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GAMA/H-ATLAS: The Local Dust Mass Function and Cosmic Density as a Function of Galaxy Type - A Benchmark for Models of Galaxy Evolution
R. A. Beeston, A. H. Wright, S. Maddox +22
We present the dust mass function (DMF) of 15,750 galaxies with redshift , drawn from the overlapping area of the GAMA and {\it H-}ATLAS surveys. The DMF is derived using t…
GAMA/G10-COSMOS/3D-HST: The 0<z<5 cosmic star-formation history, stellar- and dust-mass densities
Simon P. Driver, Stephen K. Andrews, Elisabete da Cunha +30
We use the energy-balance code MAGPHYS to determine stellar and dust masses, and dust corrected star-formation rates for over 200,000 GAMA galaxies, 170,000 G10-COSMOS galaxies and…
Galaxy And Mass Assembly (GAMA): The Consistency of GAMA and WISE Derived Mass-to-Light Ratios
T. Kettlety, J. Hesling, S. Phillipps +11
Recent work has suggested that mid-IR wavelengths are optimal for estimating the mass-to-light ratios of stellar populations and hence the stellar masses of galaxies. We compare st…
The SAMI Galaxy Survey: the intrinsic shape of kinematically selected galaxies
C. Foster, J. van de Sande, F. D'Eugenio +17
Using the stellar kinematic maps and ancillary imaging data from the Sydney AAO Multi Integral field (SAMI) Galaxy Survey, the intrinsic shape of kinematically-selected samples of…
The SAMI Galaxy Survey: understanding observations of large-scale outflows at low redshift with EAGLE simulations
E. Tescari, L. Cortese, C. Power +23
This work presents a study of galactic outflows driven by stellar feedback. We extract main sequence disc galaxies with stellar mass MM$_{\odot} \le 5.7\times1…
Photometric redshifts for the Kilo-Degree Survey. Machine-learning analysis with artificial neural networks
M. Bilicki, H. Hoekstra, M. J. I. Brown +24
We present a machine-learning photometric redshift analysis of the Kilo-Degree Survey Data Release 3, using two neural-network based techniques: ANNz2 and MLPQNA. Despite limited c…