Modelling cross-correlations of ultra-high-energy cosmic rays and galaxies
On the significance of the thick disks of disk galaxies
Abstract:
Thick disks are a prevalent feature observed in numerous disk galaxies, including our own Milky Way. Their significance has been reported to vary widely, ranging from a few percent to 100% of the disk mass, depending on the galaxy and the measurement method. We use the NewHorizon simulation, which has high spatial and stellar mass resolutions, to investigate the issue of the thick-disk mass fraction. We also use the NewHorizon2 simulation, which was run on the same initial conditions, but additionally traced nine chemical elements. Based on a sample of 27 massive disk galaxies with M* > 1010M⊙ in NewHorizon, the contribution of the thick disk was found to be 20% ± 11% in r-band luminosity or 35% ± 15% in mass to the overall galactic disk, which seems in agreement with observational data. The vertical profiles of 0, 22, and 5 galaxies are best fitted by 1, 2, or 3 sech2 components, respectively. The NewHorizon2 data show that the selection of thick-disk stars based on a single [α/Fe] cut is contaminated by stars of different kinematic properties, while missing the bulk of kinematically thick disk stars. Vertical luminosity profile fits recover the key properties of thick disks reasonably well. The majority of stars are born near the galactic midplane with high circularity and get heated with time via fluctuations in the force field. Depending on the star formation and merger histories, galaxies may naturally develop thick disks with significantly different properties.
Galaxy bias in the era of LSST: perturbative bias expansions
Abstract:
Upcoming imaging surveys will allow for high signal-to-noise measurements of galaxy clustering at small scales. In this work, we present the results of the Rubin Observatory Legacy Survey of Space and Time (LSST) bias challenge, the goal of which is to compare the performance of different nonlinear galaxy bias models in the context of LSST Year 10 (Y10) data. Specifically, we compare two perturbative approaches, Lagrangian perturbation theory (LPT) and Eulerian perturbation theory (EPT) to two variants of Hybrid Effective Field Theory (HEFT), with our fiducial implementation of these models including terms up to second order in the bias expansion as well as nonlocal bias and deviations from Poissonian stochasticity. We consider a variety of different simulated galaxy samples and test the performance of the bias models in a tomographic joint analysis of LSST-Y10-like galaxy clustering, galaxy-galaxy-lensing and cosmic shear. We find both HEFT methods as well as LPT and EPT combined with non-perturbative predictions for the matter power spectrum to yield unbiased constraints on cosmological parameters up to at least a maximal scale of kmax = 0.4 Mpc-1 for all samples considered, even in the presence of assembly bias. While we find that we can reduce the complexity of the bias model for HEFT without compromising fit accuracy, this is not generally the case for the perturbative models. We find significant detections of non-Poissonian stochasticity in all cases considered, and our analysis shows evidence that small-scale galaxy clustering predominantly improves constraints on galaxy bias rather than cosmological parameters. These results therefore suggest that the systematic uncertainties associated with current nonlinear bias models are likely to be subdominant compared to other sources of error for tomographic analyses of upcoming photometric surveys, which bodes well for future galaxy clustering analyses using these high signal-to-noise data.