We investigate the role of angular momentum in the clustering of dark matter haloes. We make use of data from two high-resolution N-body simulations spanning over four orders of magnitude in halo mass, from 10 9.8 to 10 14 h −1 M . We explore the hypothesis that mass accretion in filamentary environments alters the angular momentum of a halo, thereby driving a correlation between the spin parameter λ and the strength of clustering. However, we do not find evidence that the distribution of matter on large scales is related to the spin of haloes. We find that a halo's spin is correlated with its age, concentration, sphericity, and mass accretion rate. Removing these correlations strongly affects the strength of secondary spin bias at low halo masses. We also find that high spin haloes are slightly more likely to be found near another halo of comparable mass. These haloes that are found near a comparable mass neighbour -a twin -are strongly spatially biased. We demonstrate that this twin bias, along with the relationship between spin and mass accretion rates, statistically accounts for halo spin secondary bias.Key words: cosmology: theory -large-scale structure of the universe -dark matter -galaxies: haloes 1 Note however that abundance matching models do account for any clustering bias from substructure by design.

Abstract. Supermassive black holes can be seen as an agent of galaxy transformation. In particular, a supermassive black hole can cause a triaxial galaxy to evolve toward axisymmetry by inducing chaos in centrophilic orbit families. This is one way in which a single supermassive black hole can induce large-scale changes in the structure of its host galaxy -changes on scales far larger than the Schwarzschild radius (O(10 −5 )pc) and the radius of influence of the black hole (O(1) − O(100)pc).

The evolution of a dark matter halo in a dark matter only simulation is governed purely by Newtonian gravity, making a clean testbed to determine what halo properties drive its fate. Using machine learning, we predict the survival, mass loss, final position, and merging time of subhalos within a cosmological N-body simulation, focusing on what instantaneous initial features of the halo, interaction, and environment matter most. Survival is well predicted, with our model achieving 94.25 per cent out-of-bag accuracy using only three model inputs (edshift, subhalo-to-host-halo mass ratio, and the impact angle of the subhalo into its host) taken at the time immediately before the subhalo enters its host. However, the mass loss, final location, and merging times are much more stochastic processes, with significant errors between true and predicted quantities for much of our sample. Only five inputs (redshift, impact angle, relative velocity, and the masses of the host and subhalo) determine almost all of the subhalo evolution learned by our models. Generally, subhalos that enter their hosts at a mid-range of redshifts (z = 0.67-0.43) are the most challenging to make predictions for, across all of our final outcomes. Subhalo orbits that come in more perpendicular to the host are easier to predict, except for in the case of predicting disruption, where the opposite appears to be true. We conclude that the detailed evolution of individual subhalos within N-body simulations is difficult to predict, pointing to a stochasticity in the merging process. We discuss implications for both simulations and observations.