| 1 |
auterman |
1.1 |
// @(#)root/hist:$Id: TLimit.cxx 26221 2008-11-17 08:10:26Z brun $
|
| 2 |
|
|
// Author: Christophe.Delaere@cern.ch 21/08/2002
|
| 3 |
|
|
|
| 4 |
|
|
///////////////////////////////////////////////////////////////////////////
|
| 5 |
|
|
//
|
| 6 |
|
|
// TLimit
|
| 7 |
|
|
//
|
| 8 |
|
|
// Class to compute 95% CL limits
|
| 9 |
|
|
//
|
| 10 |
|
|
// adapted from the mclimit code from Tom Junk (CLs method)
|
| 11 |
|
|
// see http://root.cern.ch/root/doc/TomJunk.pdf
|
| 12 |
|
|
// see http://cern.ch/thomasj/searchlimits/ecl.html
|
| 13 |
|
|
// see: Tom Junk,NIM A434, p. 435-443, 1999
|
| 14 |
|
|
//
|
| 15 |
|
|
// see also the following interesting references:
|
| 16 |
|
|
// Alex Read, "Presentation of search results: the CLs technique"
|
| 17 |
|
|
// Journal of Physics G: Nucl. Part. Phys. 28 2693-2704 (2002).
|
| 18 |
|
|
// http://www.iop.org/EJ/abstract/0954-3899/28/10/313/
|
| 19 |
|
|
//
|
| 20 |
|
|
// A nice article is also available in the CERN yellow report with the proceeding
|
| 21 |
|
|
// of the 2000 CERN workshop on confidence intervals.
|
| 22 |
|
|
//
|
| 23 |
|
|
// Alex Read, "Modified Frequentist Analysis of Search Results (The CLs Method)"
|
| 24 |
|
|
// CERN 2000-005 (30 May 2000)
|
| 25 |
|
|
//
|
| 26 |
|
|
// see note about: "Should I use TRolke, TFeldmanCousins, TLimit?"
|
| 27 |
|
|
// in the TRolke class description.
|
| 28 |
|
|
//
|
| 29 |
|
|
///////////////////////////////////////////////////////////////////////////
|
| 30 |
|
|
|
| 31 |
|
|
#include "TLimit.h"
|
| 32 |
|
|
#include "TArrayD.h"
|
| 33 |
|
|
#include "TVectorD.h"
|
| 34 |
|
|
#include "TOrdCollection.h"
|
| 35 |
|
|
#include "TConfidenceLevel.h"
|
| 36 |
|
|
#include "TLimitDataSource.h"
|
| 37 |
|
|
#include "TRandom3.h"
|
| 38 |
|
|
#include "TH1.h"
|
| 39 |
|
|
#include "TObjArray.h"
|
| 40 |
|
|
#include "TMath.h"
|
| 41 |
|
|
#include "TIterator.h"
|
| 42 |
|
|
#include "TObjString.h"
|
| 43 |
|
|
#include "TClassTable.h"
|
| 44 |
|
|
#include "Riostream.h"
|
| 45 |
|
|
|
| 46 |
|
|
ClassImp(TLimit)
|
| 47 |
|
|
|
| 48 |
|
|
TArrayD *TLimit::fgTable = new TArrayD(0);
|
| 49 |
|
|
TOrdCollection *TLimit::fgSystNames = new TOrdCollection();
|
| 50 |
|
|
|
| 51 |
|
|
TConfidenceLevel *TLimit::ComputeLimit(TLimitDataSource * data,
|
| 52 |
|
|
Int_t nmc, bool stat,
|
| 53 |
|
|
TRandom * generator)
|
| 54 |
|
|
{
|
| 55 |
|
|
// class TLimit
|
| 56 |
|
|
// ------------
|
| 57 |
|
|
// Algorithm to compute 95% C.L. limits using the Likelihood ratio
|
| 58 |
|
|
// semi-bayesian method.
|
| 59 |
|
|
// It takes signal, background and data histograms wrapped in a
|
| 60 |
|
|
// TLimitDataSource as input and runs a set of Monte Carlo experiments in
|
| 61 |
|
|
// order to compute the limits. If needed, inputs are fluctuated according
|
| 62 |
|
|
// to systematics. The output is a TConfidenceLevel.
|
| 63 |
|
|
//
|
| 64 |
|
|
// class TLimitDataSource
|
| 65 |
|
|
// ----------------------
|
| 66 |
|
|
//
|
| 67 |
|
|
// Takes the signal, background and data histograms as well as different
|
| 68 |
|
|
// systematics sources to form the TLimit input.
|
| 69 |
|
|
//
|
| 70 |
|
|
// class TConfidenceLevel
|
| 71 |
|
|
// ----------------------
|
| 72 |
|
|
//
|
| 73 |
|
|
// Final result of the TLimit algorithm. It is created just after the
|
| 74 |
|
|
// time-consuming part and can be stored in a TFile for further processing.
|
| 75 |
|
|
// It contains light methods to return CLs, CLb and other interesting
|
| 76 |
|
|
// quantities.
|
| 77 |
|
|
//
|
| 78 |
|
|
// The actual algorithm...
|
| 79 |
|
|
// From an input (TLimitDataSource) it produces an output TConfidenceLevel.
|
| 80 |
|
|
// For this, nmc Monte Carlo experiments are performed.
|
| 81 |
|
|
// As usual, the larger this number, the longer the compute time,
|
| 82 |
|
|
// but the better the result.
|
| 83 |
|
|
//Begin_Html
|
| 84 |
|
|
/*
|
| 85 |
|
|
<FONT SIZE=+0>
|
| 86 |
|
|
<p>Supposing that there is a plotfile.root file containing 3 histograms
|
| 87 |
|
|
(signal, background and data), you can imagine doing things like:</p>
|
| 88 |
|
|
<p>
|
| 89 |
|
|
<BLOCKQUOTE><PRE>
|
| 90 |
|
|
TFile* infile=new TFile("plotfile.root","READ");
|
| 91 |
|
|
infile->cd();
|
| 92 |
|
|
TH1* sh=(TH1*)infile->Get("signal");
|
| 93 |
|
|
TH1* bh=(TH1*)infile->Get("background");
|
| 94 |
|
|
TH1* dh=(TH1*)infile->Get("data");
|
| 95 |
|
|
TLimitDataSource* mydatasource = new TLimitDataSource(sh,bh,dh);
|
| 96 |
|
|
TConfidenceLevel *myconfidence = TLimit::ComputeLimit(mydatasource,50000);
|
| 97 |
|
|
cout << " CLs : " << myconfidence->CLs() << endl;
|
| 98 |
|
|
cout << " CLsb : " << myconfidence->CLsb() << endl;
|
| 99 |
|
|
cout << " CLb : " << myconfidence->CLb() << endl;
|
| 100 |
|
|
cout << "< CLs > : " << myconfidence->GetExpectedCLs_b() << endl;
|
| 101 |
|
|
cout << "< CLsb > : " << myconfidence->GetExpectedCLsb_b() << endl;
|
| 102 |
|
|
cout << "< CLb > : " << myconfidence->GetExpectedCLb_b() << endl;
|
| 103 |
|
|
delete myconfidence;
|
| 104 |
|
|
delete mydatasource;
|
| 105 |
|
|
infile->Close();
|
| 106 |
|
|
</PRE></BLOCKQUOTE></p>
|
| 107 |
|
|
<p></p>
|
| 108 |
|
|
<p>More informations can still be found on
|
| 109 |
|
|
<a HREF="http://cern.ch/aleph-proj-alphapp/doc/tlimit.html">this</a> page.</p>
|
| 110 |
|
|
</FONT>
|
| 111 |
|
|
*/
|
| 112 |
|
|
//End_Html
|
| 113 |
|
|
|
| 114 |
|
|
// The final object returned...
|
| 115 |
|
|
TConfidenceLevel *result = new TConfidenceLevel(nmc);
|
| 116 |
|
|
// The random generator used...
|
| 117 |
|
|
TRandom *myrandom = generator ? generator : new TRandom3;
|
| 118 |
|
|
// Compute some total quantities on all the channels
|
| 119 |
|
|
Int_t maxbins = 0;
|
| 120 |
|
|
Double_t nsig = 0;
|
| 121 |
|
|
Double_t nbg = 0;
|
| 122 |
|
|
Int_t ncand = 0;
|
| 123 |
|
|
Int_t i;
|
| 124 |
|
|
for (i = 0; i <= data->GetSignal()->GetLast(); i++) {
|
| 125 |
|
|
maxbins = (((TH1 *) (data->GetSignal()->At(i)))->GetNbinsX() + 2) > maxbins ?
|
| 126 |
|
|
(((TH1 *) (data->GetSignal()->At(i)))->GetNbinsX() + 2) : maxbins;
|
| 127 |
|
|
nsig += ((TH1 *) (data->GetSignal()->At(i)))->Integral();
|
| 128 |
|
|
nbg += ((TH1 *) (data->GetBackground()->At(i)))->Integral();
|
| 129 |
|
|
ncand += (Int_t) ((TH1 *) (data->GetCandidates()->At(i)))->Integral();
|
| 130 |
|
|
}
|
| 131 |
|
|
result->SetBtot(nbg);
|
| 132 |
|
|
result->SetStot(nsig);
|
| 133 |
|
|
result->SetDtot(ncand);
|
| 134 |
|
|
Double_t buffer = 0;
|
| 135 |
|
|
fgTable->Set(maxbins * (data->GetSignal()->GetLast() + 1));
|
| 136 |
|
|
for (Int_t channel = 0; channel <= data->GetSignal()->GetLast(); channel++)
|
| 137 |
|
|
for (Int_t bin = 0;
|
| 138 |
|
|
bin <= ((TH1 *) (data->GetSignal()->At(channel)))->GetNbinsX()+1;
|
| 139 |
|
|
bin++) {
|
| 140 |
|
|
Double_t s = (Double_t) ((TH1 *) (data->GetSignal()->At(channel)))->GetBinContent(bin);
|
| 141 |
|
|
Double_t b = (Double_t) ((TH1 *) (data->GetBackground()->At(channel)))->GetBinContent(bin);
|
| 142 |
|
|
Double_t d = (Double_t) ((TH1 *) (data->GetCandidates()->At(channel)))->GetBinContent(bin);
|
| 143 |
|
|
// Compute the value of the "-2lnQ" for the actual data
|
| 144 |
|
|
if ((b == 0) && (s > 0)) {
|
| 145 |
|
|
cout << "WARNING: Ignoring bin " << bin << " of channel "
|
| 146 |
|
|
<< channel << " which has s=" << s << " but b=" << b << endl;
|
| 147 |
|
|
cout << " Maybe the MC statistic has to be improved..." << endl;
|
| 148 |
|
|
}
|
| 149 |
|
|
if ((s > 0) && (b > 0))
|
| 150 |
|
|
buffer += LogLikelihood(s, b, b, d);
|
| 151 |
|
|
// precompute the log(1+s/b)'s in an array to speed up computation
|
| 152 |
|
|
// background-free bins are set to have a maximum t.s. value
|
| 153 |
|
|
// for protection (corresponding to s/b of about 5E8)
|
| 154 |
|
|
if ((s > 0) && (b > 0))
|
| 155 |
|
|
fgTable->AddAt(LogLikelihood(s, b, b, 1), (channel * maxbins) + bin);
|
| 156 |
|
|
else if ((s > 0) && (b == 0))
|
| 157 |
|
|
fgTable->AddAt(20, (channel * maxbins) + bin);
|
| 158 |
|
|
}
|
| 159 |
|
|
result->SetTSD(buffer);
|
| 160 |
|
|
// accumulate MC experiments. Hold the test statistic function fixed, but
|
| 161 |
|
|
// fluctuate s and b within syst. errors for computing probabilities of
|
| 162 |
|
|
// having that outcome. (Alex Read's prescription -- errors are on the ensemble,
|
| 163 |
|
|
// not on the observed test statistic. This technique does not split outcomes.)
|
| 164 |
|
|
// keep the tstats as sum log(1+s/b). convert to -2lnQ when preparing the results
|
| 165 |
|
|
// (reason -- like to keep the < signs right)
|
| 166 |
|
|
Double_t *tss = new Double_t[nmc];
|
| 167 |
|
|
Double_t *tsb = new Double_t[nmc];
|
| 168 |
|
|
Double_t *lrs = new Double_t[nmc];
|
| 169 |
|
|
Double_t *lrb = new Double_t[nmc];
|
| 170 |
|
|
TLimitDataSource* tmp_src = (TLimitDataSource*)(data->Clone());
|
| 171 |
|
|
TLimitDataSource* tmp_src2 = (TLimitDataSource*)(data->Clone());
|
| 172 |
|
|
for (i = 0; i < nmc; i++) {
|
| 173 |
|
|
tss[i] = 0;
|
| 174 |
|
|
tsb[i] = 0;
|
| 175 |
|
|
lrs[i] = 0;
|
| 176 |
|
|
lrb[i] = 0;
|
| 177 |
|
|
// fluctuate signal and background
|
| 178 |
|
|
TLimitDataSource* fluctuated = Fluctuate(data, tmp_src, !i, myrandom, stat) ? tmp_src : data;
|
| 179 |
|
|
// make an independent set of fluctuations for reweighting pseudoexperiments
|
| 180 |
|
|
// it turns out using the same fluctuated ones for numerator and denominator
|
| 181 |
|
|
// is biased.
|
| 182 |
|
|
TLimitDataSource* fluctuated2 = Fluctuate(data, tmp_src2, false, myrandom, stat) ? tmp_src2 : data;
|
| 183 |
|
|
|
| 184 |
|
|
for (Int_t channel = 0;
|
| 185 |
|
|
channel <= fluctuated->GetSignal()->GetLast(); channel++) {
|
| 186 |
|
|
for (Int_t bin = 0;
|
| 187 |
|
|
bin <=((TH1 *) (fluctuated->GetSignal()->At(channel)))->GetNbinsX()+1;
|
| 188 |
|
|
bin++) {
|
| 189 |
|
|
if ((Double_t) ((TH1 *) (fluctuated->GetSignal()->At(channel)))->GetBinContent(bin) != 0) {
|
| 190 |
|
|
// s+b hypothesis
|
| 191 |
|
|
Double_t rate = (Double_t) ((TH1 *) (fluctuated->GetSignal()->At(channel)))->GetBinContent(bin) +
|
| 192 |
|
|
(Double_t) ((TH1 *) (fluctuated->GetBackground()->At(channel)))->GetBinContent(bin);
|
| 193 |
|
|
Double_t rand = myrandom->Poisson(rate);
|
| 194 |
|
|
tss[i] += rand * fgTable->At((channel * maxbins) + bin);
|
| 195 |
|
|
Double_t s = (Double_t) ((TH1 *) (fluctuated->GetSignal()->At(channel)))->GetBinContent(bin);
|
| 196 |
|
|
Double_t s2= (Double_t) ((TH1 *) (fluctuated2->GetSignal()->At(channel)))->GetBinContent(bin);
|
| 197 |
|
|
Double_t b = (Double_t) ((TH1 *) (fluctuated->GetBackground()->At(channel)))->GetBinContent(bin);
|
| 198 |
|
|
Double_t b2= (Double_t) ((TH1 *) (fluctuated2->GetBackground()->At(channel)))->GetBinContent(bin);
|
| 199 |
|
|
if ((s > 0) && (b2 > 0))
|
| 200 |
|
|
lrs[i] += LogLikelihood(s, b, b2, rand) - s - b + b2;
|
| 201 |
|
|
else if ((s > 0) && (b2 == 0))
|
| 202 |
|
|
lrs[i] += 20 * rand - s;
|
| 203 |
|
|
// b hypothesis
|
| 204 |
|
|
rate = (Double_t) ((TH1 *) (fluctuated->GetBackground()->At(channel)))->GetBinContent(bin);
|
| 205 |
|
|
rand = myrandom->Poisson(rate);
|
| 206 |
|
|
tsb[i] += rand * fgTable->At((channel * maxbins) + bin);
|
| 207 |
|
|
if ((s2 > 0) && (b > 0))
|
| 208 |
|
|
lrb[i] += LogLikelihood(s2, b2, b, rand) - s2 - b2 + b;
|
| 209 |
|
|
else if ((s > 0) && (b == 0))
|
| 210 |
|
|
lrb[i] += 20 * rand - s;
|
| 211 |
|
|
}
|
| 212 |
|
|
}
|
| 213 |
|
|
}
|
| 214 |
|
|
lrs[i] = TMath::Exp(lrs[i] < 710 ? lrs[i] : 710);
|
| 215 |
|
|
lrb[i] = TMath::Exp(lrb[i] < 710 ? lrb[i] : 710);
|
| 216 |
|
|
}
|
| 217 |
|
|
delete tmp_src;
|
| 218 |
|
|
delete tmp_src2;
|
| 219 |
|
|
// lrs and lrb are the LR's (no logs) = prob(s+b)/prob(b) for
|
| 220 |
|
|
// that choice of s and b within syst. errors in the ensemble. These are
|
| 221 |
|
|
// the MC experiment weights for relating the s+b and b PDF's of the unsmeared
|
| 222 |
|
|
// test statistic (in which cas one can use another test statistic if one likes).
|
| 223 |
|
|
|
| 224 |
|
|
// Now produce the output object.
|
| 225 |
|
|
// The final quantities are computed on-demand form the arrays tss, tsb, lrs and lrb.
|
| 226 |
|
|
result->SetTSS(tss);
|
| 227 |
|
|
result->SetTSB(tsb);
|
| 228 |
|
|
result->SetLRS(lrs);
|
| 229 |
|
|
result->SetLRB(lrb);
|
| 230 |
|
|
if (!generator)
|
| 231 |
|
|
delete myrandom;
|
| 232 |
|
|
return result;
|
| 233 |
|
|
}
|
| 234 |
|
|
|
| 235 |
|
|
bool TLimit::Fluctuate(TLimitDataSource * input, TLimitDataSource * output,
|
| 236 |
|
|
bool init, TRandom * generator, bool stat)
|
| 237 |
|
|
{
|
| 238 |
|
|
// initialisation: create a sorted list of all the names of systematics
|
| 239 |
|
|
if (init) {
|
| 240 |
|
|
// create a "map" with the systematics names
|
| 241 |
|
|
TIterator *errornames = input->GetErrorNames()->MakeIterator();
|
| 242 |
|
|
TObjArray *listofnames = 0;
|
| 243 |
|
|
delete fgSystNames;
|
| 244 |
|
|
fgSystNames = new TOrdCollection();
|
| 245 |
|
|
while ((listofnames = ((TObjArray *) errornames->Next()))) {
|
| 246 |
|
|
TObjString *name = 0;
|
| 247 |
|
|
TIterator *loniter = listofnames->MakeIterator();
|
| 248 |
|
|
while ((name = (TObjString *) (loniter->Next())))
|
| 249 |
|
|
if ((fgSystNames->IndexOf(name)) < 0)
|
| 250 |
|
|
fgSystNames->AddLast(name);
|
| 251 |
|
|
}
|
| 252 |
|
|
fgSystNames->Sort();
|
| 253 |
|
|
}
|
| 254 |
|
|
// if the output is not given, create it from the input
|
| 255 |
|
|
if (!output)
|
| 256 |
|
|
output = (TLimitDataSource*)(input->Clone());
|
| 257 |
|
|
// if there are no systematics, just returns the input as "fluctuated" output
|
| 258 |
|
|
if ((fgSystNames->GetSize() <= 0)&&(!stat)) {
|
| 259 |
|
|
return 0;
|
| 260 |
|
|
}
|
| 261 |
|
|
// if there are only stat, just fluctuate stats.
|
| 262 |
|
|
if (fgSystNames->GetSize() <= 0) {
|
| 263 |
|
|
output->SetOwner();
|
| 264 |
|
|
for (Int_t channel = 0; channel <= input->GetSignal()->GetLast(); channel++) {
|
| 265 |
|
|
TH1 *newsignal = (TH1*)(output->GetSignal()->At(channel));
|
| 266 |
|
|
TH1 *oldsignal = (TH1*)(input->GetSignal()->At(channel));
|
| 267 |
|
|
if(stat)
|
| 268 |
|
|
for(int i=1; i<=newsignal->GetNbinsX(); i++) {
|
| 269 |
|
|
newsignal->SetBinContent(i,oldsignal->GetBinContent(i)+generator->Gaus(0,oldsignal->GetBinError(i)));
|
| 270 |
|
|
}
|
| 271 |
|
|
newsignal->SetDirectory(0);
|
| 272 |
|
|
TH1 *newbackground = (TH1*)(output->GetBackground()->At(channel));
|
| 273 |
|
|
TH1 *oldbackground = (TH1*)(input->GetBackground()->At(channel));
|
| 274 |
|
|
if(stat)
|
| 275 |
|
|
for(int i=1; i<=newbackground->GetNbinsX(); i++)
|
| 276 |
|
|
newbackground->SetBinContent(i,oldbackground->GetBinContent(i)+generator->Gaus(0,oldbackground->GetBinError(i)));
|
| 277 |
|
|
newbackground->SetDirectory(0);
|
| 278 |
|
|
}
|
| 279 |
|
|
return 1;
|
| 280 |
|
|
}
|
| 281 |
|
|
// Find a choice for the random variation and
|
| 282 |
|
|
// re-toss all random numbers if any background or signal
|
| 283 |
|
|
// goes negative. (background = 0 is bad too, so put a little protection
|
| 284 |
|
|
// around it -- must have at least 10% of the bg estimate).
|
| 285 |
|
|
Bool_t retoss = kTRUE;
|
| 286 |
|
|
Double_t *serrf = new Double_t[(input->GetSignal()->GetLast()) + 1];
|
| 287 |
|
|
Double_t *berrf = new Double_t[(input->GetSignal()->GetLast()) + 1];
|
| 288 |
|
|
do {
|
| 289 |
|
|
Double_t *toss = new Double_t[fgSystNames->GetSize()];
|
| 290 |
|
|
for (Int_t i = 0; i < fgSystNames->GetSize(); i++)
|
| 291 |
|
|
toss[i] = generator->Gaus(0, 1);
|
| 292 |
|
|
retoss = kFALSE;
|
| 293 |
|
|
for (Int_t channel = 0;
|
| 294 |
|
|
channel <= input->GetSignal()->GetLast();
|
| 295 |
|
|
channel++) {
|
| 296 |
|
|
serrf[channel] = 0;
|
| 297 |
|
|
berrf[channel] = 0;
|
| 298 |
|
|
for (Int_t bin = 0;
|
| 299 |
|
|
bin <((TVectorD *) (input->GetErrorOnSignal()->At(channel)))->GetNrows();
|
| 300 |
|
|
bin++) {
|
| 301 |
|
|
serrf[channel] += ((TVectorD *) (input->GetErrorOnSignal()->At(channel)))->operator[](bin) *
|
| 302 |
|
|
toss[fgSystNames->BinarySearch((TObjString*) (((TObjArray *) (input->GetErrorNames()->At(channel)))->At(bin)))];
|
| 303 |
|
|
berrf[channel] += ((TVectorD *) (input->GetErrorOnBackground()->At(channel)))->operator[](bin) *
|
| 304 |
|
|
toss[fgSystNames->BinarySearch((TObjString*) (((TObjArray *) (input->GetErrorNames()->At(channel)))->At(bin)))];
|
| 305 |
|
|
}
|
| 306 |
|
|
if ((serrf[channel] < -1.0) || (berrf[channel] < -0.9)) {
|
| 307 |
|
|
retoss = kTRUE;
|
| 308 |
|
|
continue;
|
| 309 |
|
|
}
|
| 310 |
|
|
}
|
| 311 |
|
|
delete[]toss;
|
| 312 |
|
|
} while (retoss);
|
| 313 |
|
|
// adjust the fluctuated signal and background counts with a legal set
|
| 314 |
|
|
// of random fluctuations above.
|
| 315 |
|
|
output->SetOwner();
|
| 316 |
|
|
for (Int_t channel = 0; channel <= input->GetSignal()->GetLast();
|
| 317 |
|
|
channel++) {
|
| 318 |
|
|
TH1 *newsignal = (TH1*)(output->GetSignal()->At(channel));
|
| 319 |
|
|
TH1 *oldsignal = (TH1*)(input->GetSignal()->At(channel));
|
| 320 |
|
|
if(stat)
|
| 321 |
|
|
for(int i=1; i<=newsignal->GetNbinsX(); i++)
|
| 322 |
|
|
newsignal->SetBinContent(i,oldsignal->GetBinContent(i)+generator->Gaus(0,oldsignal->GetBinError(i)));
|
| 323 |
|
|
else
|
| 324 |
|
|
for(int i=1; i<=newsignal->GetNbinsX(); i++)
|
| 325 |
|
|
newsignal->SetBinContent(i,oldsignal->GetBinContent(i));
|
| 326 |
|
|
newsignal->Scale(1 + serrf[channel]);
|
| 327 |
|
|
newsignal->SetDirectory(0);
|
| 328 |
|
|
TH1 *newbackground = (TH1*)(output->GetBackground()->At(channel));
|
| 329 |
|
|
TH1 *oldbackground = (TH1*)(input->GetBackground()->At(channel));
|
| 330 |
|
|
if(stat)
|
| 331 |
|
|
for(int i=1; i<=newbackground->GetNbinsX(); i++)
|
| 332 |
|
|
newbackground->SetBinContent(i,oldbackground->GetBinContent(i)+generator->Gaus(0,oldbackground->GetBinError(i)));
|
| 333 |
|
|
else
|
| 334 |
|
|
for(int i=1; i<=newbackground->GetNbinsX(); i++)
|
| 335 |
|
|
newbackground->SetBinContent(i,oldbackground->GetBinContent(i));
|
| 336 |
|
|
newbackground->Scale(1 + berrf[channel]);
|
| 337 |
|
|
newbackground->SetDirectory(0);
|
| 338 |
|
|
}
|
| 339 |
|
|
delete[] serrf;
|
| 340 |
|
|
delete[] berrf;
|
| 341 |
|
|
return 1;
|
| 342 |
|
|
}
|
| 343 |
|
|
|
| 344 |
|
|
TConfidenceLevel *TLimit::ComputeLimit(TH1* s, TH1* b, TH1* d,
|
| 345 |
|
|
Int_t nmc, bool stat,
|
| 346 |
|
|
TRandom * generator)
|
| 347 |
|
|
{
|
| 348 |
|
|
// Compute limit.
|
| 349 |
|
|
|
| 350 |
|
|
TLimitDataSource* lds = new TLimitDataSource(s,b,d);
|
| 351 |
|
|
TConfidenceLevel* out = ComputeLimit(lds,nmc,stat,generator);
|
| 352 |
|
|
delete lds;
|
| 353 |
|
|
return out;
|
| 354 |
|
|
}
|
| 355 |
|
|
|
| 356 |
|
|
TConfidenceLevel *TLimit::ComputeLimit(TH1* s, TH1* b, TH1* d,
|
| 357 |
|
|
TVectorD* se, TVectorD* be, TObjArray* l,
|
| 358 |
|
|
Int_t nmc, bool stat,
|
| 359 |
|
|
TRandom * generator)
|
| 360 |
|
|
{
|
| 361 |
|
|
// Compute limit.
|
| 362 |
|
|
|
| 363 |
|
|
TLimitDataSource* lds = new TLimitDataSource(s,b,d,se,be,l);
|
| 364 |
|
|
TConfidenceLevel* out = ComputeLimit(lds,nmc,stat,generator);
|
| 365 |
|
|
delete lds;
|
| 366 |
|
|
return out;
|
| 367 |
|
|
}
|
| 368 |
|
|
|
| 369 |
|
|
TConfidenceLevel *TLimit::ComputeLimit(Double_t s, Double_t b, Int_t d,
|
| 370 |
|
|
Int_t nmc,
|
| 371 |
|
|
bool stat,
|
| 372 |
|
|
TRandom * generator)
|
| 373 |
|
|
{
|
| 374 |
|
|
// Compute limit.
|
| 375 |
|
|
|
| 376 |
|
|
TH1D* sh = new TH1D("__sh","__sh",1,0,2);
|
| 377 |
|
|
sh->Fill(1,s);
|
| 378 |
|
|
TH1D* bh = new TH1D("__bh","__bh",1,0,2);
|
| 379 |
|
|
bh->Fill(1,b);
|
| 380 |
|
|
TH1D* dh = new TH1D("__dh","__dh",1,0,2);
|
| 381 |
|
|
dh->Fill(1,d);
|
| 382 |
|
|
TLimitDataSource* lds = new TLimitDataSource(sh,bh,dh);
|
| 383 |
|
|
TConfidenceLevel* out = ComputeLimit(lds,nmc,stat,generator);
|
| 384 |
|
|
delete lds;
|
| 385 |
|
|
delete sh;
|
| 386 |
|
|
delete bh;
|
| 387 |
|
|
delete dh;
|
| 388 |
|
|
return out;
|
| 389 |
|
|
}
|
| 390 |
|
|
|
| 391 |
|
|
TConfidenceLevel *TLimit::ComputeLimit(Double_t s, Double_t b, Int_t d,
|
| 392 |
|
|
TVectorD* se, TVectorD* be, TObjArray* l,
|
| 393 |
|
|
Int_t nmc,
|
| 394 |
|
|
bool stat,
|
| 395 |
|
|
TRandom * generator)
|
| 396 |
|
|
{
|
| 397 |
|
|
// Compute limit.
|
| 398 |
|
|
|
| 399 |
|
|
TH1D* sh = new TH1D("__sh","__sh",1,0,2);
|
| 400 |
|
|
sh->Fill(1,s);
|
| 401 |
|
|
TH1D* bh = new TH1D("__bh","__bh",1,0,2);
|
| 402 |
|
|
bh->Fill(1,b);
|
| 403 |
|
|
TH1D* dh = new TH1D("__dh","__dh",1,0,2);
|
| 404 |
|
|
dh->Fill(1,d);
|
| 405 |
|
|
TLimitDataSource* lds = new TLimitDataSource(sh,bh,dh,se,be,l);
|
| 406 |
|
|
TConfidenceLevel* out = ComputeLimit(lds,nmc,stat,generator);
|
| 407 |
|
|
delete lds;
|
| 408 |
|
|
delete sh;
|
| 409 |
|
|
delete bh;
|
| 410 |
|
|
delete dh;
|
| 411 |
|
|
return out;
|
| 412 |
|
|
}
|
| 413 |
|
|
|
| 414 |
|
|
Double_t TLimit::LogLikelihood(Double_t s, Double_t b, Double_t b2, Double_t d)
|
| 415 |
|
|
{
|
| 416 |
|
|
// Compute LogLikelihood (static function)
|
| 417 |
|
|
|
| 418 |
|
|
return d*TMath::Log((s+b)/b2);
|
| 419 |
|
|
}
|
