Prof. Matt Jarvis

Abstract:

We present the H I emission project within the MIGHTEE survey, currently being carried out with the newly commissioned MeerKAT radio telescope. This is one of the first deep, blind, medium-wide interferometric surveys for neutral hydrogen (H I) ever undertaken, extending our knowledge of H I emission to z = 0.6. The science goals of this medium-deep, medium-wide survey are extensive, including the evolution of the neutral gas content of galaxies over the past 5 billion years. Simulations predict nearly 3000 galaxies over 0 <  z <  0.4 will be detected directly in H I, with statistical detections extending to z = 0.6. The survey allows us to explore H I as a function of galaxy environment, with massive groups and galaxy clusters within the survey volume. Additionally, the area is large enough to contain as many as 50 local galaxies with H I mass < 108 M⊙, which allows us to study the low-mass galaxy population. The 20 deg2 main survey area is centred on fields with exceptional multi-wavelength ancillary data, with photometry ranging from optical through far-infrared wavelengths, supplemented with multiple spectroscopic campaigns. We describe here the survey design and the key science goals. We also show first results from the Early Science observations, including kinematic modelling of individual sources, along with the redshift, H I, and stellar mass ranges of the sample to date.

Abstract:

We measure the harmonic-space auto-power spectrum of the galaxy overdensity in the LOFAR Two-metre Sky Survey (LoTSS) First Data Release and its cross correlation with the map of the lensing convergence of the cosmic microwave background (CMB) from the Planck collaboration. We report a ∼5σ detection of the cross-correlation. We show that the combination of the clustering power spectrum and CMB lensing cross-correlation allows us to place constraints on the high-redshift tail of the redshift distribution, one of the largest sources of uncertainty in the use of continuum surveys for cosmology. Our analysis shows a preference for a broader redshift tail than that predicted by the photometric redshifts contained in the LoTSS value added catalog, as expected, and more compatible with predictions from simulations and spectroscopic data. Although the ability of CMB lensing to constrain the width and tail of the redshift distribution could also be valuable for the analysis of current and future photometric weak lensing surveys, we show that its performance relies strongly on the redshift evolution of the galaxy bias. Assuming the redshift distribution predicted by the Square Kilometre Array Design simulations, we use our measurements to place constraints on the linear bias of radio galaxies and the amplitude of matter inhomogeneities σ8, finding σ8=0.69+0.14−0.21 assuming the galaxy bias scales with the inverse of the linear growth factor, and σ8=0.79+0.17−0.32 assuming a constant bias.

Abstract:

With the advent of deep optical-to-near-infrared extragalactic imaging on the degree scale, samples of high-redshift sources are being selected that contain both bright star-forming (SF) galaxies and faint active galactic nuclei (AGN). In this study, we investigate the transition between SF- and AGN-dominated systems at z ≃ 4 in the rest-frame ultraviolet (UV). We find a rapid transition to AGN-dominated sources brightward of MUV ≃ −23.2. The effect is observed in the rest-frame UV morphology and size–luminosity relation, where extended clumpy systems become point-source-dominated, and also in the available spectra for the sample. These results allow us to derive the rest-frame UV luminosity function (LF) for the SF- and AGN-dominated subsamples. We find the SF-dominated LF is best fit with a double power law, with a lensed Schechter function being unable to explain the existence of extremely luminous SF galaxies at MUV ≃ −23.5. If we identify AGN-dominated sources according to a point-source morphology criterion, we recover the relatively flat faint-end slope of the AGN LF determined in previous studies. If we instead separate the LF according to the current spectroscopic AGN fraction, we find a steeper faint-end slope of α = −1.83 ± 0.11. Using a simple model to predict the rest-frame AGN LF from the z = 4 galaxy LF, we find that the increasing impact of host galaxy light on the measured morphology of faint AGN can explain our observations.

Abstract:

We report the discovery of two new giant radio galaxies (GRGs) using the MeerKAT International GHz Tiered Extragalactic Exploration (MIGHTEE) survey. Both GRGs were found within a ∼1 deg2 region inside the COSMOS field. They have redshifts of z = 0.1656 and z = 0.3363 and physical sizes of 2.4 and 2.0 Mpc, respectively. Only the cores of these GRGs were clearly visible in previous high-resolution Very Large Array observations, since the diffuse emission of the lobes was resolved out. However, the excellent sensitivity and uv coverage of the new MeerKAT telescope allowed this diffuse emission to be detected. The GRGs occupy an unpopulated region of radio power – size parameter space. Based on a recent estimate of the GRG number density, the probability of finding two or more GRGs with such large sizes at z < 0.4 in a ∼1 deg2 field is only 2.7 × 10−6, assuming Poisson statistics. This supports the hypothesis that the prevalence of GRGs has been significantly underestimated in the past due to limited sensitivity to low surface brightness emission. The two GRGs presented here may be the first of a new population to be revealed through surveys like MIGHTEE that provide exquisite sensitivity to diffuse, extended emission.

Abstract:

Forthcoming large photometric surveys for cosmology require precise and accurate photometric redshift (photo-z) measurements for the success of their main science objectives. However, to date, no method has been able to produce photo-zs at the required accuracy using only the broad-band photometry that those surveys will provide. An assessment of the strengths and weaknesses of current methods is a crucial step in the eventual development of an approach to meet this challenge. We report on the performance of 13 photometric redshift code single value redshift estimates and redshift probability distributions (PDZs) on a common set of data, focusing particularly on the 0.2pdbl-pdbl2.6 redshift range that the Euclid mission will probe. We designed a challenge using emulated Euclid data drawn from three photometric surveys of the COSMOS field. The data was divided into two samples: one calibration sample for which photometry and redshifts were provided to the participants; and the validation sample, containing only the photometry to ensure a blinded test of the methods. Participants were invited to provide a redshift single value estimate and a PDZ for each source in the validation sample, along with a rejection flag that indicates the sources they consider unfit for use in cosmological analyses. The performance of each method was assessed through a set of informative metrics, using cross-matched spectroscopic and highly-accurate photometric redshifts as the ground truth. We show that the rejection criteria set by participants are efficient in removing strong outliers, that is to say sources for which the photo-z deviates by more than 0.15(1pdbl+pdblz) from the spectroscopic-redshift (spec-z). We also show that, while all methods are able to provide reliable single value estimates, several machine-learning methods do not manage to produce useful PDZs. We find that no machine-learning method provides good results in the regions of galaxy color-space that are sparsely populated by spectroscopic-redshifts, for example zpdbl> pdbl1. However they generally perform better than template-fitting methods at low redshift (zpdbl< pdbl0.7), indicating that template-fitting methods do not use all of the information contained in the photometry. We introduce metrics that quantify both photo-z precision and completeness of the samples (post-rejection), since both contribute to the final figure of merit of the science goals of the survey (e.g., cosmic shear from Euclid). Template-fitting methods provide the best results in these metrics, but we show that a combination of template-fitting results and machine-learning results with rejection criteria can outperform any individual method. On this basis, we argue that further work in identifying how to best select between machine-learning and template-fitting approaches for each individual galaxy should be pursued as a priority.