Wavelet-Based Techniques for the Gamma-Ray Sky

TL;DR

This paper introduces wavelet decomposition techniques to analyze gamma-ray sky data, enabling the extraction of structures at different scales without relying on predefined models, which aids in distinguishing signals like dark matter from astrophysical sources.

Contribution

The paper presents a novel application of wavelet analysis to gamma-ray data, allowing model-independent separation of emission components on various angular scales.

Findings

Successfully extracted extended signals from mock gamma-ray data.

Differentiated diffuse dark matter signals from point source populations.

Demonstrated robustness against foreground and background uncertainties.

Abstract

We demonstrate how the image analysis technique of wavelet decomposition can be applied to the gamma-ray sky to separate emission on different angular scales. New structures on scales that differ from the scales of the conventional astrophysical foreground and background uncertainties can be robustly extracted, allowing a model-independent characterization with no presumption of exact signal morphology. As a test case, we generate mock gamma-ray data to demonstrate our ability to extract extended signals without assuming a fixed spatial template. For some point source luminosity functions, our technique also allows us to differentiate a diffuse signal in gamma-rays from dark matter annihilation and extended gamma-ray point source populations in a data-driven way.