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Register a new userDensity-dependent clustering: I. Pulling back the curtains on motions of the BAO peak
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The most common statistic used to analyze large-scale structure surveys is the correlation function, or power spectrum. Here, we show how `slicing the correlation function on local density brings sensitivity to interesting non-Gaussian features in the large-scale structure, such as the expansion or contraction of baryon acoustic oscillations (BAO) according to the local density. The sliced correlation function measures the large-scale flows that smear out the BAO, instead of just correcting them as reconstruction algorithms do. Thus, we expect the sliced correlation function to be useful in constraining the growth factor, and modified gravity theories that involve the local density. Out of the studied cases, we find that the run of the BAO peak location with density is best revealed when slicing on a $sim 40$ Mpc/$h$ filtered density. But slicing on a $sim100$ Mpc/$h$ filtered density may be most useful in distinguishing between underdense and overdense regions, whose BAO peaks are separated by a substantial $sim 5$ Mpc/$h$ at $z=0$. We also introduce `curtain plots showing how local densities drive particle motions toward or away from each other over the course of an $N$-body simulation.
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In this contribution we present the preliminary results regarding the non-linear BAO signal in higher-order statistics of the cosmic density field. We use ensembles of N-body simulations to show that the non-linear evolution changes the amplitudes of the BAO signal, but has a negligible effect on the scale of the BAO feature. The latter observation accompanied by the fact that the BAO feature amplitude roughly doubles as one moves to higher orders, suggests that the higher-order correlation amplitudes can be used as probe of the BAO signal.
We develop an analytic method for implementing the IR-resummation of arXiv:1404.5954, which allows one to correctly and consistently describe the imprint of baryon acoustic oscillations (BAO) on statistical observables in large-scale structure. We show that the final IR-resummed correlation function can be computed analytically without relying on numerical integration, thus allowing for an efficient and accurate use of these predictions on real data in cosmological parameter fitting. In this work we focus on the one-loop correlation function and the BAO peak. We show that, compared with the standard numerical integration method of IR-resummation, the new method is accurate to better than 0.2 %, and is quite easily improvable. We also give an approximate resummation scheme which is based on using the linear displacements of a fixed fiducial cosmology, which when combined with the method described above, is about six times faster than the standard numerical integration. Finally, we show that this analytic method is generalizable to higher loop computations.
Third-order statistics of the cosmic density field provides a powerful cosmological probe containing synergistic information to the more commonly explored second-order statistics. Here, we exploit a spectroscopic catalog of 72,563 clusters of galaxies extracted from the Sloan Digital Sky Survey, providing the first detection of the baryon acoustic oscillations (BAO) peak in the three-point correlation function (3PCF) of galaxy clusters. We measure and analyze both the connected and the reduced 3PCF of SDSS clusters from intermediate ($rsim10$ Mpc/h) up to large ($rsim140$ Mpc/h) scales, exploring a variety of different configurations. From the analysis of reduced 3PCF at intermediate scales, in combination with the analysis of the two-point correlation function, we constrain both the cluster linear and non-linear bias parameters, $b_1=2.75pm0.03$ and $b_2=1.2pm0.5$. We analyze the measurements of the 3PCF at larger scales, comparing them with theoretical models. The data show clear evidence of the BAO peak in different configurations, which appears more visible in the reduced 3PCF rather than in the connected one. From the comparison between theoretical models considering or not the BAO peak, we obtain a quantitative estimate of this evidence, with a $Delta chi^2$ between 2 and 75, depending on the considered configuration. Finally, we set up a generic framework to estimate the expected signal-to-noise ratio of the BAO peak in the 3PCF exploring different possible definitions, that can be used to forecast the most favorable configurations to be explored also in different future surveys, and applied it to the case of the Euclid mission.
[abridged] We present an anisotropic analysis of the baryonic acoustic oscillation (BAO) scale in the twelfth and final data release of the Baryonic Oscillation Spectroscopic Survey (BOSS). We independently analyse the LOWZ and CMASS galaxy samples: the LOWZ sample contains contains 361 762 galaxies with an effective redshift of $z_{rm LOWZ}=0.32$; the CMASS sample consists of 777 202 galaxies with an effective redshift of $z_{rm CMASS}=0.57$. We extract the BAO peak position from the monopole power spectrum moment, $alpha_0$, and from the $mu^2$ moment, $alpha_2$, where $mu$ is the cosine of the angle to the line-of-sight. The $mu^2$-moment provides equivalent information to that available in the quadrupole but is simpler to analyse. After applying a reconstruction algorithm to reduce the BAO suppression by bulk motions, we measure the BAO peak position in the monopole and $mu^2$-moment, which are related to radial and angular shifts in scale. We report $H(z_{rm LOWZ})r_s(z_d)=(11.60pm0.60)cdot10^3 {rm km}s^{-1}$ and $D_A(z_{rm LOWZ})/r_s(z_d)=6.66pm0.16$ with a cross-correlation coefficient of $r_{HD_A}=0.41$, for the LOWZ sample; and $H(z_{rm CMASS})r_s(z_d)=(14.56pm0.37)cdot10^3 {rm km}s^{-1}$ and $D_A(z_{rm CMASS})/r_s(z_d)=9.42pm0.13$ with a cross-correlation coefficient of $r_{HD_A}=0.47$, for the CMASS sample. We combine these results with the measurements of the BAO peak position in the monopole and quadrupole correlation function of the same dataset citep[][companion paper]{Cuestaetal2015} and report the consensus values: $H(z_{rm LOWZ})r_s(z_d)=(11.63pm0.69)cdot10^3 {rm km}s^{-1}$ and $D_A(z_{rm LOWZ})/r_s(z_d)=6.67pm0.15$ with $r_{HD_A}=0.35$ for the LOWZ sample; $H(z_{rm CMASS})r_s(z_d)=(14.67pm0.42)cdot10^3 {rm km}s^{-1}$ and $D_A(z_{rm CMASS})/r_s(z_d)=9.47pm0.12$ with $r_{HD_A}=0.52$ for the CMASS sample.
Our goals are (i) to search for BAO and large-scale structure in current QSO survey data and (ii) to use these and simulation/forecast results to assess the science case for a new, >10x larger, QSO survey. We first combine the SDSS, 2QZ and 2SLAQ surveys to form a survey of ~60000 QSOs. We find a hint of a peak in the QSO 2-point correlation function, xi(s), at the same scale (~105h^-1 Mpc) as detected by Eisenstein et al (2005) in their sample of DR5 LRGs but only at low statistical significance. We then compare these data with QSO mock catalogues from the Hubble Volume simulation used by Hoyle et al (2002) and find that both routes give statistical error estimates that are consistent at ~100h^-1 Mpc scales. Mock catalogues are then used to estimate the nominal survey size needed for a 3-4 sigma detection of the BAO peak. We find that a redshift survey of ~250000 z<2.2 QSOs is required over ~3000 deg^2. This is further confirmed by static log-normal simulations where the BAO are clearly detectable in the QSO power spectrum and correlation function. The nominal survey would on its own produce the first detection of, for example, discontinuous dark energy evolution in the so far uncharted 1<z<2.2 redshift range. A survey with ~50% higher QSO sky densities and 50% bigger area will give an ~6sigma BAO detection, leading to an error ~60% of the size of the BOSS error on the dark energy evolution parameter, w_a. Another important aim for a QSO survey is to place new limits on primordial non-Gaussianity at large scales, testing tentative evidence we have found for the evolution of the linear form of the combined QSO xi(s) at z~1.6. Such a QSO survey will also determine the gravitational growth rate at z~1.6 via z-space distortions, allow lensing tomography via QSO magnification bias while also measuring the exact luminosity dependence of small-scale QSO clustering.
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