Hintze Centre for Astrophysical Surveys

Abstract:

This is the fourth paper of a series investigating the AGN fuelling/feedback processes in a sample of 11 nearby low-excitation radio galaxies (LERGs). In this paper, we present follow-up Atacama Large Millimeter/submillimeter Array (ALMA) observations of one source, NGC 3100, targeting the 12CO(1-0), 12CO(3-2), HCO+(4-3), SiO(3-2), and HNCO(6-5) molecular transitions. 12CO(1-0) and 12CO(3-2) lines are nicely detected and complement our previous 12CO(2-1) data. By comparing the relative strength of these three CO transitions, we find extreme gas excitation conditions (i.e. Tex ≳ 50 K) in regions that are spatially correlated with the radio lobes, supporting the case for a jet–ISM interaction. An accurate study of the CO kinematics demonstrates that although the bulk of the gas is regularly rotating, two distinct non-rotational kinematic components can be identified in the inner gas regions: one can be associated to inflow/outflow streaming motions induced by a two-armed spiral perturbation; the second one is consistent with a jet-induced outflow with vmax ≈ 200 km s−1 and $\dot{M}\lesssim 0.12$ M⊙ yr−1. These values indicate that the jet-CO coupling ongoing in NGC 3100 is only mildly affecting the gas kinematics, as opposed to what expected from existing simulations and other observational studies of (sub-)kpc scale jet–cold gas interactions. HCO+(4-3) emission is tentatively detected in a small area adjacent to the base of the northern radio lobe, possibly tracing a region of jet-induced gas compression. The SiO(3-2) and HNCO(6-5) shock tracers are undetected: this – along with the tentative HCO+(4-3) detection – may be consistent with a deficiency of very dense (i.e. ncrit > 106 cm−3) cold gas in the central regions of NGC 3100.

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.