Rf 3 1 4_Paramfitrange

Multidimensional models: working with parameterized ranges in a fit. This an example of a fit with an acceptance that changes per-event

pdf = exp(-t/tau) with t[tmin,5]

where t and tmin are both observables in the dataset

Author: Clemens Lange, Wouter Verkerke (C++ version)
This notebook tutorial was automatically generated with ROOTBOOK-izer from the macro found in the ROOT repository on Monday, January 17, 2022 at 10:00 AM.

In [ ]:
import ROOT

Define observables and decay pdf

Declare observables

In [ ]:
t = ROOT.RooRealVar("t", "t", 0, 5)
tmin = ROOT.RooRealVar("tmin", "tmin", 0, 0, 5)

Make parameterized range in t : [tmin,5]

In [ ]:
t.setRange(tmin, ROOT.RooFit.RooConst(t.getMax()))

Make pdf

In [ ]:
tau = ROOT.RooRealVar("tau", "tau", -1.54, -10, -0.1)
model = ROOT.RooExponential("model", "model", t, tau)

Create input data

Generate complete dataset without acceptance cuts (for reference)

In [ ]:
dall = model.generate({t}, 10000)

Generate a (fake) prototype dataset for acceptance limit values

In [ ]:
tmp = ROOT.RooGaussian("gmin", "gmin", tmin, ROOT.RooFit.RooConst(0), ROOT.RooFit.RooConst(0.5)).generate({tmin}, 5000)

Generate dataset with t values that observe (t>tmin)

In [ ]:
dacc = model.generate({t}, ProtoData=tmp)

Fit pdf to data in acceptance region

In [ ]:
r = model.fitTo(dacc, Save=True)

Plot fitted pdf on full and accepted data

Make plot frame, datasets and overlay model

In [ ]:
frame = t.frame(Title="Fit to data with per-event acceptance")
dall.plotOn(frame, MarkerColor="r", LineColor="r")

Print fit results to demonstrate absence of bias

In [ ]:

c = ROOT.TCanvas("rf314_paramranges", "rf314_paramranges", 600, 600)


Draw all canvases

In [ ]:
from ROOT import gROOT