Beecroft Institute for Particle Astrophysics and Cosmology

Abstract:

We study the nature of the faint radio source population detected in the MeerKAT International GHz Tiered Extragalactic Exploration (MIGHTEE) Early Science data in the COSMOS field, focusing on the properties of the radio-loud active galactic nuclei (AGNs). Using the extensive multiwavelength data available in the field, we are able to classify 88 per cent of the 5223 radio sources in the field with host galaxy identifications as AGNs (35 per cent) or star-forming galaxies (54 per cent). We select a sample of radio-loud AGNs with redshifts out to z ∼ 6 and radio luminosities 10²⁰ < L_1.4 GHz/W Hz⁻¹ < 10²⁷ and classify them as high-excitation and low-excitation radio galaxies (HERGs and LERGs). The classification catalogue is released with this work. We find no significant difference in the host galaxy properties of the HERGs and LERGs in our sample. In contrast to previous work, we find that the HERGs and LERGs have very similar Eddington-scaled accretion rates; in particular we identify a population of very slowly accreting AGNs that are formally classified as HERGs at these low radio luminosities, where separating into HERGs and LERGs possibly becomes redundant. We investigate how black hole mass affects jet power, and find that a black hole mass ≳ 10^7.8 M_⊙ is required to power a jet with mechanical power greater than the radiative luminosity of the AGN (L_mech/L_bol > 1). We discuss that both a high black hole mass and black hole spin may be necessary to launch and sustain a dominant radio jet.

Abstract:

Improving the theoretical description of galaxy clustering on small scales is an important challenge in cosmology, as it can considerably increase the scientific return of forthcoming galaxy surveys - e.g. tightening the bounds on neutrino masses and deviations from general relativity. In this paper, we propose and test a new model for the clustering of galaxies that is able to accurately describe redshift-space distortions even down to small scales. This model corresponds to a second-order perturbative Lagrangian bias expansion which is advected to Eulerian space employing a displacement field extracted from N-body simulations. Eulerian coordinates are then transformed into redshift space by directly employing simulated velocity fields augmented with nuisance parameters capturing various possible satellite fractions and intra-halo small-scale velocities. We quantify the accuracy of our approach against samples of physically motivated mock galaxies selected according to either stellar mass (SM) or star formation rate (SFR) at multiple abundances and at z = 0 and 1. We find our model describes the monopole, quadrupole, and hexadecapole of the galaxy-power spectra down to scales of k ≈ 0.6 [h Mpc-1] within the accuracy of our simulations. This approach could pave the way to significantly increase the amount of cosmological information to be extracted from future galaxy surveys.

Abstract:

We study the relations among the parameters of the hybrid Lagrangian bias expansion model, fitting biased auto and cross power spectra up to kmax = 0 .7 h Mpc -1. We consider ∼8000 halo and galaxy samples, with different halo masses, redshifts, galaxy number densities, and varying the parameters of the galaxy formation model. Galaxy samples are obtained through state-of-the-art extended subhalo abundance matching techniques and include both stellar mass and star formation rate selected galaxies. All of these synthetic galaxy samples are publicly available. We find that the hybrid Lagrangian bias model provides accurate fits to all of our halo and galaxy samples. The coevolution relations between galaxy bias parameters, although roughly compatible with those obtained for haloes, show systematic shifts and larger scatter. We explore possible sources of this difference in terms of dependence on halo occupation and assembly bias of each sample. The bias parameter relations displayed in this work can be used as a prior for future Bayesian analyses employing the hybrid Lagrangian bias expansion model.