Poisson Denoising on the Sphere: Application to the Fermi Gamma Ray Space Telescope

TL;DR

This paper introduces a novel multiscale Poisson noise removal method on the sphere, called MS-VSTS, tailored for low photon count data from the Fermi Gamma-Ray Space Telescope, improving detection of diffuse backgrounds and point sources.

Contribution

The paper presents MS-VSTS, a new spherical multiscale Poisson denoising technique using variance stabilization and wavelet/curvelet transforms, with extensions for background separation and inpainting.

Findings

Effective noise reduction on simulated Fermi LAT data.

Enhanced detection of point sources and diffuse background.

Fast and adaptable implementation.

Abstract

The Large Area Telescope (LAT), the main instrument of the Fermi Gamma-Ray Space Telescope, detects high energy gamma rays with energies from 20 MeV to more than 300 GeV. The two main scientific ob jectives, the study of the Milky Way diffuse background and the detection of point sources, are complicated by the lack of photons. That is why we need a powerful Poisson noise removal method on the sphere which is efficient on low count Poisson data. This paper presents a new multiscale decomposition on the sphere for data with Poisson noise, called Multi-Scale Variance Stabilizing Transform on the Sphere (MS-VSTS). This method is based on a Variance Stabilizing Transform (VST), a transform which aims to stabilize a Poisson data set such that each stabilized sample has a quasi constant variance. In addition, for the VST used in the method, the transformed data are asymptotically Gaussian. MS-VSTS consists of decomposing the data into a sparse multi-scale dictionary like wavelets or curvelets, and then applying a VST on the coefficients in order to get almost Gaussian stabilized coefficients. In this work, we use the Isotropic Undecimated Wavelet Transform (IUWT) and the Curvelet Transform as spherical multi-scale transforms. Then, binary hypothesis testing is carried out to detect significant coefficients, and the denoised image is reconstructed with an iterative algorithm based on Hybrid Steepest Descent (HSD). To detect point sources, we have to extract the Galactic diffuse background: an extension of the method to background separation is then proposed. In contrary, to study the Milky Way diffuse background, we remove point sources with a binary mask. The gaps have to be interpolated: an extension to inpainting is then proposed. The method, applied on simulated Fermi LAT data, proves to be adaptive, fast and easy to implement.