Beecroft Institute for Particle Astrophysics and Cosmology

Abstract:

We present the Extreme-Horizon (EH) cosmological simulation, which models galaxy formation with stellar and active galactic nuclei (AGN) feedback and uses a very high resolution in the intergalactic and circumgalactic medium. Its high resolution in low-density regions results in smaller-size massive galaxies at a redshift of z = 2, which is in better agreement with observations compared to other simulations. We achieve this result thanks to the improved modeling of cold gas flows accreting onto galaxies. In addition, the EH simulation forms a population of particularly compact galaxies with stellar masses of 1010−11 M⊙ that are reminiscent of observed ultracompact galaxies at z ≃ 2. These objects form primarily through repeated major mergers of low-mass progenitors and independently of baryonic feedback mechanisms. This formation process can be missed in simulations with insufficient resolution in low-density intergalactic regions.

Abstract:

We present a new method to marginalize over uncertainties in redshift distributions, N(z), within tomographic cosmological analyses applicable to current and upcoming photometric galaxy surveys. We allow for arbitrary deviations from the best-guess N(z) governed by a general covariance matrix describing the uncertainty in our knowledge of redshift distributions. In principle, this is marginalization over hundreds or thousands of new parameters describing potential deviations as a function of redshift and tomographic bin. However, by linearly expanding the theory predictions around a fiducial model, this marginalization can be performed analytically, resulting in a modified data covariance matrix that effectively downweights the modes of the data vector that are more sensitive to redshift distribution variations. We showcase this method by applying it to the galaxy clustering measurements from the Hyper Suprime-Cam first data release. We illustrate how to marginalize over sample-variance of the calibration sample and a large general systematic uncertainty in photometric estimation methods, and explore the impact of priors imposing smoothness in the redshift distributions.