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rf316_llratioplot.py File Reference

## Namespaces

namespace  rf316_llratioplot

## Detailed Description

Multidimensional models: using the likelihood ratio technique to construct a signal enhanced one-dimensional projection of a multi-dimensional pdf

import ROOT
# Create 3D pdf and data
# -------------------------------------------
# Create observables
x = ROOT.RooRealVar("x", "x", -5, 5)
y = ROOT.RooRealVar("y", "y", -5, 5)
z = ROOT.RooRealVar("z", "z", -5, 5)
# Create signal pdf gauss(x)*gauss(y)*gauss(z)
gx = ROOT.RooGaussian("gx", "gx", x, 0.0, 1.0)
gy = ROOT.RooGaussian("gy", "gy", y, 0.0, 1.0)
gz = ROOT.RooGaussian("gz", "gz", z, 0.0, 1.0)
sig = ROOT.RooProdPdf("sig", "sig", [gx, gy, gz])
# Create background pdf poly(x)*poly(y)*poly(z)
px = ROOT.RooPolynomial("px", "px", x, [-0.1, 0.004])
py = ROOT.RooPolynomial("py", "py", y, [0.1, -0.004])
pz = ROOT.RooPolynomial("pz", "pz", z)
bkg = ROOT.RooProdPdf("bkg", "bkg", [px, py, pz])
# Create composite pdf sig+bkg
fsig = ROOT.RooRealVar("fsig", "signal fraction", 0.1, 0.0, 1.0)
model = ROOT.RooAddPdf("model", "model", [sig, bkg], [fsig])
data = model.generate({x, y, z}, 20000)
# Project pdf and data on x
# -------------------------------------------------
# Make plain projection of data and pdf on x observable
frame = x.frame(Title="Projection of 3D data and pdf on X", Bins=40)
data.plotOn(frame)
model.plotOn(frame)
# Define projected signal likelihood ratio
# ----------------------------------------------------------------------------------
# Calculate projection of signal and total likelihood on (y,z) observables
# i.e. integrate signal and composite model over x
sigyz = sig.createProjection({x})
totyz = model.createProjection({x})
# Construct the log of the signal / signal+background probability
llratio_func = ROOT.RooFormulaVar("llratio", "log10(@0)-log10(@1)", [sigyz, totyz])
# Plot data with a LL ratio cut
# -------------------------------------------------------
# Calculate the llratio value for each event in the dataset
# Extract the subset of data with large signal likelihood
dataSel = data.reduce(Cut="llratio>0.7")
# Make plot frame
frame2 = x.frame(Title="Same projection on X with LLratio(y,z)>0.7", Bins=40)
# Plot select data on frame
dataSel.plotOn(frame2)
# Make MC projection of pdf with same LL ratio cut
# ---------------------------------------------------------------------------------------------
# Generate large number of events for MC integration of pdf projection
mcprojData = model.generate({x, y, z}, 10000)
# Calculate LL ratio for each generated event and select MC events with
# llratio)0.7
mcprojDataSel = mcprojData.reduce(Cut="llratio>0.7")
# Project model on x, projected observables (y,z) with Monte Carlo technique
# on set of events with the same llratio cut as was applied to data
model.plotOn(frame2, ProjWData=mcprojDataSel)
c = ROOT.TCanvas("rf316_llratioplot", "rf316_llratioplot", 800, 400)
c.Divide(2)
c.cd(1)
frame.GetYaxis().SetTitleOffset(1.4)
frame.Draw()
c.cd(2)
frame2.GetYaxis().SetTitleOffset(1.4)
frame2.Draw()
c.SaveAs("rf316_llratioplot.png")
[#1] INFO:Plotting -- RooAbsReal::plotOn(model) plot on x integrates over variables (z,y)
[#1] INFO:InputArguments -- The formula llratio>0.7 claims to use the variables (z,x,y,llratio) but only (llratio) seem to be in use.
inputs: llratio>0.7
[#1] INFO:InputArguments -- The formula llratio>0.7 claims to use the variables (z,x,y,llratio) but only (llratio) seem to be in use.
inputs: llratio>0.7
[#1] INFO:Plotting -- RooAbsReal::plotOn(model) plot on x averages using data variables (z,y)
[#1] INFO:Plotting -- RooAbsReal::plotOn(model) only the following components of the projection data will be used: (z,y)
[#1] INFO:Fitting -- using CPU computation library compiled with -mavx2
Date
February 2018

Definition in file rf316_llratioplot.py.