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

Improving Photometric Redshift Estimation using GPz: size information, post processing and improved photometry