Foreground effect on the $J$-factor estimation of classical dwarf spheroidal galaxies

TL;DR

This study evaluates how foreground star contamination affects the estimation of the J-factor in dwarf spheroidal galaxies for dark matter searches, proposing methods to mitigate this systematic uncertainty.

Contribution

It introduces a new likelihood function that accounts for foreground contamination and demonstrates its effectiveness through mock data analysis.

Findings

Foreground contamination significantly biases J-factor estimates.

Velocity and surface gravity cuts effectively reduce foreground effects.

The new likelihood accurately recovers true J-factor values in simulations.

Abstract

The gamma-ray observation of the dwarf spheroidal galaxies (dSphs) is a promising approach to search for the dark matter annihilation (or decay) signal. The dSphs are the nearby satellite galaxies with a clean environment and dense dark matter halo so that they give stringent constraints on the 1 TeV dark matter. However, recent studies have revealed that current estimation of astrophysical factors relevant for the dark matter searches are not conservative, where the various non-negligible systematic uncertainties are not taken into account. Among them, the effect of foreground stars on the astrophysical factors has not been paid much attention, which becomes more important for deeper and wider stellar surveys in the future. In this article, we assess the effects of the foreground contamination by generating the mock samples of stars and using a model of future spectrographs. We investigate various data cuts to optimize the quality of the data and find that the cuts on the velocity and surface gravity can efficiently eliminate the contamination. We also propose a new likelihood function which includes the foreground distribution function. We apply this likelihood function to the fit of the three types of the mock data (Ursa Minor, Draco with large dark matter halo, and Draco with small halo) and three cases of the observation. The likelihood successfully reproduces the input $J$-factor value while the fit without considering the foreground distribution gives large deviation from the input value by a factor of three.