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The physics of baryons in haloes, and their subsequent influence on the total matter phase space, has a rich phenomenology and must be well understood in order to pursue a vast set of questions in both cosmology and astrophysics. We use the Cosmology and Astrophysics with MachinE Learning Simulation (Camels) suite to quantify the impact of four different galaxy formation parameters/processes (as well as two cosmological parameters) on the concentration–mass relation, cvir−Mvir. We construct a simulation-informed non-linear model for concentration as a function of halo mass, redshift, and six cosmological/astrophysical parameters. This is done for two galaxy formation models, IllustrisTNG and Simba, using 1000 simulations of each. We extract the imprints of galaxy formation across a wide range in mass $M_{\rm vir}\in [10^{11}, 10^{14.5}] \, {\rm M}_\odot \, h^{-1}$ and in redshift z ∈ [0, 6] finding many strong mass- and redshift-dependent features. Comparisons between the IllustrisTNG and Simba results show the astrophysical model choices cause significant differences in the mass and redshift dependence of these baryon imprints. Finally, we use existing observational measurements of cvir−Mvir to provide rough limits on the four astrophysical parameters. Our non-linear model is made publicly available and can be used to include Camels-based baryon imprints in any halo model-based analysis.

 

Using low-temperature cathodoluminescence spectroscopy, we study the properties of N- and Al-polar AlN layers grown by molecular beam epitaxy on bulk AlN{0001}. Compared with the bulk AlN substrate, layers of both polarities feature a suppression of deep-level luminescence, a total absence of the prevalent donor with an exciton binding energy of 28 meV, and a much increased intensity of the emission from free excitons. The dominant donor in these layers is characterized by an associated exciton binding energy of 13 meV. The observation of excited exciton states up to the exciton continuum allows us to directly extract the Γ5 free exciton binding energy of 57 meV. 

Permeability of binary mixtures of soils is important for several industrial and engineering applications. Previous models for predicting the permeability of a binary mixture of soils were primarily developed from Kozeny–Carman equation with an empirical approach. The permeability is predicted based on an equivalent particle size of the two species. This study is aimed to develop a model using a more fundamental approach. Instead of an equivalent particle size, the permeability is predicted based on the bimodal void sizes of the binary mixture. Because the bimodal void sizes are not available as commonly measured physical properties. We first develop an analytical method that has the capability of predicting the bimodal void sizes of a binary mixture. A permeability model is then developed based on the bimodal void sizes of the binary mixture. The developed permeability model is evaluated by comparing the predicted and experimentally measured results for binary mixtures of glass beads, crush sand, and gravel sand. The findings can contribute to a better understanding of the important influence of pore structure on the prediction of permeability. 

Abstract