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Register a new userA New Statistic for Analyzing Baryon Acoustic Oscillations
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We introduce a new statistic omega_l for measuring and analyzing large-scale structure and particularly the baryon acoustic oscillations. omega_l is a band-filtered, configuration space statistic that is easily implemented and has advantages over the traditional power spectrum and correlation function estimators. Unlike these estimators, omega_l can localize most of the acoustic information into a single dip at the acoustic scale while also avoiding sensitivity to the poorly constrained large scale power (i.e., the integral constraint) through the use of a localized and compensated filter. It is also sensitive to anisotropic clustering through pair counting and does not require any binning. We measure the shift in the acoustic peak due to nonlinear effects using the monopole omega_0 derived from subsampled dark matter catalogues as well as from mock galaxy catalogues created via halo occupation distribution (HOD) modeling. All of these are drawn from 44 realizations of 1024^3 particle dark matter simulations in a 1h^{-1}Gpc box at z=1. We compare these shifts with those obtained from the power spectrum and conclude that the results agree. This indicates that any distance measurements obtained from omega_0 and P(k) will be consistent with each other. We also show that it is possible to extract the same amount of acoustic information using either omega_0 or P(k) from equal volume surveys.
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We investigate the utility and robustness of a new statistic, $omega_{ell}left(r_{c}right)$, for analyzing Baryon Acoustic Oscillations (BAO). We apply $omega_{ell}left(r_{c}right)$, introduced in Xu et al. (2010), to mocks and data from the Sloan Digital Sky Survey (SDSS)-III Baryon Oscillation Spectroscopic Survey (BOSS) included in the SDSS Data Release Eleven (DR11). We fit the anisotropic clustering using the monopole and quadrupole of the $omega_{ell}left(r_{c}right)$ statistic in a manner similar to conventional multipole fitting methods using the correlation function as detailed in (Xu et al. 2012). To test the performance of the $omega_{ell}left(r_{c}right)$ statistic we compare our results to those obtained using the multipoles. The results are in agreement. We also conduct a brief investigation into some of the possible advantages of using the $omega_{ell}left(r_{c}right)$ statistic for BAO analysis. The $omega_{ell}left(r_{c}right)$ analysis matches the stability of the multipoles analysis in response to artificially introduced distortions in the data, without using extra nuisance parameters to improve the fit. When applied to data with systematics, the $omega_{ell}left(r_{c}right)$ statistic again matches the performance of fitting the multipoles without using nuisance parameters. In all the analyzed circumstances, we find that fitting the $omega_{ell}left(r_{c}right)$ statistic removes the requirement for extra nuisance parameters.
Baryon Acoustic Oscillations (BAO) are frozen relics left over from the pre-decoupling universe. They are the standard rulers of choice for 21st century cosmology, providing distance estimates that are, for the first time, firmly rooted in well-understood, linear physics. This review synthesises current understanding regarding all aspects of BAO cosmology, from the theoretical and statistical to the observational, and includes a map of the future landscape of BAO surveys, both spectroscopic and photometric.
Gravitational non-linear evolution induces a shift in the position of the baryon acoustic oscillations (BAO) peak together with a damping and broadening of its shape that bias and degrades the accuracy with which the position of the peak can be determined. BAO reconstruction is a technique developed to undo part of the effect of non-linearities. We present and analyse a reconstruction method that consists of displacing pixels instead of galaxies and whose implementation is easier than the standard reconstruction method. We show that this method is equivalent to the standard reconstruction technique in the limit where the number of pixels becomes very large. This method is particularly useful in surveys where individual galaxies are not resolved, as in 21cm intensity mapping observations. We validate this method by reconstructing mock pixelated maps, that we build from the distribution of matter and halos in real- and redshift-space, from a large set of numerical simulations. We find that this method is able to decrease the uncertainty in the BAO peak position by 30-50% over the typical angular resolution scales of 21 cm intensity mapping experiments.
The density field reconstruction technique has been widely used for recovering the Baryon Acoustic Oscillation (BAO) feature in galaxy surveys that has been degraded due to nonlinearities. In this paper, we investigate the performance of iterative reconstruction on the BAO and the broadband, focusing on the iterative implementation based on citet{Seo:2010} and citet{Schmittfull:2017}. We include redshift-space distortions, halo bias, and shot noise and inspect the components of the reconstructed field in Fourier space and in configuration space using both density field-based reconstruction and displacement field-based reconstruction. We find that the displacement field reconstruction becomes quickly challenging in the presence of non-negligible shot noise and therefore present surrogate methods that can be practically applied to a much more sparse field such as galaxies. For a galaxy field, implementing a debiasing step to remove the Lagrangian bias appears crucial for the displacement field reconstruction. We show that the iterative reconstruction does not substantially improve the BAO feature beyond an optimized standard reconstruction; however, we find that such aggressive optimization of the standard reconstruction with a small smoothing kernel is achieved at the cost of degradation on large scales while taking iterative steps allows us to use a small smoothing kernel `stably, i.e., without causing a substantial deviation from the linear theory model on large scales.
We study the large-scale clustering of galaxies in the overlap region of the Baryon Oscillation Spectroscopic Survey (BOSS) CMASS sample and the WiggleZ Dark Energy Survey. We calculate the auto-correlation and cross-correlation functions in the overlap region of the two datasets and detect a Baryon Acoustic Oscillation (BAO) signal in each of them. The BAO measurement from the cross-correlation function represents the first such detection between two different galaxy surveys. After applying density-field reconstruction we report distance-scale measurements $D_V r_s^{rm fid} / r_s = (1970 pm 47, 2132 pm 67, 2100 pm 200)$ Mpc from CMASS, the cross-correlation and WiggleZ, respectively. We use correlated mock realizations to calculate the covariance between the three BAO constraints. The distance scales derived from the two datasets are consistent, and are also robust against switching the displacement fields used for reconstruction between the two surveys. This approach can be used to construct a correlation matrix, permitting for the first time a rigorous combination of WiggleZ and CMASS BAO measurements. Using a volume-scaling technique, our result can also be used to combine WiggleZ and future CMASS DR12 results. Finally, we use the cross-correlation function measurements to show that the relative velocity effect, a possible source of systematic uncertainty for the BAO technique, is consistent with zero for our samples.
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