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Jean-Marc Delouis

Publications and source records attributed to Jean-Marc Delouis.

7 recordsLinked to original sources
arXiv2026

Revealing Galactic dust beneath the cosmic infrared background anisotropies with Wavelet Phase Harmonics

Separating Galactic dust emission from cosmic infrared background (CIB) anisotropies is challenging because both the emission components have similar spectral properties. The primary objective of this work is to develop a component-separation algorithm to extract the Galactic dust emission from CIB anisotropies dominated sky regions using Wavelet Phase Harmonics statistics (WPH). We apply our algorithm to the \textit{Planck} $353\,\mathrm{GHz}$ frequency band over three sky regions spanning a range of neutral hydrogen ($\text{HI}$) column densities at high Galactic latitudes. In all three regions, we successfully recover the dust signal without significant leakage between the dust and CIB anisotropy components. We use the low signal-to-noise ($\mathrm{S/N}$) region to validate the component-separation algorithm and compare its performance with the standard template-fit approach that uses the linear dust-$\text{HI}$ correlation relation. For intermediate and high $\mathrm{S/N}$ regions, we separate the dust signal map from the contamination while preserving the statistical correlation with $\text{HI}$ column density map. Dust maps are used to analyze the relationship between dust and $\text{HI}$ emissions. We characterize the spatial variations in dust emission relative to the hydrogen column density as a function of the angular scale.

astro-ph.GA↗
arXiv2026

Untangling dust emission and cosmic infrared background anisotropies with the scattering transform statistics

The template-fit approach is often used to separate Galactic dust emission from cosmic infrared background (CIB) anisotropies in low $\text{HI}$ column density regions, under the assumption that gas and dust are tightly correlated. However, this method fails in regions where additional Galactic emission from molecular hydrogen, diffuse ionized gas, and dark gas is present. We develop and test a statistical component-separation method to extract the dust signal from contaminated $\textit{Planck}$ $353\,\rm GHz$ observations using Scattering Covariance (SC) statistics. We first obtain a set of CIB maps over $25$ square patches, each covering an area of $222\,{\rm deg}^{2}$, using the linear correlation between dust and Galactic $21\,\rm cm$ $\text{HI}$ emission, which is valid in low $\text{HI}$ column density regions, through the template-fit approach. We then construct, from these $25$ maps, a generative model of the CIB using SC statistics. Finally, we rely on this contamination model to perform 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 exhibits more structure than the corrected SFD map at $100\,\mu \rm m$. The differences observed at 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 for mapping Galactic interstellar reddening over intermediate and high Galactic latitudes.

astro-ph.GA↗
arXiv2025

Variance of dust temperature and spectral index in Planck polarization data using spin-moment expansion

Thermal dust is the major polarized foreground hindering the detection of primordial cosmic microwave background (CMB) B-modes. Its signal exhibits complex behavior in frequency space, arising from the combined variation in our Galaxy of the orientation of magnetic fields and the spectral properties of dust grains aligned with magnetic field lines. In this work, we present a new framework for analyzing the thermal dust signal using polarized microwave data. We introduce residual maps, represented as complex quantities, which capture deviations of the local polarized spectral energy distribution (SED) from the mean complex SED averaged over the sky mask. We present simple predictions that relate the values of the statistical correlation and covariances between the residual maps to the physical properties of the emitting aligned grains. Testing these predictions provides valuable information about the nature of the dust signal. We evaluated our predictions using Planck data over a 97% mask excluding the inner Galactic plane. Despite its simplicity, our model captures a significant part of the statistical properties of the data. For the SRoll2 version of the data, the spectral dependence of the covariances between residual maps is compatible with a dust model that includes only temperature variations rather than spectral index variations. In contrast, for the PR4 Planck official release, it is incompatible with both models. Our methodology can be used to analyze future high-precision polarization data and to build more accurate dust models for use by the CMB community.

astro-ph.GA↗
arXiv2024

Scattering transforms on the sphere, application to large scale structure modelling

Scattering transforms are a new type of summary statistics recently developed for the study of highly non-Gaussian processes, which have been shown to be very promising for astrophysical studies. In particular, they allow one to build generative models of complex non-linear fields from a limited amount of data. In the context of upcoming cosmological surveys, the extension of these tools to spherical data is necessary. We develop scattering transforms on the sphere and focus on the construction of maximum-entropy generative models of astrophysical fields. The quality of the generative models, both statistically and visually, is very satisfying, which therefore open up a wide range of new applications for future cosmological studies.

astro-ph.IM↗
arXiv2024

Generative models of astrophysical fields with scattering transforms on the sphere

Scattering transforms are a new type of summary statistics recently developed for the study of highly non-Gaussian processes, which have been shown to be very promising for astrophysical studies. In particular, they allow one to build generative models of complex non-linear fields from a limited amount of data, and have also been used as the basis of new statistical component separation algorithms. In the context of upcoming cosmological surveys, such as LiteBIRD for the cosmic microwave background polarization or Rubin-LSST and Euclid for study of the large scale structures of the Universe, the extension of these tools to spherical data is necessary. We develop scattering transforms on the sphere and focus on the construction of maximum-entropy generative models of several astrophysical fields. We construct, from a single target field, generative models of homogeneous astrophysical and cosmological fields, whose samples are quantitatively compared to the target fields using common statistics (power spectrum, pixel probability density function and Minkowski functionals). Our sampled fields agree well with the target fields, both statistically and visually. These generative models therefore open up a wide range of new applications for future astrophysical and cosmological studies; particularly those for which very little simulated data is available. We make our code available to the community so that this work can be easily reproduced and developed further.

astro-ph.IM↗
arXiv2022

Dust polarization spectral dependence from Planck HFI data. Turning point on CMB polarization foregrounds modelling

The search for the primordial B-modes of the cosmic microwave background (CMB) relies on the separation from the brighter foreground dust signal. In this context, the characterisation of the spectral energy distribution (SED) of thermal dust in polarization has become a critical subject of study. We present a power-spectra analysis of Planck data, which improves on previous studies by using the newly released SRoll2 maps that correct residual data systematics, and by extending the analysis to regions near the Galactic plane. Our analysis focuses on the lowest multipoles between l=4 and 32, and three sky areas with sky fractions of fsky = 80%, 90%, and 97%. The mean dust SED for polarization and the 353 GHz Q and U maps are used to compute residual maps at 100, 143 and 217 GHz, highlighting spatial variations of the dust polarization SED. Residuals are detected at the three frequencies for the three sky areas. We show that models based on total intensity data are underestimating by a significant factor the complexity of dust polarized CMB foreground. Our analysis emphasizes the need to include variations of polarization angles of the dust polarized CMB foreground. The frequency dependence of the EE and BB power spectra of the residual maps yields further insight. We find that the moments expansion to the first order of the modified black-body (MBB) spectrum provides a good fit to the EE power-spectra. This result suggests that the residuals could follow mainly from variations of dust MBB spectral parameters. However, this conclusion is challenged by cross-spectra showing that the residuals maps at the three frequencies are not fully correlated, and the fact that the BB power-spectra do not match the first order moment expansion of a MBB SED. This work sets new requirements for simulations of the dust polarized foreground and component separation methods (abridged)

astro-ph.CO↗
arXiv2020

SRoll3: A neural network approach to reduce large-scale systematic effects in the Planck High Frequency Instrument maps

In the present work, we propose a neural network based data inversion approach to reduce structured contamination sources, with a particular focus on the mapmaking for Planck High Frequency Instrument (Planck-HFI) data and the removal of large-scale systematic effects within the produced sky maps. The removal of contamination sources is rendered possible by the structured nature of these sources, which is characterized by local spatiotemporal interactions producing couplings between different spatiotemporal scales. We focus on exploring neural networks as a means of exploiting these couplings to learn optimal low-dimensional representations, optimized with respect to the contamination source removal and mapmaking objectives, to achieve robust and effective data inversion. We develop multiple variants of the proposed approach, and consider the inclusion of physics informed constraints and transfer learning techniques. Additionally, we focus on exploiting data augmentation techniques to integrate expert knowledge into an otherwise unsupervised network training approach. We validate the proposed method on Planck-HFI 545 GHz Far Side Lobe simulation data, considering ideal and non-ideal cases involving partial, gap-filled and inconsistent datasets, and demonstrate the potential of the neural network based dimensionality reduction to accurately model and remove large-scale systematic effects. We also present an application to real Planck-HFI 857 GHz data, which illustrates the relevance of the proposed method to accurately model and capture structured contamination sources, with reported gains of up to one order of magnitude in terms of contamination removal performance. Importantly, the methods developed in this work are to be integrated in a new version of the SRoll algorithm (SRoll3), and we describe here SRoll3 857 GHz detector maps that will be released to the community.

astro-ph.IM↗