Exposing the plural nature of molecular clouds : Extracting filaments and the CIB against the true scale-free interstellar medium

We present the Multiscale non-Gaussian Segmentation (MnGSeg) analysis technique. This wavelet based method combines the analysis of the probability distribution function (PDF) of map fluctuations as a function of spatial scales and the power spectrum analysis of a map. This technique allows us to extract the non-Gaussianities identified in the multiscaled PDFs usually associated with turbulence intermittency and to spatially reconstruct the Gaussian and the non-Gaussian component of the map. This new technique can be applied on any data set. Here, it is applied on a Herschel column density map of the Polaris flare cloud. The first component has by construction a self-similar fractal geometry as the one produced by fractional Brownian motion simulations. The second component is called the coherent component, by opposition to fractal, and includes a network of filamentary structures which demonstrates a spatial hierarchical scaling, i.e. filaments inside filaments. The power spectrum analysis of both components proves that the Fourier power spectrum of the initial map is dominated by the power of the coherent filamentary structures across almost all spatial scales. The coherent structures contribute progressively, more and more from large to smaller scales, without producing any break in the inertial range. We suggest that this behaviour is induced, at least partly, by inertial-range intermittency, a well known phenomenon for turbulent flows. We also demonstrate that the MnGSeg technique is a very sensitive signal analysis technique, which allows the extraction of the cosmic infrared background (CIB) signal present in the Polaris flare submillimeter observations and the detection of a characteristic scale for 0.1 < l < 0.3 pc whose origin could partly be the transition of regimes dominated by incompressible turbulence versus compressible modes and other physical processes, such as gravity.