Mass functions, luminosity functions, and completeness measurements from clustering redshifts. Issue 3 (24th April 2019)

• Record Type: Journal Article
• Authors: Bates, Dominic J; Tojeiro, Rita; Newman, Jeffrey A; Gonzalez-Perez, Violeta; Comparat, Johan; Schneider, Donald P; Lima, Marcos; Streblyanska, Alina
• Is Part Of: Monthly notices of the Royal Astronomical Society. Volume 486:Issue 3(2019)
• Page Start: 3059
• Page End: 3077
• Publication Date: 2019-04-24
• Subjects: methods: data analysis -- surveys -- galaxies: distances and redshifts -- galaxies: luminosity function, mass function; Astronomy -- Periodicals; Periodicals; 520.5
• DOI: 10.1093/mnras/stz997
• Languages: English
• ISSNs: 0035-8711

Abstract

Abstract: This paper presents stellar mass functions and i -band luminosity functions for Sloan Digital Sky Survey (SDSS) galaxies with i < 21 using clustering redshifts. From these measurements, we also compute targeting completeness measurements for the Baryon Oscillation Spectroscopic Survey (BOSS). Clustering redshifts is a method of obtaining the redshift distribution of a sample of galaxies with only photometric information by measuring the angular cross-correlation with a spectroscopic sample in different redshift bins. We construct a spectroscopic sample containing data from the BOSS + eBOSS surveys, allowing us to recover redshift distributions from photometric data out to z ≃ 2.5. We produce k -corrected i -band luminosity functions and stellar mass functions by applying clustering redshifts to SDSS DR8 galaxies in small bins of colour and magnitude. There is little evolution in the mass function between 0.2 < z < 0.8, implying that the most massive galaxies form most of their mass before z = 0.8. These mass functions are used to produce stellar mass completeness estimates for the BOSS, giving a stellar mass completeness of$80{{\ \rm per\ cent}}$ above M ⋆ > 10 11.4 between 0.2 < z < 0.7, with completeness falling significantly at redshifts higher than 0.7, and at lower masses. Large photometric data sets will be available in the near future (DECaLS, DES, Euclid), so this and similar techniques will become increasingly useful in order to fully utilize these data.