Search

Context. How galaxies form, assemble, and cease their star formation is a central question within the modern landscape of galaxy evolution studies. These processes are indelibly imprinted on the galaxy stellar mass function (SMF), and its measurement and understanding is key to uncovering a unified theory of galaxy evolution. Aims. We present constraints on the shape and evolution of the galaxy SMF, the quiescent galaxy fraction, and the cosmic stellar mass density across 90% of the history of the Universe from z = 7.5 → 0.2 as a means to study the physical processes that underpin galaxy evolution. Methods. The COSMOS survey is an ideal laboratory for studying representative galaxy samples. Now equipped with deeper and more homogeneous near-infrared coverage exploited by the COSMOS2020 catalog, we leverage the large 1.27 deg2 effective area to improve sample statistics and understand spatial variations (cosmic variance) – particularly for rare, massive galaxies – and push to higher redshifts with greater confidence and mass completeness than previous studies. We divide the total stellar mass function into star-forming and quiescent subsamples through NUVrJ color-color selection. The measurements are then fit with single- and double-component Schechter functions to infer the intrinsic galaxy stellar mass function, the evolution of its key parameters, and the cosmic stellar mass density out to z = 7.5. Finally, we compare our measurements to predictions from state-of-the-art cosmological simulations and theoretical dark matter halo mass functions. Results. We find a smooth, monotonic evolution in the galaxy stellar mass function since z = 7.5, in general agreement with previous studies. The number density of star-forming systems have undergone remarkably consistent growth spanning four decades in stellar mass from z = 7.5 → 2 whereupon high-mass systems become predominantly quiescent ("downsizing"). Meanwhile, the assembly and growth of low-mass quiescent systems only occurred recently, and rapidly. An excess of massive systems at z ≈ 2.5 − 5.5 with strikingly red colors, with some being newly identified, increase the observed number densities to the point where the SMF cannot be reconciled with a Schechter function. Conclusions. Systematics including cosmic variance and/or active galactic nuclei contamination are unlikely to fully explain this excess, and so we speculate that they may be dust-obscured populations similar to those found in far infrared surveys. Furthermore, we find a sustained agreement from z ≈ 3 − 6 between the stellar and dark matter halo mass functions for the most massive systems, suggesting that star formation in massive halos may be more efficient at early times. [ABSTRACT FROM AUTHOR]

We review the field of collisionless numerical simulations for the large-scale structure of the Universe. We start by providing the main set of equations solved by these simulations and their connection with General Relativity. We then recap the relevant numerical approaches: discretization of the phase-space distribution (focusing on N-body but including alternatives, e.g., Lagrangian submanifold and Schrödinger–Poisson) and the respective techniques for their time evolution and force calculation (direct summation, mesh techniques, and hierarchical tree methods). We pay attention to the creation of initial conditions and the connection with Lagrangian Perturbation Theory. We then discuss the possible alternatives in terms of the micro-physical properties of dark matter (e.g., neutralinos, warm dark matter, QCD axions, Bose–Einstein condensates, and primordial black holes), and extensions to account for multiple fluids (baryons and neutrinos), primordial non-Gaussianity and modified gravity. We continue by discussing challenges involved in achieving highly accurate predictions. A key aspect of cosmological simulations is the connection to cosmological observables, we discuss various techniques in this regard: structure finding, galaxy formation and baryonic modelling, the creation of emulators and light-cones, and the role of machine learning. We finalise with a recount of state-of-the-art large-scale simulations and conclude with an outlook for the next decade. [ABSTRACT FROM AUTHOR]