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Predicting the neutral hydrogen content of galaxies from optical data using machine learning
(Oxford University Press, 2018)
We develop a machine learning-based framework to predict the Hi content of galaxies using
more straightforwardly observable quantities such as optical photometry and environmental
parameters. We train the algorithm on z ...
MUFASA: The strength and evolution of galaxy conformity in various tracers
(Oxford University Press, 2017)
We investigate galaxy conformity using the Mufasa cosmological hydrodynamical
simulation. We show a bimodal distribution in galaxy colour with radius, albeit
with too many low-mass quenched satellite galaxies compared ...
Mufasa: The assembly of the red sequence
(Oxford University Press, 2017)
We examine the growth and evolution of quenched galaxies in the Mufasa cosmo-
logical hydrodynamic simulations that include an evolving halo mass-based quench-
ing prescription, with galaxy colours computed accounting ...