The nature and nature of a starburst supermassive H1 galaxy : HIZOA J0836-43

Includes abstract

The SED of the nearby HI-massive LIRG HIZOA J0836-43: from the NIR to the radio domain

HIZOA J0836-43is one of the most HI-massive galaxies in the local (z<0.1) Universe. Not only are such galaxies extremely rare, but this "coelacanth" galaxy exhibits characteristics -- in particular its active, inside-out stellar disk-building -- that appear more typical of past (z ~ 1) star formation, when large gas fractions were more common. Unlike most local giant HI galaxies, it is actively star forming. Moreover, the strong infrared emission is not induced by a merger event or AGN, as is commonly found in other local LIRGs. The galaxy is suggestive of a scaled-up version of local spiral galaxies; its extended star formation activity likely being fueled by its large gas reservoir and, as such, can aid our understanding of star formation in systems expected to dominate at higher redshifts. The multi-wavelength imaging and spectroscopic observations that have led to these deductions will be presented. These include NIR (J H K) and MIR (Spitzer; 3-24micron) imaging and photometry, MIR spectroscopy, ATCA HI-interferometry and Mopra CO line emission observations. But no optical data, as the galaxy is heavily obscured due to its location in Vela behind the Milky Way.Comment: 5 pages, 3 figures, to appear in the proceedings of IAU Symposium 284, "The Spectral Energy Distribution of Galaxies" (SED2011), 5-9 September 2011, Preston, UK, editors R.J. Tuffs & C.C.Popesc

Probing distant clusters : a pre-SALT photometric study of intermediate redshift galaxy cluster

Includes bibliographical references (p. 273-279)

Deep K_s-near-infrared Surface Photometry of 80 Dwarf Irregular Galaxies in the Local Volume

We present deep near-infrared (K_s) images and surface photometry for 80 dwarf irregular galaxies (dIs) within ~5 Mpc of the Milky Way. The galaxy images were obtained at five different facilities between 2004 and 2006. The image reductions and surface photometry have been performed using methods specifically designed for isolating faint galaxies from the high and varying near-infrared sky level. Fifty-four of the 80 dIs have surface brightness profiles which could be fit to a hyperbolic-secant (sech) function, while the remaining profiles could be fit to the sum of a sech and a Gaussian function. From these fits, we have measured central surface brightnesses, scale lengths, and integrated magnitudes. This survey is part of a larger study of the connection between large-scale structure and the global properties of dIs, the hypothesized building-blocks of more massive galaxies

Deep Ks -near-infrared surface photometry of 80 dwarf irregular galaxies in the local volume

We present deep near-infrared (K_s) images and surface photometry for 80 dwarf irregular galaxies (dIs) within ~5 Mpc of the Milky Way. The galaxy images were obtained at five different facilities between 2004 and 2006. The image reductions and surface photometry have been performed using methods specifically designed for isolating faint galaxies from the high and varying near-infrared sky level. Fifty-four of the 80 dIs have surface brightness profiles which could be fit to a hyperbolic-secant (sech) function, while the remaining profiles could be fit to the sum of a sech and a Gaussian function. From these fits, we have measured central surface brightnesses, scale lengths, and integrated magnitudes. This survey is part of a larger study of the connection between large-scale structure and the global properties of dIs, the hypothesized building-blocks of more massive galaxies

Measurement of the evolving galaxy luminosity and mass function using clustering-based redshift inference

We develop a framework for using clustering-based redshift inference (cluster-z ) to measure the evolving galaxy luminosity function (GLF) and galaxy stellar mass function (GSMF) using Wide-field Infrared Survey Explorer W1 (3.4 μm) mid-infrared photometry and positions. We use multiple reference sets from the Galaxy And Mass Assembly survey, Sloan Digital Sky Survey and Baryon Oscillation Spectroscopic Survey. Combining the resulting cluster-z s allows us to enlarge the study area, and by accounting for the specific properties of each reference set, making best use of each reference set to produce the best overall result

Galaxy And Mass Assembly (GAMA) : the large-scale structure of galaxies and comparison to mock universes

MA acknowledges funding from the University of St Andrews and the International Centre for Radio Astronomy Research. ASGR is supported by funding from a UWA Fellowship. PN acknowledges the support of the Royal Society through the award of a University Research Fellowship and the European Research Council, through receipt of a Starting Grant (DEGAS-259586). MJIB acknowledges the financial support of the Australian Research Council Future Fellowship 100100280. TMR acknowledges support from a European Research Council Starting Grant (DEGAS-259586).From a volume-limited sample of 45 542 galaxies and 6000 groups with z ≤ 0.213, we use an adapted minimal spanning tree algorithm to identify and classify large-scale structures within the Galaxy And Mass Assembly (GAMA) survey. Using galaxy groups, we identify 643 filaments across the three equatorial GAMA fields that span up to 200 h−1 Mpc in length, each with an average of eight groups within them. By analysing galaxies not belonging to groups, we identify a secondary population of smaller coherent structures composed entirely of galaxies, dubbed ‘tendrils’ that appear to link filaments together, or penetrate into voids, generally measuring around 10 h−1 Mpc in length and containing on average six galaxies. Finally, we are also able to identify a population of isolated void galaxies. By running this algorithm on GAMA mock galaxy catalogues, we compare the characteristics of large-scale structure between observed and mock data, finding that mock filaments reproduce observed ones extremely well. This provides a probe of higher order distribution statistics not captured by the popularly used two-point correlation function.Peer reviewe