Prof. Matt Jarvis

Abstract:

Using a new radio sample, 6C* designed to find radio galaxies at z > 4 along with the complete 3CRR and 6CE sample we extend the radio galaxy K-z relation to z~4.5. The 6C* K-z data significantly improve delineation of the K-z relation for radio galaxies at high redshift (z > 2). Accounting for non-stellar contamination, and for correlations between radio luminosity and estimates of stellar mass, we find little support for previous claims that the underlying scatter in the stellar luminosity of radio galaxies increases significantly at z > 2. This indicates that we are not probing into the formation epoch until at least z > 3.

Abstract:

We use three samples (3CRR, 6CE and 6C*) to investigate the radio luminosity function (RLF) for the 'most powerful' low-frequency selected radio sources. We find that the data are well fitted by a model with a constant ca-moving space density at high redshift as well as by one with a declining co-moving space density above some particular redshift. This behaviour is very similar to that inferred for steep-spectrum radio quasars by Willott et al (1998) in Line with the expectations of Unified Schemes. We conclude that there is as yet no evidence for a 'redshift cutoff' in the co-moving space densities of powerful classical double radio sources, and rule out a art-off at z less than or similar to 2.5.

Abstract:

The next generation of large scale imaging surveys (such as those conducted with the Large Synoptic Survey Telescope and Euclid) will require accurate photometric redshifts in order to optimally extract cosmological information. Gaussian Processes for photometric redshift estimation (GPz) is a promising new method that has been proven to provide efficient, accurate photometric redshift estimations with reliable variance predictions. In this paper, we investigate a number of methods for improving the photometric redshift estimations obtained using GPz (but which are also applicable to others). We use spectroscopy from the Galaxy and Mass Assembly Data Release 2 with a limiting magnitude of r<19.4 along with corresponding Sloan Digital Sky Survey visible (ugriz) photometry and the UKIRT Infrared Deep Sky Survey Large Area Survey near-IR (YJHK) photometry. We evaluate the effects of adding near-IR magnitudes and angular size as features for the training, validation and testing of GPz and find that these improve the accuracy of the results by ~15-20 per cent. In addition, we explore a post-processing method of shifting the probability distributions of the estimated redshifts based on their Quantile-Quantile plots and find that it improves the bias by ~40 per cent. Finally, we investigate the effects of using more precise photometry obtained from the Hyper Suprime-Cam Subaru Strategic Program Data Release 1 and find that it produces significant improvements in accuracy, similar to the effect of including additional features.