Including parameter dependence in the data and covariance for cosmological inference

The final step of most large-scale structure analyses involves the comparison of power spectra 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. We 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. We 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.Comment: 17 pages, 4 figures, matches version published in JCA

Matched filtering with interferometric 21cm experiments

A new generation of interferometric instruments is emerging which aim to use intensity mapping of redshifted $21\,$cm radiation to measure the large-scale structure of the Universe at $z\simeq 1-2$ over wide areas of sky. While these instruments typically have limited angular resolution, they cover huge volumes and thus can be used to provide large samples of rare objects. In this paper we study how well such instruments could find spatially extended large-scale structures, such as cosmic voids, using a matched filter formalism. Such a formalism allows us to work in Fourier space, the natural space for interferometers, and to study the impact of finite $u-v$ coverage, noise and foregrounds on our ability to recover voids. We find that in the absence of foregrounds such instruments would provide enormous catalogs of voids, with high completeness, but that control of foregrounds is key to realizing this goal.Comment: 14 pages, 8 figures, minor revisions to match version accepted by MNRA

Constraints on local primordial non-Gaussianity from large scale structure

Recent work has shown that the local non-Gaussianity parameter f_NL induces a scale-dependent bias, whose amplitude is growing with scale. Here we first rederive this result within the context of peak-background split formalism and show that it only depends on the assumption of universality of mass function, assuming halo bias only depends on mass. We then use extended Press-Schechter formalism to argue that this assumption may be violated and the scale dependent bias will depend on other properties, such as merging history of halos. In particular, in the limit of recent mergers we find the effect is suppressed. Next we use these predictions in conjunction with a compendium of large scale data to put a limit on the value of f_NL. When combining all data assuming that halo occupation depends only on halo mass, we get a limit of -29 ~ (-65)< f_NL < +70 ~(+93) at 95% (99.7%) confidence. While we use a wide range of datasets, our combined result is dominated by the signal from the SDSS photometric quasar sample. If the latter are modeled as recent mergers then the limits weaken to -31 ~(-96) < f_NL < +70 ~ (+96) . These limits are comparable to the strongest current limits from the WMAP 5 year analysis, with no evidence of a positive signal in f_NL. While the method needs to be thoroughly tested against large scale structure simulations with realistic quasar and galaxy formation models, our results indicate that this is a competitive method relative to CMB and should be further pursued both observationally and theoretically.Comment: 18 pages, 5 figures; v2 matches version accepted by JCAP, several small changes in the text, added refs and fixed typo