Galaxy formation and evolution

Abstract:

Abstract The rotation velocities of disc galaxies trace dark matter halo structure, providing direct constraints on the galaxy–halo connection. We construct a Bayesian forward model to connect the dark matter halo population predicted by ΛCDM with an observed sample of disc galaxies (SPARC) through their maximum rotation velocities. Our approach combines a subhalo abundance matching scheme (accounting for assembly bias) with a parameterised halo response to galaxy formation. When assuming no correlation between selection in the SPARC survey and halo properties, reproducing the observed velocities requires strong halo expansion, low abundance matching scatter and a halo proxy that strongly suppresses the stellar masses in satellite haloes. This is in clear tension with independent clustering constraints. Allowing for SPARC-like galaxies to preferentially populate low Vmax haloes at fixed virial mass greatly improves the goodness-of-fit and resolves these tensions: the preferred halo response shifts to mild contraction, the abundance matching scatter constraint relaxes to σSHAM < 0.30 dex at 1σ and the proxy becomes consistent with clustering. However, the inferred selection threshold is extreme, implying that SPARC galaxies occupy the lowest ~15 per cent of the Vmax, halo distribution at fixed Mvir. Moreover, even with selection, the inferred scatter remains in statistical disagreement with the low-mass clustering constraints, which are most representative of the SPARC galaxies in our sample. Our analysis highlights the advantage of augmenting clustering-based constraints on the galaxy–halo connection with kinematics and suggests a possible tension using current data.

Abstract:

Abstract We present the most complete modelling of the MOJAVE 1.5 Jansky Quarter Century active galactic nuclei (AGN) jet population, using likelihood-free simulation-based inference. Due to the complex impact of a flux-limit on observed AGN data sets, careful modelling of the parent population is required. In particular, when observing and fitting to multiple data distributions likelihoods become non-intuitive. Parameter degeneracies further complicate the problem and make it suitable for likelihood-free, simulation-based inference. This method relies on a normalising flow learning the likelihood surface or posteriors directly. We extensively validate the flow to show that previous parameter estimates for the AGN jet speed distributions underestimated parameter errors significantly and do not capture the non-gaussianity of the parameter posteriors. The new results enable a better statistical comparison to other AGN population studies, but also a more accurate comparison of supermassive black hole jets with their lower mass counterparts, X-ray binaries (XRB). We find that the AGN follow a Lorentz factor distribution of the shape N(Γ)∝Γb with $b= -1.32_{-0.19}^{+0.20}$. This slope is consistent with the XRB Lorentz factor distribution at 2σ. Simulation-based inference as a method is generally well-suited to many astrophysical problems, and this paper shows the convenient applicability of this methodology to parent population studies of jetted AGN with multiple observables specifically.