Hi, I use the following code to smear a histogram in two ways: Using the gRandom->Gaus(x,3.5) using x_res = x + 3.5 * TMath::Sqrt2() * TMath::ErfInverse(2. * rndm() - 1.) the follwoing is my code : { gStyle->SetOptStat(2211); gStyle->SetOptFit(1111); TCanvas *c1 = new TCanvas("c1","Without con…

{ gStyle->SetOptStat(2211); gStyle->SetOptFit(1111); auto c1 = new TCanvas("c1","Without constraint",800,600); c1->Divide(2,2); auto h1 = new TH1D("h1_gaus","Mass (amu);Counts",101.0,39.5,140.5); auto h2 = new TH1D("h2","h2_Mass_resolution",101.0,39.5,140.5); auto h3 = new TH1…

That works. But when I plot my histograms , I find them identical before and after smearing. What I wanted to see was that I wanted to add my resolution of 3.5amu on the gaussian histogram and wanted to see how the widths of the distribution change with experimental resolution. And here I find no…

At maximum, you have a value of about 10000, and you add a random gaussian with a sigma 3.5. So, the final value is something between 9990 and 10010 (10000 ± 3 sigma). Do you really think you can notice it in the plot?

Hi @gini Smear the data means to adding the effect of a resolution broadening your histogram. What you are doing in your code is not smearing, but just changing the number of counts in each bin. From your code I see that you have just a file with the number of counts for each bin. I sligh…

for(int j = 0; j < TMath::Nint(x); j++) {

Thanks, @Dilicus and @Wile_E_Coyote for the clear explanation. I understand now why my plots were showing Bizzard values for std after smearing. I am a bit curious now as to whether it’s possible to do exactly the reverse of smearing ie., whether it is possible to eliminate the effect of smearing …

You can obtain the values of the initial distribution like the sigma, µ and amplitude, but you cannot get the initial data before the smearing. Below you find a code to find the initial values. The codes use the convolution of the initial distribution with the gaussian for the resolution, and I us…