Modelling the dusty universe II: The clustering of submillimetre-selected galaxies

TL;DR

This paper models the clustering of submillimetre-selected galaxies using a combination of galaxy formation models, spectral energy distribution calculations, and neural networks, predicting their spatial distribution and dependence on various properties.

Contribution

It introduces a novel integrated approach combining semi-analytical models, SED calculations, and neural networks to study SMG clustering in cosmological simulations.

Findings

SMGs at z=2 are strongly clustered with r0=5.6 Mpc/h.

Predicted clustering increases at higher redshifts.

Correlation length correlates tightly with halo and stellar masses.

Abstract

We combine the GALFORM semi-analytical model of galaxy formation, which predicts the star formation and merger histories of galaxies, the GRASIL spectro-photometric code, which calculates the spectral energy distributions (SEDs) of galaxies self-consistently including reprocessing of radiation by dust, and artificial neural networks (ANN), to investigate the clustering properties of galaxies selected by their emission at submillimetre wavelengths (SMGs). We use the Millennium Simulation to predict the spatial and angular distribution of SMGs. At redshift z = 2, we find that these galaxies are strongly clustered, with a comoving correlation length of r0 = 5.6 \pm 0.9 Mpc/h for galaxies with 850{\mu}m flux densities brighter than 5 mJy, in agreement with observations. We predict that at higher redshifts these galaxies trace denser and increasingly rarer regions of the universe. We present the predicted dependence of the clustering on luminosity, submillimetre colour, halo and total stellar masses. Interestingly, we predict tight relations between correlation length and halo and stellar masses, independent of sub-mm luminosity.