How to apply the Ratio Method to estimate the photon contamination in VGamma from V+Jets.

Say you wanted the amount of V+Jet contamination in a sample of barrel tight photons with pt between 40 and 60GeV for 2011A. First count up all the barrel Photon-like Jets in that Pt range, N_plJ, and all the barrel Tight Photons, N_T. Using the f/e and alpha corresponding to that bin for the 2011A barrel, the background in that bin should be:

Equation1.png

The f/e's and alpha factors are listed on lxplus: ~abarker/RatioMethodResults/ in the following files

ratioMethod_GJ_11A_data_bar.root: main_total_err_res
ratioMethod_GJ_11B_data_bar.root: main_total_err_res
ratioMethod_GJ_11A_data_ec.root: main_total_err_ec_res
ratioMethod_GJ_11B_data_ec.root: main_total_err_ec_res
Alpha_bar_11A_PU.h
Alpha_bar_11B_PU.h
Alpha_ec_11A_PU.h
Alpha_ec_11B_PU.h

(For convenience they are also listed on cmslpc: ~abarker/forIraklis )

The f/e ratios listed in the histograms include the full error bars, which take into account statistical errors, the systematic from the template choice, and uncertainty in alpha.

The equation above is implemented for you in lxplus: ~abarker/public/RatioMethodResults/RatioMethodCalcs.h

in the function GetRatioMethodBackgroundPrediction.

float GetRatioMethodBackgroundPrediction(float N_T,float N_plJ, float !FoverE, float alpha)

(For convenience it is also listed on cmslpc at ~abarker/forIraklis )

The uncertainty in the result is also implemented in:

float GetErrorInRatioMethodBackgroundPrediction(float N_T,float N_plJ, float !FoverE, float alpha, float err_FoverE, float err_alpha)

GetRatioMethodBackgroundPrediction takes the number of Tight Photons N_T, the number of Photon Like Jets N_plJ, f/e (FoverE) and alpha and returns the ratio method's prediction of the number of background events.

GetErrorInRatioMethodBackgroundPrediction Assumes gaussian uncertainty in N_T and N_plJ with standard deviations sqrt(N_T) and sqrt(N_plJ) and assumes gaussian errors in FoverE and alpha. It then throws calculates the background prediction for each of 10^6 random combinations of parameters in the shape of their gaussian errors and repports the standard deviation of the resutls of the calculation.

-- AnthonyBarker - 13-Apr-2012

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