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Display_SignalFits_LMR_bias.cc
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Display_SignalFits_LMR_bias.cc
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// Creates the images and HTML
// for displaying changes in Signal MC
// due to JEC+1-1, and JER+1-1
#include <TH1F.h>
#include <TH2F.h>
#include <TROOT.h>
#include <TFile.h>
#include <TTree.h>
#include <TSystem.h>
#include <TChain.h>
#include <TLorentzVector.h>
#include <TLegend.h>
#include <TCanvas.h>
#include <TProfile.h>
#include <iostream>
#include <TFractionFitter.h>
#include <TStyle.h>
#include <TPaveText.h>
#include <THStack.h>
#include <TArrow.h>
#include <TColor.h>
#include <sstream>
#include <iostream>
#include <fstream>
#include <algorithm>
#include "RooAddPdf.h"
#include "RooRealVar.h"
#include "RooArgList.h"
#include "RooChebychev.h"
#include "RooDataHist.h"
#include "RooAbsPdf.h"
#include "RooWorkspace.h"
#include "RooPlot.h"
#include "RooFitResult.h"
#include "RooCBShape.h"
#include "RooGaussian.h"
std::string reg;
std::string dest_dir;
#include "CMS_lumi.c"
#include "TDRStyle.h"
int iPeriod = 4; // 1=7TeV, 2=8TeV, 3=7+8TeV, 7=7+8+13TeV
int iPos =11;
int rebin=3;
ofstream outfile;
std::string tostr(float t)
{
std::ostringstream os;
os<<t;
return os.str();
}
double quad(double a, double b, double c=0, double d=0, double e=0, double f=0, double g=0, double h=0, double i=0, double j=0, double k=0)
{
return pow(a*a+b*b+c*c+d*d+e*e+f*f+g*g+h*h+i*i+j*j+k*k, 0.5);
}
struct Params
{
double sg_p0;
double sg_p1;
double sg_p2;
double sg_p3;
double sg_p4;
double sg_p0_err;
double sg_p1_err;
double sg_p2_err;
double sg_p3_err;
double sg_p4_err;
};
TLegend* twoStatBoxes(TH1F* h1, TH1F* h2)
{
TLegend *leg=new TLegend(0.6, 0.6, 0.9, 0.85);
double wherepeak=(h2->GetMean())/(h2->GetXaxis()->GetXmax());
if (wherepeak>0.63) leg=new TLegend(0.1, 0.9, 0.4, 0.6);
leg->AddEntry(h1, "Baseline");
leg->AddEntry((TObject*)0, ("mean="+tostr(h1->GetMean())).c_str(), "");
leg->AddEntry((TObject*)0, ("rms="+tostr(h1->GetRMS())).c_str(), "");
leg->AddEntry((TObject*)0, "", "");
leg->AddEntry(h2, "JEC +1 #sigma");
leg->AddEntry((TObject*)0, ("mean="+tostr(h2->GetMean())).c_str(), "");
leg->AddEntry((TObject*)0, ("rms="+tostr(h2->GetRMS())).c_str(), "");
leg->SetFillColor(10);
return leg;
}
TLegend* threeStatBoxes(TH1F* h1, TH1F* h2, TH1F *h3)
{
TLegend *leg=new TLegend(0.6, 0.6, 0.9, 0.85);
double wherepeak=(h2->GetMean())/(h2->GetXaxis()->GetXmax());
if (wherepeak>0.63) leg=new TLegend(0.1, 0.9, 0.4, 0.6);
leg->AddEntry(h1, "Baseline");
leg->AddEntry((TObject*)0, ("mean="+tostr(h1->GetMean())).c_str(), "");
leg->AddEntry((TObject*)0, ("rms="+tostr(h1->GetRMS())).c_str(), "");
leg->AddEntry(h2, "JEC +1 #sigma");
leg->AddEntry((TObject*)0, ("mean="+tostr(h2->GetMean())).c_str(), "");
leg->AddEntry((TObject*)0, ("rms="+tostr(h2->GetRMS())).c_str(), "");
leg->AddEntry(h3, "JEC -1 #sigma");
leg->AddEntry((TObject*)0, ("mean="+tostr(h3->GetMean())).c_str(), "");
leg->AddEntry((TObject*)0, ("rms="+tostr(h3->GetRMS())).c_str(), "");
leg->SetFillColor(10);
return leg;
}
TLegend* fiveStatBoxes(TH1F* h1, TH1F* h2, TH1F *h3, TH1F *h4, TH1F *h5)
{
TLegend *leg=new TLegend(0.6, 0.4, 0.9, 0.85);
double wherepeak=(h2->GetMean())/(h2->GetXaxis()->GetXmax());
if (wherepeak>0.63) leg=new TLegend(0.1, 0.9, 0.4, 0.6);
leg->AddEntry(h1, "Baseline");
leg->AddEntry((TObject*)0, ("mean="+tostr(h1->GetMean())).c_str(), "");
leg->AddEntry((TObject*)0, ("rms="+tostr(h1->GetRMS())).c_str(), "");
leg->AddEntry(h2, "JEC +1 #sigma");
leg->AddEntry((TObject*)0, ("mean="+tostr(h2->GetMean())).c_str(), "");
leg->AddEntry((TObject*)0, ("rms="+tostr(h2->GetRMS())).c_str(), "");
leg->AddEntry(h3, "JEC -1 #sigma");
leg->AddEntry((TObject*)0, ("mean="+tostr(h3->GetMean())).c_str(), "");
leg->AddEntry((TObject*)0, ("rms="+tostr(h3->GetRMS())).c_str(), "");
leg->AddEntry(h4, "JER +1 #sigma");
leg->AddEntry((TObject*)0, ("mean="+tostr(h4->GetMean())).c_str(), "");
leg->AddEntry((TObject*)0, ("rms="+tostr(h4->GetRMS())).c_str(), "");
leg->AddEntry(h5, "JER -1 #sigma");
leg->AddEntry((TObject*)0, ("mean="+tostr(h5->GetMean())).c_str(), "");
leg->AddEntry((TObject*)0, ("rms="+tostr(h5->GetRMS())).c_str(), "");
leg->SetFillColor(10);
return leg;
}
TLegend* elevenStatBoxes(TH1F* h1, TH1F* h2, TH1F *h3, TH1F *h4, TH1F *h5, TH1F *h6, TH1F *h7, TH1F *h8, TH1F *h9, TH1F *h10, TH1F *h11)
{
TLegend *leg=new TLegend(0.7, 0.1, 0.9, 0.85);
double wherepeak=(h2->GetMean())/(h2->GetXaxis()->GetXmax());
if (wherepeak>0.63) leg=new TLegend(0.1, 0.9, 0.3, 0.1);
leg->AddEntry(h1, "Baseline");
leg->AddEntry((TObject*)0, ("mean="+tostr(h1->GetMean())).c_str(), "");
leg->AddEntry((TObject*)0, ("rms="+tostr(h1->GetRMS())).c_str(), "");
leg->AddEntry(h2, "JEC +1 #sigma");
leg->AddEntry((TObject*)0, ("mean="+tostr(h2->GetMean())).c_str(), "");
leg->AddEntry((TObject*)0, ("rms="+tostr(h2->GetRMS())).c_str(), "");
leg->AddEntry(h3, "JEC -1 #sigma");
leg->AddEntry((TObject*)0, ("mean="+tostr(h3->GetMean())).c_str(), "");
leg->AddEntry((TObject*)0, ("rms="+tostr(h3->GetRMS())).c_str(), "");
leg->AddEntry(h4, "JER +1 #sigma");
leg->AddEntry((TObject*)0, ("mean="+tostr(h4->GetMean())).c_str(), "");
leg->AddEntry((TObject*)0, ("rms="+tostr(h4->GetRMS())).c_str(), "");
leg->AddEntry(h5, "JER -1 #sigma");
leg->AddEntry((TObject*)0, ("mean="+tostr(h5->GetMean())).c_str(), "");
leg->AddEntry((TObject*)0, ("rms="+tostr(h5->GetRMS())).c_str(), "");
leg->AddEntry(h6, "SF_{bc} +1 #sigma");
leg->AddEntry((TObject*)0, ("mean="+tostr(h6->GetMean())).c_str(), "");
leg->AddEntry((TObject*)0, ("rms="+tostr(h6->GetRMS())).c_str(), "");
leg->AddEntry(h7, "SF_{bc} -1 #sigma");
leg->AddEntry((TObject*)0, ("mean="+tostr(h7->GetMean())).c_str(), "");
leg->AddEntry((TObject*)0, ("rms="+tostr(h7->GetRMS())).c_str(), "");
leg->AddEntry(h8, "SF_{l} +1 #sigma");
leg->AddEntry((TObject*)0, ("mean="+tostr(h8->GetMean())).c_str(), "");
leg->AddEntry((TObject*)0, ("rms="+tostr(h8->GetRMS())).c_str(), "");
leg->AddEntry(h9, "SF_{l} -1 #sigma");
leg->AddEntry((TObject*)0, ("mean="+tostr(h9->GetMean())).c_str(), "");
leg->AddEntry((TObject*)0, ("rms="+tostr(h9->GetRMS())).c_str(), "");
leg->AddEntry(h10, "Trig SF +1 #sigma");
leg->AddEntry((TObject*)0, ("mean="+tostr(h10->GetMean())).c_str(), "");
leg->AddEntry((TObject*)0, ("rms="+tostr(h10->GetRMS())).c_str(), "");
leg->AddEntry(h11, "Trig SF -1 #sigma");
leg->AddEntry((TObject*)0, ("mean="+tostr(h11->GetMean())).c_str(), "");
leg->AddEntry((TObject*)0, ("rms="+tostr(h11->GetRMS())).c_str(), "");
leg->SetFillColor(10);
return leg;
}
TLegend* thirteenStatBoxes(TH1F* h1, TH1F* h2, TH1F *h3, TH1F *h4, TH1F *h5, TH1F *h6, TH1F *h7, TH1F *h8, TH1F *h9, TH1F *h10, TH1F *h11, TH1F *h12, TH1F *h13)
{
TLegend *leg=new TLegend(0.7, 0.1, 0.9, 0.85);
double wherepeak=(h2->GetMean())/(h2->GetXaxis()->GetXmax());
if (wherepeak>0.63) leg=new TLegend(0.1, 0.9, 0.3, 0.1);
leg->AddEntry(h1, "Baseline");
leg->AddEntry((TObject*)0, ("mean="+tostr(h1->GetMean())).c_str(), "");
leg->AddEntry((TObject*)0, ("rms="+tostr(h1->GetRMS())).c_str(), "");
leg->AddEntry(h2, "JEC +1 #sigma");
leg->AddEntry((TObject*)0, ("mean="+tostr(h2->GetMean())).c_str(), "");
leg->AddEntry((TObject*)0, ("rms="+tostr(h2->GetRMS())).c_str(), "");
leg->AddEntry(h3, "JEC -1 #sigma");
leg->AddEntry((TObject*)0, ("mean="+tostr(h3->GetMean())).c_str(), "");
leg->AddEntry((TObject*)0, ("rms="+tostr(h3->GetRMS())).c_str(), "");
leg->AddEntry(h4, "JER +1 #sigma");
leg->AddEntry((TObject*)0, ("mean="+tostr(h4->GetMean())).c_str(), "");
leg->AddEntry((TObject*)0, ("rms="+tostr(h4->GetRMS())).c_str(), "");
leg->AddEntry(h5, "JER -1 #sigma");
leg->AddEntry((TObject*)0, ("mean="+tostr(h5->GetMean())).c_str(), "");
leg->AddEntry((TObject*)0, ("rms="+tostr(h5->GetRMS())).c_str(), "");
leg->AddEntry(h6, "SF_{bc} +1 #sigma");
leg->AddEntry((TObject*)0, ("mean="+tostr(h6->GetMean())).c_str(), "");
leg->AddEntry((TObject*)0, ("rms="+tostr(h6->GetRMS())).c_str(), "");
leg->AddEntry(h7, "SF_{bc} -1 #sigma");
leg->AddEntry((TObject*)0, ("mean="+tostr(h7->GetMean())).c_str(), "");
leg->AddEntry((TObject*)0, ("rms="+tostr(h7->GetRMS())).c_str(), "");
leg->AddEntry(h8, "SF_{l} +1 #sigma");
leg->AddEntry((TObject*)0, ("mean="+tostr(h8->GetMean())).c_str(), "");
leg->AddEntry((TObject*)0, ("rms="+tostr(h8->GetRMS())).c_str(), "");
leg->AddEntry(h9, "SF_{l} -1 #sigma");
leg->AddEntry((TObject*)0, ("mean="+tostr(h9->GetMean())).c_str(), "");
leg->AddEntry((TObject*)0, ("rms="+tostr(h9->GetRMS())).c_str(), "");
leg->AddEntry(h10, "Trig SF (CSV) +1 #sigma");
leg->AddEntry((TObject*)0, ("mean="+tostr(h10->GetMean())).c_str(), "");
leg->AddEntry((TObject*)0, ("rms="+tostr(h10->GetRMS())).c_str(), "");
leg->AddEntry(h11, "Trig SF (CSV) -1 #sigma");
leg->AddEntry((TObject*)0, ("mean="+tostr(h11->GetMean())).c_str(), "");
leg->AddEntry((TObject*)0, ("rms="+tostr(h11->GetRMS())).c_str(), "");
leg->AddEntry(h12, "Trig SF (p_{T}) +1 #sigma");
leg->AddEntry((TObject*)0, ("mean="+tostr(h12->GetMean())).c_str(), "");
leg->AddEntry((TObject*)0, ("rms="+tostr(h12->GetRMS())).c_str(), "");
leg->AddEntry(h13, "Trig SF (p_{T}) -1 #sigma");
leg->AddEntry((TObject*)0, ("mean="+tostr(h13->GetMean())).c_str(), "");
leg->AddEntry((TObject*)0, ("rms="+tostr(h13->GetRMS())).c_str(), "");
leg->SetFillColor(10);
return leg;
}
RooPlot* fitSignal(TH1F *h, std::string mass, int color, TLegend *leg, Params ¶ms, double bias, bool kinFit=false)
{
RooMsgService::instance().setGlobalKillBelow(RooFit::WARNING) ; gErrorIgnoreLevel = kWarning;
RooRealVar *x, *sg_p0, *sg_p1, *sg_p2, *sg_p3, *sg_p4;
double rangeLo=-1, rangeHi=-1;
if (!kinFit)
{
if (mass=="260")
{
rangeLo=250., rangeHi=630.;
sg_p0=new RooRealVar("sg_p0", "sg_p0", 230., 270.);
sg_p1=new RooRealVar("sg_p1", "sg_p1", 2., 25.);
sg_p2=new RooRealVar("sg_p2", "sg_p2", 10., 590.);
sg_p3=new RooRealVar("sg_p3", "sg_p3", 10., 300.);
sg_p4=new RooRealVar("sg_p4", "sg_p4", 0.5, 1.);
}
else if (mass=="270")
{
rangeLo=250, rangeHi=600.;
sg_p0=new RooRealVar("sg_p0", "sg_p0", 250., 280.);
sg_p1=new RooRealVar("sg_p1", "sg_p1", 2., 15.);
sg_p2=new RooRealVar("sg_p2", "sg_p2", 10., 530.);
sg_p3=new RooRealVar("sg_p3", "sg_p3", 10., 300.);
sg_p4=new RooRealVar("sg_p4", "sg_p4", 0.5, 1.);
}
else if (mass=="300")
{
rangeLo=250., rangeHi=400.;
sg_p0=new RooRealVar("sg_p0", "sg_p0", 250., 320.);
sg_p1=new RooRealVar("sg_p1", "sg_p1", 5., 35.);
sg_p2=new RooRealVar("sg_p2", "sg_p2", 250., 360.);
sg_p3=new RooRealVar("sg_p3", "sg_p3", 10., 130.);
sg_p4=new RooRealVar("sg_p4", "sg_p4", 0., 1.);
}
else if (mass=="350")
{
rangeLo=250., rangeHi=550.;
sg_p0=new RooRealVar("sg_p0", "sg_p0", 310., 380.);
sg_p1=new RooRealVar("sg_p1", "sg_p1", 5., 35.);
sg_p2=new RooRealVar("sg_p2", "sg_p2", 330., 420.);
sg_p3=new RooRealVar("sg_p3", "sg_p3", 10., 100.);
sg_p4=new RooRealVar("sg_p4", "sg_p4", 0., 1.);
}
else if (mass=="400")
{
rangeLo=300., rangeHi=600.;
sg_p0=new RooRealVar("sg_p0", "sg_p0", 360., 430.);
sg_p1=new RooRealVar("sg_p1", "sg_p1", 3., 40.);
sg_p2=new RooRealVar("sg_p2", "sg_p2", 370.,460.);
sg_p3=new RooRealVar("sg_p3", "sg_p3", 10., 100.);
sg_p4=new RooRealVar("sg_p4", "sg_p4", 0., 1.);
}
else if (mass=="450")
{
rangeLo=300., rangeHi=600.;
sg_p0=new RooRealVar("sg_p0", "sg_p0", 410., 480.);
sg_p1=new RooRealVar("sg_p1", "sg_p1", 3., 35.);
sg_p2=new RooRealVar("sg_p2", "sg_p2", 410., 490.);
sg_p3=new RooRealVar("sg_p3", "sg_p3", 10., 100.);
sg_p4=new RooRealVar("sg_p4", "sg_p4", 0., 1.);
}
else if (mass=="500")
{
rangeLo=300., rangeHi=600.;
sg_p0=new RooRealVar("sg_p0", "sg_p0", 460., 520.);
sg_p1=new RooRealVar("sg_p1", "sg_p1", 7., 40.);
sg_p2=new RooRealVar("sg_p2", "sg_p2", 400., 590.);
sg_p3=new RooRealVar("sg_p3", "sg_p3", 10., 100.);
sg_p4=new RooRealVar("sg_p4", "sg_p4", 0., 1.);
}
else if (mass=="550")
{
rangeLo=350., rangeHi=650.;
sg_p0=new RooRealVar("sg_p0", "sg_p0", 510., 580.);
sg_p1=new RooRealVar("sg_p1", "sg_p1", 7., 40.);
sg_p2=new RooRealVar("sg_p2", "sg_p2", 400., 590.);
sg_p3=new RooRealVar("sg_p3", "sg_p3", 10., 100.);
sg_p4=new RooRealVar("sg_p4", "sg_p4", 0., 1.);
}
else
{
rangeLo=500., rangeHi=700.;
sg_p0=new RooRealVar("sg_p0", "sg_p0", 560., 650.);
sg_p1=new RooRealVar("sg_p1", "sg_p1", 7., 40.);
sg_p2=new RooRealVar("sg_p2", "sg_p2", 500., 650.);
sg_p3=new RooRealVar("sg_p3", "sg_p3", 10., 130.);
sg_p4=new RooRealVar("sg_p4", "sg_p4", 0.7, 1.);
}
}
else
{
if (mass=="260")
{
rangeLo=250., rangeHi=350.;
sg_p0=new RooRealVar("sg_p0", "sg_p0", 250., 270.);
sg_p1=new RooRealVar("sg_p1", "sg_p1", 2., 6.);
sg_p2=new RooRealVar("sg_p2", "sg_p2", 8., 350.);
sg_p3=new RooRealVar("sg_p3", "sg_p3", 10., 300.);
sg_p4=new RooRealVar("sg_p4", "sg_p4", 0.5, 1.);
}
else if (mass=="270")
{
rangeLo=250, rangeHi=350.;
sg_p0=new RooRealVar("sg_p0", "sg_p0", 260., 280.);
sg_p1=new RooRealVar("sg_p1", "sg_p1", 2., 8.);
sg_p2=new RooRealVar("sg_p2", "sg_p2", 9., 350.);
sg_p3=new RooRealVar("sg_p3", "sg_p3", 10., 300.);
sg_p4=new RooRealVar("sg_p4", "sg_p4", 0.5, 1.);
}
else if (mass=="300")
{
rangeLo=250., rangeHi=350.;
sg_p0=new RooRealVar("sg_p0", "sg_p0", 290., 320.);
sg_p1=new RooRealVar("sg_p1", "sg_p1", 5., 9.);
sg_p2=new RooRealVar("sg_p2", "sg_p2", 170., 350.);
sg_p3=new RooRealVar("sg_p3", "sg_p3", 10., 130.);
sg_p4=new RooRealVar("sg_p4", "sg_p4", 0., 1.);
}
else if (mass=="350")
{
rangeLo=300., rangeHi=430.;
sg_p0=new RooRealVar("sg_p0", "sg_p0", 330., 380.);
sg_p1=new RooRealVar("sg_p1", "sg_p1", 5., 9.);
sg_p2=new RooRealVar("sg_p2", "sg_p2", 100., 430.);
sg_p3=new RooRealVar("sg_p3", "sg_p3", 10., 150.);
sg_p4=new RooRealVar("sg_p4", "sg_p4", 0.65, 1.);
}
else if (mass=="400")
{
rangeLo=350., rangeHi=450.;
sg_p0=new RooRealVar("sg_p0", "sg_p0", 370., 430.);
sg_p1=new RooRealVar("sg_p1", "sg_p1", 3., 15.);
sg_p2=new RooRealVar("sg_p2", "sg_p2", 220.,450.);
sg_p3=new RooRealVar("sg_p3", "sg_p3", 10., 150.);
sg_p4=new RooRealVar("sg_p4", "sg_p4", 0., 1.);
}
else if (mass=="450")
{
rangeLo=350., rangeHi=550.;
sg_p0=new RooRealVar("sg_p0", "sg_p0", 420., 480.);
sg_p1=new RooRealVar("sg_p1", "sg_p1", 3., 25.);
sg_p2=new RooRealVar("sg_p2", "sg_p2", 380., 490.);
sg_p3=new RooRealVar("sg_p3", "sg_p3", 10., 150.);
sg_p4=new RooRealVar("sg_p4", "sg_p4", 0., 1.);
}
else if (mass=="500")
{
rangeLo=300., rangeHi=600.;
sg_p0=new RooRealVar("sg_p0", "sg_p0", 480., 520.);
sg_p1=new RooRealVar("sg_p1", "sg_p1", 7., 30.);
sg_p2=new RooRealVar("sg_p2", "sg_p2", 400., 590.);
sg_p3=new RooRealVar("sg_p3", "sg_p3", 10., 150.);
sg_p4=new RooRealVar("sg_p4", "sg_p4", 0., 1.);
}
else if (mass=="550")
{
rangeLo=350., rangeHi=650.;
sg_p0=new RooRealVar("sg_p0", "sg_p0", 510., 580.);
sg_p1=new RooRealVar("sg_p1", "sg_p1", 7., 40.);
sg_p2=new RooRealVar("sg_p2", "sg_p2", 400., 590.);
sg_p3=new RooRealVar("sg_p3", "sg_p3", 10., 100.);
sg_p4=new RooRealVar("sg_p4", "sg_p4", 0., 1.);
}
else
{
rangeLo=500., rangeHi=700.;
sg_p0=new RooRealVar("sg_p0", "sg_p0", 560., 650.);
sg_p1=new RooRealVar("sg_p1", "sg_p1", 7., 40.);
sg_p2=new RooRealVar("sg_p2", "sg_p2", 500., 650.);
sg_p3=new RooRealVar("sg_p3", "sg_p3", 10., 130.);
sg_p4=new RooRealVar("sg_p4", "sg_p4", 0.7, 1.);
}
}
x=new RooRealVar("x", "m_{X} (GeV)", rangeLo-50., rangeHi+50.);
RooGaussian signalCore("signalCore", "Signal Prediction", *x, *sg_p0, *sg_p1);
RooGaussian signalComb("signalComb", "Combinatoric", *x, *sg_p2, *sg_p3);
RooAddPdf signal("signal", "signal", RooArgList(signalCore, signalComb), *sg_p4);
RooDataHist signalHistogram("signalHistogram", "Signal Histogram", RooArgList(*x), h);
if (kinFit) signal.fitTo(signalHistogram, RooFit::Range(rangeLo, rangeHi), RooFit::Save());
else signal.fitTo(signalHistogram, RooFit::Range(rangeLo-50, rangeHi), RooFit::Save());
std::cout << mass << std::endl;
params.sg_p0=sg_p0->getVal(); params.sg_p0_err=sg_p0->getError(); std::cout << sg_p0->getVal() << " +- " << sg_p0->getError() << std::endl;
params.sg_p1=sg_p1->getVal(); params.sg_p1_err=sg_p1->getError(); std::cout << sg_p1->getVal() << " +- " << sg_p1->getError() << std::endl;
params.sg_p2=sg_p2->getVal(); params.sg_p2_err=sg_p2->getError();
params.sg_p3=sg_p3->getVal(); params.sg_p3_err=sg_p3->getError();
params.sg_p4=sg_p4->getVal(); params.sg_p4_err=sg_p4->getError();
RooPlot *plot=x->frame();
if (color==kBlack)
{
signalHistogram.plotOn(plot, RooFit::MarkerColor(color), RooFit::MarkerSize(1.2));
signal.plotOn(plot, RooFit::LineColor(color), RooFit::LineWidth(3));
}
else
{
signalHistogram.plotOn(plot, RooFit::MarkerColor(color));
signal.plotOn(plot, RooFit::LineColor(color), RooFit::LineWidth(0));
}
// leg->AddEntry((TObject*)0, ("norm="+tostr(h->GetSumOfWeights())).c_str(), "");
// leg->AddEntry((TObject*)0, ("mean="+tostr(sg_p2->getVal())+"#pm"+tostr(sg_p2->getError())).c_str(), "");
// leg->AddEntry((TObject*)0, ("rms="+tostr(sg_p3->getVal())+"#pm"+tostr(sg_p3->getError())).c_str(), "");
// std::cout<<"chi2/dof = "<<plot->chiSquare()<<std::endl;
// Save modified signal shape to workspace
if (color==kBlack)
{
RooRealVar signal_p0("signal_p0", "signal_p0", sg_p0->getVal());
RooRealVar signal_p1("signal_p1", "signal_p1", sg_p1->getVal());
RooRealVar signal_p2("signal_p2", "signal_p2", sg_p2->getVal());
RooRealVar signal_p3("signal_p3", "signal_p3", sg_p3->getVal());
RooRealVar signal_p4("signal_p4", "signal_p4", sg_p4->getVal());
RooGaussian signalCore_fixed("signalCore_fixed", "Signal Prediction", *x, signal_p0, signal_p1);
RooGaussian signalComb_fixed("signalComb_fixed", "Combinatoric", *x, signal_p2, signal_p3);
RooAddPdf signal_fixed("signal_fixed", "signal", RooArgList(signalCore_fixed, signalComb_fixed), signal_p4);
RooRealVar signal_p0_1("signal_p0_1", "signal_p0_1", sg_p0->getVal());
RooRealVar signal_p1_1("signal_p1_1", "signal_p1_1", sg_p1->getVal());
RooRealVar signal_p2_1("signal_p2_1", "signal_p2_1", sg_p2->getVal());
RooRealVar signal_p3_1("signal_p3_1", "signal_p3_1", sg_p3->getVal());
RooRealVar signal_p4_1("signal_p4_1", "signal_p4v", sg_p4->getVal());
RooGaussian signalCore_fixed_1("signalCore_fixed_1", "Signal Prediction", *x, signal_p0_1, signal_p1_1);
RooGaussian signalComb_fixed_1("signalComb_fixed_1", "Combinatoric", *x, signal_p2_1, signal_p3_1);
RooAddPdf signal_bkg("signal_bkg", "signal", RooArgList(signalCore_fixed_1, signalComb_fixed_1), signal_p4_1);
RooRealVar *signal_bkg_norm= new RooRealVar("signal_bkg_norm","signal_bkg_norm", 0., -abs(bias), abs(bias),"");
RooWorkspace *w=new RooWorkspace("HbbHbb");
w->import(signal_fixed);
w->import(signal_bkg);
w->import(*signal_bkg_norm);
w->SaveAs((dest_dir+"/"+"SignalFits_LMR"+reg+"/w_signal_"+mass+".root").c_str());
}
return plot;
}
RooPlot* fitSignal_Gaussian(TH1F *h, std::string mass, int color, TLegend *leg, Params ¶ms, bool kinFit=false)
{
RooRealVar *x, *sg_p0, *sg_p1;
double rangeLo=-1, rangeHi=-1;
if (!kinFit)
{
if (mass=="270")
{
rangeLo=170., rangeHi=470.;
sg_p0=new RooRealVar("sg_p0", "sg_p0", 250., 290.);
sg_p1=new RooRealVar("sg_p1", "sg_p1", 10., 60.);
}
else if (mass=="300")
{
rangeLo=200., rangeHi=600.;
sg_p0=new RooRealVar("sg_p0", "sg_p0", 280., 320.);
sg_p1=new RooRealVar("sg_p1", "sg_p1", 10., 60.);
}
else if (mass=="350")
{
rangeLo=250., rangeHi=650.;
sg_p0=new RooRealVar("sg_p0", "sg_p0", 330., 370.);
sg_p1=new RooRealVar("sg_p1", "sg_p1", 10., 60.);
}
else if (mass=="400")
{
rangeLo=300., rangeHi=600.;
sg_p0=new RooRealVar("sg_p0", "sg_p0", 380., 420.);
sg_p1=new RooRealVar("sg_p1", "sg_p1", 10., 50.);
}
else if (mass=="450")
{
rangeLo=350., rangeHi=650.;
sg_p0=new RooRealVar("sg_p0", "sg_p0", 430., 470.);
sg_p1=new RooRealVar("sg_p1", "sg_p1", 10., 50.);
}
else if (mass=="500")
{
rangeLo=300., rangeHi=700.;
sg_p0=new RooRealVar("sg_p0", "sg_p0", 470., 530.);
sg_p1=new RooRealVar("sg_p1", "sg_p1", 10., 30.);
}
else if (mass=="550")
{
rangeLo=350., rangeHi=750.;
sg_p0=new RooRealVar("sg_p0", "sg_p0", 530., 580.);
sg_p1=new RooRealVar("sg_p1", "sg_p1", 10., 30.);
}
else if (mass=="600")
{
rangeLo=400., rangeHi=800.;
sg_p0=new RooRealVar("sg_p0", "sg_p0", 580., 620.);
sg_p1=new RooRealVar("sg_p1", "sg_p1", 20., 30.);
}
else if (mass=="650")
{
rangeLo=450., rangeHi=850.;
sg_p0=new RooRealVar("sg_p0", "sg_p0", 630., 680.);
sg_p1=new RooRealVar("sg_p1", "sg_p1", 20., 30.);
}
else if (mass=="700")
{
rangeLo=500., rangeHi=900.;
sg_p0=new RooRealVar("sg_p0", "sg_p0", 680., 720.);
sg_p1=new RooRealVar("sg_p1", "sg_p1", 25., 35.);
}
else if (mass=="800")
{
rangeLo=600., rangeHi=1000.;
sg_p0=new RooRealVar("sg_p0", "sg_p0", 780., 850.);
sg_p1=new RooRealVar("sg_p1", "sg_p1", 25., 40.);
}
else if (mass=="900")
{
rangeLo=700., rangeHi=1100.;
sg_p0=new RooRealVar("sg_p0", "sg_p0", 880., 950.);
sg_p1=new RooRealVar("sg_p1", "sg_p1", 25., 40.);
}
else if (mass=="1000")
{
rangeLo=800., rangeHi=1200.;
sg_p0=new RooRealVar("sg_p0", "sg_p0", 980., 1050.);
sg_p1=new RooRealVar("sg_p1", "sg_p1", 25., 40.);
}
else if (mass=="1100")
{
rangeLo=900., rangeHi=1300.;
sg_p0=new RooRealVar("sg_p0", "sg_p0", 1080., 1150.);
sg_p1=new RooRealVar("sg_p1", "sg_p1", 25., 40.);
}
}
else
{
if (mass=="270")
{
rangeLo=200., rangeHi=400.;
sg_p0=new RooRealVar("sg_p0", "sg_p0", 250., 290.);
sg_p1=new RooRealVar("sg_p1", "sg_p1", 10., 60.);
}
else if (mass=="300")
{
rangeLo=250., rangeHi=350.;
sg_p0=new RooRealVar("sg_p0", "sg_p0", 280., 320.);
sg_p1=new RooRealVar("sg_p1", "sg_p1", 7., 60.);
}
else if (mass=="350")
{
rangeLo=300., rangeHi=400.;
sg_p0=new RooRealVar("sg_p0", "sg_p0", 330., 370.);
sg_p1=new RooRealVar("sg_p1", "sg_p1", 10., 60.);
}
else if (mass=="400")
{
// rangeLo=300., rangeHi=600.;
rangeLo=380., rangeHi=440.;
sg_p0=new RooRealVar("sg_p0", "sg_p0", 390., 410.);
sg_p1=new RooRealVar("sg_p1", "sg_p1", 5., 15.);
}
else if (mass=="450")
{
rangeLo=430., rangeHi=480.;
sg_p0=new RooRealVar("sg_p0", "sg_p0", 430., 470.);
sg_p1=new RooRealVar("sg_p1", "sg_p1", 10., 50.);
}
else if (mass=="500")
{
// rangeLo=300., rangeHi=700.;
rangeLo=475., rangeHi=540.;
sg_p0=new RooRealVar("sg_p0", "sg_p0", 470., 530.);
sg_p1=new RooRealVar("sg_p1", "sg_p1", 10., 30.);
}
else if (mass=="550")
{
rangeLo=520., rangeHi=600.;
sg_p0=new RooRealVar("sg_p0", "sg_p0", 530., 580.);
sg_p1=new RooRealVar("sg_p1", "sg_p1", 10., 30.);
}
else if (mass=="600")
{
// rangeLo=400., rangeHi=800.;
rangeLo=575., rangeHi=650.;
sg_p0=new RooRealVar("sg_p0", "sg_p0", 600., 620.);
sg_p1=new RooRealVar("sg_p1", "sg_p1", 10., 30.);
}
else if (mass=="650")
{
rangeLo=630., rangeHi=700.;
sg_p0=new RooRealVar("sg_p0", "sg_p0", 630., 680.);
sg_p1=new RooRealVar("sg_p1", "sg_p1", 10., 30.);
}
else if (mass=="700")
{
// rangeLo=500., rangeHi=900.;
rangeLo=675., rangeHi=750.;
sg_p0=new RooRealVar("sg_p0", "sg_p0", 680., 750.);
sg_p1=new RooRealVar("sg_p1", "sg_p1", 15., 30.);
}
else if (mass=="800")
{
// rangeLo=600., rangeHi=1000.;
rangeLo=770., rangeHi=860.;
sg_p0=new RooRealVar("sg_p0", "sg_p0", 780., 850.);
sg_p1=new RooRealVar("sg_p1", "sg_p1", 15., 30.);
}
else if (mass=="900")
{
rangeLo=860., rangeHi=970.;
sg_p0=new RooRealVar("sg_p0", "sg_p0", 880., 950.);
sg_p1=new RooRealVar("sg_p1", "sg_p1", 20., 40.);
}
else if (mass=="1000")
{
rangeLo=950., rangeHi=1080.;
sg_p0=new RooRealVar("sg_p0", "sg_p0", 980., 1050.);
sg_p1=new RooRealVar("sg_p1", "sg_p1", 25., 40.);
}
else if (mass=="1100")
{
rangeLo=1040., rangeHi=1180.;
sg_p0=new RooRealVar("sg_p0", "sg_p0", 1080., 1150.);
sg_p1=new RooRealVar("sg_p1", "sg_p1", 30., 50.);
}
}
x=new RooRealVar("x", "m_{X} (GeV)", rangeLo-50, rangeHi+50);
RooGaussian signal("signal", "Signal Prediction", *x, *sg_p0, *sg_p1);
RooDataHist signalHistogram("signalHistogram", "Signal Histogram", RooArgList(*x), h);
signal.fitTo(signalHistogram, RooFit::Range(rangeLo, rangeHi), RooFit::Save());
params.sg_p0=sg_p0->getVal(); params.sg_p0_err=sg_p0->getError();
params.sg_p1=sg_p1->getVal(); params.sg_p1_err=sg_p1->getError();
params.sg_p2=-1;
params.sg_p3=-1;
RooPlot *plot=x->frame();
if (color==kBlack)
{
signalHistogram.plotOn(plot, RooFit::MarkerColor(color), RooFit::MarkerSize(1.2));
signal.plotOn(plot, RooFit::LineColor(color), RooFit::LineWidth(3));
}
else
{
signalHistogram.plotOn(plot, RooFit::MarkerColor(color));
signal.plotOn(plot, RooFit::LineColor(color), RooFit::LineWidth(0));
}
// leg->AddEntry((TObject*)0, ("norm="+tostr(h->GetSumOfWeights())).c_str(), "");
// leg->AddEntry((TObject*)0, ("mean="+tostr(sg_p0->getVal())+"#pm"+tostr(sg_p0->getError())).c_str(), "");
// leg->AddEntry((TObject*)0, ("rms="+tostr(sg_p1->getVal())+"#pm"+tostr(sg_p1->getError())).c_str(), "");
// std::cout<<"chi2/dof = "<<plot->chiSquare()<<std::endl;
// Save modified signal shape to workspace
if (color==kBlack)
{
RooRealVar signal_p0("signal_p0", "signal_p0", sg_p0->getVal());
RooRealVar signal_p1("signal_p1", "signal_p1", sg_p1->getVal());
RooGaussian signal_fixed("signal", "Signal Prediction Fixed", *x, signal_p0, signal_p1);
RooWorkspace *w=new RooWorkspace("HbbHbb");
w->import(signal_fixed);
w->SaveAs((dest_dir+"/"+"SignalFits_LMR"+reg+"/w_signal_Gaussian_"+mass+".root").c_str());
}
return plot;
}
double lnN(double b, double a, double c)
{
// std::cout<<"a = "<<a<<", b = "<<b<<", c = "<<c<<std::endl;
// std::cout<<"1.+(a-c)/(2.*b) = "<<1.+fabs(a-c)/(2.*b)<<std::endl;
double err=0;
if (b>0) err=1.+fabs(a-c)/(2.*b);
return err;
}
int Display_SignalFits_LMR_bias(std::string dir_preselection="PreselectedWithRegressionDeepCSV", std::string _reg = "reg",
std::string dir_selection="LMRSelection_chi2", std::string _dest_dir=".",
std::string file_histograms="Histograms_GluGluToBulkGravitonToHHTo4B_M-",
int _mass=400,
bool focus=false, int range = 1)
{
double bias;
if(range==1){
switch(_mass){
case 260: bias=0.1464; break;
case 270: bias=0.1360; break;
case 300: bias=-0.2192; break;
default: break;
}
}
else{
switch(_mass){
case 300: bias=0.0154; break;
case 350: bias=-0.0224; break;
case 400: bias=0.0182; break;
case 450: bias=0.0130; break;
case 500: bias=0.0029; break;
case 550: bias=-0.0092; break;
case 600: bias=-0.0035; break;
default: break;
}
}
reg = _reg;
dest_dir=_dest_dir;
std::vector<std::string> masses;
string _massstring; // string which will contain the result
ostringstream convert; // stream used for the conversion
convert << _mass; // insert the textual representation of 'Number' in the characters in the stream
_massstring = convert.str(); // set 'Result' to the contents of the stream
masses.push_back(_massstring);
writeExtraText = true; // if extra text
extraText = "Simulation"; // default extra text is "Preliminary"
lumi_13TeV = "35.9 fb^{-1}"; // default is "19.7 fb^{-1}"
gROOT->SetStyle("Plain");
gStyle->SetOptStat(000000000);
// gStyle->SetPalette(1);
gSystem->Load("PDFs/ExpGaussExp_cxx.so");
TStyle *myStyle=setTDRStyle();
myStyle->cd();
myStyle->SetOptTitle(0);
myStyle->SetOptStat(0);
// Calculate nSignal events given production cross section, branching fractions and efficiency
double totalLumi=35867; // /pb
double prodXsec_1=1.; // pb
// Interpolation Plots
std::vector<double> v_sg_p0, v_sg_p0_err;
std::vector<double> v_sg_p1, v_sg_p1_err;
std::vector<double> v_sg_p2, v_sg_p2_err;
std::vector<double> v_sg_p3, v_sg_p3_err;
std::vector<double> v_sg_p4, v_sg_p4_err;
std::vector<double> v_zero;
for (unsigned int i=0; i<masses.size(); ++i)
{
v_zero.push_back(0);
string width = "";
if(masses.at(i) =="1000" || masses.at(i) =="1200") file_histograms = "Histograms_LMR_BulkGravTohhTohbbhbb_narrow_M-";
else width = "_narrow";
TFile *file=new TFile((dir_preselection+"/"+dir_selection+"/"+file_histograms+masses.at(i)+width+"_13TeV-madgraph.root").c_str());
TH1F *h_H1_mass=(TH1F*)file->Get("h_H1_mass");
TH1F *h_H2_mass=(TH1F*)file->Get("h_H2_mass");
TH1F *h_mX_SR=(TH1F*)file->Get("h_mX_SR");
TH1F *h_mX_SR_KinFit=(TH1F*)file->Get("h_mX_SR_kinFit");
TH1F *h_Count=(TH1F*)file->Get("Count");
double nSignal_init=h_Count->GetBinContent(1);
std::cout<<"nSignal_init = "<<nSignal_init<<std::endl;
focus=false;
TFile *file_JECp1;
if (focus) file_JECp1=file;
else file_JECp1=new TFile((dir_preselection+"_JECp1/"+dir_selection+"/"+file_histograms+masses.at(i)+width+"_13TeV-madgraph.root").c_str());
TH1F *h_H1_mass_JECp1=(TH1F*)file_JECp1->Get("h_H1_mass")->Clone("h_H1_mass_JECp1");
TH1F *h_H2_mass_JECp1=(TH1F*)file_JECp1->Get("h_H2_mass")->Clone("h_H2_mass_JECp1");
TH1F *h_mX_SR_JECp1=(TH1F*)file_JECp1->Get("h_mX_SR")->Clone("h_mX_SR_JECp1");
TH1F *h_mX_SR_JECp1_KinFit=(TH1F*)file_JECp1->Get("h_mX_SR_kinFit")->Clone("h_mX_SR_JECp1_KinFit");
TFile *file_JECm1;
if (focus) file_JECm1=file;
else file_JECm1=new TFile((dir_preselection+"_JECm1/"+dir_selection+"/"+file_histograms+masses.at(i)+width+"_13TeV-madgraph.root").c_str());
TH1F *h_H1_mass_JECm1=(TH1F*)file_JECm1->Get("h_H1_mass")->Clone("h_H1_mass_JECm1");
TH1F *h_H2_mass_JECm1=(TH1F*)file_JECm1->Get("h_H2_mass")->Clone("h_H2_mass_JECm1");;
TH1F *h_mX_SR_JECm1=(TH1F*)file_JECm1->Get("h_mX_SR")->Clone("h_mX_SR_JECm1");
TH1F *h_mX_SR_JECm1_KinFit=(TH1F*)file_JECm1->Get("h_mX_SR_kinFit")->Clone("h_mX_SR_JECm1_KinFit");
TFile *file_JERp1;
if (focus) file_JERp1=file;
else file_JERp1=new TFile((dir_preselection+"_JERp1/"+dir_selection+"/"+file_histograms+masses.at(i)+width+"_13TeV-madgraph.root").c_str());
TH1F *h_H1_mass_JERp1=(TH1F*)file_JERp1->Get("h_H1_mass")->Clone("h_H1_mass_JERp1");
TH1F *h_H2_mass_JERp1=(TH1F*)file_JERp1->Get("h_H2_mass")->Clone("h_H2_mass_JERp1");
TH1F *h_mX_SR_JERp1=(TH1F*)file_JERp1->Get("h_mX_SR")->Clone("h_mX_SR_JERp1");
TH1F *h_mX_SR_JERp1_KinFit=(TH1F*)file_JERp1->Get("h_mX_SR_kinFit")->Clone("h_mX_SR_JERp1_KinFit");
TFile *file_JERm1;
if (focus) file_JERm1=file;
else file_JERm1=new TFile((dir_preselection+"_JERm1/"+dir_selection+"/"+file_histograms+masses.at(i)+width+"_13TeV-madgraph.root").c_str());
TH1F *h_H1_mass_JERm1=(TH1F*)file_JERm1->Get("h_H1_mass")->Clone("h_H1_mass_JERm1");
TH1F *h_H2_mass_JERm1=(TH1F*)file_JERm1->Get("h_H2_mass")->Clone("h_H2_mass_JERm1");;
TH1F *h_mX_SR_JERm1=(TH1F*)file_JERm1->Get("h_mX_SR")->Clone("h_mX_SR_JERm1");
TH1F *h_mX_SR_JERm1_KinFit=(TH1F*)file_JERm1->Get("h_mX_SR_kinFit")->Clone("h_mX_SR_JERm1_KinFit");
TFile *file_bTagDown;
if (focus) file_bTagDown=file;
else file_bTagDown=new TFile((dir_preselection+"_bTagm1/"+dir_selection+"/"+file_histograms+masses.at(i)+width+"_13TeV-madgraph.root").c_str());
TH1F *h_H1_mass_bTagDown=(TH1F*)file_bTagDown->Get("h_H1_mass")->Clone("h_H1_mass_bTagDown");
TH1F *h_H2_mass_bTagDown=(TH1F*)file_bTagDown->Get("h_H2_mass")->Clone("h_H2_mass_bTagDown");
TH1F *h_mX_SR_bTagDown=(TH1F*)file_bTagDown->Get("h_mX_SR")->Clone("h_mX_SR_bTagDown");
TH1F *h_mX_SR_bTagDown_KinFit=(TH1F*)file_bTagDown->Get("h_mX_SR_kinFit")->Clone("h_mX_SR_bTagDown_KinFit");
TFile *file_bTagUp;
if (focus) file_bTagUp=file;
else file_bTagUp=new TFile((dir_preselection+"_bTagp1/"+dir_selection+"/"+file_histograms+masses.at(i)+width+"_13TeV-madgraph.root").c_str());
TH1F *h_H1_mass_bTagUp=(TH1F*)file_bTagUp->Get("h_H1_mass")->Clone("h_H1_mass_bTagUp");
TH1F *h_H2_mass_bTagUp=(TH1F*)file_bTagUp->Get("h_H2_mass")->Clone("h_H2_mass_bTagUp");;
TH1F *h_mX_SR_bTagUp=(TH1F*)file_bTagUp->Get("h_mX_SR")->Clone("h_mX_SR_bTagUp");
TH1F *h_mX_SR_bTagUp_KinFit=(TH1F*)file_bTagUp->Get("h_mX_SR_kinFit")->Clone("h_mX_SR_bTagUp_KinFit");
TCanvas *c_H1_mass=new TCanvas("c_H1_mass", "c_H1_mass", 700, 700);
h_H1_mass->SetLineWidth(2);
h_H1_mass_JECp1->SetLineStyle(9); h_H1_mass_JECp1->SetLineColor(kRed);
h_H1_mass_JECm1->SetLineStyle(9); h_H1_mass_JECm1->SetLineColor(kRed+2);
h_H1_mass->Draw("same");
h_H1_mass_JECp1->Draw("same");
h_H1_mass_JECm1->Draw("same");
threeStatBoxes(h_H1_mass,
h_H1_mass_JECp1,
h_H1_mass_JECm1)->Draw();
c_H1_mass->SaveAs((dest_dir+"/"+"SignalFits_LMR"+reg+"/c_H1_mass_"+masses.at(i)+".png").c_str());
TCanvas *c_H2_mass=new TCanvas("c_H2_mass", "c_H2_mass", 700, 700);
h_H2_mass->SetLineWidth(2);
h_H2_mass_JECp1->SetLineStyle(9); h_H2_mass_JECp1->SetLineColor(kRed);
h_H2_mass_JECm1->SetLineStyle(9); h_H2_mass_JECm1->SetLineColor(kRed+2);
h_H2_mass->Draw("same");
h_H2_mass_JECp1->Draw("same");
h_H2_mass_JECm1->Draw("same");
threeStatBoxes(h_H2_mass,
h_H2_mass_JECp1,
h_H2_mass_JECm1)->Draw();
c_H2_mass->SaveAs((dest_dir+"/"+"SignalFits_LMR"+reg+"/c_H2_mass_"+masses.at(i)+".png").c_str());
TCanvas *c_mX_SR=new TCanvas(("c_mX_SR_"+masses.at(i)).c_str(), ("c_mX_SR_"+masses.at(i)).c_str(), 700, 700);
h_mX_SR->SetTitle(("m_{X} Peak in Signal MC (m_{X}="+masses.at(i)+" GeV); m_{X} (GeV)").c_str());
h_mX_SR->Rebin(rebin);
h_mX_SR_JECp1->Rebin(rebin);
h_mX_SR_JECm1->Rebin(rebin);
h_mX_SR_JECp1->SetLineColor(kRed);
h_mX_SR_JECm1->SetLineColor(kRed+2);
h_mX_SR->GetXaxis()->SetRangeUser(0, 1200);
h_mX_SR_JECp1->GetXaxis()->SetRangeUser(0, 1200);
h_mX_SR_JECm1->GetXaxis()->SetRangeUser(0, 1200);
TLegend *leg=new TLegend(0.6, 0.55, 0.9, 0.85);
leg->AddEntry(h_mX_SR, "Baseline");
Params par, par_JECp1, par_JECm1;
RooPlot *plot=fitSignal(h_mX_SR, masses.at(i), kBlack, leg, par, bias);
RooPlot *plot_JECp1, *plot_JECm1;
if (!focus)
{
leg->AddEntry(h_mX_SR_JECp1, "JEC +1 #sigma");
leg->AddEntry(h_mX_SR_JECm1, "JEC -1 #sigma");
plot_JECp1=fitSignal(h_mX_SR_JECp1, masses.at(i), kRed, leg, par_JECp1, bias);
plot_JECm1=fitSignal(h_mX_SR_JECm1, masses.at(i), kRed+2, leg, par_JECm1, bias);
}
plot->SetMaximum(plot->GetMaximum()*1.2);
plot->Draw();
if (!focus)
{
plot_JECp1->Draw("same");
plot_JECm1->Draw("same");
}
plot->Draw("same");
leg->SetFillColor(0);
leg->Draw();
c_mX_SR->SaveAs((dest_dir+"/"+"SignalFits_LMR"+reg+"/c_mX_SR_"+masses.at(i)+".png").c_str());
double xPad = 0.3;
TCanvas *c_mX_SR_KinFit=new TCanvas(("c_mX_SR_KinFit_"+masses.at(i)).c_str(), ("c_mX_SR_KinFit_"+masses.at(i)).c_str(), 700*(1.-xPad), 700);
TPad *p_1=new TPad("p_1", "p_1", 0, xPad, 1, 1);
p_1->SetFillStyle(4000);
p_1->SetFrameFillColor(0);
p_1->SetBottomMargin(0.02);
p_1->SetTopMargin(0.06);
TPad* p_2 = new TPad("p_2", "p_2",0,0,1,xPad);
p_2->SetBottomMargin((1.-xPad)/xPad*0.13);
p_2->SetTopMargin(0.03);
p_2->SetFillColor(0);
p_2->SetBorderMode(0);
p_2->SetBorderSize(2);
p_2->SetFrameBorderMode(0);
p_2->SetFrameBorderMode(0);
p_1->Draw();
p_2->Draw();
p_1->cd();
h_mX_SR_KinFit->SetTitle(("m_{X} Peak in Signal MC (m_{X}="+masses.at(i)+" GeV); m_{X} (GeV)").c_str());
leg = new TLegend(0.75,0.75,0.5,0.9,NULL,"brNDC");
leg->SetBorderSize(0);
leg->SetTextSize(0.035);
leg->SetTextFont(42);
leg->SetLineColor(1);
leg->SetLineStyle(1);
leg->SetLineWidth(2);
leg->SetFillColor(0);
leg->SetFillStyle(0);
leg->SetTextFont(42);
leg->AddEntry(h_mX_SR_KinFit, "Signal MC");
h_mX_SR_KinFit->Rebin(rebin);
h_mX_SR_JECp1_KinFit->Rebin(rebin);
h_mX_SR_JECm1_KinFit->Rebin(rebin);
h_mX_SR_JECp1_KinFit->SetLineColor(kRed);
h_mX_SR_JECm1_KinFit->SetLineColor(kRed+2);
h_mX_SR_KinFit->GetXaxis()->SetRangeUser(0, 1200);
h_mX_SR_JECp1_KinFit->GetXaxis()->SetRangeUser(0, 1200);
h_mX_SR_JECm1_KinFit->GetXaxis()->SetRangeUser(0, 1200);
h_mX_SR_JERp1_KinFit->Rebin(rebin);
h_mX_SR_JERm1_KinFit->Rebin(rebin);
h_mX_SR_JERp1_KinFit->SetLineColor(kAzure+1);
h_mX_SR_JERm1_KinFit->SetLineColor(kBlue+1);
h_mX_SR_JERp1_KinFit->GetXaxis()->SetRangeUser(0, 1200);
h_mX_SR_JERm1_KinFit->GetXaxis()->SetRangeUser(0, 1200);
h_mX_SR_bTagDown_KinFit->Rebin(rebin);
h_mX_SR_bTagUp_KinFit->Rebin(rebin);
h_mX_SR_bTagDown_KinFit->SetLineColor(kGreen+1);
h_mX_SR_bTagUp_KinFit->SetLineColor(kGreen+3);
h_mX_SR_bTagDown_KinFit->GetXaxis()->SetRangeUser(0, 1200);
h_mX_SR_bTagUp_KinFit->GetXaxis()->SetRangeUser(0, 1200);
Params par_KinFit, par_JECp1_KinFit, par_JECm1_KinFit;
RooPlot *plot_KinFit=fitSignal(h_mX_SR_KinFit, masses.at(i), kBlack, leg, par_KinFit, bias, true);
std::cout<<" fit done " <<std::endl;
plot_KinFit->SetTitle("");
plot_KinFit->GetXaxis()->SetRangeUser(_mass-100, _mass+150);
plot_KinFit->GetXaxis()->SetLabelOffset(0.03);
plot_KinFit->GetXaxis()->SetNdivisions(505);
plot_KinFit->Draw();
v_sg_p0.push_back(par_KinFit.sg_p0); v_sg_p0_err.push_back(par_KinFit.sg_p0_err);
v_sg_p1.push_back(par_KinFit.sg_p1); v_sg_p1_err.push_back(par_KinFit.sg_p1_err);
v_sg_p2.push_back(par_KinFit.sg_p2); v_sg_p2_err.push_back(par_KinFit.sg_p2_err);
v_sg_p3.push_back(par_KinFit.sg_p3); v_sg_p3_err.push_back(par_KinFit.sg_p3_err);
v_sg_p4.push_back(par_KinFit.sg_p4); v_sg_p4_err.push_back(par_KinFit.sg_p4_err);
leg=new TLegend(0.59, 0.60, 0.91, 0.90);
leg->SetFillStyle(0);
h_mX_SR_KinFit->SetLineColor(kBlack);
h_mX_SR_KinFit->SetLineWidth(2);
h_mX_SR_KinFit->SetMarkerStyle(20);
if (focus)
{
leg->AddEntry(h_mX_SR_KinFit, "Gaus+Gaus fit", "L");
leg->AddEntry(h_mX_SR_KinFit, ("m_{X} = "+masses.at(i)+" GeV signal").c_str(), "LEP");
}
RooPlot *plot_JECp1_KinFit, *plot_JECm1_KinFit;
Params par_JERp1_KinFit, par_JERm1_KinFit;
RooPlot *plot_JERp1_KinFit, *plot_JERm1_KinFit;
Params par_Trigp1_KinFit, par_Trigm1_KinFit;
RooPlot *plot_Trigp1_KinFit, *plot_Trigm1_KinFit;
Params par_bTagDown_KinFit, par_bTagUp_KinFit;
RooPlot *plot_bTagDown_KinFit, *plot_bTagUp_KinFit;
if (!focus) {
leg->AddEntry(h_mX_SR_JECp1_KinFit, "JEC +1 #sigma");
leg->AddEntry(h_mX_SR_JECm1_KinFit, "JEC -1 #sigma");
plot_JECp1_KinFit=fitSignal(h_mX_SR_JECp1_KinFit, masses.at(i), kRed, leg, par_JECp1_KinFit, bias, true);
plot_JECm1_KinFit=fitSignal(h_mX_SR_JECm1_KinFit, masses.at(i), kRed+2, leg, par_JECm1_KinFit, bias, true);
leg->AddEntry(h_mX_SR_JERp1_KinFit, "JER +1 #sigma");
leg->AddEntry(h_mX_SR_JERm1_KinFit, "JER -1 #sigma");
plot_JERp1_KinFit=fitSignal(h_mX_SR_JERp1_KinFit, masses.at(i), kAzure+1, leg, par_JERp1_KinFit, bias, true);
plot_JERm1_KinFit=fitSignal(h_mX_SR_JERm1_KinFit, masses.at(i), kBlue+1, leg, par_JERm1_KinFit, bias, true);
leg->AddEntry(h_mX_SR_bTagDown_KinFit, "bTag +1 #sigma");
leg->AddEntry(h_mX_SR_bTagUp_KinFit, "bTag -1 #sigma");
plot_bTagDown_KinFit=fitSignal(h_mX_SR_bTagDown_KinFit, masses.at(i), kGreen+1, leg, par_bTagDown_KinFit, bias, true);
plot_bTagUp_KinFit=fitSignal(h_mX_SR_bTagUp_KinFit, masses.at(i), kGreen+3, leg, par_bTagUp_KinFit, bias, true);
}
plot_KinFit->SetMaximum(plot_KinFit->GetMaximum()*1.2);
plot_KinFit->Draw();
if (!focus)
{
plot_JECp1_KinFit->Draw("same");
plot_JECm1_KinFit->Draw("same");
plot_JERp1_KinFit->Draw("same");
plot_JERm1_KinFit->Draw("same");
plot_bTagUp_KinFit->Draw("same");
plot_bTagDown_KinFit->Draw("same");
}
plot_KinFit->Draw("same");
leg->SetFillColor(0);
leg->SetLineColor(0);
leg->Draw();
CMS_lumi(p_1, iPeriod, iPos );