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We present the Multiscale non-Gaussian Segmentation (MnGSeg) analysistechnique. This wavelet based method combines the analysis of the probabilitydistribution function (PDF) of map fluctuations as a function of spatial scalesand the power spectrum analysis of a map. This technique allows us to extractthe non-Gaussianities identified in the multiscaled PDFs usually associatedwith turbulence intermittency and to spatially reconstruct the Gaussian and thenon-Gaussian component of the map. This new technique can be applied on anydata set. Here, it is applied on a Herschel column density map of the Polarisflare cloud. The first component has by construction a self-similar fractalgeometry as the one produced by fractional Brownian motion simulations. Thesecond component is called the coherent component, by opposition to fractal,and includes a network of filamentary structures which demonstrates a spatialhierarchical scaling, i.e. filaments inside filaments. The power spectrumanalysis of both components proves that the Fourier power spectrum of theinitial map is dominated by the power of the coherent filamentary structuresacross almost all spatial scales. The coherent structures contributeprogressively, more and more from large to smaller scales, without producingany break in the inertial range. We suggest that this behaviour is induced, atleast partly, by inertial-range intermittency, a well known phenomenon forturbulent flows. We also demonstrate that the MnGSeg technique is a verysensitive signal analysis technique, which allows the extraction of the cosmicinfrared background (CIB) signal present in the Polaris flare submillimeterobservations and the detection of a characteristic scale for 0.1 < l < 0.3 pcwhose origin could partly be the transition of regimes dominated byincompressible turbulence versus compressible modes and other physicalprocesses, such as gravity.

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