Organised randoms: Learning and correcting for systematic galaxy clustering patterns in KiDS using self-organising maps

¹ Institute for Theoretical Physics, Utrecht University, Princetonplein 5, 3584 CE Utrecht, The Netherlands
e-mail: h.s.johnston@uu.nl
² Department of Physics and Astronomy, University College London, Gower Street, London WC1E 6BT, UK
³ Ruhr University Bochum, Faculty of Physics and Astronomy, Astronomical Institute (AIRUB), German Centre for Cosmological Lensing, 44780 Bochum, Germany
⁴ Center for Theoretical Physics, Polish Academy of Sciences, al. Lotników 32/46, 02-668 Warsaw, Poland
⁵ Argelander-Institut für Astronomie, Auf dem Hügel 71, 53121 Bonn, Germany
⁶ Institute for Astronomy, University of Edinburgh, Royal Observatory, Blackford Hill, Edinburgh EH9 3HJ, UK
⁷ Leiden Observatory, Leiden University, PO Box 9513, Leiden 2300 RA, The Netherlands
⁸ Department of Physics, University of Oxford, Keble Road, Oxford OX1 3RH, UK
⁹ Waterloo Centre for Astrophysics, University of Waterloo, 200 University Ave W, Waterloo, ON N2L 3G1, Canada

Received: 15 December 2020
Accepted: 9 February 2021

Abstract

We present a new method for the mitigation of observational systematic effects in angular galaxy clustering through the use of corrective random galaxy catalogues. Real and synthetic galaxy data from the Kilo Degree Survey’s (KiDS) 4th Data Release (KiDS-1000) and the Full-sky Lognormal Astro-fields Simulation Kit package, respectively, are used to train self-organising maps to learn the multivariate relationships between observed galaxy number density and up to six systematic-tracer variables, including seeing, Galactic dust extinction, and Galactic stellar density. We then create ‘organised’ randoms; random galaxy catalogues with spatially variable number densities, mimicking the learnt systematic density modes in the data. Using realistically biased mock data, we show that these organised randoms consistently subtract spurious density modes from the two-point angular correlation function w(ϑ), correcting biases of up to 12σ in the mean clustering amplitude to as low as 0.1σ, over an angular range of 7 − 100 arcmin with high signal-to-noise ratio. Their performance is also validated for angular clustering cross-correlations in a bright, flux-limited subset of KiDS-1000, comparing against an analogous sample constructed from highly complete spectroscopic redshift data. Each organised random catalogue object is a clone carrying the properties of a real galaxy, and is distributed throughout the survey footprint according to the position of the parent galaxy in systematics space. Thus, sub-sample randoms are readily derived from a single master random catalogue through the same selection as applied to the real galaxies. Our method is expected to improve in performance with increased survey area, galaxy number density, and systematic contamination, making organised randoms extremely promising for current and future clustering analyses of faint samples.