Prof. Matt Jarvis

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, utilizing a homogeneous sample of 19 H i-selected galaxies out to . We introduce a novel approach of measuring resolved stellar masses using spectral energy distribution 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 , consistent with previous studies. Adopting a spatially varying mass-to-light ratio yields the tightest RAR with an intrinsic scatter of only dex, highlighting the importance of resolved stellar mass measurements in accurately characterizing 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.

Abstract:

We present the discovery of the most distant OH megamaser to be observed in the main lines, using data from the MeerKAT International Giga-Hertz Tiered Extragalactic Exploration (MIGHTEE) survey. At a newly measured redshift of 𝑧 = 0.7092, the system has strong emission in both the 1665 MHz (𝐿 ≈ 2500 L⊙) and 1667 MHz (𝐿 ≈ 4.5×104 L⊙) transitions, with both narrow and broad components. We interpret the broad line as a high-velocity-dispersion component of the 1667 MHz transition, with velocity 𝑣 ∼ 330 km s−1 with respect to the systemic velocity. The host galaxy has a stellar mass of 𝑀★ = 2.95 × 1010 M⊙ and a star-formation rate of SFR = 371 M⊙ yr−1 , placing it ∼ 1.5 dex above the main sequence for star-forming galaxies at this redshift, and can be classified as an ultra-luminous infrared galaxy. Alongside the optical imaging data, which exhibits evidence for a tidal tail, this suggests that the OH megamaser arises from a system that is currently undergoing a merger, which is stimulating star formation and providing the necessary conditions for pumping the OH molecule to saturation. The OHM is likely to be lensed, with a magnification factor of ∼ 2.5, and perhaps more if the maser emitting region is compact and suitably offset relative to the centroid of its host galaxy’s optical light. This discovery demonstrates that spectral line mapping with the new generation of radio interferometers may provide important information on the cosmic merger history of galaxies.

Abstract:

We study the nature of the faint radio source population detected in the MeerKAT International GHz Tiered Extragalactic Exploration (MIGHTEE) Early Science data in the COSMOS field, focusing on the properties of the radio-loud active galactic nuclei (AGNs). Using the extensive multiwavelength data available in the field, we are able to classify 88 per cent of the 5223 radio sources in the field with host galaxy identifications as AGNs (35 per cent) or star-forming galaxies (54 per cent). We select a sample of radio-loud AGNs with redshifts out to z ∼ 6 and radio luminosities 10²⁰ < L_1.4 GHz/W Hz⁻¹ < 10²⁷ and classify them as high-excitation and low-excitation radio galaxies (HERGs and LERGs). The classification catalogue is released with this work. We find no significant difference in the host galaxy properties of the HERGs and LERGs in our sample. In contrast to previous work, we find that the HERGs and LERGs have very similar Eddington-scaled accretion rates; in particular we identify a population of very slowly accreting AGNs that are formally classified as HERGs at these low radio luminosities, where separating into HERGs and LERGs possibly becomes redundant. We investigate how black hole mass affects jet power, and find that a black hole mass ≳ 10^7.8 M_⊙ is required to power a jet with mechanical power greater than the radiative luminosity of the AGN (L_mech/L_bol > 1). We discuss that both a high black hole mass and black hole spin may be necessary to launch and sustain a dominant radio jet.

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:

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.