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Abstract We study the black hole mass–host galaxy stellar mass relation,M_BH–M_*, of a sample ofz< 4 optically variable active galactic nuclei (AGNs) in the COSMOS field. The parent sample of 491 COSMOS AGNs were identified by optical variability from the Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP) program. Using publicly available catalogs and spectra, we consolidate their spectroscopic redshifts and estimate virial black hole masses using broad-line widths and luminosities. We show that variability searches with deep, high-precision photometry like the HSC-SSP can identity AGNs in low-mass galaxies up toz∼ 1. However, their black holes are more massive given their host galaxy stellar masses than predicted by the local relation for active galaxies. We report thatz∼ 0.5–4 variability-selected AGNs are meanwhile more consistent with theM_BH–M_*relation for local inactive early-type galaxies. This result is in agreement with most previous studies of theM_BH–M_*relation at similar redshifts and indicates that AGNs selected from variability are not intrinsically different from the broad-line Type 1 AGN population at similar luminosities. Our results demonstrate the need for robust black hole and stellar mass estimates for intermediate-mass black hole candidates in low-mass galaxies at similar redshifts to anchor this scaling relation. Assuming that these results do not reflect a selection bias, they appear to be consistent with self-regulated feedback models wherein the central black hole and stars in galaxies grow in tandem. 

ABSTRACT The next generation of wide-field deep astronomical surveys will deliver unprecedented amounts of images through the 2020s and beyond. As both the sensitivity and depth of observations increase, more blended sources will be detected. This reality can lead to measurement biases that contaminate key astronomical inferences. We implement new deep learning models available through Facebook AI Research’s detectron2 repository to perform the simultaneous tasks of object identification, deblending, and classification on large multiband co-adds from the Hyper Suprime-Cam (HSC). We use existing detection/deblending codes and classification methods to train a suite of deep neural networks, including state-of-the-art transformers. Once trained, we find that transformers outperform traditional convolutional neural networks and are more robust to different contrast scalings. Transformers are able to detect and deblend objects closely matching the ground truth, achieving a median bounding box Intersection over Union of 0.99. Using high-quality class labels from the Hubble Space Telescope, we find that when classifying objects as either stars or galaxies, the best-performing networks can classify galaxies with near 100 per cent completeness and purity across the whole test sample and classify stars above 60 per cent completeness and 80 per cent purity out to HSC i-band magnitudes of 25 mag. This framework can be extended to other upcoming deep surveys such as the Legacy Survey of Space and Time and those with the Roman Space Telescope to enable fast source detection and measurement. Our code, deepdisc, is publicly available at https://github.com/grantmerz/deepdisc. 

Abstract High-quality Extragalactic Legacy-field Monitoring (HELM) is a long-term observing program that photometrically monitors several well-studied extragalactic legacy fields with the Dark Energy Camera (DECam) imager on the CTIO 4 m Blanco telescope. Since 2019 February, HELM has been monitoring regions within COSMOS, XMM-LSS, CDF-S, S-CVZ, ELAIS-S1, and SDSS Stripe 82 with few-day cadences in the (u)gri(z) bands, over a collective sky area of ∼38 deg². The main science goal of HELM is to provide high-quality optical light curves for a large sample of active galactic nuclei (AGNs), and to build decades-long time baselines when combining past and future optical light curves in these legacy fields. These optical images and light curves will facilitate the measurements of AGN reverberation mapping lags, as well as studies of AGN variability and its dependencies on accretion properties. In addition, the time-resolved and coadded DECam photometry will enable a broad range of science applications from galaxy evolution to time-domain science. We describe the design and implementation of the program and present the first data release that includes source catalogs and the first ∼3.5 yr of light curves during 2019A–2022A.