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 The radial acceleration relation (RAR) is a fundamental relation linking baryonic and dark matter in galaxies by relating the observed acceleration derived from dynamics to the one estimated from the baryonic mass. This relation exhibits small scatter, thus providing key constraints for models of galaxy formation and evolution—allowing us to map the distribution of dark matter in galaxies—as well as models of modified dynamics. However, it has only been extensively studied in the very local Universe with largely heterogeneous samples. We present a new measurement of the RAR, utilising a homogeneous sample of 19 H i-selected galaxies out to z = 0.08. We introduce a novel approach of measuring resolved stellar masses using spectral energy distribution (SED) fitting across 10 photometric bands to determine the resolved mass-to-light ratio, which we show is essential for measuring the acceleration due to baryons in the low-acceleration regime. Our results reveal a tight RAR with a low-acceleration power-law slope of ∼0.5, consistent with previous studies. Adopting a spatially varying mass-to-light ratio yields the tightest RAR with an intrinsic scatter of only 0.045 ± 0.022 dex, highlighting the importance of resolved stellar mass measurements in accurately characterising the gravitational contribution of the baryons in low-mass, gas-rich galaxies. We also find the first tentative evidence for redshift evolution in the acceleration scale, but more data will be required to confirm this. Adopting a more general MOND interpolating function, we find that our results ameliorate the tension between previous RAR analyses, the Solar System quadrupole and wide-binary test.