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We present an analysis of the non-Gaussianity in the distribution of Ly$alpha$ forest lines in the QSO absorption spectra. Statistical tests performed on this data indicate that there may be large scale structure even though the power spectrum of the Ly$alpha$ line distribution on large scales is found to be flat. It is apparent that higher (than two) order statistics are crucial in quantifying the clustering behavior of Ly$alpha$ clouds. Using the discrete wavelet on three independent data sets of Ly$alpha$ forests, we find that the distribution of Ly$alpha$ forests does show non-Gaussian behavior on scales from 5 to 10 h$^{-1}$ Mpc with confidence level larger than 95%. Two data sets available on large scales are found to be non-Gaussian on even larger scales. These techniques are effective in discriminating among models of the Ly$alpha$ forest formation, which are degenerate at second and lower order statistics (abridged).
We calculate the structure function and intermittent exponent of the 1.) Keck data, which consists of 29 high resolution, high signal to noise ratio (S/N) QSO Ly$alpha$ absorption spectra, and 2.)the Ly$alpha$ forest simulation samples produced via t
We have compiled all available data on chemical abundances in damped Lyman alpha absorption systems for comparison with results from our combined chemical and spectrophotometric galaxy evolution models. Preliminary results from chemically consistent
Using a discrete wavelet based space-scale decomposition (SSD), the spectrum of the skewness and kurtosis is developed to describe the non-Gaussian signatures in cosmologically interesting samples. Because the basis of the discrete wavelet is compact
Broad Absorption Line Quasars (BAL QSOs) have been found to be associated with extremely compact radio sources. These reduced dimensions can be either due to projection effects or these objects might actually be intrinsically small. Exploring these t
We have developed two independent methods to measure the one-dimensional power spectrum of the transmitted flux in the Lyman-$alpha$ forest. The first method is based on a Fourier transform, and the second on a maximum likelihood estimator. The two m