmer statistical ground, it opens questions as to the timescale differences between galaxy merger and BSBH formation, or the efficacy of the radial-velocity-shift-based selection of subparsec BSBH candidates. 

eXplainable Artificial Intelligence (XAI) has garnered significant attention for enhancing transparency and trust in machine learning models. However, the scopes of most existing explanation techniques focus either on offering a holistic view of the explainee model (global explanation) or on individual instances (local explanation), while the middle ground, i.e., cohort-based explanation, is less explored. Cohort explanations offer insights into the explainee's behavior on a specific group or cohort of instances, enabling a deeper understanding of model decisions within a defined context. In this paper, we discuss the unique challenges and opportunities associated with measuring cohort explanations, define their desired properties, and create a generalized framework for generating cohort explanations based on supervised clustering. 

Abstract High spatial and spectral resolution observations are essential for identifying subarcsecond dual and lensed quasars and confirming their redshifts. We present Gemini/Gemini Multi-Object Spectrograph and Hubble Space Telescope/STIS optical spectra for 27 dual quasar candidates selected based on their variability-induced astrometric noise or double detections in Gaia (the Varstrometry for Off-nucleus and Dual sub-Kpc AGN (VODKA) project). From this follow-up, we spectroscopically identify 11 star superpositions and seven dual/lensed quasars. Among the remaining targets, two are likely dual/lensed quasars based on additional radio imaging, while the rest are quasars with unknown companions. Without prior photometric or spectroscopic selection, we find the star contamination rate to be 41%–67%, while the dual/lensed quasar fraction is ≳26% in the follow-up VODKA sample. However, when combined with existing unresolved spectra and spatially resolved two-band color cuts, the dual/lensed quasar fraction can be increased to ≳67%. Our study highlights the need for high-quality spectral data, including a signal-to-noise ratio of at least 20, spatial resolution that is at least twice finer than the source separation, and a spectral resolution ofR ≳ 1000, in order to separate close sources, exclude stellar superpositions, and reliably identify dual quasars. 

Abstract Cooperative catalysis with an enzyme and a small‐molecule photocatalyst has recently emerged as a potentially general activation mode to advance novel biocatalytic reactions with synthetic utility. Herein, we report cooperative photobiocatalysis involving an engineered nonheme Fe enzyme and a tailored photoredox catalyst to achieve enantioconvergent decarboxylative azidation, thiocyanation, and isocyanation of redox‐active esters via a radical mechanism. We repurposed and further evolved metapyrocatechase (MPC), a nonheme Fe extradiol dioxygenase not previously studied in new‐to‐nature biocatalysis, for the enantioselective C─N₃, C─SCN, and C─NCO bond formation via an enzymatic Fe─X intermediate (X═N₃, NCS, and NCO). A range of primary, secondary, and tertiary alkyl radical precursors were effectively converted by our engineered MPC, allowing the syntheses of organic azides, thiocyanates, and isocyanates with good to excellent enantiocontrol. Further derivatization of these products furnished valuable compounds including enantioenriched amines, triazoles, ureas, and SCF₃‐containing products. DFT and MD simulations shed light on the mechanism as well as the binding poses of the alkyl radical intermediate in the enzyme active site and the π‐facial selectivity in the enantiodetermining radical rebound. Overall, cooperative photometallobiocatalysis with nonheme Fe enzymes provides a means to develop challenging asymmetric radical transformations eluding small‐molecule catalysis.