Use of the likelihood principle in physics. Statistics II

Transcription

1 Use of the likelihood principle in physics Statistics II 1

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4 + Bayesians vs Frequentists 4

5 Why ML does work? hypothesis observation 5

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17 Fit of Histograms corresponds This formula is from ML!!!! 17

18 The extended likelihood When the total number of events is a poissonian variable: } If and is the product of two poissonians, with averages and Conclusions: the number of events in any channel follows the Poisson statistics

19 The extended likelihood L(, n) N ni i i 1 n! i i e ln L(, n) k i 1 n i ln[ ( )] i k i 1 ( ) i Since N i p i ( ) N ln L(, n) k i 1 n i ln[ p i ( )] N( ) N is a function of q as in the case of a detector efficiency, If there is no functional relation between N and the result is the same as for the non extended likelihood 19

20 Binomial p=0.5 p= p= Gaussian =70 s=10 = s = = s = L c 2 20

21 The other branch of Statistics: Hypothesis Testing 21

22 P(H 0 ) P(H 1 )... in Physics 1 a b a exp value 1 b power 22

23 ) A Milestone: the Neyman-Pearson theorem Likelihood Ratio Test 23

24 A Milestone: the Neyman-Pearson theorem: limitations 24

25 NP theorem application x ln R / ln( ab) 25

26 Likelihood Ratio n i from MC samples! 26

27 Steps of the likelihood ratio test Determine the ratio s i /b i for each bin (model + MC simulation) Find lnq pdf simulating n i from background (with the same experimental statistics) Find lnq pdf simulating n i with signal (with the same experimental statistics) Calculate the lnq for the data n i and make the test n i 27

28 The Higgs at LEP in

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30 LEP real data Three selections of the reconstructed Higgs mass of 115 GeV to obtain 0.5/1/2/ times as many expected signal as Background above 109 GeV 30

31 ALEP, DELPHI, L3, OPAL, 2003 One can sum-up over the bins of histograms from different experiments and to construct a GLOBAL statistics! 31

32 MC toy model First problem: due to detector efficiencies and to undetected neutrinos which accompain the Higgs decay products, the reconstructed mass could not coincide with the true mass The figure shows the weight ln(1+s/b) when the reconstructed mass is 110 GeV and the weights are calculated for true Higgs masses bewtween GeV The weight plot was called spaghetti plot 32

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34 Steps of the likelihood ratio test Determine the ratio s i /b i for each bin (model + MC simulation) Find lnq pdf simulating n i from background (with the same experimental statistics) Find lnq pdf simulating n i with signal (with the same experimental statistics) Calculate the lnq for the datum n i and make the test n i 34

35 MC toy model s i red b i yellow Crosses: M data, Background only ln(1+s/b) plot 1,2,3,4,5,...n 35

36 MC toy model s i red b i yellow Crosses: M data, Background + Signal ln(1+s/b) plot 1,2,3,4,5,...n (in red is the previous one with background only) 36

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38 With a mass of 116 GeV 10% of the background only experiments give the observed signal With a Higgs mass of 110 GeV the data are consistent with the background only hypothesis With a Higgs mass of 120 GeV the data are not able to discriminate between the hypotheses 38

39 maximum likelihood zone 39

40 3s effect! 40

41 ALEPH DELPHI L3 OPAL 2003 m H 5% m H GeV/c 2 CL=95% 41

42 Conclusions 42

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45 Bayesian Hypothesis test 45

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48 Conclusions The maximum likelihood (ML) is the best estimator in the case of parametric statistics problems The likelihood ratio is the maximum power test, that maximize the discovery potential The likelihood ratio permits to match toghether different experiments and to realize the Neyman frequentist scheme 48

49 MC samples background signal With a mass of 116 GeV 10% of the background only experiments give the observed signal With a Higgs mass of 120 GeV the data are not able to discriminate between the hypotheses With a Higgs mass of 110 GeV the data are consistent with the background only hypothesis 49

50 LEP real data 52