| 168 |
|
|
| 169 |
|
} |
| 170 |
|
|
| 171 |
< |
vector<float> compute_one_upper_limit(float mceff,float mcefferr, int ibin, string mcjzb, bool doobserved=false) { |
| 171 |
> |
vector<float> compute_one_upper_limit(float mceff,float mcefferr, int ibin, string mcjzb, string plotfilename, bool doobserved) { |
| 172 |
|
float sigma95=-9.9,sigma95A=-9.9; |
| 173 |
< |
int nuisancemodel=1; |
| 173 |
> |
int nuisancemodel=0; |
| 174 |
> |
/* |
| 175 |
> |
USAGE OF ROOSTATS_CL95 |
| 176 |
> |
" Double_t limit = roostats_cl95(ilum, slum, eff, seff, bck, sbck, n, gauss = false, nuisanceModel, method, plotFileName, seed); \n" |
| 177 |
> |
" LimitResult expected_limit = roostats_clm(ilum, slum, eff, seff, bck, sbck, ntoys, nuisanceModel, method, seed); \n" |
| 178 |
> |
" Double_t average_limit = roostats_cla(ilum, slum, eff, seff, bck, sbck, nuisanceModel, method, seed); \n" |
| 179 |
> |
" \n" |
| 180 |
> |
" |
| 181 |
> |
" Double_t obs_limit = limit.GetObservedLimit(); \n" |
| 182 |
> |
" Double_t exp_limit = limit.GetExpectedLimit(); \n" |
| 183 |
> |
" Double_t exp_up = limit.GetOneSigmaHighRange(); \n" |
| 184 |
> |
" Double_t exp_down = limit.GetOneSigmaLowRange(); \n" |
| 185 |
> |
" Double_t exp_2up = limit.GetTwoSigmaHighRange(); \n" |
| 186 |
> |
" Double_t exp_2down = limit.GetTwoSigmaLowRange(); \n" |
| 187 |
> |
*/ |
| 188 |
|
if(mceff<=0) { |
| 189 |
|
write_warning(__FUNCTION__,"Cannot compute upper limit in this configuration as the efficiency is negative:"); |
| 190 |
|
dout << "mc efficiency=" << mceff << " +/- " << mcefferr; |
| 193 |
|
sigmas.push_back(-1); |
| 194 |
|
return sigmas; |
| 195 |
|
} else { |
| 196 |
< |
dout << "Now calling : CL95(" << luminosity << "," << lumiuncert*luminosity << "," << mceff << "," << mcefferr << "," << Npred[ibin] << "," << Nprederr[ibin] << "," << Nobs[ibin] << "," << false << "," << nuisancemodel<< ") " << endl; |
| 196 |
> |
int nlimittoysused=1; |
| 197 |
> |
if(doobserved) nlimittoysused=nlimittoys; |
| 198 |
> |
/* dout << "Now calling : CL95(" << luminosity << "," << lumiuncert*luminosity << "," << mceff << "," << mcefferr << "," << Npred[ibin] << "," << Nprederr[ibin] << "," << Nobs[ibin] << "," << false << "," << nuisancemodel<< ") " << endl; |
| 199 |
|
sigma95 = CL95(luminosity, lumiuncert*luminosity, mceff, mcefferr, Npred[ibin], Nprederr[ibin], Nobs[ibin], false, nuisancemodel); |
| 200 |
< |
if(doobserved) { |
| 201 |
< |
dout << "Now calling : CLA(" << luminosity << "," << lumiuncert*luminosity << "," << mceff << "," << mcefferr << "," << Npred[ibin] << "," << Nprederr[ibin] << "," << nuisancemodel<< ") " << endl; |
| 202 |
< |
sigma95A = CLA(luminosity, lumiuncert*luminosity, mceff, mcefferr, Npred[ibin], Nprederr[ibin], nuisancemodel); |
| 203 |
< |
} |
| 200 |
> |
*/ |
| 201 |
> |
dout << "Now calling : roostats_limit(" << luminosity << "," << lumiuncert*luminosity << ","<<mceff <<","<<mcefferr<<","<<Npred[ibin]<<","<<Nprederr[ibin] << ",n=" << nlimittoysused << ",gauss=" << false << ", nuisanceModel="<<nuisancemodel<<",method="<<limitmethod<<",plotfilename="<<plotfilename<<",seed=1) " << endl; |
| 202 |
> |
LimitResult limit = roostats_limit(luminosity,lumiuncert*luminosity,mceff,mcefferr,Npred[ibin],Nprederr[ibin],nlimittoysused,false,nuisancemodel,limitmethod,plotfilename,1); |
| 203 |
> |
|
| 204 |
|
vector<float> sigmas; |
| 205 |
+ |
sigmas.push_back(limit.GetExpectedLimit());//expected |
| 206 |
+ |
sigmas.push_back(limit.GetObservedLimit());//observed |
| 207 |
+ |
//up to here for backward compatibility |
| 208 |
+ |
sigmas.push_back(limit.GetOneSigmaHighRange());//expected, up |
| 209 |
+ |
sigmas.push_back(limit.GetTwoSigmaHighRange());//expected, 2 up |
| 210 |
+ |
sigmas.push_back(limit.GetOneSigmaLowRange());//expected, down |
| 211 |
+ |
sigmas.push_back(limit.GetTwoSigmaLowRange());//expected, 2 down |
| 212 |
+ |
// if(doobserved) { |
| 213 |
+ |
// dout << "Now calling : CLA(" << luminosity << "," << lumiuncert*luminosity << "," << mceff << "," << mcefferr << "," << Npred[ibin] << "," << Nprederr[ibin] << "," << nuisancemodel<< ") " << endl; |
| 214 |
+ |
// sigma95A = CLA(luminosity, lumiuncert*luminosity, mceff, mcefferr, Npred[ibin], Nprederr[ibin], nuisancemodel); |
| 215 |
+ |
// } |
| 216 |
+ |
|
| 217 |
+ |
/* vector<float> sigmas; |
| 218 |
|
sigmas.push_back(sigma95); |
| 219 |
< |
sigmas.push_back(sigma95A); |
| 219 |
> |
sigmas.push_back(sigma95A);*/ |
| 220 |
|
return sigmas; |
| 221 |
+ |
|
| 222 |
+ |
|
| 223 |
|
} |
| 224 |
+ |
write_warning(__FUNCTION__,"STILL MISSING SIGMAS, LIMITS, EVERYTHING ..."); |
| 225 |
|
} |
| 226 |
|
|
| 227 |
|
void compute_upper_limits_from_counting_experiment(vector<vector<float> > uncertainties,vector<float> jzbcuts, string mcjzb, bool doobserved) { |
| 247 |
|
// fill_result_histos(observed,observederr, null,null,null,null,null,null,null,mcjzb,JZBcutat,14000,(int)5,result,(signalsamples.FindSample(signalsamples.collection[isample].filename)),signalsamples); |
| 248 |
|
// observed-=result;//this is the actual excess we see! |
| 249 |
|
// float expected=observed/luminosity; |
| 250 |
< |
|
| 250 |
> |
string plotfilename=(string)(TString(signalsamples.collection[isample].samplename)+TString("___JZB_geq_")+TString(any2string(JZBcutat))); |
| 251 |
|
dout << "Sample: " << signalsamples.collection[isample].samplename << ", JZB>"<<JZBcutat<< " : " << mceff << " +/- " << staterr << " (stat) +/- " << systerr << " (syst) --> toterr = " << toterr << endl; |
| 252 |
< |
vector<float> sigmas = compute_one_upper_limit(mceff,toterr,ibin,mcjzb,doobserved); |
| 252 |
> |
vector<float> sigmas = compute_one_upper_limit(mceff,toterr,ibin,mcjzb,plotfilename,doobserved); |
| 253 |
|
|
| 254 |
|
if(doobserved) { |
| 255 |
|
// rows.push_back(any2string(sigmas[0])+";"+any2string(sigmas[1])+";"+"("+any2string(expected)+")"); |
| 287 |
|
|
| 288 |
|
if(!doobserved) { |
| 289 |
|
dout << endl << endl << "LIMITS: (Tex)" << endl; |
| 290 |
+ |
tout << "\\begin{table}[hbtp]" << endl; |
| 291 |
+ |
tout << "\\renewcommand{\arraystretch}{1.3}" << endl; |
| 292 |
+ |
tout << "\\begin{center}" << endl; |
| 293 |
+ |
tout << "\\caption{Observed upper limits on the cross section of different LM benchmark points " << (ConsiderSignalContaminationForLimits?" (accounting for signal contamination)":" (not accounting for signal contamination)") << "}\\label{tab:lmresults}" << endl; |
| 294 |
+ |
tout << "" << endl; |
| 295 |
|
tout << "\\begin{tabular}{ | l | "; |
| 296 |
|
for (int irow=0;irow<jzbcuts.size();irow++) tout << " l |"; |
| 297 |
|
tout << "} " << endl << " \\hline " << endl << "& \t "; |
| 309 |
|
tout << " \\\\ \\hline " << endl; |
| 310 |
|
} |
| 311 |
|
tout << "\\end{tabular}" << endl; |
| 312 |
+ |
tout << " \\end{tabular}"<< endl; |
| 313 |
+ |
tout << "\\end{center}"<< endl; |
| 314 |
+ |
tout << "\\end{table} "<< endl; |
| 315 |
+ |
|
| 316 |
|
}//do observed |
| 317 |
|
|
| 318 |
|
dout << endl << endl << "Final selection efficiencies with total statistical and systematic errors, and corresponding observed and expected upper limits (UL) on ($\\sigma\\times$ BR $\\times$ acceptance) for the LM4 and LM8 scenarios, in the different regions. The last column contains the predicted ($\\sigma \\times $BR$\\times$ acceptance) at NLO obtained from Monte Carlo simulation." << endl; |
| 319 |
|
dout << "Scenario \t Efficiency [%] \t Upper limits [pb] \t \\sigma [pb]" << endl; |
| 320 |
|
for(int icut=0;icut<jzbcuts.size();icut++) { |
| 321 |
< |
dout << "Region with JZB>" << jzbcuts[icut] << endl; |
| 321 |
> |
dout << "Region with JZB>" << jzbcuts[icut] << (ConsiderSignalContaminationForLimits?" (accounting for signal contamination)":" (not accounting for signal contamination)") << endl; |
| 322 |
|
for(int isample=0;isample<signalsamples.collection.size();isample++) { |
| 323 |
|
dout << limits[isample][0] << "\t" << Round(100*uncertainties[isample*jzbcuts.size()+icut][1],3) << "+/-" << Round(100*uncertainties[isample*jzbcuts.size()+icut][2],3) << " (stat) +/- " << Round(100*uncertainties[isample*jzbcuts.size()+icut][3],3) << " (syst) \t" << Round((vlimits[isample][2*icut]),3) << "\t" << Round(vlimits[isample][2*icut+1],3) << endl; |
| 324 |
|
} |
