Day 1397

Wednesday.  Thursday.

1804.03667
Preparing for the cosmic shear data flood: optimal data extraction and simulation requirements for stage IV dark energy experiments
Taylor, Kitching, McEwen

It remains an open question of how to optimal extract the information and how well the matter power spectrum must be known to obtain unbiased cosmo parameter estimates.  By performing a PCA, quantify the sensitivity of 3d cosmic shear and tomography with different binning strategies to different regions of the lensing kernel and matter PS, and hence the BG geometry and growth of structure in the Universe.  Find that a large number of equally spaced tomographic bins in redshift can extract nearly all the cosmo information without the additional computational expense of 3d cosmic shear.   Meanwhile a large fraction of the information comes from small poorly understood scales in the matter PS, that can lead to biases on measurements of cosmological parameters.  In light of this, define and compute a cosmology-independent measure of the bias due to imperfect knowledge of the power spectrum.  For a Euclid-like survey, find that the PS must be known to an accuracy of less than 1% on scales with k=7 h/Mpc.  This requirement is not absolute since the bias depends on the magnitude of modeling errors, where they occur in k-z space, and the correlation between them, all of which are specific to any particular model.  Therefore compare the bias in several of the most likely modeling scenarios and introduce a general formalism and public code, RequiSim, to compute the expected bias from any non-linear model.


1804.04055
Some assembly required: assembly bias in massive dark matter halos
Chue, Dalal, White

Study halo assembly bias for cluster-sized halos.  Previous work has found little evidence for correlations between large-scale bias and halo mass assembly history for simulated cluster-sized haloes, in contrast to the significant correlation found between bias and concentration for haloes of this mass.  This difference in behavior is surprising, given that both concentration and assembly history are closely related to the same properties of the linear-density peaks that collapse to form haloes.  Using publicly available simulations, show that significant assembly bias is indeed found in the most massive haloes with M~1e15 Msun, using essentially any definition of halo age.  For lower halo masses M~1e14 Msun, no correlation is found between bias and the commonly used age indicator a_0.5, the half-mass time.  Show that this is a mere accident, and that significant assembly bias exists for other definitions of halo age, including those based on the time when the halo progenitor acquires some fraction f of the ultimate mass at z=0.  For halos with M_vir~1e14 Msun, the sense of assembly bias changes sign at f=0.5.  Explore the origin of this behavior, and argue that it arises because standard definitions of halo mass in halo finders do not correspond to the collapsed, virtualized mass that appears in the spherical collapse model used to predict large-scale clustering.  Because bias depends strongly on halo mass, these errors in mass definition can masquerade as or even obscure the assembly bias that is physically present.  More physically motivated halo dentitions using splash back should be free of this particular defect of standard halo finders.