Mohamad-Ziad Charif - Antares

6.4.1.2 Background estimationThe background estimation in the direction of the Sun is done similarly as shownin section 5.6. However, since now we have a different MC for each run, thescrambling of the Sun is done within the time-frame of the run to which the eventbelong to. In figure 6.8 we can see a comparison of the estimation of the backgroundin the direction of the Sun as a function of the half-cone with scrambleddata and the MC. While the agreement is not perfect, this should not be a problemas again we will be using the scrambled data as our estimator of the background.Again we find that for low ∆(Ψ) the background is compatible with a randomdistribution of events N events ∝ ∆(Ψ) 2Figure 6.8: The estimation of the background in the direction of the Sun as afunction of the half-cone centered around it. The scrambled data in addition to theMC are presented here. A cut of χ 2 < 2 was applied here.6.4.1.3 Search variables and optimizationThe search variables for this part of the analysis are going to be again χ 2 and∆(Ψ ◦ ). And again the search region for the χ 2 variable would be between χ 2 =1.2 and χ 2 = 2 , and as for ∆(Ψ) it would be between ∆(Ψ) = 0 ◦ and ∆(Ψ) = 10 ◦ .Similarly to chapter 5, we find one χ 2 cut for all of our dark matter models, thisvalue is again 1.6 . Figure 6.9 shows a comparison of the estimated backgroundin the direction of the Sun from scrambled data and one dark matter model forthe optimized χ 2 cut. Figure 6.10 shows the value of the optimized ∆(Ψ ◦ ) cutas a function of the WIMP mass, we can notice that for M WIMP = 150 GeV the117