MIGHTEE: are giant radio galaxies more common than we thought?
Monthly Notices of the Royal Astronomical Society Oxford University Press 501:3 (2020) 3833-3845
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.Augmenting machine learning photometric redshifts with Gaussian mixture models
Monthly Notices of the Royal Astronomical Society Oxford University Press 498:4 (2020) 5498-5510
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.The rest-frame UV luminosity function at z≃4 : a significant contribution of AGN to the bright-end of the galaxy population
Monthly Notices of the Royal Astronomical Society Oxford University Press 494:2 (2020) 1771-1783
Abstract:
We measure the rest-frame UV luminosity function (LF) at z ∼ 4 self-consistently over a wide range in absolute magnitude (−27 . MUV . −20). The LF is measured with 46,904 sources selected using a photometric redshift approach over ∼ 6 deg2 of the combined COSMOS and XMM-LSS fields. We simultaneously fit for both AGN and galaxy LFs using a combination of Schechter or Double Power Law (DPL) functions alongside a single power law for the faint-end slope of the AGN LF. We find a lack of evolution in the shape of the bright-end of the LBG component when compared to other studies at z ' 5 and evolutionary recipes for the UV LF. Regardless of whether the LBG LF is fit with a Schechter function or DPL, AGN are found to dominate at MUV < −23.5. We measure a steep faint-end slope of the AGN LF with αAGN = −2.09+0.35 −0.38 (−1.66+0.29 −0.58) when fit alongside a Schechter function (DPL) for the galaxies. Our results suggest that if AGN are morphologically selected it results in a bias to lower number densities. Only by considering the full galaxy population over the transition region from AGN to LBG domination can an accurate measurement of the total LF be attained.A lack of evolution in the very bright-end of the galaxy luminosity function from z ≃ 8-10
Monthly Notices of the Royal Astronomical Society Oxford University Press 493:2 (2020) 2059-2084
Abstract:
We utilize deep near-infrared survey data from the UltraVISTA fourth data release (DR4) and the VIDEO survey, in combination with overlapping optical and Spitzer data, to search for bright star-forming galaxies at z ≳ 7.5. Using a full photometric redshift fitting analysis applied to the ∼6 deg2 of imaging searched, we find 27 Lyman break galaxies (LBGs), including 20 new sources, with best-fitting photometric redshifts in the range 7.4 < z < 9.1. From this sample, we derive the rest-frame UV luminosity function at z = 8 and z = 9 out to extremely bright UV magnitudes (MUV ≃ −23) for the first time. We find an excess in the number density of bright galaxies in comparison to the typically assumed Schechter functional form derived from fainter samples. Combined with previous studies at lower redshift, our results show that there is little evolution in the number density of very bright (MUV ∼ −23) LBGs between z ≃ 5 and z ≃ 9. The tentative detection of an LBG with best-fitting photometric redshift of z = 10.9 ± 1.0 in our data is consistent with the derived evolution. We show that a double power-law fit with a brightening characteristic magnitude (ΔM*/Δz ≃ −0.5) and a steadily steepening bright-end slope (Δβ/Δz ≃ −0.5) provides a good description of the z > 5 data over a wide range in absolute UV magnitude (−23 < MUV < −17). We postulate that the observed evolution can be explained by a lack of mass quenching at very high redshifts in combination with increasing dust obscuration within the first ∼1Gyr of galaxy evolution.Obscured star formation in bright z ≃ 7 Lyman-break galaxies
Monthly Notices of the Royal Astronomical Society Oxford University Press 481:2 (2018) 1631-1644