Separation of dust emission from the cosmic infrared background in Herschel observations with wavelet phase harmonics

Abstract

The low-brightness dust emission at high Galactic latitudes is of interest with respect to studying the interplay among the physical processes involved in shaping the structure of the interstellar medium (ISM), as well as in statistical characterizations of the dust emission as a foreground to the cosmic microwave background (CMB). Progress in this avenue of research has been hampered by the difficulty related to separating the dust emission from the cosmic infrared background (CIB). We demonstrate that the dust and CIB may be effectively separated based on their different structure on the sky and we use the separation to characterize the structure of diffuse dust emission on angular scales, where the CIB is a significant component in terms of power. We used scattering transform statistics, wavelet phase harmonics (WPH) to perform a statistical component separation using Herschel SPIRE observations. This component separation is done only from observational data using non-Gaussian properties as a lever arm and is done at a single 250 µm frequency. This method, which we validated on mock data, gives us access to non-Gaussian statistics of the interstellar dust and an output dust map that is essentially free from CIB contamination. Our statistical modeling characterizes the non-Gaussian structure of the diffuse ISM down to the smallest scales observed by Herschel. We recovered the power law shape of the dust power spectrum up to k = 2 arcmin−1, where the dust signal represents 2% of the total power. Going beyond the standard power spectra analysis, we show that the non-Gaussian properties of the dust emission are not scale-invariant. The output dust map reveals coherent structures at the smallest scales, which had been hidden by the CIB anisotropies. This aspect opens up new observational perspectives on the formation of structure in the diffuse ISM, which we discuss here in reference to a previous work. We have succeeded in performing a statistical separation from the observational data at a single frequency by using non-Gaussian statistics.