August 29th Meeting: Efficiency Histogram & Expected Events

[A-A-Abdelhamid]

The binning of the histogram hist = ROOT.TH2F("hist", "hist",10, 65, 765, 8, 0, 400) is taken from the original code used in the paper. To make the same histogram with almost the same color gradient I used a color pick tool:

# Number of points in gradient
nRGBs = 5

# Stop points at 0, 0.2, 0.3, 0.4, and 0.6
stops = array('d', [0.0, 0.2, 0.3, 0.4, 0.6])

# RGB values for the colors at the stops (scaled between 0 and 1)
red   = array('d', [236/255.0, 97/255.0, 33/255.0, 30/255.0, 21/255.0])
green = array('d', [228/255.0, 203/255.0, 191/255.0, 172/255.0, 121/255.0])
blue  = array('d', [241/255.0, 221/255.0, 212/255.0, 190/255.0, 133/255.0])

# Create the gradient color table
ROOT.TColor.CreateGradientColorTable(nRGBs, stops, red, green, blue, 255)
ROOT.gStyle.SetNumberContours(255)
hist =     ROOT.TH2F("hist", "hist",10, 65, 765, 8, 0, 400)
hist.SetStats(0)

Pt = []
d0 = []
eff = []

# Read the JSON file
file_path = "2.json"
with open(file_path, 'r') as f:
    json_data = json.load(f)

values_data = json_data.get('values', None)

for entry in values_data:
    Pt.append(float(entry['x'][0]['value']))
    d0.append(float(entry['x'][1]['value']))
    eff.append(float(entry['y'][0]['value']))

# Fill the histogram
for i in range(len(Pt)):
    # Adding 1 to ensure we start from bin 1
    x_bin = hist.GetXaxis().FindBin(Pt[i])
    y_bin = hist.GetYaxis().FindBin(d0[i])
    hist.SetBinContent(x_bin, y_bin, eff[i])

# Draw the histogram
c = ROOT.TCanvas("canvas", "Pt-do eff", 800, 600)
hist.GetXaxis().SetTitle("Pt (GeV)")
hist.GetYaxis().SetTitle("d0 (mm)")
hist.SetTitle("Muon reconstruction efficiency")
hist.Draw("COLZ")
c.Update()

This is an assurance that we have populated the histogram correctly.

Applying the efficiency selection code (did not have time to make it cleaner):

Number of passed events is:  2275  event
Number of expected events:  8.255053624999999

Where Ntotal is the total number of the generated events we started with in our MC simulation (i.e. MadGraph)

BUT, there is an issue here.

Any muon with a momentum of ~ 765 GeV or more does not have an efficiency in the table or the histogram. The histogram has 10 bins for the x-axis (Pt), and the GetBin method returns bin number 11 for any +765 GeV muon.

This is the case with any empty bin in the histogram too, e.g. Pt of 592.6 GeV and do of 166.5 mm