Pre-launch estimates for GLAST sensitivity to Dark Matter annihilation signals

TL;DR

This paper assesses GLAST's (Fermi) potential to detect dark matter WIMP signals through gamma-ray observations, highlighting its sensitivity, expected detection capabilities, and challenges in background modeling.

Contribution

It provides the first sensitivity estimates for GLAST to detect dark matter annihilation signals across various models and sources, including extragalactic and UED scenarios.

Findings

GLAST can detect WIMP signals for cuspy dark matter profiles.

Sensitivity estimates vary with source location and dark matter distribution.

Background modeling is crucial for accurate detection limits.

Abstract

We investigate the sensitivity of the Gamma-ray Large Area Space Telescope (GLAST) to indirectly detect weakly interacting massive particles (WIMPs) through the $\gamma$-ray signal that their pair annihilation produces. WIMPs are among the favorite candidates to explain the compelling evidence that about 80% of the mass in the Universe is non-baryonic dark matter (DM). They are serendipitously motivated by various extensions of the standard model of particle physics such as Supersymmetry and Universal Extra Dimensions (UED). With its unprecedented sensitivity and its very large energy range (20 MeV to more than 300 GeV) the main instrument on board the GLAST satellite, the Large Area Telescope (LAT), will open a new window of discovery. As our estimates show, the LAT will be able to detect an indirect DM signature for a large class of WIMP models given a cuspy profile for the DM distribution. Using the current state of the art Monte Carlo and event reconstruction software developed within the LAT collaboration, we present preliminary sensitivity studies for several possible sources inside and outside the Galaxy. We also discuss the potential of the LAT to detect UED via the electron/positron channel. Diffuse background modeling and other background issues that will be important in setting limits or seeing a signal are presented.