rf310_sliceplot.py File Reference

Namespaces
namespace	rf310_sliceplot

Detailed Description

Multidimensional models: projecting pdf and data slices in discrete observables

 
import ROOT
 
# Create B decay pdf with mixing
# ----------------------------------------------------------
 
# Decay time observables
dt = ROOT.RooRealVar("dt", "dt", -20, 20)
 
# Discrete observables mixState (B0tag==B0reco?) and tagFlav
# (B0tag==B0(bar)?)
mixState = ROOT.RooCategory("mixState", "B0/B0bar mixing state")
tagFlav = ROOT.RooCategory("tagFlav", "Flavour of the tagged B0")
 
# Define state labels of discrete observables
mixState.defineType("mixed", -1)
mixState.defineType("unmixed", 1)
tagFlav.defineType("B0", 1)
tagFlav.defineType("B0bar", -1)
 
# Model parameters
dm = ROOT.RooRealVar("dm", "delta m(B)", 0.472, 0., 1.0)
tau = ROOT.RooRealVar("tau", "B0 decay time", 1.547, 1.0, 2.0)
w = ROOT.RooRealVar("w", "Flavor Mistag rate", 0.03, 0.0, 1.0)
dw = ROOT.RooRealVar(
    "dw", "Flavor Mistag rate difference between B0 and B0bar", 0.01)
 
# Build a gaussian resolution model
bias1 = ROOT.RooRealVar("bias1", "bias1", 0)
sigma1 = ROOT.RooRealVar("sigma1", "sigma1", 0.01)
gm1 = ROOT.RooGaussModel("gm1", "gauss model 1", dt, bias1, sigma1)
 
# Construct a decay pdf, with single gaussian resolution model
bmix_gm1 = ROOT.RooBMixDecay(
    "bmix",
    "decay",
    dt,
    mixState,
    tagFlav,
    tau,
    dm,
    w,
    dw,
    gm1,
    ROOT.RooBMixDecay.DoubleSided)
 
# Generate BMixing data with above set of event errors
data = bmix_gm1.generate(ROOT.RooArgSet(dt, tagFlav, mixState), 20000)
 
# Plot full decay distribution
# ----------------------------------------------------------
 
# Create frame, data and pdf projection (integrated over tagFlav and
# mixState)
frame = dt.frame(ROOT.RooFit.Title("Inclusive decay distribution"))
data.plotOn(frame)
bmix_gm1.plotOn(frame)
 
# Plot decay distribution for mixed and unmixed slice of mixState
# -------------------------------------------------------------------------------------------
 
# Create frame, data (mixed only)
frame2 = dt.frame(ROOT.RooFit.Title("Decay distribution of mixed events"))
data.plotOn(frame2, ROOT.RooFit.Cut("mixState==mixState::mixed"))
 
# Position slice in mixState at "mixed" and plot slice of pdf in mixstate
# over data (integrated over tagFlav)
bmix_gm1.plotOn(frame2, ROOT.RooFit.Slice(mixState, "mixed"))
 
# Create frame, data (unmixed only)
frame3 = dt.frame(ROOT.RooFit.Title(
    "Decay distribution of unmixed events"))
data.plotOn(frame3, ROOT.RooFit.Cut("mixState==mixState::unmixed"))
 
# Position slice in mixState at "unmixed" and plot slice of pdf in
# mixstate over data (integrated over tagFlav)
bmix_gm1.plotOn(frame3, ROOT.RooFit.Slice(mixState, "unmixed"))
 
c = ROOT.TCanvas("rf310_sliceplot", "rf310_sliceplot", 1200, 400)
c.Divide(3)
c.cd(1)
ROOT.gPad.SetLeftMargin(0.15)
frame.GetYaxis().SetTitleOffset(1.4)
ROOT.gPad.SetLogy()
frame.Draw()
c.cd(2)
ROOT.gPad.SetLeftMargin(0.15)
frame2.GetYaxis().SetTitleOffset(1.4)
ROOT.gPad.SetLogy()
frame2.Draw()
c.cd(3)
ROOT.gPad.SetLeftMargin(0.15)
frame3.GetYaxis().SetTitleOffset(1.4)
ROOT.gPad.SetLogy()
frame3.Draw()
 
c.SaveAs("rf310_sliceplot.png")

Date

February 2018

Authors

Clemens Lange, Wouter Verkerke (C++ version)

Definition in file rf310_sliceplot.py.