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We present Chandra X-ray Observatory observations and Space Telescope Imaging Spectrograph spectra of NGC 5972, one of the 19 ‘Voorwerpjes’ galaxies. This galaxy contains an extended emission-line region (EELR) and an arcsecond scale nuclear bubble. NGC 5972 is a faded active galactic nucleus (AGN), with EELR luminosity suggesting a 2.1 dex decrease in Lbol in the last ∼5 × 104 yr. We investigate the role of AGN feedback in exciting the EELR and bubble given the long-term variability and potential accretion state changes. We detect broad-band (0.3–8 keV) X-ray emission in the near-nuclear regions, coincident with the [O iii] bubble, as well as diffuse soft X-ray emission coincident with the EELR. The soft nuclear (0.5–1.5 keV) emission is spatially extended and the spectra are consistent with two apec thermal populations (∼0.80 and ∼0.10 keV). We find a bubble age >2.2 Myr, suggesting formation before the current variability. We find evidence for efficient feedback with $P_{\textrm {kin}}/L_{\textrm {bol}}\sim 0.8~{{\ \rm per\ cent}}$, which may be overestimated given the recent Lbol variation. [O iii] kinematics show a 300 km s−1 high-ionization velocity consistent with disturbed rotation or potentially the line-of-sight component of a ∼780 km s−1 thermal X-ray outflow capable of driving strong shocks to photoionize the precursor material. We explore possibilities to explain the overall jet, radio lobe and EELR misalignment including evidence for a double supermassive black hole which could support a complex misaligned system.

 

We report the discovery of a candidate dual QSO atz= 1.889, a redshift that is in the era known as “cosmic noon” where most of the universe’s black hole and stellar mass growth occurred. The source was identified in Hubble Space Telescope WFC3/IR images of a dust-reddened QSO that showed two closely separated point sources at a projected distance of 0.″26, or 2.2 kpc. This red QSO was targeted for imaging to explore whether red QSOs are hosted by merging galaxies. We subsequently obtained a spatially resolved Space Telescope Imaging Spectrograph spectrum of the system, covering the visible spectral range, and verifying the presence of two distinct QSO components. We also obtained high-resolution radio continuum observations with the Very Long Baseline Array at 1.4 GHz (21 cmLband) and found two sources coincident with the optical positions. The sources have similar black hole masses, bolometric luminosities, and radio-loudness parameters. However, their colors and reddenings differ significantly. The redder QSO has a higher Eddington ratio, consistent with previous findings. We consider the possibility of gravitational lensing and find that it would require extreme and unlikely conditions. If confirmed as a bona fide dual QSO, this system would link dust reddening to galaxy and supermassive black hole mergers, opening up a new population in which to search for samples of dual active galactic nuclei.

 

Abstract Mergers play a complex role in galaxy formation and evolution. Continuing to improve our understanding of these systems requires ever larger samples, which can be difficult (even impossible) to select from individual surveys. We use the new platform ESA Datalabs to assemble a catalog of interacting galaxies from the Hubble Space Telescope science archives; this catalog is larger than previously published catalogs by nearly an order of magnitude. In particular, we apply the Zoobot convolutional neural network directly to the entire public archive of HST F814W images and make probabilistic interaction predictions for 126 million sources from the Hubble Source Catalog. We employ a combination of automated visual representation and visual analysis to identify a clean sample of 21,926 interacting galaxy systems, mostly with z < 1. Sixty-five percent of these systems have no previous references in either the NASA Extragalactic Database or Simbad. In the process of removing contamination, we also discover many other objects of interest, such as gravitational lenses, edge-on protoplanetary disks, and “backlit” overlapping galaxies. We briefly investigate the basic properties of this sample, and we make our catalog publicly available for use by the community. In addition to providing a new catalog of scientifically interesting objects imaged by HST, this work also demonstrates the power of the ESA Datalabs tool to facilitate substantial archival analysis without placing a high computational or storage burden on the end user. 

ABSTRACT Astronomers have typically set out to solve supervised machine learning problems by creating their own representations from scratch. We show that deep learning models trained to answer every Galaxy Zoo DECaLS question learn meaningful semantic representations of galaxies that are useful for new tasks on which the models were never trained. We exploit these representations to outperform several recent approaches at practical tasks crucial for investigating large galaxy samples. The first task is identifying galaxies of similar morphology to a query galaxy. Given a single galaxy assigned a free text tag by humans (e.g. ‘#diffuse’), we can find galaxies matching that tag for most tags. The second task is identifying the most interesting anomalies to a particular researcher. Our approach is 100 per cent accurate at identifying the most interesting 100 anomalies (as judged by Galaxy Zoo 2 volunteers). The third task is adapting a model to solve a new task using only a small number of newly labelled galaxies. Models fine-tuned from our representation are better able to identify ring galaxies than models fine-tuned from terrestrial images (ImageNet) or trained from scratch. We solve each task with very few new labels; either one (for the similarity search) or several hundred (for anomaly detection or fine-tuning). This challenges the longstanding view that deep supervised methods require new large labelled data sets for practical use in astronomy. To help the community benefit from our pretrained models, we release our fine-tuning code zoobot. Zoobot is accessible to researchers with no prior experience in deep learning. 

Galaxy Zoo: Clump Scout  is a web-based citizen science project designed to identify and spatially locate giant star forming clumps in galaxies that were imaged by the Sloan Digital Sky Survey Legacy Survey. We present a statistically driven software framework that is designed to aggregate two-dimensional annotations of clump locations provided by multiple independent Galaxy Zoo: Clump Scout volunteers and generate a consensus label that identifies the locations of probable clumps within each galaxy. The statistical model our framework is based on allows us to assign false-positive probabilities to each of the clumps we identify, to estimate the skill levels of each of the volunteers who contribute to Galaxy Zoo: Clump Scout and also to quantitatively assess the reliability of the consensus labels that are derived for each subject. We apply our framework to a data set containing 3561 454 two-dimensional points, which constitute 1739 259 annotations of 85 286 distinct subjects provided by 20 999 volunteers. Using this data set, we identify 128 100 potential clumps distributed among 44 126 galaxies. This data set can be used to study the prevalence and demographics of giant star forming clumps in low-redshift galaxies. The code for our aggregation software framework is publicly available at: https://github.com/ou-astrophysics/BoxAggregator

 

Abstract We describe the Gems of the Galaxy Zoos (Zoo Gems) project, a gap-filler project using short windows in the Hubble Space Telescope's schedule. As with previous snapshot programs, targets are taken from a pool based on position; we combine objects selected by volunteers in both the Galaxy Zoo and Radio Galaxy Zoo citizen-science projects. Zoo Gems uses exposures with the Advanced Camera for Surveys to address a broad range of topics in galaxy morphology, interstellar-medium content, host galaxies of active galactic nuclei, and galaxy evolution. Science cases include studying galaxy interactions, backlit dust in galaxies, post-starburst systems, rings and peculiar spiral patterns, outliers from the usual color–morphology relation, Green Pea compact starburst systems, double radio sources with spiral host galaxies, and extended emission-line regions around active galactic nuclei. For many of these science categories, final selection of targets from a larger list used public input via a voting process. Highlights to date include the prevalence of tightly wound spiral structure in blue, apparently early-type galaxies, a nearly complete Einstein ring from a group lens, redder components at lower surface brightness surrounding compact Green Pea starbursts, and high-probability examples of spiral galaxies hosting large double radio sources. 

ABSTRACT We present Galaxy Zoo DECaLS: detailed visual morphological classifications for Dark Energy Camera Legacy Survey images of galaxies within the SDSS DR8 footprint. Deeper DECaLS images (r = 23.6 versus r = 22.2 from SDSS) reveal spiral arms, weak bars, and tidal features not previously visible in SDSS imaging. To best exploit the greater depth of DECaLS images, volunteers select from a new set of answers designed to improve our sensitivity to mergers and bars. Galaxy Zoo volunteers provide 7.5 million individual classifications over 314 000 galaxies. 140 000 galaxies receive at least 30 classifications, sufficient to accurately measure detailed morphology like bars, and the remainder receive approximately 5. All classifications are used to train an ensemble of Bayesian convolutional neural networks (a state-of-the-art deep learning method) to predict posteriors for the detailed morphology of all 314 000 galaxies. We use active learning to focus our volunteer effort on the galaxies which, if labelled, would be most informative for training our ensemble. When measured against confident volunteer classifications, the trained networks are approximately 99 per cent accurate on every question. Morphology is a fundamental feature of every galaxy; our human and machine classifications are an accurate and detailed resource for understanding how galaxies evolve.