In this tutorial, we use the H->WW->lnln analysis as an example to illustrate how to build a physics analysis based on both cut-n-count and shape method and calculate the upper limits, significance. For simplification, we use ideal MC sample without reconstruction and include only one background (continuum WW) in the search.

Disclaimer: Numbers in this tutorial are motivated by the HWW analysis, but do not have one-to-one correspondance to the HWW results. Please do not be alarmed that your result is not the same as in the official note or AN.

**note: as for the rest of CMSSW software, this requires scientific linux 5, so you should log into lxplus5.cern.ch and not lxplus.cern.ch**

setenv SCRAM_ARCH slc5_amd64_gcc472 # use export SCRAM_ARCH=slc5_amd64_gcc472 for bash cmsrel CMSSW_6_1_1 cd CMSSW_6_1_1/src cmsenv addpkg HiggsAnalysis/CombinedLimit V03-01-12 scramv1 b

After this, you have an excutable called "combine" that can be used to do tons of statistical calculations. Use `combine help`

to see the ultimate list of options.

In a cut and count analysis, we need to know what is the number of signal and background events expected, the number of events observed in data and associated systematic uncertainties. Each source of the systematic uncertainty is labelled as a nusiance. It is important to notice that even the MC statistic error for a given background is assumed as a nusiance.

Datacard is the input to run the statistical tools in the Higgs analysis in CMS. This can be adapted for other search program as well. There are two different softwares in CMS that does this computation, one is in LandS and other is in HiggsLimit/combination. The common data card for a cut-based analysis is given below.

Here is an example of the card called hww_20fb_cut.txt. In this example we consider 20% systematic uncertainty for the signal and 10% for the background.

imax 1 number of bins jmax 1 number of processes minus 1 kmax 2 number of nuisance parameters ---------------------------------------------------- bin of0j observation 505.0 ---------------------------------------------------- bin of0j of0j process ggH qqWW process 0 1 rate 90.0000 430.0000 ---------------------------------------------------- uncert_HWW lnN 1.2 1.0 uncert_qqWW lnN 1.0 1.1

- This card can be combined with other channels for combination. Suppose we have another channel with input hww_20fb_1j_cut.txt. You can combine the two channels together as follows

combineCards.py of0j=hww_20fb_cut.txt of1j=hww_20fb_1j_cut.txt > hww_20fb_cut_comb.txt

In addition to use only the number of events, shape analysis exploits the kinematic shapes as well. This is equivalent to sub-dividing the analysis into more categories according to the kinematic shape.

Here is an example card

imax 1 number of bins jmax 1 number of processes minus 1 kmax 3 number of nuisance parameters ---------------------------------------------------------------------------------------------- shapes * ofj0 hww_20fb_shape.input.root histo_$PROCESS histo_$PROCESS_$SYSTEMATIC shapes data_obs ofj0 hww_20fb_shape.input.root histo_Data ---------------------------------------------------------------------------------------------- bin ofj0 observation 5729.0 ---------------------------------------------------------------------------------------------- bin ofj0 ofj0 process ggH qqWW process 0 1 rate 228.1320 3981.6820 ---------------------------------------------------------------------------------------------- CMS_hww_0j_WW_8TeV_SHAPE lnN - 1.1 CMS_hww_MVAWWBounding shape - 1.0 uncert_HWW lnN 1.2 1.0

Compare to the cut-based analysis you would notice the following

- You need an input file of the signal and background shapes, currently called "hww_20fb_shape.input.root". You can find this at
`/afs/cern.ch/user/y/yygao/public/hww_20fb_shape.input.root`

- Inside the input root file, you can see the histograms with specific naming as declared at the beginning of the data card

KEY: TH1D histo_ggH;1 histo_ggH KEY: TH1D histo_Data;1 histo_Data KEY: TH1D histo_qqWW;1 histo_qqWW KEY: TH1D histo_qqWW_CMS_hww_MVAWWBoundingUp;1 histo_qqWW_CMS_hww_MVAWWBoundingUp KEY: TH1D histo_qqWW_CMS_hww_MVAWWBoundingDown;1 histo_qqWW_CMS_hww_MVAWWBoundingDown

- The number of signal and background events are much larger than in cut-based analysis. This is because we apply much looser analysis cut in the shape analysis.

combine -d hww_20fb_cut_comb.txt -M Asymptotic Output: -- Asymptotic -- Observed Limit: r < 1.8221 Expected 2.5%: r < 0.6019 Expected 16.0%: r < 0.8016 Expected 50.0%: r < 1.1289 Expected 84.0%: r < 1.6194 Expected 97.5%: r < 2.2782

This indicates that the 95% upperlimit observed is 1.8, while the median expected is 1.1 with 1sigma band [0.8, 1.6] and 2sigma band [0.6, 2.2]. This result tells us that we observed an excess in data at the level of 1-2 sigma.

combine -d hww_20fb_cut_comb.txt -M ProfileLikelihood -v 1 --significance --expectSignal=1 -t -1 -m 125 -n Expected Output: -- Profile Likelihood -- Significance: 1.80964 (p-value = 0.035176)

combine -d hww_20fb_cut_comb.txt -M ProfileLikelihood -v 1 --significance --expectSignal=1 -t -1 -m 125 Output: -- Profile Likelihood -- Significance: 1.52713 (p-value = 0.063364)

A maximum likelihood scan is performed to get the +/- 1 sigma error.

combine -d hww_20fb_cut_comb.txt -M MaxLikelihoodFit Output: --- MaxLikelihoodFit --- Best fit r: 0.832828 -0.535222/+0.559076 (68% CL) nll S+B -> -6.19048 nll B -> -5.02441

- Note, The fit result is also saved in the file called "mlfit.root", which contains all the results, including the best fit nusiances and the signal strength, correlations between the nuisances etc.

- Above tutorial is a super slimmed version of the complete tool, see the documentation here, Documentation of the RooStats-based statistics tools CMS

This topic: Sandbox > WebPreferences > LPCStatsHandsOnTutorial

Topic revision: r3 - 2013-06-26 - YanyanGao

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