Prof Chris Lintott

Abstract:

We use Bayesian convolutional neural networks and a novel generative model of Galaxy Zoo volunteer responses to infer posteriors for the visual morphology of galaxies. Bayesian CNN can learn from galaxy images with uncertain labels and then, for previously unlabelled galaxies, predict the probability of each possible label. Our posteriors are well-calibrated (e.g. for predicting bars, we achieve coverage errors of 11.8 per cent within a vote fraction deviation of 0.2) and hence are reliable for practical use. Further, using our posteriors, we apply the active learning strategy BALD to request volunteer responses for the subset of galaxies which, if labelled, would be most informative for training our network. We show that training our Bayesian CNNs using active learning requires up to 35–60 per cent fewer labelled galaxies, depending on the morphological feature being classified. By combining human and machine intelligence, Galaxy zoo will be able to classify surveys of any conceivable scale on a time-scale of weeks, providing massive and detailed morphology catalogues to support research into galaxy evolution.

Abstract:

© 2021 2020 The Author(s) Published by Oxford University Press on behalf of the Royal Astronomical Society. We present the results from the first two years of the Planet Hunters TESS (PHT) citizen science project, which identifies planet candidates in the TESS (Transiting Exoplanet Survey Satellite) data by engaging members of the general public. Over 22 000 citizen scientists from around the world visually inspected the first 26 sectors of TESS data in order to help identify transit-like signals. We use a clustering algorithm to combine these classifications into a ranked list of events for each sector, the top 500 of which are then visually vetted by the science team. We assess the detection efficiency of this methodology by comparing our results to the list of TESS Objects of Interest (TOIs) and show that we recover 85 per cent of the TOIs with radii greater than 4 R and 51 per cent of those with radii between 3 and 4 R. Additionally, we present our 90 most promising planet candidates that had not previously been identified by other teams, 73 of which exhibit only a single-transit event in the TESS light curve, and outline our efforts to follow these candidates up using ground-based observatories. Finally, we present noteworthy stellar systems that were identified through the Planet Hunters TESS project.

Abstract:

The growth of supermassive black holes appears to be driven by galaxy mergers, violent merger-free processes and/or `secular' processes. In order to quantify the effects of secular evolution on black hole growth, we study a sample of active galactic nuclei (AGN) in galaxies with a calm formation history free of significant mergers, a population that heretofore has been difficult to locate. Here we present an initial sample of 13 AGN in massive (M_* >~ 1e10 M_sun) bulgeless galaxies -- which lack the classical bulges believed inevitably to result from mergers -- selected from the Sloan Digital Sky Survey using visual classifications from Galaxy Zoo. Parametric morphological fitting confirms the host galaxies lack classical bulges; any contributions from pseudobulges are very small (typically < 5%). We compute black hole masses for the two broad-line objects in the sample (4.2e6 and 1.2e7 M_sun) and place lower limits on black hole masses for the remaining sample (typically M_BH >~ 1e6 M_sun), showing that significant black hole growth must be possible in the absence of mergers or violent disk instabilities. The black hole masses are systematically higher than expected from established bulge-black hole relations. However, if the mean Eddington ratio of the systems with measured black hole masses (L/L_Edd = 0.065) is typical, 10 of 13 sources are consistent with the correlation between black hole mass and total stellar mass. That pure disk galaxies and their central black holes may be consistent with a relation derived from elliptical and bulge-dominated galaxies with very different formation histories implies the details of stellar galaxy evolution and dynamics may not be fundamental to the co-evolution of galaxies and black holes.

Abstract:

Abstract Does the environment of a galaxy directly influence the kinematics of its bar? We present observational evidence that bars in high-density environments exhibit significantly slower rotation rates than bars in low-density environments. Galactic bars are central, extended structures composed of stars, dust and gas, present in approximately 30 to 70 per cent of luminous spiral galaxies in the local Universe. Recent simulation studies have suggested that the environment can influence the bar rotation rate, $\mathcal {R}$, which is used to classify bars as either fast ($1\le \mathcal {R}\le 1.4$) or slow ($\mathcal {R}>1.4$). We use estimates of $\mathcal {R}$ obtained with the Tremaine–Weinberg method applied to Integral Field Unit spectroscopy from MaNGA and CALIFA. After cross-matching these with the projected neighbour density, log Σ, we retain 286 galaxies. The analysis reveals that bars in high-density environments are significantly slower (median $\mathcal {R} = 1.65^{+0.13}_{-0.11}$) compared to bars in low-density environments (median $\mathcal {R} =1.39^{+0.09}_{-0.08}$); Anderson–Darling p-value of pAD = 0.002 (3.1 σ). This study marks the first empirical test of the hypothesis that fast bars are formed by global instabilities in isolated galaxies, while slow bars are triggered by tidal interactions in dense environments, in agreement with predictions from numerous N-body simulations. Future studies would benefit from a larger sample of galaxies with reliable Integral Field Unit data, required to measure bar rotation rates. Specifically, more data are necessary to study the environmental influence on bar formation within dense settings (i.e. groups, clusters and filaments).

Abstract:

Abstract We present morphological classifications of over 41 000 galaxies out to zphot ∼ 2.5 across six square degrees of the Euclid Deep Field North (EDFN) from the Hawaii Twenty Square Degree (H20) survey, a part of the wider Cosmic Dawn survey. Galaxy Zoo citizen scientists play a crucial role in the examination of large astronomical data sets through crowdsourced data mining of extragalactic imaging. This iteration, Galaxy Zoo: Cosmic Dawn (GZCD), saw tens of thousands of volunteers and the deep learning foundation model Zoobot collectively classify objects in ultra-deep multiband Hyper Suprime-Cam (HSC) imaging down to a depth of mHSC − i = 21.5. Here, we present the details and general analysis of this iteration, including the use of Zoobot in an active learning cycle to improve both model performance and volunteer experience, as well as the discovery of 51 new gravitational lenses in the EDFN. We also announce the public data release of the classifications for over 45 000 subjects, including more than 41 000 galaxies (median zphot of 0.42 ± 0.23), along with their associated image cutouts. This data set provides a valuable opportunity for follow-up imaging of objects in the EDFN as well as acting as a truth set for training deep learning models for application to ground-based surveys like that of the Ultraviolet Near-Infrared Optical Northern Survey (UNIONS) collaboration and the newly operational Vera C. Rubin Observatory.