# Number of signal and background events in ROOT

Hi,

I would like to fit a histogram in ROOT with a Gaussian (signal) and a Polynomial (background) and get the number of Signal Background events.
I was able to fit the histogram with one (background + Signal) fit function and deduce the total number of events.

However I would like to fit them separately. I tried to follow this example :https://root.cern.ch/root/html/tutorials/fit/combinedFit.C.html but my fits are not working.
Could anyone please let me know what I’m doing wrong ?

This is the piece of code :

``````Double_t background(Double_t *x, Double_t *par)  {
return par[0] + par[1]*x[0] + par[2]*x[0]*x[0];

}

Double_t GAUSS(Double_t *x, Double_t *par){
return par[0]*exp(-0.5*(((x[0]-par[1])/par[2])*((x[0]-par[1])/par[2])));

}

Double_t fitFunction(Double_t *x, Double_t *par) {
return background(x,par) + GAUSS(x,&par[3]);

}

void TEST(){
n.Draw("Mph>>histo(100,3.04,3.19)");
histo->SetLineWidth(2);
histo->SetMarkerStyle(20);

TF1 *fitFcn = new TF1("fitFcn",fitFunction,3.04,3.17,6);
fitFcn->SetParameters(1,1,1,1,3.095,0.004);
histo->SetMinimum(0);
histo->Fit("fitFcn","0");

fitFcn->SetParameter(4,0.004);
fitFcn->SetParameter(5,3.095);

histo->Fit("fitFcn","V+","ep");

TF1 *backFcn = new TF1("backFcn",background,3.04,3.17,3);
backFcn->SetLineColor(kRed);
TF1 *signalFcn = new TF1("signalFcn",GAUSS,3.04,3.17,3);
signalFcn->SetLineColor(kBlue);
signalFcn->SetNpx(500);
Double_t par[6];
fitFcn->GetParameters(par);
backFcn->SetParameters(par);
backFcn->Draw("same");
signalFcn->SetParameters(&par[3]);
signalFcn->Draw("same");

double w=histo->GetBinWidth(1);
int integral = backFcn->Integral(3.04,3.19)/w;
int integral2=signalFcn->Integral(3.04,3.19)/w;

cout<<"Integral 1::::::"<<integral1<<endl;
cout<<"Integral 2::::::"<<integral2<<endl;

TLegend *legend=new TLegend(0.6,0.65,0.88,0.85);
legend->SetTextFont(72);
legend->SetTextSize(0.04);
legend->Draw();

}

``````

You already fit the data and what you need is to get all your fitting parameters which means you get your fit function back and you can draw the (gotten fitting) gaussian and background function separately and you can deduce the signal and background events at the same time.

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