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We present a per cent-level accurate model of the line-of-sight velocity distribution of galaxies around dark matter haloes as a function of projected radius and halo mass. The model is developed and tested using synthetic galaxy catalogues generated with the UniverseMachine run on the Multi-Dark Planck 2 N-body simulations. The model decomposes the galaxies around a cluster into three kinematically distinct classes: orbiting, infalling, and interloping galaxies. We demonstrate that: (1) we can statistically distinguish between these three types of galaxies using only projected line-of-sight velocity information; (2) the halo edge radius inferred from the line-of-sight velocity dispersion is an excellent proxy for the three-dimensional halo edge radius; and (3) we can accurately recover the full velocity dispersion profile for each of the three populations of galaxies. Importantly, the velocity dispersion profiles of the orbiting and infalling galaxies contain five independent parameters – three distinct radial scales and two velocity dispersion amplitudes – each of which is correlated with mass. Thus, the velocity dispersion profile of galaxy clusters has inherent redundancies that allow us to perform non-trivial systematics checks from a single data set. We discuss several potential applications of our new model for detecting the edge radius and constraining cosmology and astrophysics using upcoming spectroscopic surveys.

 

Dark matter haloes have long been recognized as one of the fundamental building blocks of large-scale structure formation models. Despite their importance – or perhaps because of it! – halo definitions continue to evolve towards more physically motivated criteria. Here, we propose a new definition that is physically motivated, effectively unique, and parameter-free: ‘A dark matter halo is comprised of the collection of particles orbiting in their own self-generated potential’. This definition is enabled by the fact that, even with as few as ≈300 particles per halo, nearly every particle in the vicinity of a halo can be uniquely classified as either orbiting or infalling based on its dynamical history. For brevity, we refer to haloes selected in this way as physical haloes. We demonstrate that (1) the mass function of physical haloes is Press–Schechter, provided the critical threshold for collapse is allowed to vary slowly with peak height; and (2) the peak-background split prediction of the clustering amplitude of physical haloes is statistically consistent with the simulation data, with accuracy no worse than ≈5 per cent.

 

We develop a machine-learning (ML) algorithm that generates high-resolution thermal Sunyaev–Zeldovich (SZ) maps of novel galaxy clusters given only halo mass and mass accretion rate (MAR). The algorithm uses a conditional variational autoencoder (CVAE) in the form of a convolutional neural network and is trained with SZ maps generated from the IllustrisTNG simulation. Our method can reproduce many of the details of galaxy clusters that analytical models usually lack, such as internal structure and aspherical distribution of gas created by mergers, while achieving the same computational feasibility, allowing us to generate mock SZ maps for over 105 clusters in 30 s on a laptop. We show that the model is capable of generating novel clusters (i.e. not found in the training set) and that the model accurately reproduces the effects of mass and MAR on the SZ images, such as scatter, asymmetry, and concentration, in addition to modelling merging sub-clusters. This work demonstrates the viability of ML-based methods for producing the number of realistic, high-resolution maps of galaxy clusters necessary to achieve statistical constraints from future SZ surveys.

 

Abstract We forecast the number of galaxy clusters that can be detected via the thermal Sunyaev–Zel’dovich (tSZ) signals by future cosmic microwave background (CMB) experiments, primarily the wide area survey of the CMB-S4 experiment but also CMB-S4's smaller de-lensing survey and the proposed CMB-HD experiment. We predict that CMB-S4 will detect 75,000 clusters with its wide survey of f sky = 50% and 14,000 clusters with its deep survey of f sky = 3%. Of these, approximately 1350 clusters will be at z ≥ 2, a regime that is difficult to probe by optical or X-ray surveys. We assume CMB-HD will survey the same sky as the S4-Wide, and find that CMB-HD will detect three times more overall and an order of magnitude more z ≥ 2 clusters than CMB-S4. These results include galactic and extragalactic foregrounds along with atmospheric and instrumental noise. Using CMB-cluster lensing to calibrate the cluster tSZ–mass scaling relation, we combine cluster counts with primary CMB to obtain cosmological constraints for a two-parameter extension of the standard model (ΛCDM + ∑ m ν + w 0 ). In addition to constraining σ ( w 0 ) to ≲1%, we find that both surveys can enable a ∼2.5–4.5 σ detection of ∑ m ν , substantially strengthening CMB-only constraints. We also study the evolution of the intracluster medium by modeling the cluster virialization v( z ) and find tight constraints from CMB-S4, with further factors of three to four improvement for CMB-HD. 

Galaxy cluster masses, rich with cosmological information, can be estimated from internal dark matter (DM) velocity dispersions, which in turn can be observationally inferred from satellite galaxy velocities. However, galaxies are biased tracers of the DM, and the bias can vary over host halo and galaxy properties as well as time. We precisely calibrate the velocity bias, bv – defined as the ratio of galaxy and DM velocity dispersions – as a function of redshift, host halo mass, and galaxy stellar mass threshold ($M_{\rm \star , sat}$), for massive haloes ($M_{\rm 200c}\gt 10^{13.5} \, {\rm M}_\odot$) from five cosmological simulations: IllustrisTNG, Magneticum, Bahamas + Macsis, The Three Hundred Project, and MultiDark Planck-2. We first compare scaling relations for galaxy and DM velocity dispersion across simulations; the former is estimated using a new ensemble velocity likelihood method that is unbiased for low galaxy counts per halo, while the latter uses a local linear regression. The simulations show consistent trends of bv increasing with M200c and decreasing with redshift and $M_{\rm \star , sat}$. The ensemble-estimated theoretical uncertainty in bv is 2–3 per cent, but becomes percent-level when considering only the three highest resolution simulations. We update the mass–richness normalization for an SDSS redMaPPer cluster sample, and find our improved bv estimates reduce the normalization uncertainty from 22 to 8 per cent, demonstrating that dynamical mass estimation is competitive with weak lensing mass estimation. We discuss necessary steps for further improving this precision. Our estimates for $b_v(M_{\rm 200c}, M_{\rm \star , sat}, z)$ are made publicly available.

 

Self-gravitating gaseous filaments exist on many astrophysical scales, from sub-pc filaments in the interstellar medium to Mpc scale streams feeding galaxies from the cosmic web. These filaments are often subject to Kelvin–Helmholtz Instability (KHI) due to shearing against a confining background medium. We study the non-linear evolution of KHI in pressure-confined self-gravitating gas streams initially in hydrostatic equilibrium, using analytic models and hydrodynamic simulations, not including radiative cooling. We derive a critical line mass, or mass per unit length, as a function of the stream Mach number and density contrast with respect to the background, μcr(Mb, δc) ≤ 1, where μ = 1 is normalized to the maximal line mass for which initial hydrostatic equilibrium is possible. For μ < μcr, KHI dominates the stream evolution. A turbulent shear layer expands into the background and leads to stream deceleration at a similar rate to the non-gravitating case. However, with gravity, penetration of the shear layer into the stream is halted at roughly half the initial stream radius by stabilizing buoyancy forces, significantly delaying total stream disruption. Streams with μcr < μ ≤ 1 fragment and form round, long-lived clumps by gravitational instability (GI), with typical separations roughly eight times the stream radius, similar to the case without KHI. When KHI is still somewhat effective, these clumps are below the spherical Jeans mass and are partially confined by external pressure, but they approach the Jeans mass as μ → 1 and GI dominates. We discuss potential applications of our results to streams feeding galaxies at high redshift, filaments in the ISM, and streams resulting from tidal disruption of stars near the centres of massive galaxies.