E(2) Equivariant Self-Attention for Radio Astronomy

ArXiv 2111.04742 (2021)

Authors:

Micah Bowles, Matthew Bromley, Max Allen, Anna Scaife

Practical Galaxy Morphology Tools from Deep Supervised Representation Learning

(2021)

Authors:

Mike Walmsley, Anna MM Scaife, Chris Lintott, Michelle Lochner, Verlon Etsebeth, Tobias Géron, Hugh Dickinson, Lucy Fortson, Sandor Kruk, Karen L Masters, Kameswara Bharadwaj Mantha, Brooke D Simmons

Galaxy zoo: stronger bars facilitate quenching in star-forming galaxies

Monthly Notices of the Royal Astronomical Society Oxford University Press (OUP) 507:3 (2021) 4389-4408

Authors:

Tobias Géron, RJ Smethurst, Chris Lintott, Sandor Kruk, Karen L Masters, Brooke Simmons, David V Stark

Galaxy Zoo DECaLS: detailed visual morphology measurements from volunteers and deep learning for 314 000 galaxies

Monthly Notices of the Royal Astronomical Society Oxford University Press 509:3 (2021) 3966-3988

Authors:

Mike Walmsley, Chris Lintott, Tobias Géron, Sandor Kruk, Coleman Krawczyk, Kyle W Willett, Steven Bamford, Lee S Kelvin, Lucy Fortson, Yarin Gal, William Keel, Karen L Masters, Vihang Mehta, Brooke D Simmons, Rebecca Smethurst, Lewis Smith, Elisabeth M Baeten, Christine Macmillan

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.

Kiloparsec-scale AGN outflows and feedback in merger-free galaxies

Monthly Notices of the Royal Astronomical Society Oxford University Press 507:3 (2021) 3985-3997

Authors:

Rj Smethurst, Bd Simmons, A Coil, Cj Lintott, W Keel, Kl Masters, E Glikman, Gck Leung, J Shanahan, Il Garland

Abstract:

Recent observations and simulations have challenged the long-held paradigm that mergers are the dominant mechanism driving the growth of both galaxies and supermassive black holes (SMBH), in favour of non-merger (secular) processes. In this pilot study of merger-free SMBH and galaxy growth, we use Keck Cosmic Web Imager spectral observations to examine four low-redshift (0.043 < z < 0.073) disc-dominated ‘bulgeless’ galaxies hosting luminous active galactic nucleus (AGN), assumed to be merger-free. We detect blueshifted broadened [O III] emission from outflows in all four sources, which the [OIII]/Hβ ratios reveal are ionized by the AGN. We calculate outflow rates in the range 0.12−0.7 M⊙ yr−1⁠, with velocities of 675−1710 km s−1⁠, large radial extents of 0.6−2.4 kpc⁠, and SMBH accretion rates of 0.02−0.07 M⊙ yr−1⁠. We find that the outflow rates, kinematics, and energy injection rates are typical of the wider population of low-redshift AGN, and have velocities exceeding the galaxy escape velocity by a factor of ∼30, suggesting that these outflows will have a substantial impact through AGN feedback. Therefore, if both merger-driven and non-merger-driven SMBH growth lead to co-evolution, this suggests that co-evolution is regulated by feedback in both scenarios. Simulations find that bars and spiral arms can drive inflows to galactic centers at rates an order of magnitude larger than the combined SMBH accretion and outflow rates of our four targets. This work therefore provides further evidence that non-merger processes are sufficient to fuel SMBH growth and AGN outflows in disc galaxies.