Magnification bias in galaxy surveys with complex sample selection functions

• Published in: Monthly notices of the Royal Astronomical Society Vol. 504; no. 1; pp. 1452 - 1465
• Main Authors: von Wietersheim-Kramsta, Maximilian, Joachimi, Benjamin, van den Busch, Jan Luca, Heymans, Catherine, Hildebrandt, Hendrik, Asgari, Marika, Tr’oster, Tilman, Unruh, Sandra, Wright, Angus H
• Format: Journal Article
• Language: English
• Published: Oxford University Press 01.06.2021
• Subjects: gravitational lensing: weak; methods: observational; methods: data analysis
• ISSN: 0035-8711; 1365-2966
• DOI: 10.1093/mnras/stab1000

ABSTRACT Gravitational lensing magnification modifies the observed spatial distribution of galaxies and can severely bias cosmological probes of large-scale structure if not accurately modelled. Standard approaches to modelling this magnification bias may not be applicable in practice as many galaxy samples have complex, often implicit, selection functions. We propose and test a procedure to quantify the magnification bias induced in clustering and galaxy–galaxy lensing (GGL) signals in galaxy samples subject to a selection function beyond a simple flux limit. The method employs realistic mock data to calibrate an effective luminosity function slope, αobs, from observed galaxy counts that can then be used with the standard formalism. We demonstrate this method for two galaxy samples derived from the Baryon Oscillation Spectroscopic Survey (BOSS) in the redshift ranges 0.2 < z ≤ 0.5 and 0.5 < z ≤ 0.75, complemented by mock data built from the MICE2 simulation. We obtain αobs = 1.93 ± 0.05 and αobs = 2.62 ± 0.28 for the two BOSS samples. For BOSS-like lenses, we forecast a contribution of the magnification bias to the GGL signal between the multipole moments, ℓ, of 100 and 4600 with a cumulative signal-to-noise ratio between 0.1 and 1.1 for sources from the Kilo-Degree Survey (KiDS), between 0.4 and 2.0 for sources from the Hyper Suprime-Cam survey (HSC), and between 0.3 and 2.8 for ESA Euclid-like source samples. These contributions are significant enough to require explicit modelling in future analyses of these and similar surveys. Our code is publicly available within the MagBEt module (https://github.com/mwiet/MAGBET).