Day 938

Tuesday.  Wednesday.


1508.00035
Combining spectroscopic and photometric surveys using angular cross-correlatons III: galaxy bias and stochastisity
Eriksen, Gaztanaga

Paper I studied the effect of BAO, RSD and WL on measurements of angular cross-correlations in narrow redshift bins.  Paper II presented a multitracer forecast as FoM, combining a photometric and spectroscopic stage-IV survey.  The uncertainties from galaxy bias, the way light traces mass, is an important ingredient in the forecast.  Fixing the bias would increase our FoM equivalent to 3.3x larger area for the combined constraints.  This paper focus on how the modelling of bias affects these results.  In the combined forecast, lensing both help and benefit from the improved bias measurements in overlapping surveys after marginalizing over the cosmo parameters.  Adding a second lens population in counts-shear does not have a large impact on bias error, but removing all counts-shear information increases the bias error in a significant way.  Also discuss the relative impact of WL, magnification, RSD and BAO, and how results changes as a function of bias amplitude, photo-z error and sample density.  By default, use one bias parameter per bin (with 72 narrow bins), but show that the results do not change much when other parameterizations are used, with at least 3 parameters in total.  Bias stochasticity, even when added as one new free parameter per bin, only produce moderate decrease in the FoM.  In general, find that the degradation in the FoM caused by the uncertainties in the knowledge of bias is significantly smaller for overlapping surveys.


1508.00566
Including parameter dependence in the data and covariance for cosmological inference
White, Padmanabhan

The final step of most LSS analyses involves the comparison of PS or correlation functions to theoretical models.  It is clear that the theoretical models have parameter dependence, but frequently the measurements and the covariance matrix depend upon some of the parameters as well.  Show that a very simple interpolation scheme from an unstructured mesh allows for an efficient way to include this parameter dependence self-consistently in the analysis at modest computational expense.  Describe two schemes for covariance matrices.  The scheme which uses the geometric structure of such matrices performs roughly twice as well as the simplest scheme, though both perform very well.