The SAMI Galaxy Survey: first detection of a transition in spin orientation with respect to cosmic filaments in the stellar kinematics of galaxies
Monthly Notices of the Royal Astronomical Society Oxford University Press 491:2 (2019) 2864-2884
Abstract:
We present the first detection of mass dependent galactic spin alignments with local cosmic filaments with >2σ confidence using IFS kinematics. The 3D network of cosmic filaments is reconstructed on Mpc scales across GAMA fields using the cosmic web extractor DisPerSe. We assign field galaxies from the SAMI survey to their nearest filament segment in 3D and estimate the degree of alignment between SAMI galaxies’ kinematic spin axis and their nearest filament in projection. Low-mass galaxies align their spin with their nearest filament while higher mass counterparts are more likely to display an orthogonal orientation. The stellar transition mass from the first trend to the second is bracketed between 1010.4 M⊙ and 1010.9 M⊙, with hints of an increase with filament scale. Consistent signals are found in the Horizon-AGN cosmological hydrodynamic simulation. This supports a scenario of early angular momentum build-up in vorticity rich quadrants around filaments at low stellar mass followed by progressive flip of spins orthogonal to the cosmic filaments through mergers at high stellar mass. Conversely, we show that dark-matter only simulations post-processed with a semi-analytic model treatment of galaxy formation struggles to reproduce this alignment signal. This suggests that gas physics is key in enhancing the galaxy-filament alignment.Galaxy zoo: Probabilistic morphology through Bayesian CNNs and active learning
Monthly Notices of the Royal Astronomical Society Oxford University Press 491:2 (2019) 1554-1574
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.A ghost in the toast: TESS background light produces a false “transit” across τ Ceti
Research Notes of the AAS American Astronomical Society 3:10 (2019) 145
Inferring high redshift large-scale structure dynamics from the Lyman-alpha forest
A&A 2019
Abstract:
One of the major science goals over the coming decade is to test fundamental physics with probes of the cosmic large-scale structure out to high redshift. Here we present a fully Bayesian approach to infer the three-dimensional cosmic matter distribution and its dynamics at z>2 from observations of the Lyman-α forest. We demonstrate that the method recovers the unbiased mass distribution and the correct matter power spectrum at all scales. Our method infers the three-dimensional density field from a set of one-dimensional spectra, interpolating the information between the lines of sight. We show that our algorithm provides unbiased mass profiles of clusters, becoming an alternative for estimating cluster masses complementary to weak lensing or X-ray observations. The algorithm employs a Hamiltonian Monte Carlo method to generate realizations of initial and evolved density fields and the three-dimensional large-scale flow, revealing the cosmic dynamics at high redshift. The method correctly handles multi-modal parameter distributions, which allow constraining the physics of the intergalactic medium (IGM) with high accuracy. We performed several tests using realistic simulated quasar spectra to test and validate our method. Our results show that detailed and physically plausible inference of three-dimensional large-scale structures at high redshift has become feasible.