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The stochastic order redshift technique (SORT) is a simple, efficient, androbust method to improve cosmological redshift measurements. The method reliesupon having a small ($\sim$10 per cent) reference sample of high-qualityredshifts. Within pencil-beam-like sub-volumes surrounding each galaxy, we usethe precise dN/d$z$ distribution of the reference sample to recover newredshifts and assign them one-to-one to galaxies such that the original rankorder of redshifts is preserved. Preserving the rank order is motivated by thefact that random variables drawn from Gaussian probability density functionswith different means but equal standard deviations satisfy stochastic ordering.The process is repeated for sub-volumes surrounding each galaxy in the survey.This results in every galaxy with an uncertain redshift being assigned multiple"recovered" redshifts from which a new redshift estimate can be determined. Anearlier paper applied SORT to a mock Sloan Digital Sky Survey at $z \lesssim$0.2 and accurately recovered the two-point correlation function on scales$\gtrsim$4 $h^{-1}$Mpc. In this paper, we test the performance of SORT insurveys spanning the redshift range 0.75$<z<$2.25. We used two mock surveysextracted from the Small MultiDark-Planck and Bolshoi-Planck N-body simulationswith dark matter haloes that were populated by the Santa Cruz semi-analyticmodel. We find that SORT is able to improve redshift estimates and recoverdistinctive large-scale features of the cosmic web. Further, it providesunbiased estimates of the redshift-space two-point correlation function$\xi(s)$ on scales $\gtrsim$2.5 $h^{-1}$Mpc, as well as local densities inregions of average or higher density. This may allow improved understanding ofhow galaxy properties relate to their local environments.

Galaxy Correlation Function and Local Density from Photometric Redshifts Using the Stochastic Order Redshift Technique (SORT)