Data di Pubblicazione:
2017
Citazione:
Constraints on halo formation from cross-correlations with correlated Variables / E. Castorina, A. Paranjape, R.K. Sheth. - In: MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY. - ISSN 0035-8711. - 468:4(2017 Jul), pp. 3813-3827. [10.1093/mnras/stx701]
Abstract:
Cross-correlations between biased tracers and the dark matter field encode information about the physical variables that characterize these tracers. However, if the physical variables of interest are correlated with one another, then extracting this information is not as straightforward as one might naively have thought.We show how to exploit these correlations so as to estimate scale-independent bias factors of all orders in a model-independent way. We also show that failure to account for this will lead to incorrect conclusions about which variables matter and which do not. Moreover, accounting for this allows one to use the scale dependence of bias to constrain the physics of halo formation; to date, the argument has been phrased the other way around. We illustrate by showing that the scale dependence of linear and non-linear bias, measured on non-linear scales, can be used to provide consistent estimates of how the critical density for halo formation depends on halo mass. Our methods work even when the bias is non-local and stochastic, such as when, in addition to the spherically averaged density field and its derivatives, the quadrupolar shear field also matters for halo formation. In such models, the non-local bias factors are closely related to the more familiar local non-linear bias factors, which are much easier to measure. Our analysis emphasizes the fact that biased tracers are biased because they do not sample fields (density, velocity, shear, etc.) at all positions in space in the same way as the dark matter does.
Tipologia IRIS:
01 - Articolo su periodico
Keywords:
Methods: Analytical-methods: Numerical-large-scale structure of universe-cosmology: Theory
Elenco autori:
E. Castorina, A. Paranjape, R.K. Sheth
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