Sparse estimation of model-based diffuse thermal dust emission

TL;DR

This paper introduces a new sparse estimation method called premise for separating thermal dust emission from CIB in Planck HFI data, providing more accurate all-sky maps of dust properties especially in low signal-to-noise regions.

Contribution

The paper presents premise, a novel parameter estimation technique exploiting the sparsity of thermal dust emission, improving separation accuracy over existing methods in challenging regions.

Findings

Premise achieves 2.8-7.2% accuracy in estimating dust parameters.

Performs comparably to GNILC in high SNR regions.

Outperforms GNILC outside the Galactic plane as SNR decreases.

Abstract

Component separation for the Planck HFI data is primarily concerned with the estimation of thermal dust emission, which requires the separation of thermal dust from the cosmic infrared background (CIB). For that purpose, current estimation methods rely on filtering techniques to decouple thermal dust emission from CIB anisotropies, which tend to yield a smooth, low- resolution, estimation of the dust emission. In this paper we present a new parameter estimation method, premise: Parameter Recovery Exploiting Model Informed Sparse Estimates. This method exploits the sparse nature of thermal dust emission to calculate all-sky maps of thermal dust temperature, spectral index and optical depth at 353 GHz. premise is evaluated and validated on full-sky simulated data. We find the percentage difference between the premise results and the true values to be 2.8, 5.7 and 7.2 per cent at the 1{\sigma} level across the full sky for thermal dust temperature, spectral index and optical depth at 353 GHz, respectively. Comparison between premise and a GNILC-like method over selected regions of our sky simulation reveals that both methods perform comparably within high signal-to-noise regions. However outside of the Galactic plane premise is seen to outperform the GNILC-like method with increasing success as the signal-to-noise ratio worsens.