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Eres fit

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ecal/scripts/rec_emcal_resolution_analysis.cxx 0 → 100644
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////////////////////////////////////////
// Read reconstruction ROOT output file
// Plot energy resolution
// 2021/01/20
////////////////////////////////////////
#include "ROOT/RDataFrame.hxx"
#include <iostream>
#include "dd4pod/Geant4ParticleCollection.h"
#include "dd4pod/CalorimeterHitCollection.h"
#include "dd4pod/TrackerHitCollection.h"
#include "eicd/CalorimeterHitCollection.h"
#include "eicd/CalorimeterHitData.h"
#include "eicd/ClusterCollection.h"
#include "eicd/ClusterData.h"
#include "TCanvas.h"
#include "TStyle.h"
#include "TMath.h"
#include "TH1.h"
#include "TH1D.h"
// If root cannot find a dd4pod header make sure to add the include dir to ROOT_INCLUDE_PATH:
// export ROOT_INCLUDE_PATH=$HOME/include:$ROOT_INCLUDE_PATH
// export ROOT_INCLUDE_PATH=/home/jihee/stow/development/include:$ROOT_INCLUDE_PATH
using ROOT::RDataFrame;
using namespace ROOT::VecOps;
void rec_emcal_resolution_analysis(const char* input_fname = "rec_emcal_uniform_electrons.root")
{
// Setting for graphs
gROOT->SetStyle("Plain");
gStyle->SetOptFit(1);
gStyle->SetLineWidth(2);
gStyle->SetPadTickX(1);
gStyle->SetPadTickY(1);
gStyle->SetPadGridX(1);
gStyle->SetPadGridY(1);
gStyle->SetPadLeftMargin(0.14);
gStyle->SetPadRightMargin(0.14);
ROOT::EnableImplicitMT();
ROOT::RDataFrame d0("events", input_fname);
// Number of Clusters
auto ncluster = [] (const std::vector<eic::ClusterData>& evt) {return (int) evt.size(); };
// Cluster Energy [GeV]
auto clusterE = [] (const std::vector<eic::ClusterData>& evt) {
std::vector<double> result;
for (const auto& i: evt)
result.push_back(i.energy);
return result;
};
// Thrown Energy [GeV]
auto E_thr = [](std::vector<dd4pod::Geant4ParticleData> const& input) {
std::vector<double> result;
result.push_back(TMath::Sqrt(input[2].psx*input[2].psx + input[2].psy*input[2].psy + input[2].psz*input[2].psz + input[2].mass*input[2].mass));
return result;
};
// Energy Resolution = delta(E)/E
// delta(E) = (Erec - Ethr)
// E = Ethr
auto E_res = [] (const std::vector<double>& Erec, const std::vector<double>& Ethr) {
std::vector<double> result;
for (const auto& E1 : Ethr) {
for (const auto& E2 : Erec) {
result.push_back((E2-E1)/E1);
}
}
return result;
};
// Define variables
auto d1 = d0.Define("ncluster", ncluster, {"EcalClusters"})
.Define("clusterE", clusterE, {"EcalClusters"})
.Define("E_thr", E_thr, {"mcparticles2"})
.Define("E_res", E_res, {"clusterE","E_thr"})
;
// Define Cuts
// d2: Select Events with One Cluster
// d3: Select Events which determined their center of cluster not being on the edge of detector in addition to d2 cut
auto d2 = d1.Filter("ncluster==1");
auto d3 = d2.Filter([=] (const std::vector<eic::ClusterData>& evt) {
for(const auto& i: evt) {
auto pos_x = i.position.x;
auto pos_y = i.position.y;
auto radial_dist = TMath::Sqrt(pos_x*pos_x + pos_y*pos_y);
if (radial_dist > 8.0 && radial_dist < 30.0)
return true;
}
return false;}, {"EcalClusters"});
// Define histograms
auto hEresNC1 = d2.Histo1D({"hEresNC1", "Energy Resolution CE vs Ethr w/ NC1; #DeltaE/E; Events", 100, -0.5, 0.5}, "E_res");
auto hEresNC1EGCUT = d3.Histo1D({"hEresNC1EGCUT", "Energy Resolution CE vs Ethr w/ NC1 EGCUT; #DeltaE/E; Events", 100, -0.5, 0.5}, "E_res");
// Energy Resolution Graphs
// Divided into serveral energy bin group
// Do gaussian git to extract mean and sigma
int nbin = 12; // number of energy bin group
double maxEnergy = 30.0; // maximum of energy in dataset
double multiple = maxEnergy / (double)nbin; // energy interval between energy bin group
double maximumE; // high end in each energy bin group
double minimumE; // low end in each energy bin group
double mean[nbin]; // mean from gaussian fit
double sigma[nbin]; // sigma from gaussian fit
double dmean[nbin]; // error on mean
double dsigma[nbin]; // error on sigma
double gEbin[nbin]; // center of energy in each group
double invSqrtgEbin[nbin]; // inverse sqrt center of energy in each group
double resolution[nbin]; // resolution = sigma/mean
double resolutionError[nbin]; // error on resolution
double resolutionErrorSigma; // error on resolution sigma term
double resolutionErrorMean; // error on resolution mean term
double xerror[nbin]; // set to 0.0 for all
// Loop over each energy bin group
// Define hitogram and do gaussian fit
// Save fit results; mean, sigma, error on mean & sigam
// Calculate resolution and resolution error
for(int n=1; n <= nbin; n++) {
maximumE = n * multiple;
minimumE = maximumE - multiple;
gEbin[n-1] = (maximumE + minimumE) / 2.0;
invSqrtgEbin[n-1] = 1.0 / TMath::Sqrt(gEbin[n-1]);
// Define histogram
auto h = d3.Filter([=] (const std::vector<eic::ClusterData>& evt) {
for (const auto& i: evt) {
auto eng = i.energy;
if (eng >= minimumE && eng < maximumE)
return true;
}
return false;}, {"EcalClusters"})
.Histo1D({"h", "Energy Resolution; #DeltaE/E; Events", 100, -0.5, 0.5}, "E_res");
// Gaussian fit
h->Fit("gaus","W,E");
// Save fit results
// how to access how many events in histogram: h->GetEntries();
if(h->GetFunction("gaus")) {
mean[n-1] = TMath::Abs(h->GetFunction("gaus")->GetParameter(1));
dmean[n-1] = h->GetFunction("gaus")->GetParError(1);
sigma[n-1] = h->GetFunction("gaus")->GetParameter(2);
dsigma[n-1] = h->GetFunction("gaus")->GetParError(2);
resolution[n-1] = sigma[n-1] / mean[n-1];
resolutionErrorSigma = dsigma[n-1] / mean[n-1];
resolutionErrorMean = dmean[n-1] * sigma[n-1];
resolutionError[n-1] = TMath::Sqrt(resolutionErrorSigma*resolutionErrorSigma + resolutionErrorMean*resolutionErrorMean);
xerror[n-1] = 0.0;
}
// Draw histogram, but
// Just save fit results
//TCanvas *c = new TCanvas("c", "c", 500, 500);
//h->GetYaxis()->SetTitleOffset(1.4);
//h->SetLineWidth(2);
//h->SetLineColor(kBlue);
//h->Fit("gaus","W,E");
//h->DrawClone();
//c->SaveAs("results/h.png");
}
// Generate graphs using gaussian fit results
// Cluster energy VS Simga
TGraphErrors* gCEVsSigmaNC1EGCUT = new TGraphErrors(nbin,gEbin,sigma,xerror,dsigma);
TCanvas *c1 = new TCanvas("c1","c1",500,500);
gCEVsSigmaNC1EGCUT->SetTitle("Cluster Energy vs #sigma");
gCEVsSigmaNC1EGCUT->GetYaxis()->SetTitleOffset(1.4);
gCEVsSigmaNC1EGCUT->GetXaxis()->SetTitle("Cluster Energy [GeV]");
gCEVsSigmaNC1EGCUT->GetYaxis()->SetTitle("#sigma");
gCEVsSigmaNC1EGCUT->GetYaxis()->SetRangeUser(0.0,0.05);
gCEVsSigmaNC1EGCUT->SetMarkerStyle(21);
gCEVsSigmaNC1EGCUT->DrawClone("AEP");
c1->SaveAs("results/emcal_electrons_gCEVsSigmaNC1EGCUT.png");
c1->SaveAs("results/emcal_electrons_gCEVsSigmaNC1EGCUT.pdf");
// Cluster energy VS Mean
TGraphErrors* gCEVsMeanNC1EGCUT = new TGraphErrors(nbin,gEbin,mean,xerror,dmean);
TCanvas *c2 = new TCanvas("c2","c2",500,500);
gCEVsMeanNC1EGCUT->SetTitle("Cluster Energy vs #mu");
gCEVsMeanNC1EGCUT->GetYaxis()->SetTitleOffset(1.4);
gCEVsMeanNC1EGCUT->GetXaxis()->SetTitle("Cluster Energy [GeV]");
gCEVsMeanNC1EGCUT->GetYaxis()->SetTitle("#mu");
gCEVsMeanNC1EGCUT->GetYaxis()->SetRangeUser(0.0,0.1);
gCEVsMeanNC1EGCUT->SetMarkerStyle(21);
gCEVsMeanNC1EGCUT->DrawClone("AEP");
c2->SaveAs("results/emcal_electrons_gCEVsMeanNC1EGCUT.png");
c2->SaveAs("results/emcal_electrons_gCEVsMeanNC1EGCUT.pdf");
// Inverse of sqrt(cluster energy) VS resolution
TGraphErrors* gInvSqCEVsSigmaENC1EGCUT = new TGraphErrors(nbin,invSqrtgEbin,resolution,xerror,resolutionError);
TCanvas *c3 = new TCanvas("c3","c3",500,500);
gInvSqCEVsSigmaENC1EGCUT->SetTitle("#sigma/E [%] vs 1/sqrt(Cluster Energy)");
gInvSqCEVsSigmaENC1EGCUT->GetYaxis()->SetTitleOffset(1.4);
gInvSqCEVsSigmaENC1EGCUT->GetXaxis()->SetTitle("1/sqrt(Cluster Energy)");
gInvSqCEVsSigmaENC1EGCUT->GetYaxis()->SetTitle("#sigma/E [%]");
gInvSqCEVsSigmaENC1EGCUT->GetYaxis()->SetRangeUser(0.0,1.5);
gInvSqCEVsSigmaENC1EGCUT->GetXaxis()->SetRangeUser(0.0,1.5);
gInvSqCEVsSigmaENC1EGCUT->SetMarkerStyle(21);
gInvSqCEVsSigmaENC1EGCUT->DrawClone("AEP");
c3->SaveAs("results/emcal_electrons_gInvSqCEVsSimgaENC1EGCUT.png");
c3->SaveAs("results/emcal_electrons_gInvSqCEVsSigmaENC1EGCUT.pdf");
// Fit Function
// sigma/E = S/sqrt(E) + N/E + C; added in quadrature
// S: Stochastic term
// N: Niose term
// C: Constant term
TF1 *f1 = new TF1("f1","sqrt([0]*[0] + pow([1]/sqrt(x),2) + pow([2]/x,2))", 0.5, 30.0);
f1->SetParNames("C", "S", "N"); // param names
f1->SetParameters(0.30,2.80,0.12); // initial param values
f1->SetLineWidth(2);
f1->SetLineColor(kRed);
// Cluster energy VS resolution
TGraphErrors* gCEVsSigmaENC1EGCUT = new TGraphErrors(nbin,gEbin,resolution,xerror,resolutionError);
TCanvas *c4 = new TCanvas("c4","c4",500,500);
gCEVsSigmaENC1EGCUT->SetTitle("Cluster Energy vs #sigma/E [%]");
gCEVsSigmaENC1EGCUT->GetYaxis()->SetTitleOffset(1.4);
gCEVsSigmaENC1EGCUT->GetXaxis()->SetTitle("Cluster Energy [GeV]");
gCEVsSigmaENC1EGCUT->GetYaxis()->SetTitle("#sigma/E [%]");
gCEVsSigmaENC1EGCUT->GetYaxis()->SetRangeUser(0.0,1.5);
gCEVsSigmaENC1EGCUT->SetMarkerStyle(21);
gCEVsSigmaENC1EGCUT->Fit("f1","W,E");
gCEVsSigmaENC1EGCUT->DrawClone("AEP");
c4->SaveAs("results/emcal_electrons_gCEVsSimgaENC1EGCUT.png");
c4->SaveAs("results/emcal_electrons_gCEVsSigmaENC1EGCUT.pdf");
// Event Counts
auto nevents_thrown = d1.Count();
auto nevents_cluster1 = d2.Count();
auto nevents_cluster1cut = d3.Count();
std::cout << "Number of Events: " << (*nevents_cluster1) << " / " << (*nevents_thrown) << " = single cluster events/all \n";
std::cout << "Number of Events: " << (*nevents_cluster1cut) << " / " << (*nevents_thrown) << " = single cluster events that passed edge cut/all \n";
}