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S/N evaluation

S/N evaluation. Mix sample. Signal(S). I used the notation as in Thomas Ulrich’s note for the signal/background I choose for the range of evaluation Range = [mean-3 ; mean- 3 ] where mean = 1.86 MeV/c 2 and  =0.014 MeV/c 2 (values taken from the pure sample). Totalcount(T).

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S/N evaluation

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  1. S/N evaluation Mix sample

  2. Signal(S) • I used the notation as in Thomas Ulrich’s note for the signal/background • I choose for the range of evaluation Range = [mean-3; mean-3] where mean = 1.86 MeV/c2 and  =0.014 MeV/c2 (values taken from the pure sample) Totalcount(T) background(B) • Then for each values in Range, I calculated S,T,B • The significance as in the note is S / Sqrt(S+2B) • comments : • Every plots used probability > 0.01(it was set already in the macro) • the signal is the histogram value ,not the fit function • The background is the value of the function that fits the background

  3. Example :no cut in slength/dslength Raw histogram : T= S+B Background fit function T = 52038 B = 51257 S = 781 Then significance = 2.42

  4. Summary (when increasing L/dL)

  5. No cut L/dL>0 L/dL>3 L/dL>1

  6. comments • We see an increase in significance up to L/dL > 1 • For high cut (L/dL>3) ,the signal (S) is very low comparing to the background • This method depends on the bin range for the S,T,B evaluation : • For range =Mean±3, significance = 4.07 (L/dL>2.5) • For range =Mean±2, significance = 4.41 (L/dL>2.5)

  7. Summary (when increasing prob)

  8. Changing the parameters • As I’m using the value of the fit function for the background and the bin entry for the S+B, sometimes S+B is under the fit function. • The significance can be biased • It also can biased the significance by the range of the bin counting • In the following plot (prob>.1 && (L/dL)>1), the mean is 1.864 (pdg value) and range is 2 ; it gives then range of fitting [1.836 ;1.892] (shown in green). • In that case, the range fits well the signal and I get a significance of 4.17 (instead of 4.31 for the default range)

  9. Comments (of slide 7) • From prob>0.01 to prob>.7, the significance is increasing (I have to understand why from 0.01 to 0.1 it’s decreasing) • What is interesting is that for a given probability cut, the signal is slowly decreasing whereas the background is considerably reduced : • For prob>.1, S increases from 362 to 572 whereas B decreases from 39979 to 8527 (almost x5 reduction) • For prob>.3, S decreases from 564 to 425 whereas B decreases from 27863 to 5748 (again almost x5 reduction

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