Geometry of Gaussian signals
classification
❄️ cond-mat.dis-nn
cond-mat.stat-mech
keywords
deltaalphagaussiandistributionindependentlimitmomentsprobability
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We consider Gaussian signals, i.e. random functions $u(t)$ ($t/L \in [0,1]$) with independent Gaussian Fourier modes of variance $\sim 1/q^{\alpha}$, and compute their statistical properties in small windows $[x, x+\delta]$. We determine moments of the probability distribution of the mean square width of $u(t)$ in powers of the window size $\delta$. We show that the moments, in the small-window limit $\delta \ll 1$, become universal, whereas they strongly depend on the boundary conditions of $u(t)$ for larger $\delta$. For $\alpha > 3$, the probability distribution is computed in the small-window limit and shown to be independent of $\alpha$.
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