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CERN-THESIS-2012-153 26/07/2012 - CERN Document Server

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than or equal to the value observed, Qobs:<br />

CLs+b = Ps+b(Q ≤ Qobs), (8.1)<br />

where Ps+b(Q ≤ Qobs) can be expressed in terms of the probability distribution function of the test-statistic,<br />

to be described below, for signal+background experiments. Small values of CLs+b indicate poor compatibility<br />

with the signal+background hypothesis and favor the background-only hypothesis. In a similar way, the<br />

confidence in the background-only hypothesis is given by the probability that the test-statistic is less than<br />

or equal to the value observed:<br />

CLb = Pb(Q ≤ Qobs), (8.2)<br />

where Pb(Q ≤ Qobs) is expressed in terms of the probability distribution of the test-statistic for background-<br />

only experiments.<br />

The technique used by the CLs method, to avoid results that are more sensitive to fluctuations of the<br />

known background than to the hypothetical signal, is to normalize the confidence level observed for the<br />

signal+background hypothesis, CLs+b, to the confidence level observed in the background-only hypothesis,<br />

CLb. This makes it possible to obtain sensible exclusion limits on the signal even if the observed rate is<br />

very low. In addition, the limits on the signal hypothesis obtained by this results will be conservative. The<br />

normalization just described is then:<br />

CLs ≡ CLs+b/CLb. (8.3)<br />

The signal hypothesis will be then considered excluded at the confidence level CL when:<br />

1 − CLs = CL. (8.4)<br />

The consequence of CLs not being a confidence, rather a ratio of confidences, is that the difference bet-<br />

ween CLs and the actual false exclusion rate will increase as the probability density functions of the sig-<br />

nal+background and background-only hypotheses become more similar. This means, the use of CLs reduces<br />

the range of model parameters for which an exclusion result is possible [106,1<strong>07</strong>].<br />

For this case, the CLs method is used with a likelihood ratio as a test-statistic. The likelihood ratio,<br />

Q( X), is the ratio of the probability densities for a given experimental result X for two hypothesis. This<br />

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