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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. 

Fast inactivation is a key feature of voltage-gated sodium channels and is pivotal for countless physiological functions. Despite the prevalence of the canonical ball-and-chain model, more recent structural results suggest that fast inactivation requires multiple conformational changes beyond the binding of the inactivation particle, the IFM motif. Combining ionic current, gating current, and fluorescent measurements, here we showed that a double mutant at the bottom of the pore domain (CW) removes fast inactivation by interrupting the communication of the IFM motif and the pore. Instead of triggering fast inactivation, the IFM motif binding in CW allows the channel to enter an alternative open state. This alternative open state severely influenced the voltage sensor movements and was not accessible to wild type or other fast inactivation–deficient channels. Our results highlight the multistep nature of the fast inactivation process in mammalian voltage-gated sodium channels and demonstrate that CW modifies the channel behaviors more profoundly than simple removal of fast inactivation. 

Many voltage-gated potassium (Kv) channels display a time-dependent phenomenon called C-type inactivation, whereby prolonged activation by voltage leads to the inhibition of ionic conduction, a process that involves a conformational change at the selectivity filter toward a non-conductive state. Recently, a high-resolution structure of a strongly inactivated triple-mutant channel kv1.2-kv2.1-3m revealed a novel conformation of the selectivity filter that is dilated at its outer end, distinct from the well-characterized conductive state. While the experimental structure was interpreted as the elusive non-conductive state, our molecular dynamics simulations and electrophysiological measurements show that the dilated filter of kv1.2-kv2.1-3m is conductive and, as such, cannot completely account for the inactivation of the channel observed in the structural experiments. The simulation shows that an additional conformational change, implicating isoleucine residues at position 398 along the pore lining segment S6, is required to effectively block ion conduction. The I398 residues from the four subunits act as a state-dependent hydrophobic gate located immediately beneath the selectivity filter. These observations are corroborated by electrophysiological experiments showing that ion permeation can be resumed in the kv1.2-kv2.1-3m channel when I398 is mutated to an asparagine—a mutation that does not abolish C-type inactivation since digitoxin (AgTxII) fails to block the ionic permeation of kv1.2-kv2.1-3m_I398N. As a critical piece of the C-type inactivation machinery, this structural feature is the potential target of a broad class of quaternary ammonium (QA) blockers and negatively charged activators thus opening new research directions toward the development of drugs that specifically modulate gating states of Kv channels. 

Photometric redshifts will be a key data product for the Rubin Observatory Legacy Survey of Space and Time (LSST) as well as for future ground and space-based surveys. The need for photometric redshifts, or photo-zs, arises from sparse spectroscopic coverage of observed galaxies. LSST is expected to observe billions of objects, making it crucial to have a photo-z estimator that is accurate and efficient. To that end, we present DeepDISC photo-z, a photo-z estimator that is an extension of the DeepDISC framework. The base DeepDISC network simultaneously detects, segments, and classifies objects in multi-band coadded images. We introduce photo-z capabilities to DeepDISC by adding a redshift estimation Region of Interest head, which produces a photo-z probability distribution function for each detected object. On simulated LSST images, DeepDISC photo-z outperforms traditional catalog-based estimators, in both point estimate and probabilistic metrics. We validate DeepDISC by examining dependencies on systematics including galactic extinction, blending and PSF effects. We also examine the impact of the data quality and the size of the training set and model. We find that the biggest factor in DeepDISC photo-z quality is the signal-to-noise of the imaging data, and see a reduction in photo-z scatter approximately proportional to the image data signal-to-noise. Our code is fully public and integrated in the RAIL photo-z package for ease of use and comparison to other codes at https://github.com/LSSTDESC/rail_deepdisc 

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.