rf613_global_observables.py File Reference

Namespaces
namespace	rf613_global_observables

Detailed Description

This tutorial explains the concept of global observables in RooFit, and showcases how their values can be stored either in the model or in the dataset.

Introduction

Note: in this tutorial, we are multiplying the likelihood with an additional likelihood to constrain the parameters with auxiliary measurements. This is different from the rf604_constraints tutorial, where the likelihood is multiplied with a Bayesian prior to constrain the parameters.

With RooFit, you usually optimize some model parameters p to maximize the likelihood L given the per-event or per-bin ## observations x:

\[ L( x | p ) \]

Often, the parameters are constrained with some prior likelihood C, which doesn't depend on the observables x:

\[ L'( x | p ) = L( x | p ) * C( p ) \]

Usually, these constraint terms depend on some auxiliary measurements of other observables g. The constraint term is then the likelihood of the so-called global observables:

\[ L'( x | p ) = L( x | p ) * C( g | p ) \]

For example, think of a model where the true luminosity lumi is a nuisance parameter that is constrained by an auxiliary measurement lumi_obs with uncertainty lumi_obs_sigma:

\[ L'(data | mu, lumi) = L(data | mu, lumi) * Gauss(lumi_obs | lumi, lumi_obs_sigma) \]

As a Gaussian is symmetric under exchange of the observable and the mean parameter, you can also sometimes find this equivalent but less conventional formulation for Gaussian constraints:

\[ L'(data | mu, lumi) = L(data | mu, lumi) * Gauss(lumi | lumi_obs, lumi_obs_sigma) \]

If you wanted to constrain a parameter that represents event counts, you would use a Poissonian constraint, e.g.:

\[ L'(data | mu, count) = L(data | mu, count) * Poisson(count_obs | count) \]

Unlike a Guassian, a Poissonian is not symmetric under exchange of the observable and the parameter, so here you need to be more careful to follow the global observable prescription correctly.

 
 
import ROOT
 
 
# Setting up the model and creating toy dataset
# ---------------------------------------------
 
# l'(x | mu, sigma) = l(x | mu, sigma) * Gauss(mu_obs | mu, 0.2)
 
# event observables
x = ROOT.RooRealVar("x", "x", -10, 10)
 
# parameters
mu = ROOT.RooRealVar("mu", "mu", 0.0, -10, 10)
sigma = ROOT.RooRealVar("sigma", "sigma", 1.0, 0.1, 2.0)
 
# Gaussian model for event observables
gauss = ROOT.RooGaussian("gauss", "gauss", x, mu, sigma)
 
# global observables (which are not parameters so they are constant)
mu_obs = ROOT.RooRealVar("mu_obs", "mu_obs", 1.0, -10, 10)
mu_obs.setConstant()
# note: alternatively, one can create a constant with default limits using `RooRealVar("mu_obs", "mu_obs", 1.0)`
 
# constraint pdf
constraint = ROOT.RooGaussian("constraint", "constraint", mu_obs, mu, ROOT.RooFit.RooConst(0.2))
 
# full pdf including constraint pdf
model = ROOT.RooProdPdf("model", "model", [gauss, constraint])
 
# Generating toy data with randomized global observables
# ------------------------------------------------------
 
# For most toy-based statistical procedures, it is necessary to also
# randomize the global observable when generating toy datasets.
#
# To that end, let's generate a single event from the model and take the
# global observable value (the same is done in the RooStats:ToyMCSampler
# class):
 
dataGlob = model.generate({mu_obs}, 1)
 
# Next, we temporarily set the value of `mu_obs` to the randomized value for
# generating our toy dataset:
mu_obs_orig_val = mu_obs.getVal()
 
ROOT.RooArgSet(mu_obs).assign(dataGlob.get(0))
 
# actually generate the toy dataset
data = model.generate({x}, 1000)
 
# When fitting the toy dataset, it is important to set the global
# observables in the fit to the values that were used to generate the toy
# dataset. To facilitate the bookkeeping of global observable values, you
# can attach a snapshot with the current global observable values to the
# dataset like this (new feature introduced in ROOT 6.26):
 
data.setGlobalObservables({mu_obs})
 
# reset original mu_obs value
mu_obs.setVal(mu_obs_orig_val)
 
# Fitting a model with global observables
# ---------------------------------------
 
# Create snapshot of original parameters to reset parameters after fitting
modelParameters = model.getParameters(data.get())
origParameters = modelParameters.snapshot()
 
# When you fit a model that includes global observables, you need to
# specify them in the call to RooAbsPdf::fitTo with the
# RooFit::GlobalObservables command argument. By default, the global
# observable values attached to the dataset will be prioritized over the
# values in the model, so the following fit correctly uses the randomized
# global observable values from the toy dataset:
print("1. model.fitTo(*data, GlobalObservables(mu_obs))")
print("------------------------------------------------\n")
model.fitTo(data, GlobalObservables=mu_obs, PrintLevel=-1, Save=True).Print()
modelParameters.assign(origParameters)
 
# In our example, the set of global observables is attached to the toy
# dataset. In this case, you can actually drop the GlobalObservables()
# command argument, because the global observables are automatically
# figured out from the data set (this fit result should be identical to the
# previous one).
print("2. model.fitTo(*data)")
print("---------------------\n")
model.fitTo(data, PrintLevel=-1, Save=True).Print()
modelParameters.assign(origParameters)
 
# If you want to explicitly ignore the global observables in the dataset,
# you can do that by specifying GlobalObservablesSource("model"). Keep in
# mind that now it's also again your responsability to define the set of
# global observables.
print('3. model.fitTo(*data, GlobalObservables(mu_obs), GlobalObservablesSource("model"))')
print("------------------------------------------------\n")
model.fitTo(data, GlobalObservables=mu_obs, GlobalObservablesSource="model", PrintLevel=-1, Save=True).Print()
modelParameters.assign(origParameters)

Date

January 2022

Author

Jonas Rembser

Definition in file rf613_global_observables.py.