MUSE observations of M87: radial gradients for the stellar initial-mass function and the abundance of sodium
Monthly Notices of the Royal Astronomical Society Oxford University Press (OUP) 478:3 (2018) 4084-4100
SDSS-IV MaNGA: The intrinsic shape of slow rotator early-type galaxies
Astrophysical Journal Letters American Astronomical Society 863:2 (2018) L19
Abstract:
By inverting the distributions of galaxies' apparent ellipticities and misalignment angles (measured around the projected half-light radius R e) between their photometric and kinematic axes, we study the intrinsic shape distribution of 189 slow rotator early-type galaxies with stellar masses 2 × 1011 M ⊙ < M * < 2 × 1012 M ⊙, extracted from a sample of about 2200 galaxies with integral-field stellar kinematics from the data release 14 (DR14) of the fourth-generation Sloan Digital Sky Survey IV (SDSS-IV) Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) integral field unit (IFU) survey. Thanks to the large sample of slow rotators, Graham et al. showed that there is clear structure in the misalignment angle distribution, with two peaks at both 0° and 90° misalignment (characteristic of oblate and prolate rotation, respectively). Here we invert the observed distribution from Graham et al. The large sample allows us to go beyond the known fact that slow rotators are weakly triaxial and to place useful constraints on their intrinsic triaxiality distribution (around 1 R e) for the first time. The shape inversion is generally non-unique. However, we find that, for a wide set of model assumptions, the observed distribution clearly requires a dominant triaxial-oblate population. For some of our models, the data suggest a minor triaxial-prolate population, but a dominant prolate population is ruled out.The far-infrared radio correlation at low radio frequency with LOFAR/H-ATLAS
Monthly Notices of the Royal Astronomical Society Oxford University Press 480:4 (2018) 5625-5644
Abstract:
The radio and far-infrared luminosities of star-forming galaxies are tightly correlated over several orders of magnitude; this is known as the far-infrared radio correlation (FIRC). Previous studies have shown that a host of factors conspire to maintain a tight and linear FIRC, despite many models predicting deviation. This discrepancy between expectations and observations is concerning since a linear FIRC underpins the use of radio luminosity as a star-formation rate indicator. Using LOFAR 150MHz , FIRST 1.4GHz , and Herschel infrared luminosities derived from the new LOFAR/H-ATLAS catalogue, we investigate possible variation in the monochromatic ( 250μm) FIRC at low and high radio frequencies. We use statistical techniques to probe the FIRC for an optically selected sample of 4082 emission-line classified star-forming galaxies as a function of redshift, effective dust temperature, stellar mass, specific star formation rate, and mid-infrared colour (an empirical proxy for specific star formation rate). Although the average FIRC at high radio frequency is consistent with expectations based on a standard power-law radio spectrum, the average correlation at 150MHz is not. We see evidence for redshift evolution of the FIRC at 150MHz, and find that the FIRC varies with stellar mass, dust temperature, and specific star formation rate, whether the latter is probed using MAGPHYS fitting, or using mid-infrared colour as a proxy. We can explain the variation, to within 1σ, seen in the FIRC over mid-infrared colour by a combination of dust temperature, redshift, and stellar mass using a Bayesian partial correlation technique.Large-scale three-dimensional Gaussian process extinction mapping
Monthly Notices of the Royal Astronomical Society Oxford University Press (OUP) (2018)
Abstract:
Gaussian processes are the ideal tool for modelling the Galactic ISM, combining statistical flexibility with a good match to the underlying physics. In an earlier paper we outlined how they can be employed to construct three-dimensional maps of dust extinction from stellar surveys. Gaussian processes scale poorly to large datasets though, which put the analysis of realistic catalogues out of reach. Here we show how a novel combination of the Expectation Propagation method and certain sparse matrix approximations can be used to accelerate the dust mapping problem. We demonstrate, using simulated Gaia data, that the resultant algorithm is fast, accurate and precise. Critically, it can be scaled up to map the Gaia catalogue.A pilot survey for transients and variables with the Australian Square Kilometre Array Pathfinder
MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY 478:2 (2018) 1784-1794