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Register a new userHierarchical clustering and the BAO signature
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Wojciech A. Hellwing
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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.
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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.
We study the methodology and potential theoretical systematics of measuring Baryon Acoustic Oscillations (BAO) using the angular correlation functions in tomographic bins. We calibrate and optimize the pipeline for the Dark Energy Survey Year 1 dataset using 1800 mocks. We compare the BAO fitting results obtained with three estimators: the Maximum Likelihood Estimator (MLE), Profile Likelihood, and Markov Chain Monte Carlo. The fit results from the MLE are the least biased and their derived 1-$sigma$ error bar are closest to the Gaussian distribution value after removing the extreme mocks with non-detected BAO signal. We show that incorrect assumptions in constructing the template, such as mismatches from the cosmology of the mocks or the underlying photo-$z$ errors, can lead to BAO angular shifts. We find that MLE is the method that best traces this systematic biases, allowing to recover the true angular distance values. In a real survey analysis, it may happen that the final data sample properties are slightly different from those of the mock catalog. We show that the effect on the mock covariance due to the sample differences can be corrected with the help of the Gaussian covariance matrix or more effectively using the eigenmode expansion of the mock covariance. In the eigenmode expansion, the eigenmodes are provided by some proxy covariance matrix. The eigenmode expansion is significantly less susceptible to statistical fluctuations relative to the direct measurements of the covariance matrix because of the number of free parameters is substantially reduced
We determine an optimized clustering statistic to be used for galaxy samples with significant redshift uncertainty, such as those that rely on photometric redshifts. To do so, we study the baryon acoustic oscillation (BAO) information content as a function of the orientation of galaxy clustering modes with respect to their angle to the line-of-sight (LOS). The clustering along the LOS, as observed in a redshift-space with significant redshift uncertainty, has contributions from clustering modes with a range of orientations with respect to the true LOS. For redshift uncertainty $sigma_z geq 0.02(1+z)$ we find that while the BAO information is confined to transverse clustering modes in the true space, it is spread nearly evenly in the observed space. Thus, measuring clustering in terms of the projected separation (regardless of the LOS) is an efficient and nearly lossless compression of the signal for $sigma_z geq 0.02(1+z)$. For reduced redshift uncertainty, a more careful consideration is required. We then use more than 1700 realizations (combining two separate sets) of galaxy simulations mimicking the Dark Energy Survey Year 1 sample to validate our analytic results and optimized analysis procedure. We find that using the correlation function binned in projected separation, we can achieve uncertainties that are within 10 per cent of those predicted by Fisher matrix forecasts. We predict that DES Y1 should achieve a 5 per cent distance measurement using our optimized methods. We expect the results presented here to be important for any future BAO measurements made using photometric redshift data.
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.
Recently, Hierarchical Clustering (HC) has been considered through the lens of optimization. In particular, two maximization objectives have been defined. Moseley and Wang defined the emph{Revenue} objective to handle similarity information given by a weighted graph on the data points (w.l.o.g., $[0,1]$ weights), while Cohen-Addad et al. defined the emph{Dissimilarity} objective to handle dissimilarity information. In this paper, we prove structural lemmas for both objectives allowing us to convert any HC tree to a tree with constant number of internal nodes while incurring an arbitrarily small loss in each objective. Although the best-known approximations are 0.585 and 0.667 respectively, using our lemmas we obtain approximations arbitrarily close to 1, if not all weights are small (i.e., there exist constants $epsilon, delta$ such that the fraction of weights smaller than $delta$, is at most $1 - epsilon$); such instances encompass many metric-based similarity instances, thereby improving upon prior work. Finally, we introduce Hierarchical Correlation Clustering (HCC) to handle instances that contain similarity and dissimilarity information simultaneously. For HCC, we provide an approximation of 0.4767 and for complementary similarity/dissimilarity weights (analogous to $+/-$ correlation clustering), we again present nearly-optimal approximations.
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