rf204_extrangefit.py

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## \file
## \ingroup tutorial_roofit
## \notebook -nodraw
## Addition and convolution: extended maximum likelihood fit with alternate range definition
## for observed number of events.
##
## \macro_code
##
## \date February 2018
## \authors Clemens Lange, Wouter Verkerke (C++ version)
 
import ROOT
 
# Set up component pdfs
# ---------------------------------------
 
# Declare observable x
x = ROOT.RooRealVar("x", "x", 0, 10)
 
# Create two Gaussian PDFs g1(x,mean1,sigma) anf g2(x,mean2,sigma) and
# their parameters
mean = ROOT.RooRealVar("mean", "mean of gaussians", 5)
sigma1 = ROOT.RooRealVar("sigma1", "width of gaussians", 0.5)
sigma2 = ROOT.RooRealVar("sigma2", "width of gaussians", 1)
 
sig1 = ROOT.RooGaussian("sig1", "Signal component 1", x, mean, sigma1)
sig2 = ROOT.RooGaussian("sig2", "Signal component 2", x, mean, sigma2)
 
# Build Chebychev polynomial pdf
a0 = ROOT.RooRealVar("a0", "a0", 0.5, 0.0, 1.0)
a1 = ROOT.RooRealVar("a1", "a1", -0.2, 0.0, 1.0)
bkg = ROOT.RooChebychev("bkg", "Background", x, [a0, a1])
 
# Sum the signal components into a composite signal pdf
sig1frac = ROOT.RooRealVar("sig1frac", "fraction of component 1 in signal", 0.8, 0.0, 1.0)
sig = ROOT.RooAddPdf("sig", "Signal", [sig1, sig2], [sig1frac])
 
# Construct extended comps with range spec
# ------------------------------------------------------------------------------
 
# Define signal range in which events counts are to be defined
x.setRange("signalRange", 4, 6)
 
# Associated nsig/nbkg as expected number of events with sig/bkg
# _in_the_range_ "signalRange"
nsig = ROOT.RooRealVar("nsig", "number of signal events in signalRange", 500, 0.0, 10000)
nbkg = ROOT.RooRealVar("nbkg", "number of background events in signalRange", 500, 0, 10000)
esig = ROOT.RooExtendPdf("esig", "extended signal pdf", sig, nsig, "signalRange")
ebkg = ROOT.RooExtendPdf("ebkg", "extended background pdf", bkg, nbkg, "signalRange")
 
# Sum extended components
# ---------------------------------------------
 
# Construct sum of two extended pdf (no coefficients required)
model = ROOT.RooAddPdf("model", "(g1+g2)+a", [ebkg, esig])
 
# Sample data, fit model
# -------------------------------------------
 
# Generate 1000 events from model so that nsig, come out to numbers <<500
# in fit
data = model.generate({x}, 1000)
 
# Perform unbinned extended ML fit to data
r = model.fitTo(data, Extended=True, Save=True)
r.Print()