Search for type-III seesaw heavy leptons in
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ATLAS CONF Note
ATLAS-CONF-2018-020
May 31, 2018
Search for type-III seesaw
√ heavy leptons in
proton-proton collisions at s = 13 TeV with the
ATLAS detector
The ATLAS Collaboration
A search for the pair production of heavy leptons (N 0 , L ± ) as predicted by the type-III seesaw
mechanism is presented. The search uses proton-proton collision data at a centre-of-mass
energy of 13 TeV corresponding to 79.8 fb−1 of integrated luminosity recorded in 2015, 2016
and 2017 by the ATLAS detector at the Large Hadron Collider. The analysis focuses on the N 0
ATLAS-CONF-2018-020
and L ± decays with intermediate W bosons and yielding two final-state light leptons (electrons
or muons) of different flavor and charge combinations, with at least two jets and large missing
transverse momentum in the final state. The search is optimized in six channels distinguished
by the flavor combination and charge product of the final state lepton pair, where same
04 June 2018
charge or opposite charge final states are considered. Good agreement is observed between
the number of events in data and Standard Model predictions. The results are translated
into exclusion limits on heavy lepton m(N 0, L ± ) masses, where N 0 and L ± are considered
mass-degenerate. The observed lower 95 % confidence level limit on the mass of the type-III
seesaw heavy leptons, where the branching fractions to all lepton flavors are assumed to be
equal, is 560 GeV.
© 2018 CERN for the benefit of the ATLAS Collaboration.
Reproduction of this article or parts of it is allowed as specified in the CC-BY-4.0 license.
1 Introduction
The experimental observation of neutrino oscillation provides strong evidence that neutrinos have non-zero
masses which are much smaller than those of the charged leptons (see Ref. [1] and references therein).
In the Standard Model (SM) only the charged fermions acquire masses by coupling to the Higgs (H)
boson, while neutrino mass terms in the lagrangian are introduced with recourse to new physics beyond
the SM. The seesaw mechanism [2, 3] provides an elegant framework to explain the relative smallness
of the neutrino masses, compared to other fundamental particles. It introduces a neutrino mass matrix
with Majorana mass terms and very light neutrino mass eigenvalues, emerging from the existence of a
heavy neutrino partner for each of the light neutrino species. Several models implementing the seesaw
mechanism exist (see e.g. Ref. [4] for a concise overview) with different particle content. Among these, the
type-III model [5] introduces at least two extra triplets of heavy fermionic fields with zero hyper-charge in
the adjoint representation of SU(2)L that couple to electroweak (EW) gauge bosons and generate neutrino
masses through Yukawa couplings to the Higgs boson and neutrinos. Consequently, these new charged
and neutral heavy leptons could be produced in EW processes in proton–proton collisions at the Large
Hadron Collider (LHC).
Several type-III seesaw heavy lepton searches were performed in the past. The search presented in this note
√
is an extension of a similar search performed by ATLAS in Run 1 at s = 8 TeV [6], which excluded the
heavy leptons with masses below 335 GeV for the same decay channels as analyzed in this note. In Run 1
this search was was complemented by another ATLAS search for heavy leptons using the three-lepton
final state [7], excluding heavy lepton masses below 470 GeV. A Run 2 search by the CMS experiment
√
at s = 13 TeV [8] was performed on multi-lepton final states using at least three leptons, excluding the
type-III seesaw heavy leptons with masses in the range up to 840 GeV.
The search presented in this note extends results from existing ATLAS searches to higher mass regions.
A minimal type-III seesaw model [9] is used to optimize the analysis strategy and interpret the search
results. This model introduces a single fermionic triplet of unknown (heavy) masses with one neutral
and two oppositely-charged leptons denoted by (L +, L −, N 0 ). Here L + is the antiparticle of L − and N 0 is
a Majorana particle. These heavy leptons decay into a SM lepton and a W, Z or H boson. The heavy
leptons are assumed to be degenerate in mass. This assumption does not affect the result because the
theoretical calculations predict only a small mass splitting due to radiative corrections and the resulting
possible decays among the heavy leptons are highly suppressed [10].
The dominant production mechanism for type-III seesaw heavy leptons in pp collisions is pair-production
through pp → W ∗ → N 0 L ± , and the largest branching fraction is the one with two W bosons in
the final state: N 0 → W ± ` ∓ (` = e, µ, τ), and L ± → W ± ν (ν = νe, νµ, ντ ). Branching fractions through
intermediate W bosons range between approximately 80 % down to approximately 60 % over the considered
heavy lepton mass range. This search is consequently optimized for the dominant processes pp →
N 0 L ± → W ± ` ∓W ± ν, where one W boson decays leptonically and the other decays hadronically (Feynman
diagrams in Figure 1). Only final states containing electrons and muons are considered, also including
those from leptonic tau decays.
In summary, the exclusive topology of the final state consists of two jets from the hadronically decaying
W boson, large missing transverse momentum (ETmiss ) and a lepton pair with either the same-sign charge
(SS) or with the opposite-sign charge (OS) and with either same-flavor (ee or µµ) or different-flavor (eµ)
combinations.
2l± l±
q q
l∓ q
N0 N0
W∓ W∓
W± ν W± q
q l±
W± W±
L± q L± ν
q q
ν ν
(a) (b)
Figure 1: Feynman diagrams of the considered production process in the (a) OS and (b) SS final state cases.
The remaining degrees of freedom in the simplified model are the unknown mixing angles Vα (α = e, µ, τ)
between the SM leptons and the new heavy lepton states, which enter only in the expressions for the L ±
and N 0 decay widths. The production cross section does not depend on these mixing angles Vα because
the heavy leptons are produced through the coupling to the EW bosons. Only the branching fraction BRα
of the L ± and N 0 decays to lepton flavor α depends on the values of the mixing angles and is proportional
to BRα = |Vα | 2 /(|Ve | 2 + |Vµ | 2 + |Vτ | 2 ). In this analysis the decay branching ratios are considered equal for
all three lepton flavors, with BRe = BRµ = BRτ = 1/3.
32 ATLAS detector
The ATLAS detector [11] at the LHC is a multi-purpose particle detector with a forward-backward
symmetric cylindrical geometry and a near 4π coverage in solid angle around the collision point. It
consists of an inner tracking detector surrounded by a thin superconducting solenoid, electromagnetic
and hadronic calorimeters, and a muon spectrometer incorporating three large superconducting toroidal
magnets.
The inner-detector system (ID) is immersed in a 2 T axial magnetic field and provides charged particle
tracking in the range1 |η| < 2.5. The inner tracking detector consists of a silicon pixel detector, which
has an additional innermost layer (positioned at a radial distance of 3.3 cm from the beam line) that has
been installed since the end of Run 1 (IBL) [12], and a silicon microstrip detector surrounding the pixel
detector, both covering |η| < 2.5, followed by a transition radiation straw-tube tracker covering |η| < 2.
The calorimeter system covers the pseudorapidity range |η| < 4.9. Within the region |η| < 3.2, electro-
magnetic calorimetry is provided by barrel and endcap high-granularity lead/liquid-argon (LAr) electro-
magnetic calorimeters, with an additional thin LAr presampler covering |η| < 1.8, to correct for energy
loss in material upstream of the calorimeters. Hadronic calorimetry is provided by the steel/scintillating-
tile calorimeter, segmented into three barrel structures within |η| < 1.7, and two copper/LAr hadronic
endcap calorimeters. The solid angle coverage is completed with forward copper/LAr and tungsten/LAr
calorimeter modules optimized for electromagnetic and hadronic measurements respectively.
The muon spectrometer (MS) comprises separate trigger and high-precision tracking chambers measuring
the deflection of muons in a magnetic field generated by superconducting air-core toroids. The field
integral of the toroids ranges between 2.0 and 6.0 T m across most of the detector. A set of precision
chambers covers the region |η| < 2.7 with three layers of monitored drift tubes, complemented by cathode
strip chambers in the forward region, where the background is highest. The muon trigger system covers
the range |η| < 2.4 with resistive plate chambers in the barrel, and thin gap chambers in the endcap
regions.
A two-level trigger system is used to select interesting events [13]. The first-level trigger is implemented
in hardware and uses a subset of detector information to reduce the event rate to a design value of at most
100 kHz. This is followed by a software-based trigger which further reduce the event rate to approximately
1 kHz.
1 ATLAS uses a right-handed coordinate system with its origin at the nominal interaction point (IP) in the centre of the detector
and the z-axis along the beam pipe. The x-axis points from the IP to the centre of the LHC ring, and the y-axis points
upwards. Cylindrical coordinates (r, φ) are used in the transverse plane, φ being the azimuthal angle around the z-axis.
The pseudorapidity
p is defined in terms of the polar angle θ as η = − ln tan(θ/2). Angular distance is measured in units of
∆R ≡ (∆η)2 + (∆φ)2 .
43 Data and simulated samples
The data used in this analysis correspond to proton-proton (pp) collisions recorded at the LHC with
proton bunches colliding every 25 ns. Data quality criteria are applied to ensure that events were recorded
with stable beam conditions and with all relevant sub-detector systems operational. After data quality
requirements, the samples used for this analysis correspond to 3.2 fb−1 , 33.0 fb−1 , and 43.6 fb−1 of inte-
grated luminosity recorded in 2015, 2016 and 2017, respectively, for a total of 79.8 fb−1 . In each of the
three periods of data taking, the average number of interactions per bunch crossing was 13, 25, and 38,
respectively.
The uncertainty on the combined 2015–2017 integrated luminosity is 2.0 %. It is derived, following a
methodology similar to that detailed in Ref. [14], from calibrations of the luminosity scale using x − y
beam-separation scans performed in August 2015, May 2016 and July 2017 (the results for 2017 are still
preliminary).
Samples of signal and background processes are simulated using several Monte Carlo (MC) gener-
ators. The signal considered in the simplified type-III seesaw model is implemented in the Mad-
Graph5_aMC@NLO [15] generator at leading-order (LO) using FeynRules [9]. For the simulated
signal production MadGraph5_aMC@NLO is interfaced to Pythia 8.212 [16] for parton showering.
The A14 parameter set [17] is used for tuning the shower. The NNPDF3.0LO [18] parton distribution
function (PDF) set enters in the matrix element calculation and the NNPDF2.3LO [19] is used in the
parton shower.
Simulated background samples include Drell–Yan (q q̄ → Z/γ ∗ → ` + ` − (` = e, µ, τ)), diboson (WW,
Z Z, W Z), top quark pair (t t¯) and single top quark production processes. The generators used for the MC
samples and the cross section calculation used for their normalization are provided in Table 1. However,
the normalization of some MC samples is considered as a free parameters in the final likelihood fit, as
described in Section 8, to ensure that any possible normalization mis-modeling of the simulated samples
is corrected in the specific phase space considered in this search.
Table 1: Simulated signal and background event samples: the corresponding event generator, parton shower, cross
section normalization, PDF set used for the Matrix Element (ME) calculation and set of tuned parameters are
shown for each sample. The generator cross section of the generator used to generate the sample is used where not
specifically stated otherwise.
Physics process Event generator ME PDF set Cross-section Parton shower Parton shower
normalization tune
Type-III Seesaw N 0 /L ± MadGraph5_aMC@NLO [15] NNPDF3.0LO [18] LO Pythia 8.212 [16] A14 [17]
Drell-Yan
Z/γ ∗ → e+ e− /µ + µ − /τ + τ − Sherpa 2.2.1 [20] NNPDF3.0NNLO [18] NLO [21] Pythia 8.212 Sherpa default
Top physics
t t̄ Powheg-Box v2 [22–24] NNPDF3.0NNLO NNLO [25] Pythia 8.212 A14
Single t Powheg-Box v2 CT10 [26] NLO [27] Pythia 6.428 [28] Perugia 2012 [29]
Diboson
Z Z, W Z, WW Sherpa 2.2.1 & 2.2.2 [20] NNPDF3.0NNLO NLO [30] Pythia 8.212 Sherpa default
The SM Drell–Yan processes with decays Z → ee, Z → µµ, and Z → ττ are simulated using the
Sherpa 2.2.1 event generator [20]. Matrix elements are calculated for up to 2 partons at next-to-leading-
5order (NLO) and 4 partons at LO using Comix [31] and OpenLoops [32] and merged with the Sherpa
parton shower [33] according to the ME+PS@NLO prescription [34]. The NNPDF3.0NNLO [18] PDF
set is used.
The t t¯ process is simulated using the NLO QCD generator Powheg-Box v2 [22–24] interfaced to
Pythia 8.212 for parton showering. The A14 parameter set is used for tuning the shower. The
NNPDF3.0NNLO PDF set enters in the matrix element calculation while it is NNPDF2.3NLO in the
parton shower. Additionally, top-quark spin correlations are preserved through the use of MadSpin [35].
The predicted t t¯ production cross section is 831.8+19.8
−29.2 (scale) ± 35.0 (PDF + αS ) pb as calculated with
Top++2.0 [25] at the NNLO in perturbative QCD, including soft-gluon resummation to next-to-next-to-
leading-log order. The top-quark mass is assumed to be 172.5 GeV. The scale uncertainty results from
an independent variation of the factorization and renormalisation scales, while the second uncertainty
is associated with variations of the PDF set and αS , following the PDF4LHC [36] prescription using
MSTW2008 68 % CL NNLO [37], CT10 NNLO [38], and NNPDF2.3 PDF.
Single-top-quark events produced in Wt final states are generated by Powheg-Box v2 with the CT10 PDF
set used in the matrix element calculations. The parton shower, fragmentation, and underlying event are
simulated with Pythia 6.428 using the CTEQ6L1 PDF sets and the corresponding Perugia 2012 tune.
The top-quark mass is set to 172.5 GeV. The EvtGen v1.2.0 program [39] is used to model bottom and
charm hadron decays. The NLO cross sections used to normalize these MC samples are summarized in
Ref. [27]. The s- and t-channel processes are included in the fake background described in Section 6.
Diboson processes with four charged leptons, three charged leptons plus one neutrino or two charged
leptons and two neutrinos are simulated with the Sherpa 2.2.2 event generator. Matrix elements contain
all diagrams with four electroweak vertices. They are calculated for up to one (4l, 2l+2ν) or zero partons
(3l+1ν) at NLO and up to three partons at LO using Comix and OpenLoops, and merged with the Sherpa
parton shower according to the ME+PS@NLO prescription. The NNPDF3.0NNLO PDF set is used in
conjunction with dedicated parton shower tuning. The event generator cross sections are used in this case
(already at NLO).
Diboson processes with one of the bosons decaying hadronically and the other leptonically are simulated
with the Sherpa 2.2.1 event generator. They are calculated for up to one additional parton at NLO and up
to three additional partons at LO using Comix and OpenLoops, and merged with the Sherpa parton shower
according to the ME+PS@NLO prescription. The NNPDF3.0NNLO PDF set is used in conjunction with
dedicated parton shower tuning. The event generator cross sections are used in this case (already at
NLO).
For all samples, a full simulation of the ATLAS detector response [40] using the Geant 4 toolkit [41]
was performed. The effect of multiple pp interactions in the same and neighboring bunch crossings
(pileup) is included by overlaying minimum-bias events, simulated with Pythia 8.212 using the A3 set of
tuned parameters [42] and the NNPDF2.3LO PDF set of tuned parameters, on each generated signal and
background event. The number of overlaid events is such that the distribution of the average number of
interactions per pp bunch crossing in the simulation matches that observed in data.
Other rare SM processes not mentioned in this section, e.g. t t¯ + V decays, where V = Z, W, H, do not
contribute significantly to the regions defined in this analysis and have therefore not been considered.
64 Event reconstruction
In addition to data quality criteria which ensure that the detector was functioning properly, events are
rejected if they contain reconstructed jets not associated to real energy deposits that can arise from
hardware problems, beam conditions or cosmic ray showers using the methods from Ref. [43]. To further
increase the purity and quality of the data sample by rejecting non-collision events originating from
cosmic rays and beam-halo events, at least one reconstructed primary vertex is required with at least two
associated tracks. The primary vertex is chosen as the pp vertex candidate with the highest sum of the
squared transverse momenta of all associated tracks.
Electron candidates are reconstructed from energy deposits in the electromagnetic calorimeter associated
with a charged-particle track measured in the inner detector. The electron candidates are required to pass
the Medium likelihood-based identification selection [44, 45], to have transverse momentum pT > 30 GeV
and to be in the fiducial volume of the inner detector, |η| < 2.47. The transition region between the
barrel and end-cap calorimeters (1.37 < |η| < 1.52) is excluded. The track associated with the electron
candidate must have an impact parameter evaluated at the point of closest approach between the track and
the beam axis in the transverse plane (d0 ) that satisfies |d0 |/σ(d0 ) < 5, where σ(d0 ) is the uncertainty on
d0 . A longitudinal impact parameter value of |z0 sin(θ)| < 0.5 mm is also required for electrons, where z0
is the distance between the track and the interaction point and θ is the azimuthal angle of the track. The
combined identification and reconstruction efficiency for Medium electrons ranges from 73 % to 92 % in
the pT range from 10 GeV to 80 GeV as measured in Z → ee events [45]. The electron candidates must
also pass the loose isolation criterion, which uses calorimeter-based and track-based isolation requirements
with an efficiency of about 99 %, as also measured in Z → ee events described in Ref. [45]. Electron
candidates are ignored if their angular distance to a jet is within 0.2 < ∆R < 0.4.
Muon candidates are constructed by matching an inner detector track with a track reconstructed in the
muon spectrometer [46]. The muon candidates are required to have pT > 30 GeV, |η| < 2.5 and the impact
parameter significance of |d0 |/σ(d0 ) < 3. As it is done for electrons, a longitudinal impact parameter
value of |z0 sin(θ)| < 0.5 mm is required for muons. Muon candidates are required to pass the Medium
muon identification requirements and must also fulfill the required track-based isolation requirements.
This results in a reconstruction and identification efficiency of above 95 % over the full phase space as
measured in Z → µµ events [46]. Muons within ∆R < 0.4 of a jet having more than three tracks and
with pT (µ)/pT (jet) < 0.5 are discarded. If a muon overlapping with an electron leaves a sufficiently high
energy deposit in the calorimeter and shares a track reconstructed in the inner detector with the electron
the muon is also discarded.
Jets are reconstructed by clustering energy deposits in the calorimeter using the anti-k t algorithm [47]
with the radius parameter of 0.4. The measured jet transverse momentum is corrected for detector effects
by weighting energy deposits arising from electromagnetic and hadronic showers differently. For all jets
the expected average transverse energy contribution from pileup is corrected using a subtraction method.
This is based on the level of diffuse noise added to the event per unit area by a pileup event which is
subtracted from the jet area in η and φ space and a residual correction derived from the MC simulation,
as detailed in Ref. [48, 49]. To reduce the contamination from jets coming from pileup, a ‘Jet Vertex
Tagger’ (JVT) algorithm [50] is used for jets with pT < 60 GeV and |η| < 2.4. It employs a multivariate
technique that relies on jet energy and tracking variables to determine the likelihood that the jet originates
from pileup. The Medium JVT working point is used with the average efficiency of 92 %. Jets within
∆R < 0.2 of an electron are discarded. Jets within ∆R < 0.2 of a muon and featuring less than three tracks
or having pT (µ)/pT (jet) > 0.5 are also removed. Jets considered in this analysis are required to have
7pT > 20 GeV with |η| < 2.5. Jets fulfilling these kinematic criteria are identified as containing b-hadrons
and categorized as b-tagged jets if tagged by a multivariate algorithm which uses information about the
impact parameters of inner-detector tracks matched to the jet, the presence of displaced secondary vertices,
and the reconstructed flight paths of b- and c-hadrons inside the jet [51–53]. The algorithm is used at the
working point providing a b-tagging efficiency of 77 %, as determined in a simulated sample of t t¯ events.
This corresponds to a rejection factor of approximately 134, 6 and 22 for light-quark and gluon jets, c-jets,
and τ-leptons decaying hadronically, respectively.
Missing transverse momentum p® miss
T (with magnitude ETmiss ) is present in events with unbalanced kinemat-
ics in the transverse plane. This originates from undetected momenta in the event, due to either neutrinos
escaping detection or to other particles outside the detector acceptance, badly reconstructed, or failing
reconstruction. The ETmiss is calculated as the modulus of the negative vectorial sum of the pT of the fully
calibrated and reconstructed physics objects in the event. Jets in the forward direction are considered if
they satisfy pT > 30 GeV. An additional ‘soft term’ is added, accounting for all tracks in the event which
are not associated with any reconstructed object, but are associated with the identified primary vertex.
The usage of this track-based ‘soft term’ is motivated by an improved performance in ETmiss reconstruction
in a high pileup environment [54].
Correction factors to account for differences in the identification and selection efficiency, reconstructed
energy and energy resolution between data and MC simulation are applied to the selected electrons, muons
and jets in MC simulation, as described in Ref. [44–46, 49].
85 Analysis strategy and event selection
All selected events are required to contain at least a pair of light leptons (electrons or muons) in one of
the possible flavor combinations (ee, eµ or µµ) where the pairs can be of either SS or OS. All events and
reconstructed physics objects must pass the requirements defined in Section 4. Accordingly, the six OS
and SS analysis channels are defined, according to the flavor combination ee, eµ, µµ. Events must pass
either single or double lepton triggers using the pT thresholds summarized in Table 2.
Table 2: Summary of the triggers used to select events for the three analysis channels during 2015, 2016 and 2017
data taking, presenting the trigger flavor type and pT thresholds. For the electron+muon trigger the first number
corresponds to the electron threshold, the second to the muon threshold.
Analysis Channel Trigger pT thresholds [GeV]
(OS and SS) flavor type 2015 2016 2017
ee dielectron 12/12 17/17 24/24
eµ electron+muon 17/14 17/14 17/14
µµ single muon 26 26 26
Selected events are then categorized into exclusive categories called analysis regions based on different
sets of requirements on reconstructed objects. These regions are grouped according to their purpose: in
control regions (CR) one fits the background normalizations to the data; in validation regions (VR) one
can validate the background estimation methods by comparing the background model with data; finally,
signal regions (SR), with enhanced signal-to-background ratios, are used to compare data to the expected
signal-and-background hypothesis using statistical methodology detailed in Section 8.
All regions used in this analysis, with the corresponding selection criteria, are summarized in Table 3 and
described below. The region selection criteria are the same for all analysis channel flavor combinations.
Table 3: Summary of all analysis regions defined in the analysis. The region definitions are the same for all analysis
channel flavor combinations. The selection criteria specific to a particular region are marked in bold.
OS (` + ` − = e+ e− , e± µ∓ , µ+ µ− ) SS (` ± ` ± = e± e± , e± µ± , µ± µ± )
Top CR Z + jets VR m j j VR SR Z + jets VR m j j VR m j j CR SR
N(jet) ≥2 ≥2 ≥2 ≥2 ≥2 ≥2 ≥2 ≥2
N(b-jet) ≥2 0 0 0 0 0 0 0
m j j [GeV] [60, 100) [60, 100) [35, 60) ∪ [100, 125) [60, 100) [60, 100) [0, 60) ∪ [100, 300) [0, 60) ∪ [100, 300) [60, 100)
m`` [GeV] [110, ∞) [70, 110) [110, ∞) [110, ∞) [70, 100) [100, ∞) [100, ∞) [100, ∞)
Sig(ETmiss ) ≥5 ≥5 ≥ 10 ≥ 10 ≥5 ≥5 ≥5 ≥ 7.5
∆φ(ETmiss, l)min ≥1
pT ( j j) [GeV] [100, ∞) [60, ∞)
pT (``) [GeV] [100, ∞) [100, ∞)
HT + ETmiss [GeV] [300, ∞) [300, ∞) [300, ∞) [300, ∞) [500, ∞) [300, 500) [300, ∞)
As the signal process contains neutrinos in the final state, one of the most important selection criteria is
based on the ETmiss significance Sig(ETmiss ) [54]. The value of Sig(ETmiss ) is calculated using a maximum
likelihood ratio method considering the direction of the ETmiss and the calibrated objects as well their
respective resolutions. The SR selection criteria were optimized to maximize the analysis sensitivity. This
results in different selection values for the OS and SS channels due to a different background composition
and resulting event topologies. Consequently the ETmiss significance selection value is Sig(ETmiss ) > 10 for
the OS channels and Sig(ETmiss ) > 7.5 for SS channels.
9At least 2 jets with pT > 20 GeV and |η| < 2.5 are required for each region. A b-jet veto is applied on
all the jets in SR events to reject background coming from SM processes involving top quarks. For each
signal event, the invariant dijet mass (m j j ) of the two leading (highest pT ) jets is expected to be close to
the W mass. The dijet invariant mass is thus required to be in a window 60 GeV < m j j < 100 GeV.
A lower bound on the invariant mass of the lepton pair (m`` ) in SR events is applied. It is chosen to be
110 GeV in the OS regions and 100 GeV in the SS regions. This choice aims to remove the events in the
Drell–Yan Z → ee peak, including events with mis-reconstructed charge.
In the OS channels the azimuthal angle ∆φ(ETmiss, l)min between the directions of four momenta of ETmiss
and closest lepton has a very good separation power, exploiting the different nature of ETmiss between signal
and background, where the latter tends to have a spurious component due to mis-reconstructed jets. For
this variable, a requirement ∆φ(ETmiss, l)min > 1 is used.
To further increase the expected signal significance additional selection criteria are introduced: the dijet
transverse momentum pT ( j j) is required to be greater than 100 GeV (60 GeV) for the OS (SS) regions,
while the dilepton transverse momentum pT (``) must be greater than 100 GeV for both OS and SS events.
These selection requirements exploit the boosted decay topology of object pairs, which would be induced
by the presence of heavy leptons. For the SS regions the requirement on pT ( j j) is somewhat looser than
is OS SR and has been optimized to maximize signal sensitivity.
The dominant background contribution in the OS channels is the SM t t¯ production in which the two W
bosons in the final state decay leptonically. To estimate the contribution from the t t¯ and single top decays,
an OS control region enriched in top quark events is defined (Top CR). The events in the Top CR pass all
the SR selections with the exception of an inverted b-tagging requirement: here at least two b-tagged jets
are required instead of imposing a b-jet veto. The normalization of the t t¯ MC sample is considered as a
free parameter in the simultaneous likelihood fit across all CRs, as described in Section 8.
The SS channels are particularly sensitive to charge mis-reconstruction, therefore dedicated Z + jets VR
are defined in order to validate the simulated charge reconstruction modeling. This region is also very
good for the Drell–Yan process validation even though it is not expected to have a sizable contribution in
the SR. For the Z + jets VR, the dilepton mass requirement is reversed to be 60 GeV < m`` < 110 GeV for
the OS and 60 GeV < m`` < 100 GeV for the SS channels.
The dijet mass selection is inverted to form a validation region (m j j VR) for both the OS and SS channels.
The events selected in the m j j VR are expected to have very similar kinematics to the SR events but with
low contributions from the possible signal contaminations and are thus a good VR for the background in
both OS and SS channels.
The m j j region is for SS events split into a m j j CR and m j j VR by further restricting this selection into
300 GeV < HT + ETmiss < 500 GeV for CR and HT + ETmiss > 500 GeV for VR. The normalization of the
diboson MC sample used in the analysis is then estimated in this CR because this (side-band) region results
in a good purity of diboson events. This is achieved by considering the normalization value of the diboson
MC as a free parameter in the simultaneous likelihood fit across all CRs, as described in Section 8.
In most of the defined CRs and VRs the Sig(ETmiss ) selection is relaxed to Sig(ETmiss ) > 5, with the exception
of the OS m j j VR, and all pT selection criteria are removed to increase the statistical precision in the
regions.
The signal process topology is expected to contain high-pT leptons, jets and neutrinos, which can best be
described by two reconstructed variables: the sum of the ETmiss and the scalar sum of the transverse momenta
10HT . A HT + ETmiss > 300 GeV selection criterion is applied in all analysis regions with the exception of the
same-sign Z + jets VR, where the reduction of the sample statistics would be too severe. The HT +ETmiss
is also the observable used in the final likelihood fit, described in Section 8. For the likelihood fit, the
CRs and SRs are binned in the HT +ETmiss variable in bins uniformly defined in log(HT + ETmiss ) in the
range 300 GeV < HT + ETmiss < 2 TeV, where the last bin also includes any overflow values. The SRs
have six, the Top CRs three and m j j CRs two bins.The VRs are binned in the same way for presentation
purposes.
116 Background composition and estimation
The final-state topologies of the six analysis channels have different background compositions. Back-
ground estimation techniques, implemented consistently among the channels, are discussed in this sec-
tion. In general, the number of expected background events and the associated kinematic distributions are
derived from a mixture of data-driven methods and simulation.
Irreducible backgrounds containing real prompt leptons originate from SM processes producing opposite-
sign and same-sign lepton pairs. Irreducible background and signal model predictions are obtained from
Monte Carlo simulated samples which are summarized in Section 3. The final normalizations of t t¯ and
diboson MC samples are not taken from MC calculations but are derived in the simultaneous likelihood
fit to the data in dedicated Top and m j j CRs, as introduced in Section 5 and detailed in Section 8.
The same MC samples also provide a source of reducible background due to charge mis-identification
(charge-flip) in channels that contain electrons. The effect of muon charge mis-identification has been
shown to be negligible. The modeling of charge misidentification in MC simulation deviates from data
and consequently charge reconstruction scale factors are derived in a data-driven way by comparing the
charge misidentification probability measured in data to the one in simulation. These scale factors are
then applied to the simulated background events to compensate for the differences.
Electron charge misidentification is caused predominantly by bremsstrahlung. The emitted photon can
either convert to an electron–positron pair, which happens in most of the cases, or traverse the inner detector
without creating any track. In the first case, the cluster corresponding to the initial electron can be matched
to the wrong-charge track, or most of the energy is transferred from one track to the other because of the
photon. In case of photon emission without subsequent pair production, the electron track has usually
very few hits only in the silicon pixel layers, and thus a short lever arm on its curvature. Because the
electron charge is derived from the track curvature, it could be incorrectly determined while the electron
energy is likely appropriate as the emitted photon deposits all of its energy in the EM calorimeter as well.
For a similar reason high-energy electrons are more often affected by charge misidentification, as their
tracks are approximately straight and therefore challenging for the curvature measurement. The modeling
of charge misidentification in simulation deviates from data due to the complex processes involved, which
particularly rely on a very precise description of the detector material.
The charge misidentification probability is extracted by performing a likelihood fit on a dedicated Z → ee
data sample (see Figure 2). The Z → ee events in this special sample are divided into two separate regions
of OS and SS events, called the OC (opposite-charge) and SC (same-charge) regions, respectively. These
two regions are then further divided into three sub-regions, according to the invariant mass of the electron
pair m(ee): a central region including the Z peak and two orthogonal side-bands. The purpose of the
side-bands is to estimate the non-Z background, which is assumed to be uniformly distributed in these
regions, and subtract it from the central region.
The OC and SC Z peaks are presented in Figure 2. The Z peak in the SC region is shifted by approximately
1.5 GeV to lower energies and the width is slightly broader compared to the Z peak in the OC region. This
is due to electron charge-flip events arising from an electron radiating a photon which is then converted
into an electron-positron pair. The particle with the wrong charge is reconstructed, which has lower energy
than the parent electron. The definition of the OC and SC regions is presented in Table 4.
12Table 4: Definitions of the OC and SC regions and the three sub-regions, central and side-band used in charge
misidentification probability estimation. The position of the Z peak as measured in the two regions is indicated by
mOC (Z) and mSC (Z).
|m(ee) − mSC,OC (Z)|
Region central sub-region side-bands
OC < 11.8 GeV 11.8 GeV − 17.7 GeV
SC < 13.6 GeV 13.6 GeV − 27.2 GeV
×106 ×103
Events / (1.0 GeV)
4.5
ATLAS Preliminary SC data
4 Z → ee peak SC SM sim. 30
s = 13 TeV, 79.8 fb-1 OC data
3.5 25
OC SM sim.
3
20
2.5
2 15
1.5 10
1
5
0.5
0 0
70 75 80 85 90 95 100 105 110
m(ee) [GeV]
Figure 2: Dielectron invariant mass distributions for opposite-charge (OC, black) and same-charge (SC, red) pairs
for data (circles) and MC simulation (continuous line). The latter includes a correction for charge misidentification.
The hatched band indicates the statistical error and the luminosity uncertainty summed in quadrature applied to MC
simulated events.Please note that the scales for OC and SC are different and given at the left side (OC) and right
side (SC), respectively.
ij ij
The numbers of OC and SC electron pairs in the two regions (N i j = NOC + NSC ), where i and j indicate
the kinematic configurations of the two electrons in the pair, are then used as inputs of the likelihood fit.
ij
The probability to observe NSC same-charge pairs is the Poisson probability:
ij
ij λ NSC e−λ
f (NSC ; λ) = ij
,
NSC !
with λ = N i j (Pi (1 − P j ) + P j (1 − Pi )) denoting the expected number of same-charge pairs, given the
ij
charge misidentification probabilities Pi and P j . NSC is the measured number of same-charge pairs. The
formula for the negative log likelihood used in the likelihood fit is given in Eq. 1:
Õ
ij
− log L(P|NSC, N ) = log(N i j (Pi (1 − P j ) + P j (1 − Pi )))NSC − N i j (Pi (1 − P j ) + P j (1 − Pi )). (1)
i, j
13The charge misidentification probability is parameterized as a function of electron pT and η, P(pT, η) =
σ(pT ) × f (η). The binned values, σ(pT ) and f (η), are free parameters in the likelihood fit. To ensure the
proper normalization of P(pT, η), the area of the distribution describing f (η) was set to unity. The charge
misidentification probability is measured with the same method in a simulated Z/γ ∗ → ee sample and
in data. All prompt electrons in simulated events are corrected with charge reconstruction scale factors.
The scale factors are defined as P(pT, η; data)/P(pT, η; MC) if the charge is wrongly reconstructed and
(1 − P(pT, η; data)) /(1 − P(pT, η; MC)) if the charge is properly reconstructed and range in values between
0.8 and 1.2.
Another source of reducible background is given by events with at least one fake/non-prompt electron or
muon, collectively called ‘fakes’. For both, electrons and muons, this contribution is caused by secondary
decays into light leptons of light-flavor or heavy-flavor mesons, embedded within jets. For electrons,
a significant component of fakes arises from jets which satisfy the electron reconstruction criteria and
from photon conversions. MC samples are not used to estimate this background because the simulation
of jets and hadronization has large uncertainties. Instead, a data-driven approach is used to assess this
contribution from production of W+ jets, t t¯ and multi-jet events. The method is also validated in a
data-driven way using specialized VRs.
The fake-lepton background is estimated with a data-driven approach, the so-called ‘fake factor’ method,
as described in Ref. [55]. The b-jet veto significantly reduces fake leptons from heavy-flavor decays. Most
of the fake leptons still passing the analysis selection originate from in-flight decays of mesons inside
jets, jets mis-reconstructed as electrons, and conversions of initial- and final-state radiation photons. The
fake factor method provides an estimation of events with fake leptons in analysis regions by extrapolating
the yields from the regions kinematically ‘adjacent’ to the signal region. For each analysis region a
corresponding adjacent region is defined. It requires exactly the same selection and lepton multiplicity
except that at least one lepton must fail to satisfy the nominal (tight, T) selection criteria but pass a relaxed
(loose, L) selection. For electrons the loose selection is defined as the electron candidate passing the
Loose identification criterion or failing the isolation criterion. For muons the loose selection requires the
muon candidate passing the same Medium identification criterion but failing the isolation criterion and in
addition the impact parameter significance cut is relaxed to |d0 |/σ(d0 ) < 10.
The ratio of tight to loose leptons is measured in dedicated Fake-Enriched regions described in Table 5.
The ratio is determined as a function of lepton flavor, pT , and η, and referred to as the ‘fake factor’
(F(pT, η, flavor)). It describes the probability for a fake lepton to be identified as a tight lepton. In the
measurement of the fake factor, a requirement on the ETmiss is imposed to reject W+ jets events and to
further enrich the regions with fake leptons. The fake factor method relies on the assumption that no
prompt leptons appear in the fake-enriched samples. This assumption is not fully correct with the imposed
selection. Therefore, the number of residual prompt leptons in the Fake-Enriched regions is estimated
using simulation and subtracted from the numbers of tight and loose leptons used to measure the fake
factors.
The number of events in the analysis regions containing at least one fake lepton, N fake , is estimated from
the adjacent regions, where one of the leptons fails the tight criteria (TL, LT, LL). Data are weighted with
fake factors according to the loose lepton multiplicity of the region:
prompt ` only
N fake = F(NT L + NLT ) − F 2 NLL data − F(NT L + NLT ) − F 2 NLL MC ,
14with NT L , NLT , NLL denoting the number of events in the corresponding adjacent region and one again
subtracts the prompt lepton contribution using the irreducible MC samples to account for the prompt
lepton contamination in the adjacent regions.
The m j j VR, as defined in Table 3, is used to verify the data-driven fake-lepton estimation in regions as
similar to the signal regions as possible.
Table 5: Selection criteria defining the dedicated Fake-Enriched regions for electrons and muons.
Muons Electrons
Single-muon trigger Single-electron trigger
b-jet veto b-jet veto
One muon and one jet One electron and at least two jets
pT (jet) > 35 GeV –
∆φ(µ, jet) > 2.7 –
ETmiss < 40 GeV 25 GeV < ETmiss < 100 GeV
157 Systematic uncertainties
Several sources of systematic uncertainty are accounted for in the analysis. These correspond to ex-
perimental and theoretical sources affecting both background and signal predictions. The impact of
systematic uncertainties on both the total event yields as well as the changes in the shape of kinematic
distributions is taken into account when performing the statistical analysis, as described in Section 8,
where the systematics impact on the result is summarized in Figure 6.
Theoretical uncertainties for the predicted background yields and kinematic distributions of the MC sam-
ples (t t¯, diboson and Drell–Yan production) include variations in parton-shower parameters, uncertainties
in the QCD renomalization/factorization scales, αS (mZ ) uncertainty and uncertainties due to PDFs used.
In particular, the PDF set choice uncertainty is evaluated by comparing different PDF sets (NNPDF3.0 [18],
CT10NNLO [21] and MMHT14 [56]) and taking into account the largest deviations from the nominal
choice.
A significant contribution to the total systematic uncertainty arises from the statistical uncertainty of the
MC samples. The corresponding MC statistical uncertainty in the signal regions varies from 12 % to
22 %, depending on the region considered.
Experimental systematic uncertainties due to different reconstruction, identification, isolation, and trigger
efficiencies of leptons in data compared to simulation are also taken into account [45, 46], as well as the
uncertainties in absolute lepton energy calibration [44]. Likewise, experimental systematic uncertainties
due to different reconstruction and b-tagging efficiency of reconstructed jets in data compared to simulation
are also taken into account [51–53], as well as absolute jet and ETmiss energy scales and resolutions [49,
54], which also translate into experimental uncertainties. The uncertainty of pileup simulation, derived
from the comparison of data and simulation is also taken into account [50]. All experimental systematic
uncertainties discussed here affect the signal samples as well as the background.
The derivation of the charge misidentification probability, described as the Section 6, relies on analyzing
the events in both dedicated data and simulated Z/γ ∗ → ee events selected in a narrow window around
the mZ peak. Possible systematic effects were investigated by varying the selection requirements imposed
on the invariant mass window used to select these events. The effects estimated with this method were
found to be negligible compared to the statistical uncertainty of both the data and the simulated events
used in the method.
The experimental systematic uncertainty of the data-driven estimate of the fake-lepton background is
evaluated by varying the nominal fake factor to account for different systematic sources. The nominal
ETmiss requirement for the fakes-enriched region was varied in order to probe the variation of the fake
factor in a region with lower W+ jets and proportionally higher hadronic multi-jet content. In addition, the
systematic impact of flavor composition of the muon fakes is estimated by changing the definition of the
recoiling jet and the transverse impact parameter criterion for tight muons, described in Section 6 by one
standard deviation. Finally, the normalization of the simulated samples, used to account for real leptons
in the fake factor estimation procedure, was varied by 10 % to account for cross section and luminosity
uncertainties as well as the theoretical modeling of these samples. The statistical uncertainty of the data
sample used is added in quadrature to the total systematic error. The total uncertainty ranges between
10 % and 20 % across all pT and η bins.
168 Statistical analysis and results
The statistical analysis package HistFitter [57] was used to implement a binned maximum-likelihood fit
of the HT + ETmiss variable distribution in all control and signal regions to obtain the numbers of signal
and background events; the binning used is described in Section 5. The likelihood is the product of a
Poisson probability density function describing the observed number of events and Gaussian distributions
to constrain the nuisance parameters associated with the systematic uncertainties. The widths of the
Gaussian distributions correspond to the magnitudes of these uncertainties, whereas Poisson distributions
are used for MC simulation statistical uncertainties.
Additional free parameters are introduced for the t t¯ and the diboson background contributions, to fit their
yields in the analysis Top and m j j control regions, respectively. Fitting the yields of the largest backgrounds
reduces the systematic uncertainty in the predicted yield from SM sources. The fitted normalizations are
compatible with their SM predictions within the uncertainties. The agreement between the fitted yields,
fake background estimates and data is shown in two representative post-fit distributions of the HT + ETmiss
variable in the VRs in Figure 3. Good agreement between measured data and predictions is observed.
50
Events
Events
250 ATLAS Preliminary Data Total SM ATLAS Preliminary Data Total SM
s=13 TeV, 79.8 fb-1
±
Top quarks Diboson s=13 TeV, 79.8 fb-1 Top quarks Diboson
OS mjj VR (e± e ) Drell-Yan Fakes 40 SS Z+jets VR (e± µ± ) Fakes
200
30
150
100 20
50 10
0 0
2
Data/SM
Data/SM
1.4
1.2 1.5
1 1
0.8 0.5
0.6 0
400 500 600 1000 2000 200 300 400 1000 2000
HT + Emiss
T
[GeV] HT + Emiss
T
[GeV]
(a) (b)
Figure 3: Post-fit distributions of the HT + ETmiss for data and SM background predictions in two validation regions,
namely (a) in the OS ee m j j VR and (b) in the SS eµ Z + jets VR. The hatched bands include all systematic
uncertainties post-fit with the correlations between various sources taken into account.
Fit Results
Post-fit binned distributions of HT + ETmiss are shown in Figure 4 for the signal regions. After the fit, the
compatibility between the data and the expected background is assessed and good agreement is observed,
with a p-value of 0.5 for the background-only hypothesis. In absence of a significant deviation from
expectations within uncertainties, 95 % confidence level (CL) upper limits were derived on the signal
strength µSIG and hence on the signal production cross section, using the CLs method [58]. All the
plots and tables presented in this note are then derived from a background-only likelihood fit, assuming
µSIG = 0.
17Events
Events
ATLAS Preliminary Data Total SM 12 ATLAS Preliminary Data Total SM
12
s=13 TeV, 79.8 fb-1
±
Top quarks Diboson s=13 TeV, 79.8 fb-1 Top quarks Diboson
OS SR (e± e ) Drell-Yan Fakes 10 SS SR (e± e± ) Drell-Yan Fakes
10 0 ± 0 ±
m(N ,L ) = 400 GeV m(N ,L ) = 400 GeV
0 0
8 m(N ,L± ) = 500 GeV 8 m(N ,L± ) = 500 GeV
0 0
m(N ,L± ) = 600 GeV m(N ,L± ) = 600 GeV
6 6
4 4
2 2
0 0
2 2
Data/SM
Data/SM
1.5 1.5
1 1
0.5 0.5
0 0
400 500 600 1000 2000 400 500 600 1000 2000
HT + Emiss
T
[GeV] HT + Emiss
T
[GeV]
(a) (b)
Events
Events
ATLAS Preliminary Data Total SM 16 ATLAS Preliminary Data Total SM
10 s=13 TeV, 79.8 fb-1 Top quarks Diboson s=13 TeV, 79.8 fb-1 Top quarks Diboson
OS SR (e± µ )
±
Drell-Yan Fakes
14 SS SR (e± µ± ) Drell-Yan Fakes
0 ± 0 ±
8 m(N ,L ) = 400 GeV 12 m(N ,L ) = 400 GeV
0 ± 0 ±
m(N ,L ) = 500 GeV m(N ,L ) = 500 GeV
0 10 0
m(N ,L± ) = 600 GeV m(N ,L± ) = 600 GeV
6
8
4 6
4
2
2
0 0
2 2
Data/SM
Data/SM
1.5 1.5
1 1
0.5 0.5
0 0
400 500 600 1000 2000 400 500 600 1000 2000
HT + Emiss
T
[GeV] HT + Emiss
T
[GeV]
(c) (d)
Events
Events
ATLAS Preliminary Data Total SM 4 ATLAS Preliminary Data Total SM
12
s=13 TeV, 79.8 fb-1 Top quarks Diboson 3.5 s=13 TeV, 79.8 fb-1 Diboson Fakes
0
m(N ,L± ) = 400 GeV
±
10 OS SR (µ± µ ) Drell-Yan Fakes SS SR (µ± µ± )
0 0
m(N ,L± ) = 400 GeV 3 m(N ,L± ) = 500 GeV
0 ± 0
8 m(N ,L ) = 500 GeV m(N ,L± ) = 600 GeV
0 2.5
m(N ,L± ) = 600 GeV
6 2
1.5
4
1
2 0.5
0 0
2 2
Data/SM
Data/SM
1.5 1.5
1 1
0.5 0.5
0 0
400 500 600 1000 2000 400 500 600 1000 2000
HT + Emiss
T
[GeV] HT + Emiss
T
[GeV]
(e) (f)
Figure 4: Distributions of HT + ETmiss in all signal regions (OS in the left and SS in the right column), namely
(a) and (b) the electron–electron signal regions, (c) and (d) the electron–muon signal regions, and (e) and (f) the
muon–muon signal regions after the background-only fit described in the text. The hatched bands include all
systematic uncertainties post-fit with the correlations between various sources taken into account. The solid colored
lines correspond to signal samples with the N 0 and L ± mass marked in the legend.
18Figure 5 shows a good agreement within the uncertainties between expected background and observed
events in all the regions and channels considered in the analysis.
Events
105 ATLAS Preliminary Data Top quarks
4 s=13 TeV, 79.8 fb-1 Total SM Diboson
10
Drell-Yan
103 Fakes
102
10
CRs VRs SRs
1
2
Data/SM
pre-fit ratio
1.5
1
0.5
0
±
±
±
±
±
±
±
±
±
±
±
±
e±e±
e±µ±
µ± µ±
e±e±
e±µ±
µ± µ±
e±e±
e±µ±
µ± µ±
e±e±
e±µ±
µ± µ±
e±e
e±e
e±e
e±e
e±µ
µ± µ
e±µ
µ± µ
e±µ
µ± µ
e±µ
µ± µ
Top CR mjj CR Z+jets VR mjj VR Signal region
Figure 5: Number of observed and expected events in the control, validation, and signal regions for all considered
channels, split by flavor and electric charge combination. The background expectation is the result of the background-
only fit described in the text. The hatched bands include all post-fit systematic uncertainties with the correlations
between various sources taken into account.
The total relative systematic uncertainty after the fit, and its breakdown into components, is presented in
Figure 6.
Predicted numbers of background events in signal regions are compared to the observed number of events
in data in Table 6, where also expected signal yields for several mass points are given for comparison.
In all considered channels, the data are compatible with the background-only hypothesis within the
uncertainties.
The calculated upper limits on the signal strength µSIG and production cross sections of the process
pp → W ∗ → N 0 L ± → W ± ` ∓W ± ν at the 95 % CL are derived as a function of the heavy lepton mass. The
resulting exclusion limits on both the µSIG and signal cross section are shown in Figure 7. The type-III
seesaw heavy leptons N 0 and L ± expected limit is at 550+68
−77 GeV, where the uncertainties on the limit are
extracted from the ±1 σ band, while the observed limit has been placed at 560 GeV, excluding the mass
values below this point.
1940
Relative Uncertainty [%]
Uncorrelated: Total uncertainty ATLAS Preliminary
35
Luminosity (correlated) s=13 TeV, 79.8 fb-1
30 Theory post-fit
Experimental
25 Fakes
20 Charge-Flip
Yield fit
15
10
5
0
±
±
±
±
±
±
e±e±
e±µ±
µ ±µ ±
e±e±
e±µ±
µ ±µ ±
e±e±
e±µ±
µ ±µ ±
e±e±
e±µ±
µ ±µ ±
e±e±
e±µ±
µ ±µ ±
e±e±
e±µ±
µ ±µ ±
e±e
e±e
e±µ
µ ±µ
e±µ
µ ±µ
Top CR mjj CR Z+jets VR mjj VR Signal region
Figure 6: Relative uncertainties in the total background yield estimation after the fit. They are calculated in an
uncorrelated way by shifting in turn only one nuisance parameter from the post-fit value by one standard deviation,
keeping all the other parameters at their post-fit values, and comparing the resulting event yield to the nominal yield.
‘Luminosity’ corresponds to the uncertainty in the luminosity. ‘Theory’ indicates the theoretical uncertainty in the
physics model used for simulation (e.g. cross sections). ‘Fakes’ is the uncertainty associated with the model of the
fake background. ‘Charge-flip’ corresponds to the uncertainty associated with the electron charge-flip scale factors.
‘Yield fit’ is the uncertainty arising from fitting the yield of top and diboson backgrounds. ‘Experimental’ indicates
the uncertainty in the simulation of object efficiencies (e.g. trigger, identification) and uncertainties associated with
ETmiss and pileup. Individual uncertainties can be correlated, and do not necessarily add in quadrature to the total
background uncertainty, which is indicated by ‘Total uncertainty’.
Table 6: Number of observed events in the different signal regions, and corresponding number of background events
in the same regions, after the background-only likelihood fit. Uncertainties correspond to the total uncertainties in
the predicted event yields, and are smaller for the total than for the individual contributions because the latter are
anti-correlated. Due to rounding the totals can differ from the sums of components. Top and diboson normalizations
are floating in the fit. Signal expectations are presented at the bottom of the table.
Signal regions OS ee OS eµ OS µµ SS ee SS eµ SS µµ
Observed events 11 13 12 13 19 4
Total background 9.9 ± 3.2 15.7 ± 2.7 10.7 ± 3.7 12.5 ± 4.4 13.4 ± 2.8 5.0 ± 1.9
Top quarks 6.1 ± 2.7 9.1 ± 2.5 5.7 ± 2.9 3.1 ± 1.5 2.3 ± 2.7
Diboson 3.2 ± 0.9 4.3 ± 1.2 2.5 ± 1.5 5.7 ± 1.5 9.5 ± 1.9 3.4 ± 1.3
Drell–Yan 0.2 ± 0.2 < 0.001 0.1 ± 1.0 0.1 ± 0.1 0.1 ± 0.1 < 0.001
Fakes 0.5 ± 2.0 2.3 ± 1.5 2.4 ± 1.3 3.6 ± 4.8 1.4 ± 1.7 1.6 ± 1.6
Signal expectation
m(N 0, L ± ) = 400 GeV 4.2 ± 0.7 7.1 ± 0.7 3.9 ± 0.6 3.6 ± 0.6 5.8 ± 0.6 3.4 ± 0.5
m(N 0, L ± ) = 500 GeV 1.9 ± 0.4 3.6 ± 0.5 1.9 ± 0.4 1.8 ± 0.4 2.7 ± 0.4 1.4 ± 0.3
m(N 0, L ± ) = 600 GeV 1.0 ± 0.3 1.7 ± 0.3 0.9 ± 0.3 0.8 ± 0.3 1.3 ± 0.3 0.6 ± 0.3
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