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We present a new method for the mitigation of observational systematiceffects in angular galaxy clustering via corrective random galaxy catalogues.Real and synthetic galaxy data, from the Kilo Degree Survey's (KiDS)4$^{\rm{th}}$ Data Release (KiDS-$1000$) and the Full-sky LognormalAstro-fields Simulation Kit (FLASK) package respectively, are used to trainself-organising maps (SOMs) to learn the multivariate relationships betweenobserved galaxy number density and up to six systematic-tracer variables,including seeing, Galactic dust extinction, and Galactic stellar density. Wethen create `organised' randoms, i.e. random galaxy catalogues with spatiallyvariable number densities, mimicking the learnt systematic density modes in thedata. Using realistically biased mock data, we show that these organisedrandoms consistently subtract spurious density modes from the two-point angularcorrelation function $w(\vartheta)$, correcting biases of up to $12\sigma$ inthe mean clustering amplitude to as low as $0.1\sigma$, over a highsignal-to-noise angular range of 7-100 arcmin. Their performance is alsovalidated for angular clustering cross-correlations in a bright, flux-limitedsubset of KiDS-$1000$, comparing against an analogous sample constructed fromhighly-complete spectroscopic redshift data. Each organised random catalogueobject is a `clone' carrying the properties of a real galaxy, and isdistributed throughout the survey footprint according to the parent galaxy'sposition in systematics-space. Thus, sub-sample randoms are readily derivedfrom a single master random catalogue via the same selection as applied to thereal galaxies. Our method is expected to improve in performance with increasedsurvey area, galaxy number density, and systematic contamination, makingorganised randoms extremely promising for current and future clusteringanalyses of faint samples.

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