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Register a new userProbing Hierarchical Clustering by Scale-Scale Correlations of Wavelet Coefficients
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It is of fundamental importance to determine if and how hierarchical clustering is involved in large-scale structure formation of the universe. Hierarchical evolution is characterized by rules which specify how dark matter halos are formed by the merging of halos at smaller scales. We show that scale-scale correlations of the matter density field are direct and sensitive measures to quantify this merging tree. Such correlations are most conveniently determined from discrete wavelet transforms. Analyzing two samples of Ly-alpha forests of QSOs absorption spectra, we find significant scale-scale correlations whose dependence is typical for a branching process. Therefore, models which predict a history independent evolution are ruled out and the halos hosting the Ly-alpha clouds must have gone through a history dependent merging process during their formation.
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We present a detailed review of large-scale structure (LSS) study using the discrete wavelet transform (DWT). After describing how one constructs a wavelet decomposition we show how this bases can be used as a complete statistical discription of LSS. Among the topics studied are the the DWT estimation of the probability distribution function; the reconstruction of the power spectrum; the regularization of complex geometry in observational samples; cluster identification; extraction and identification of coherent structures; scale-decomposition of non-Gaussianity, such as spectra of skewnes and kurtosis and scale-scale correlations. These methods are applied to both observational and simulated samples of the QSO Lyman-alpha forests. It is clearly demonstrated that the statistical measures developed using the DWT are needed to distinguish between competing models of structure formation. The DWT also reveals physical features in these distributions not detected before. We conclude with a look towards the future of the use of the DWT in LSS.
We study the asymptotic behavior of wavelet coefficients of random processes with long memory. These processes may be stationary or not and are obtained as the output of non--linear filter with Gaussian input. The wavelet coefficients that appear in the limit are random, typically non--Gaussian and belong to a Wiener chaos. They can be interpreted as wavelet coefficients of a generalized self-similar process.
We propose a large-scale hologram calculation using WAvelet ShrinkAge-Based superpositIon (WASABI), a wavelet transform-based algorithm. An image-type hologram calculated using the WASABI method is printed on a glass substrate with the resolution of $65,536 times 65,536$ pixels and a pixel pitch of $1 mu$m. The hologram calculation time amounts to approximately 354 s on a commercial CPU, which is approximately 30 times faster than conventional methods.
We show that scale-scale correlations are a generic feature of slow-roll inflation theories. These correlations result from the long-time tails characteristic of the time dependent correlations because the long wavelength density perturbation modes are diffusion-like. A relationship between the scale-scale correlations and time-correlations is established providing a way to reveal the time correlations of the perturbations during inflation. This mechanism provides for a testable prediction that the scale-scale correlations at two different spatial points will vanish.
We generalize the stochastic theory of hierarchical clustering presented in paper I by Lapi & Danese (2020) to derive the (conditional) halo progenitor mass function and the related large-scale bias. Specifically, we present a stochastic differential equation that describes fluctuations in the mass growth of progenitor halos of given descendant mass and redshift, as driven by a multiplicative Gaussian white noise involving the power spectrum and the spherical collapse threshold of density perturbations. We demonstrate that, as cosmic time passes, the noise yields an average drift of the progenitors toward larger masses, that quantitatively renders the expectation from the standard extended Press & Schechter (EPS) theory. We solve the Fokker-Planck equation associated to the stochastic dynamics, and obtain as an exact, stationary solution the EPS progenitor mass function. Then we introduce a modification of the stochastic equation in terms of a mass-dependent collapse threshold modulating the noise, and solve analytically the associated Fokker-Planck equation for the progenitor mass function. The latter is found to be in excellent agreement with the outcomes of $N-$body simulations; even more remarkably, this is achieved with the same shape of the collapse threshold used in paper I to reproduce the halo mass function. Finally, we exploit the above results to compute the large-scale halo bias, and find it in pleasing agreement with the $N-$body outcomes. All in all, the present paper illustrates that the stochastic theory of hierarchical clustering introduced in paper I can describe effectively not only halos abundance, but also their progenitor distribution and their correlation with the large-scale environment across cosmic times.
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