Rf 5 1 4_ Roo Customizer¶

Using the RooCustomizer to create multiple PDFs that share a lot of properties, but have unique parameters for each category. As an extra complication, some of the new parameters need to be functions of a mass parameter.

Author: Stephan Hageboeck, CERN
This notebook tutorial was automatically generated with ROOTBOOK-izer from the macro found in the ROOT repository on Saturday, November 28, 2020 at 10:58 AM.

In [1]:
%%cpp -d
#include "RooRealVar.h"
#include "RooGaussian.h"
#include "RooPolynomial.h"
#include "RooCustomizer.h"
#include "RooCategory.h"
#include "RooFormulaVar.h"
#include <iostream>

In [2]:
// Define a proto model that will be used as the template for each category
// ---------------------------------------------------------------------------

RooRealVar E("Energy","Energy",0,3000);

RooRealVar meanG("meanG","meanG", 100., 0., 3000.);
RooRealVar sigmaG("sigmaG","sigmaG", 3.);
RooGaussian gauss("gauss", "gauss", E, meanG, sigmaG);

RooRealVar pol1("pol1", "Constant of the polynomial", 1, -10, 10);
RooPolynomial linear("linear", "linear", E, pol1);

RooRealVar yieldSig("yieldSig", "yieldSig", 1, 0, 1.E4);
RooRealVar yieldBkg("yieldBkg", "yieldBkg", 1, 0, 1.E4);

RooAddPdf model("model", "S + B model",
RooArgList(gauss,linear),
RooArgList(yieldSig, yieldBkg));

std::cout << "The proto model before customisation:" << std::endl;
model.Print("T"); // "T" prints the model as a tree

// Build the categories
RooCategory sample("sample","sample");
sample["Sample1"] = 1;
sample["Sample2"] = 2;
sample["Sample3"] = 3;

// Start to customise the proto model that was defined above.
// ---------------------------------------------------------------------------

// We need two sets for bookkeeping of PDF nodes:
RooArgSet newLeafs;           // This set collects leafs that are created in the process.
RooArgSet allCustomiserNodes; // This set lists leafs that have been used in a replacement operation.

// 1. Each sample should have its own mean for the gaussian
// The customiser will make copies of meanG for each category.
// These will all appear in the set newLeafs, which will own the new nodes.
RooCustomizer cust(model, sample, newLeafs, &allCustomiserNodes);
cust.splitArg(meanG, sample);

// 2. Each sample should have its own signal yield, but there is an extra complication:
// We need the yields 1 and 2 to be a function of the variable "mass".
// For this, we pre-define nodes with exacly the names that the customiser would have created automatically,
// that is, "<nodeName>_<categoryName>", and we register them in the set of customiser nodes.
// The customiser will pick them up instead of creating new ones.
// If we don't provide one (e.g. for "yieldSig_Sample3"), it will be created automatically by cloning yieldSig.
RooRealVar mass("M", "M", 1, 0, 12000);
RooFormulaVar yield1("yieldSig_Sample1", "Signal yield in the first sample", "M/3.360779", mass);
RooFormulaVar yield2("yieldSig_Sample2", "Signal yield in the second sample", "M/2", mass);

// Instruct the customiser to replace all yieldSig nodes for each sample:
cust.splitArg(yieldSig, sample);

// Now we can start building the PDFs for all categories:
auto pdf1 = cust.build("Sample1");
auto pdf2 = cust.build("Sample2");
auto pdf3 = cust.build("Sample3");

// And we inspect the two PDFs
std::cout << "\nPDF 1 with a yield depending on M:" << std::endl;
pdf1->Print("T");
std::cout << "\nPDF 2 with a yield depending on M:" << std::endl;
pdf2->Print("T");
std::cout << "\nPDF 3 with a free yield:" << std::endl;
pdf3->Print("T");

std::cout << "\nThe following leafs have been created automatically while customising:" << std::endl;
newLeafs.Print("V");

// If we needed to set reasonable values for the means of the gaussians, this could be done as follows:
auto& meanG1 = static_cast<RooRealVar&>(allCustomiserNodes["meanG_Sample1"]);
meanG1.setVal(200);
auto& meanG2 = static_cast<RooRealVar&>(allCustomiserNodes["meanG_Sample2"]);
meanG2.setVal(300);

std::cout << "\nThe following leafs have been used while customising"
<< "\n\t(partial overlap with the set of automatically created leaves."
<< "\n\ta new customiser for a different PDF could reuse them if necessary.):" << std::endl;
allCustomiserNodes.Print("V");

RooFit v3.60 -- Developed by Wouter Verkerke and David Kirkby
Copyright (C) 2000-2013 NIKHEF, University of California & Stanford University

[#0] WARNING:InputArguments -- The parameter 'sigmaG' with range [-1e+30, 1e+30] of the RooGaussian 'gauss' exceeds the safe range of (0, inf). Advise to limit its range.
The proto model before customisation:
0x7fd241818a50/V- RooGaussian::gauss = 0 [Auto,Dirty]
0x7fd241818000/V- RooRealVar::Energy = 1500
0x7fd241818370/V- RooRealVar::meanG = 100
0x7fd2418186e0/V- RooRealVar::sigmaG = 3
0x7fd241819768/V- RooRealVar::yieldSig = 1
0x7fd2418192a0/V- RooPolynomial::linear = 1501 [Auto,Dirty]
0x7fd241818000/V- RooRealVar::Energy = 1500
0x7fd241818f30/V- RooRealVar::pol1 = 1

PDF 1 with a yield depending on M:
0x7fd2418192a0/V- RooPolynomial::linear = 1501 [Auto,Dirty]
0x7fd241818000/V- RooRealVar::Energy = 1500
0x7fd241818f30/V- RooRealVar::pol1 = 1
0x7fd22c6f2640/V- RooGaussian::gauss_Sample1 = 0 [Auto,Dirty]
0x7fd241818000/V- RooRealVar::Energy = 1500
0x7fd2418186e0/V- RooRealVar::sigmaG = 3
0x7fd22c705e70/V- RooRealVar::meanG_Sample1 = 100
0x7fd24181b018/V- RooFormulaVar::yieldSig_Sample1 = 0.29755 [Auto,Clean]
0x7fd24181aca8/V- RooRealVar::M = 1

PDF 2 with a yield depending on M:
0x7fd2418192a0/V- RooPolynomial::linear = 1501 [Auto,Dirty]
0x7fd241818000/V- RooRealVar::Energy = 1500
0x7fd241818f30/V- RooRealVar::pol1 = 1
0x7fd22c6c96f0/V- RooGaussian::gauss_Sample2 = 0 [Auto,Dirty]
0x7fd241818000/V- RooRealVar::Energy = 1500
0x7fd2418186e0/V- RooRealVar::sigmaG = 3
0x7fd22c7211a0/V- RooRealVar::meanG_Sample2 = 100
0x7fd24181b3d8/V- RooFormulaVar::yieldSig_Sample2 = 0.5 [Auto,Clean]
0x7fd24181aca8/V- RooRealVar::M = 1

PDF 3 with a free yield:
0x7fd2418192a0/V- RooPolynomial::linear = 1501 [Auto,Dirty]
0x7fd241818000/V- RooRealVar::Energy = 1500
0x7fd241818f30/V- RooRealVar::pol1 = 1
0x7fd22c708b60/V- RooGaussian::gauss_Sample3 = 0 [Auto,Dirty]
0x7fd241818000/V- RooRealVar::Energy = 1500
0x7fd2418186e0/V- RooRealVar::sigmaG = 3
0x7fd22c6eca60/V- RooRealVar::meanG_Sample3 = 100
0x7fd22c578210/V- RooRealVar::yieldSig_Sample3 = 1

The following leafs have been created automatically while customising:
1) RooRealVar::    meanG_Sample1 = 100
2) RooRealVar::    meanG_Sample2 = 100
3) RooRealVar::    meanG_Sample3 = 100
4) RooRealVar:: yieldSig_Sample3 = 1

The following leafs have been used while customising
(partial overlap with the set of automatically created leaves.
a new customiser for a different PDF could reuse them if necessary.):
1) RooFormulaVar:: yieldSig_Sample1 = 0.29755
2) RooFormulaVar:: yieldSig_Sample2 = 0.5
3) RooRealVar::    meanG_Sample1 = 200
4) RooRealVar::    meanG_Sample2 = 300
5) RooRealVar::    meanG_Sample3 = 100
6) RooRealVar:: yieldSig_Sample3 = 1