Zooniverse

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.

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.

Abstract:

The challenge of consistent identification of internal structure in galaxies - in particular disc galaxy components like spiral arms, bars, and bulges - has hindered our ability to study the physical impact of such structure across large samples. In this paper we present Galaxy Zoo: 3D (GZ:3D) a crowdsourcing project built on the Zooniverse platform that we used to create spatial pixel (spaxel) maps that identify galaxy centres, foreground stars, galactic bars, and spiral arms for 29 831 galaxies that were potential targets of the MaNGA survey (Mapping Nearby Galaxies at Apache Point Observatory, part of the fourth phase of the Sloan Digital Sky Surveys or SDSS-IV), including nearly all of the 10 010 galaxies ultimately observed. Our crowdsourced visual identification of asymmetric internal structures provides valuable insight on the evolutionary role of non-axisymmetric processes that is otherwise lost when MaNGA data cubes are azimuthally averaged. We present the publicly available GZ:3D catalogue alongside validation tests and example use cases. These data may in the future provide a useful training set for automated identification of spiral arm features. As an illustration, we use the spiral masks in a sample of 825 galaxies to measure the enhancement of star formation spatially linked to spiral arms, which we measure to be a factor of three over the background disc, and how this enhancement increases with radius.