Soo, John Yue Han; (2018) Enhancing Photometric Redshifts for the Era of Precision Cosmology. Doctoral thesis (Ph.D), UCL (University College London).

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Abstract

Photometric redshifts (photo-z's) are vital for the success of current and forthcoming cosmological galaxy surveys. This work focuses on three different approaches to enhance photo-z's. Firstly, we study the extent to which galaxy morphology improves photo-z's. Using artificial neural networks, we compare the performances of several morphological parameters and find that galaxy size and surface brightness bring about the most improvement to photo-z's in bright samples. When multiple morphological parameters are used, the improvement in scatter reaches as high as 12% for the Main Galaxy Sample (MGS) of the Sloan Digital Sky Survey (SDSS). We also find that the improvement becomes significant under suboptimal conditions: when surveys have limited numbers of bands, low quality photometry, or an imperfect star-galaxy separator. Next we study aspects of photo-z probability density functions (PDFs) and the resulting redshift distributions of galaxy samples in the context of the Canada-France-Hawaii Telescope Stripe-82 (CS82) Survey. We discover that, while galaxy morphology brings marginal improvement to both, we are able to produce accurate redshift distributions using a single photometric band and multiple galaxy morphological parameters, and apply this to the CS82 survey. As part of the photo-z Working Group of the Large Synoptic Survey Telescope Dark Energy Science Collaboration (LSST-DESC), we use several metrics to assess the performances of two state-of-the-art photo-z codes, ANNz2 and Delight, and concluded that the photo-z's produced by both are close to the standard for the current photo-z requirements of LSST. Finally, we explore the performances of multiple photo-z codes on narrowband surveys. Using simulated and real data from the 40-narrowband Physics of the Accelerating Universe (PAU) Survey, we find that the hybrid spectral template-machine learning code Delight outperforms monolithic machine learning as well as template codes. Using the large suite of spectral templates and well-calibrated additional broadband fluxes, we are able to produce competitive photo-z's close to the nominal PAU requirement at 40% quality cut. We believe these method would be useful for the next generation of photometric surveys, like Euclid and LSST.

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