Untangling dust emission and CIB anisotropies with the Scattering Transform Statistics

Abstract

Template-fit approach is often used to separate the Galactic dust emission and the cosmic infrared background (CIB) anisotropies at low $\text{HI}$ column density regions with an underlying assumption that the gas and dust are tightly correlated. However, this method fails in regions where additional Galactic emission within the molecular hydrogen, diffuse ionized gas, and dark gas are present. We develop and test a statistical component separation to extract the dust signal from the contaminated $\textit{Planck}$ $353\,\rm GHz$ observations using the Scattering Covariance (SC) statistics. We first obtain a set CIB maps over $25$ square patches, each with a sky area of $222\,{\rm deg}^{2}$, using the linear correlation of dust and Galactic $21\,\rm cm$ $\text{HI}$ emission valid at low $\text{HI}$ column density regions using the template-fit approach. We then construct, from these $25$ maps, a generative model of CIB using the SC statistics. We finally rely on this contamination model to perform a component separation of dust and CIB in the $\textit{Planck}$ data for different sky regions. Applying our algorithm to the $\textit{Planck}$ $353\,\rm GHz$ observations, we recover a dust map for a test sky region that has more structures as compared to the corrected SFD map at $100\,μ\rm m$. The differences seen in the map level can be explained by decomposing the recovered $\textit{Planck}$ dust map into two gas phases: dust associated with $N_{\text{HI}}$ and dust associated with $N_{\text{H}_{2}}$. This work provides a clear pathway to map the Galactic interstellar reddening over intermediate and high Galactic latitudes.

Figures (18)

• Figure 1: Orthographic projection of the residual map obtained from the template-fit approach. The northern (southern) Galactic hemisphere in on the left (right). White regions are masked from the analysis as it comprises of $N_{\textup{Hi} \textup{Hi}\xspace }$ cut-off pixels and the pixels masked due to iterative masking.
• Figure 2: The spatial distribution of the residual maps in the selected 25 square patches.
• Figure 3: The black points show the average power spectrum, in terms of $\ell C_{\ell}$, with multipole $\ell$ over all the 25 selected square patches. The error bars on them are the standard deviation obtained on the spectra. The black solid line represents Lenz:2019 best-fit CIB model at $353$$\mathrm{GHz}$$\mathrm{GHz}\xspace$.
• Figure 4: The variation of $\mathcal{V}_{0}$, $\mathcal{V}_{1}$ and $\mathcal{V}_{2}$ with pixel threshold of the Minkowski functionals over all the 25 patches. The perimeter and Euler functions are scaled with $10^{2}$ and $10^{4}$ respectively for visualisation purpose. The black solid line represents the MFs computed from the average of 10 Gaussian CIB realisations and 10 FFP10 noise realisations over a given patch.
• Figure 5: Comparison of $S_{1}$ and $S_{2}$ statistics of mean and standard deviation of 25 contamination maps obtained from the template-fit approach $r_{\rm B}$ regions (black circles) and 300 synthetic contamination maps $r_{\rm syn}$ (gray crosses). The gray crosses are shifted in the $x$-axis.