Efficiency versus a variable with confidence intervals

rf.efficiency fills the entries passing selection (all here) and those also passing passed with one binning, and draws their ratio with the interval ROOT's TEfficiency gives: Clopper-Pearson for unweighted entries, the normal approximation for weighted ones like these, which has no width at 100 % (two Z + jets bins); interval="wilson-effective" keeps one. The muon identification efficiency versus transverse momentum, for two samples; panel="ratio" draws the efficiency ratio of the second to the first below, with the intervals propagated asymmetrically (a scale factor when data is compared with simulation).

DefaultATLASCMSLHCbALICEDUNE

import rootfig as rf

style = rf.Style()

rf.efficiency(
    [signal, zjets], pt, passed="Muon_isTight", ylim=(0.5, None), panel="ratio", style=style
)

import rootfig as rf

style = rf.Style(experiment="ATLAS", status="Internal", lumi=140, com=13.6)

rf.efficiency(
    [signal, zjets], pt, passed="Muon_isTight", ylim=(0.5, None), panel="ratio", style=style
)

import rootfig as rf

style = rf.Style(experiment="CMS", status="Preliminary", lumi=138, com=13.6)

rf.efficiency(
    [signal, zjets], pt, passed="Muon_isTight", ylim=(0.5, None), panel="ratio", style=style
)

import rootfig as rf

style = rf.Style(experiment="LHCb", status="Preliminary", lumi=9, com=13.6)

rf.efficiency(
    [signal, zjets], pt, passed="Muon_isTight", ylim=(0.5, None), panel="ratio", style=style
)

import rootfig as rf

style = rf.Style(experiment="ALICE", status="Preliminary")

rf.efficiency(
    [signal, zjets], pt, passed="Muon_isTight", ylim=(0.5, None), panel="ratio", style=style
)

import rootfig as rf

style = rf.Style(experiment="DUNE", status="Preliminary")

rf.efficiency(
    [signal, zjets], pt, passed="Muon_isTight", ylim=(0.5, None), panel="ratio", style=style
)

Setup

The code reads the toy dataset. In a checkout of rootfig, python examples/gallery writes it to examples/out/ in a few seconds; run the code inside that directory.

It uses these samples and variables, shared by the gallery examples (see Samples, variables, cuts and styles):

signal = rf.Sample("signal.root", tree="events", label="Signal", weight="weight", scale=0.03)
zjets = rf.Sample("background.root", tree="events", label="Z + jets", weight="weight")
diboson = rf.Sample("diboson.root", tree="events", label="Diboson", weight="weight", scale=0.15)
data = rf.Sample("data.root", tree="events", label="Data", is_data=True)
mc = [zjets, diboson, signal]  # stacked bottom to top

pt = rf.Variable("Muon_pt", bins=(30, 0, 300), label=r"$p_T^{\mu}$", unit="GeV")
mll = rf.Variable("m_ll", bins=(70, 50, 260), label=r"$m_{\ell\ell}$", unit="GeV")
met = rf.Variable("MET", bins=(40, 0, 400), label=r"$E_T^{miss}$", unit="GeV")