Prof. Matt Jarvis

Abstract:

The MIGHTEE large survey project will survey four of the most well-studied extragalactic deep fields, totalling 20 square degrees to $\mu$Jy sensitivity at Giga-Hertz frequencies, as well as an ultra-deep image of a single ~1 square degree MeerKAT pointing. The observations will provide radio continuum, spectral line and polarisation information. As such, MIGHTEE, along with the excellent multi-wavelength data already available in these deep fields, will allow a range of science to be achieved. Specifically, MIGHTEE is designed to significantly enhance our understanding of, (i) the evolution of AGN and star-formation activity over cosmic time, as a function of stellar mass and environment, free of dust obscuration; (ii) the evolution of neutral hydrogen in the Universe and how this neutral gas eventually turns into stars after moving through the molecular phase, and how efficiently this can fuel AGN activity; (iii) the properties of cosmic magnetic fields and how they evolve in clusters, filaments and galaxies. MIGHTEE will reach similar depth to the planned SKA all-sky survey, and thus will provide a pilot to the cosmology experiments that will be carried out by the SKA over a much larger survey volume.

Abstract:

It has long been known that environment has a large effect on star formation in galaxies. There are several known plausible mechanisms to remove the cool gas needed for star formation, such as strangulation, harassment and ram-pressure stripping. It is unclear which process is dominant, and over what range of stellar mass. In this paper, we find evidence for suppression of the cross-correlation function between massive galaxies and less massive star-forming galaxies, giving a measure of how less likely a galaxy is to be star-forming in the vicinity of a more massive galaxy. We develop a formalism for modelling environmental quenching mechanisms within the Halo Occupation Distribution formalism. We find that at $z \sim 2$ environment is not a significant factor in determining quenching of star-forming galaxies, and that galaxies are quenched with similar probabilities in group environments as they are globally. However, by $z \sim 0.5$ galaxies are much less likely to be star forming when in a group environment than when not. This increased probability of being quenched does not appear to have significant radial dependence within the halo, supportive of the quenching being caused by the halting of fresh inflows of pristine gas, as opposed to by tidal stripping. Furthermore, by separating the massive sample into passive and star-forming, we see that this effect is further enhanced when the central galaxy is passive. This effect is present only in the 1-halo term (within a halo) at high redshifts ($z>1$), but is apparent in the 2-halo term at lower redshifts ($z<1$), a manifestation of galactic conformity.

Abstract:

Wide-area imaging surveys are one of the key ways of advancing our understanding of cosmology, galaxy formation physics, and the large-scale structure of the Universe in the coming years. These surveys typically require calculating redshifts for huge numbers (hundreds of millions to billions) of galaxies – almost all of which must be derived from photometry rather than spectroscopy. In this paper, we investigate how using statistical models to understand the populations that make up the colour–magnitude distribution of galaxies can be combined with machine learning photometric redshift codes to improve redshift estimates. In particular, we combine the use of Gaussian mixture models with the high-performing machine-learning photo-z algorithm GPz and show that modelling and accounting for the different colour–magnitude distributions of training and test data separately can give improved redshift estimates, reduce the bias on estimates by up to a half, and speed up the run-time of the algorithm. These methods are illustrated using data from deep optical and near-infrared data in two separate deep fields, where training and test data of different colour–magnitude distributions are constructed from the galaxies with known spectroscopic redshifts, derived from several heterogeneous surveys.

Abstract:

ABSTRACT We study the $M_{\rm H\, {\small {I}}}-M_{\star }$ relation over the last billion years using the MIGHTEE-H i sample. We first model the upper envelope of the $M_{\rm H\, {\small {I}}}-M_{\star }$ relation with a Bayesian technique applied to a total number of 249 H i-selected galaxies, without binning the data sets, while taking account of the intrinsic scatter. We fit the envelope with both linear and non-linear models, and find that the non-linear model is preferred over the linear one with a measured transition stellar mass of log10(M⋆/M⊙) = 9.15 ± 0.87, beyond which the slope flattens. This finding supports the view that the lack of H i gas is ultimately responsible for the decreasing star formation rate observed in the massive main-sequence galaxies. For spirals alone, which are biased towards the massive galaxies in our sample, the slope beyond the transition mass is shallower than for the full sample, indicative of distinct gas processes ongoing for the spirals/high-mass galaxies from other types with lower stellar masses. We then create mock catalogues for the MIGHTEE-H i detections and non-detections with two main galaxy populations of late- and early-type galaxies to measure the underlying $M_{\rm H\, {\small {I}}}-M_{\star }$ relation. We find that the turnover in this relation persists whether considering the two galaxy populations as a whole or separately. We note that an underlying linear relation could mimic this turnover in the observed scaling relation, but a model with a turnover is strongly preferred. Measurements on the logarithmic average of H i masses against the stellar mass are provided as a benchmark for future studies.