rs_numbercountingutils.py

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## \file
## \ingroup tutorial_roostats
## \notebook -nodraw
## 'Number Counting Utils' RooStats tutorial
##
## This tutorial shows an example of the RooStats standalone
## utilities that calculate the p-value or Z value (eg. significance in
## 1-sided Gaussian standard deviations) for a number counting experiment.
## This is a hypothesis test between background only and signal-plus-background.
## The background estimate has uncertainty derived from an auxiliary or sideband
## measurement.
##
## Documentation for these utilities can be found here:
## https://root.cern.ch/doc/master/namespaceNumberCountingUtils.html
##
##
## This problem is often called a proto-type problem for high energy physics.
## In some references it is referred to as the on/off problem.
##
## The problem is treated in a fully frequentist fashion by
## interpreting the relative background uncertainty as
## being due to an auxiliary or sideband observation
## that is also Poisson distributed with only background.
## Finally, one considers the test as a ratio of Poisson means
## where an interval is well known based on the conditioning on the total
## number of events and the binomial distribution.
## For more on this, see
##  - http://arxiv.org/abs/0905.3831
##  - http://arxiv.org/abs/physics/physics/0702156
##  - http://arxiv.org/abs/physics/0511028
##
##
## \macro_image
## \macro_output
## \macro_code
##
## \date July 2022
## \authors Artem Busorgin, Kyle Cranmer (C++ version)
 
import ROOT
 
# From the root prompt, you can see the full list of functions by using tab-completion
# ~~~{.bash}
# root [0] RooStats::NumberCountingUtils::  <tab>
# BinomialExpZ
# BinomialWithTauExpZ
# BinomialObsZ
# BinomialWithTauObsZ
# BinomialExpP
# BinomialWithTauExpP
# BinomialObsP
# BinomialWithTauObsP
# ~~~
 
# For each of the utilities you can inspect the arguments by tab completion
# ~~~{.bash}
# root [1] NumberCountingUtils::BinomialExpZ( <tab>
# Double_t BinomialExpZ(Double_t sExp, Double_t bExp, Double_t fractionalBUncertainty)
# ~~~
 
# -------------------------------------------------
# Here we see common usages where the experimenter
# has a relative background uncertainty, without
# explicit reference to the auxiliary or sideband
# measurement
 
# -------------------------------------------------------------
# Expected p-values and significance with background uncertainty
sExpected = 50
bExpected = 100
relativeBkgUncert = 0.1
 
pExp = ROOT.RooStats.NumberCountingUtils.BinomialExpP(sExpected, bExpected, relativeBkgUncert)
zExp = ROOT.RooStats.NumberCountingUtils.BinomialExpZ(sExpected, bExpected, relativeBkgUncert)
print("expected p-value = {}  Z value (Gaussian sigma) = {}".format(pExp, zExp))
 
# -------------------------------------------------
# Expected p-values and significance with background uncertainty
observed = 150
pObs = ROOT.RooStats.NumberCountingUtils.BinomialObsP(observed, bExpected, relativeBkgUncert)
zObs = ROOT.RooStats.NumberCountingUtils.BinomialObsZ(observed, bExpected, relativeBkgUncert)
print("observed p-value = {}  Z value (Gaussian sigma) = {}".format(pObs, zObs))
 
# ---------------------------------------------------------
# Here we see usages where the experimenter has knowledge
# about the properties of the auxiliary or sideband
# measurement.  In particular, the ratio tau of background
# in the auxiliary measurement to the main measurement.
# Large values of tau mean small background uncertainty
# because the sideband is very constraining.
 
# Usage:
# ~~~{.bash}
# root [0] RooStats::NumberCountingUtils::BinomialWithTauExpP(
# Double_t BinomialWithTauExpP(Double_t sExp, Double_t bExp, Double_t tau)
# ~~~
 
# --------------------------------------------------------------
# Expected p-values and significance with background uncertainty
tau = 1
 
pExpWithTau = ROOT.RooStats.NumberCountingUtils.BinomialWithTauExpP(sExpected, bExpected, tau)
zExpWithTau = ROOT.RooStats.NumberCountingUtils.BinomialWithTauExpZ(sExpected, bExpected, tau)
print("observed p-value = {}  Z value (Gaussian sigma) = {}".format(pExpWithTau, zExpWithTau))
 
# ---------------------------------------------------------------
# Expected p-values and significance with background uncertainty
pObsWithTau = ROOT.RooStats.NumberCountingUtils.BinomialWithTauObsP(observed, bExpected, tau)
zObsWithTau = ROOT.RooStats.NumberCountingUtils.BinomialWithTauObsZ(observed, bExpected, tau)
print("observed p-value = {}  Z value (Gaussian sigma) = {}".format(pObsWithTau, zObsWithTau))