Physics Letters B 739 (2014) 23–43 Contents lists available at ScienceDirect Physics Letters B www.elsevier.com/locate/physletb Measurement of the tt production cross section in pp collisions at √ s = 8 TeV in dilepton final states containing one τ lepton .CMS Collaboration � CERN, Switzerland a r t i c l e i n f o a b s t r a c t Article history: Received 24 July 2014 Received in revised form 9 October 2014 Accepted 10 October 2014 Available online 16 October 2014 Editor: M. Doser Keywords: CMS Physics Top quark Tau The top-quark pair production cross section is measured in final states with one electron or muon and one hadronically decaying τ lepton from the process tt → (�ν�)(τντ )bb, where � = e, μ. The data sample corresponds to an integrated luminosity of 19.6 fb−1 collected with the CMS detector in proton– proton collisions at √ s = 8 TeV. The measured cross section σtt = 257 ± 3 (stat) ± 24 (syst) ± 7 (lumi) pb, assuming a top-quark mass of 172.5 GeV, is consistent with the standard model prediction. © 2014 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/3.0/). Funded by SCOAP3. 1. Introduction Top quarks at the CERN LHC are mostly produced in pairs with the subsequent decays tt → W+bW−b. The decay modes of the two W bosons determine the event signature. The dilepton decay channel corresponds to the case in which both W bosons decay into leptons, where the term lepton usually refers to electrons or muons, as studied in Refs. [1,2]. In this letter we measure the production cross section of top-quark pairs by considering dilep- ton decays where one W boson promptly decays into �ν� , with � = e or μ, and the other decays into τντ , tt → (�ν�)(τντ )bb. The expected fraction of these events is 4/81 of all tt decays. The τ lep- ton is identified by means of its hadronic decay products, with a branching fraction B(τ → hadrons + ντ ) � 65%, to produce a nar- row jet with a small number of charged hadrons, denoted as τh. The cross section is measured by counting the number of �τh + X events consistent with originating from tt production, after sub- tracting the contributions from other processes, and correcting for the efficiency of the event selection. A similar method was used in pp collisions at a centre-of-mass energy of √ s = 7 TeV [3]. This “τ dilepton” channel is of particular interest because it is a nat- ural background process to the search for a charged Higgs boson [4,5] with a mass smaller than that of the top quark. In this case, the production chain tt → H+bW−b, with H+ → τ+ντ (or the � E-mail address: cms-publication-committee-chair@cern.ch. corresponding charge-conjugate particles) could give rise to dif- ferences with respect to the standard model (SM) prediction of the number of tt events with a τ lepton [6]. The present mea- surement is based on data collected by the CMS experiment in pp collisions at √ s = 8 TeV corresponding to an integrated luminosity of 19.6 fb−1. The relative accuracy of this measurement improves over previous results [7–11], thanks to the inclusion of additional data and improved analysis techniques. The CMS detector is briefly introduced in Section 2, followed by details of the simulated samples in Section 3, and a brief descrip- tion of the event reconstruction and event selection in Section 4. The descriptions of the background determination and the system- atic uncertainties are given in Sections 5 and 6, respectively. The measurement of the cross section is discussed in Section 7, and the results are summarised in Section 8. 2. The CMS detector The central feature of the CMS apparatus is a superconducting solenoid of 6 m internal diameter and 13 m in length, provid- ing a magnetic field of 3.8 T. Within the superconducting solenoid volume are a silicon pixel and strip tracker, a lead tungstate crystal electromagnetic calorimeter, and a brass/scintillator hadron calorimeter, each composed of a barrel and two endcap sections. The calorimetry provides high-resolution energy and direction measurements of electrons and hadronic jets. Muons are identified using gas-ionisation detectors embedded in the steel flux-return http://dx.doi.org/10.1016/j.physletb.2014.10.032 0370-2693/© 2014 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/3.0/). Funded by SCOAP3. http://dx.doi.org/10.1016/j.physletb.2014.10.032 http://www.ScienceDirect.com/ http://www.elsevier.com/locate/physletb http://creativecommons.org/licenses/by/3.0/ mailto:cms-publication-committee-chair@cern.ch http://dx.doi.org/10.1016/j.physletb.2014.10.032 http://creativecommons.org/licenses/by/3.0/ http://crossmark.crossref.org/dialog/?doi=10.1016/j.physletb.2014.10.032&domain=pdf 24 CMS Collaboration / Physics Letters B 739 (2014) 23–43 yoke outside the solenoid. Extensive forward calorimetry comple- ments the coverage provided by the barrel and endcap detectors. The CMS experiment uses a right-handed coordinate system, with the origin at the nominal interaction point, the x axis pointing to the centre of the LHC ring, the y axis pointing up (perpendic- ular to the LHC plane), and the z axis along the anticlockwise- beam direction. The polar angle θ is measured from the positive z axis and the azimuthal angle ϕ is measured in the x–y plane. Charged particle trajectories are measured covering 0 < ϕ ≤ 2π in azimuth and |η| < 2.5, where the pseudorapidity η is defined as η = − ln[tan(θ/2)]. The detector is nearly hermetic, allowing for energy balance measurements in the plane transverse to the beam directions. A two-level trigger system selects the most inter- esting proton–proton collision events for use in physics analyses. A more detailed description of the CMS detector can be found else- where [12]. 3. Data and simulation samples Events are selected online by a trigger requiring a single isolated electron (muon) with transverse momentum pT > 27 (24) GeV and |η| < 2.5 (2.1). This measurement makes use of simulated samples of tt events as well as other processes that mimic the �τh decay signature. These samples are used to optimise the event selection, to cal- culate the acceptance for tt events, and to estimate some of the backgrounds in the analysis. The signal acceptance and tt dilepton background are evalu- ated using a version of MadGraph which includes the effects of spin correlations [13,14]. The number of expected tt events is es- timated with the next-to-next-to-leading-order (NNLO) SM cross section of 251.7+6.3 −8.6 (scale) ± 6.5 (PDF) pb [15–19] for a top-quark mass of 172.5 GeV, where the first uncertainty is due to renormali- sation and factorisation scales, and the second is due to the choice of parton distribution functions (PDFs). The generated events are subsequently processed with pythia 6.426 [20] which performs the hadronisation of partons. Soft radiation is matched to the contri- butions from direct emissions accounted for in the matrix-element calculations using the kT-MLM approach [21]. The τ lepton decays are simulated using tauola 27.121.5 [22], which accounts for the τ -lepton polarisation. The samples containing W + jet and Z + jet events are simu- lated using the MadGraph 5.1.3.30 event generator [23]. The elec- troweak production of single top quarks is considered as a back- ground process and is simulated with powheg 1.0, r1380 [24–28]. The diboson production processes WW, WZ, and ZZ are generated with pythia 6.424. In each case, the pythia parameters for the un- derlying event are set according to the Z2* tune [29], which uses the CTEQ6L PDFs [30]. Simulated events are processed using the full CMS detector simulation based on Geant4 [31,32], followed by a detailed trig- ger emulation and event reconstruction. For both signal and back- ground events, additional pp interactions (pileup) in the same or nearby bunch crossings are simulated with pythia and superim- posed on the hard collision, using a pileup multiplicity distribution that reflects the luminosity profile of the analysed data. 4. Event selection Events are reconstructed with the particle-flow (PF) algorithm [33,34], which combines information from all sub-detectors to identify and reconstruct individual electrons, muons, photons, charged and neutral hadrons. The primary collision vertex is cho- sen as the reconstructed vertex with the largest ∑ p2 T of the associated tracks. Electrons are identified with a multivariate dis- criminant combining several quantities describing the track quality, the shape of the energy deposits in the electromagnetic calorime- ter, and the compatibility of the measurements from the tracker and the electromagnetic calorimeter [35], and are reconstructed with an average efficiency of approximately 95%. Muons are iden- tified with additional requirements on the quality of the track reconstruction and on the number of measurements in the tracker and the muon systems [36], and are reconstructed with an aver- age efficiency of approximately 96%. Charged and neutral particles provide the input to the anti-kT jet clustering algorithm with a distance parameter of 0.5 [37]. The jet momentum is determined from the vector sum of particle momenta in the jet. After jet en- ergies are corrected for additional pileup contributions and for detector effects, they are found in simulations to be within 5–10% of the actual jet momentum [38]. The missing transverse energy Emiss T is calculated as the magnitude of the vector sum of mo- menta from all reconstructed particles in the plane transverse to the beam. In addition, higher-level observables such as b-tagging discrim- inators and lepton isolation variables are used. The lepton relative isolation is defined as the transverse energy contributions de- posited by charged hadrons (ET,ch), neutral hadrons (ET,nh), and photons (ET,ph) in a cone of radius R = √ (�ϕ)2 + (�η)2 = 0.4 centred on the lepton candidate track, relative to the lepton’s transverse momentum (pT), Irel = (ET,ch + ET,nh + ET,ph)/pT. An electron (muon) candidate is considered to be non-isolated and is rejected if Irel > 0.1 (>0.12). The hadronic products of the τ -lepton decay are reconstructed using a jet as the initial seed, and are then classified as hav- ing one or three charged hadrons with the “hadron-plus-strips” algorithm [39,40]. In the “hadron-plus-strips” algorithm, calorime- ter energy deposits clustered along strips in the ϕ direction are used for neutral pion identification. Then, the decay modes, four- momenta, and isolation quantities of the τh are determined, and the following categories are considered: single hadron, hadron plus a strip, hadron plus two strips, and three hadrons. These cate- gories together encompass approximately 95% of hadronic τ -lepton decays. The sum of the charged hadron charges provides the τh charge. The τh-jet momentum is required to match the direction of the original jet within a maximum distance R = 0.1. Isolation criteria require that there be no additional charged hadrons with pT > 1.0 GeV or photons with transverse energy ET > 1.5 GeV within a cone of size R = 0.5 around the direction of the τh jet. Electrons and muons misidentified as τh are suppressed using al- gorithms that combine information from the tracker, calorimeters, and muon detectors [12]. The τh identification efficiency is de- fined as the ratio of the number of selected τh candidates divided by the number of hadronic τ -lepton decays in tt events; the ra- tio depends on pT and η of the τh, and is on average 50% for pτh T > 20 GeV, with a probability of approximately 1% for generic jets to be misidentified as a τh jet. The combined secondary vertex (CSV) algorithm [41] is used to identify jets originating from the hadronisation of b quarks. The al- gorithm combines the information about track impact parameters and secondary vertices within jets into a likelihood discriminant to provide separation between b jets and jets originating from light quarks, gluons, or charm quarks. The output of this CSV discrim- inant has values between zero and one; a jet with a CSV value above a certain threshold is referred to as being “b tagged”. We choose a working point where the b-tagging efficiency is approxi- mately 60%, as measured in a data sample of events enriched with jets from semileptonic b-hadron decays. The misidentification rate of light-flavour jets is estimated from inclusive jet studies and is measured to be about 0.1% for jets with pT > 30 GeV. CMS Collaboration / Physics Letters B 739 (2014) 23–43 25 Fig. 1. The b-tagged jet multiplicity after the full event selection. The simulated contributions are normalised to the SM predicted values. The hatched area shows the total uncertainty. Events are preselected by requiring exactly one isolated electron (muon) with transverse momentum pT > 35 (30) GeV and |η| < 2.5 (2.1), at least two jets with pT > 30 GeV, and one additional jet with pT > 20 GeV. The selected jets must be within |η| < 2.4. The electron or muon is required to be separated from any jet in the (η, ϕ) plane by a distance R > 0.4. Events with any additional loosely isolated, Irel < 0.2, electron (muon) of pT > 15 (10) GeV are rejected. Further event selection requirements include Emiss T > 40 GeV and only one τh with pT > 20 GeV and |η| < 2.4. The τh and the lepton are required to have electric charges of opposite sign (OS). At least one of the jets is required to be identified as originating from b-quark hadronisation (b-tagged). Fig. 1 shows, for the sum of the eτh and μτh final states, a com- parison between data and simulation of the number of b-tagged jets in each event Nb-tag after all the selection criteria have been applied. The distributions of the τh pT and Emiss T after the final event selection are shown in the top and bottom panels of Fig. 2, respectively. The distributions show agreement between the ob- served numbers of events and the expected numbers of signal and background events obtained from the simulated distributions nor- malised to the integrated luminosity of the selected data sample. Following the final selection, additional kinematic features of the tt events are studied to evaluate the agreement between the observed data and the predicted sum of signal and background. For each event, two invariant mass combinations are reconstructed by pairing the τh with the two candidate b-jets: (1) in events with two or more b-tagged jets, the two combinations are based on the two b-tagged jets with the highest value of the discriminator; (2) in events with one b-tagged jet, this is used for the first com- bination, while the non-b-tagged jet with the highest pT is used to form the second combination. For the two combinations, the in- variant mass with the lowest value is shown in Fig. 3 (top), for the eτh and μτh channels combined. For each event, the top-quark mass mtop is reconstructed using the KINb algorithm [42,43]. Due to the multiple neutrinos in the event, the reconstruction of mtop leads to an underconstrained sys- tem. The KINb algorithm applies constraints on the W boson mass, the mass difference between the top and anti-top quark, and the longitudinal momentum of the tt system. For each event, solutions to the kinematic equations are evaluated, varying the jet momenta and the direction of Emiss T within their resolutions. For each set of variations and each lepton–jet combination, the kinematic equa- tions allow up to four solutions; the one with the lowest tt in- variant mass is accepted if the mass difference between the two Fig. 2. Distribution of the τh pT (top) and Emiss T (bottom) after the full event se- lection, for the eτh and μτh channels combined. The simulated contributions are normalised to the SM predicted values. The hatched area shows the total uncer- tainty. The last bins include the overflow events. top quarks is less than 3 GeV. For each event, the accepted solu- tions corresponding to the two possible lepton–jet combinations are counted and the combination with the largest number of so- lutions is chosen and mtop is obtained by fitting the peak of this distribution. The events in which solutions are found are shown in Fig. 3 (bottom). Data are in agreement with the expected sum of signal and background events. 5. Background estimate The main background (misidentified τh) comes from events with one lepton (electron or muon), significant Emiss T , and three or more jets, where one jet is misidentified as a τh jet [6]. The dominant source is tt lepton + jet events. The misidentified τh background accounts also for events with W bosons produced in association with jets, either genuine W + jet or single-top-quark production, and for QCD multijet events. In order to estimate this background from data, the misidentification probability w(jet → τh) is parameterised as a function of the jet pT, η, and width (R jet). The quantity R jet is defined as √ σ 2 η + σ 2 ϕ , where ση (σϕ ) expresses the extent in η (ϕ) of the jet cluster [38]. The probability w(jet → τh) is evaluated from two control sam- ples: • wW+jets: from a W + jet event sample, selected by requiring one isolated muon with pT > 20 GeV and |η| < 2.1, and at least one jet with pT > 20 GeV and |η| < 2.4; 26 CMS Collaboration / Physics Letters B 739 (2014) 23–43 Fig. 3. (Top) Minimum invariant mass reconstructed by pairing the τh with either a b-tagged jet or with the highest pT non-b-tagged jet, as described in the text. (Bottom) Distribution of the reconstructed top-quark mass mtop for the �τh candi- date events after the full event selection. Data (points) are compared with the sum of signal and background yields, for the eτh and μτh channels combined. The sim- ulated contributions are normalised to the SM predicted values. The hatched area shows the total uncertainty. The last bins include the overflow events. • wQCD: from a QCD multijet sample, triggered by one jet with pT > 40 GeV, selected by requiring events to have at least two jets with pT > 20 GeV and |η| < 2.4, where the triggering jet is removed from the misidentification rate calculation to avoid a trigger bias. Both probabilities are evaluated in simulated events as well as in data, with good agreement found between the results from simu- lation and data [39]. The number of events containing misidentified τh candidates is then determined as Nmisid = M∑ i m∑ j w j i (jet → τ ) − Nother, (1) where j is the jet index of event i, and m is the number of jets in each event and M is the total number of events. The quan- tity Nother is the expected �20% contamination from signal and other processes to the misidentified background as estimated from simulated samples. The value of Nother is evaluated by applying the procedure described above to simulated events of Z/γ ∗ → ττ , single-top-quark production, diboson production, and the tt pro- cesses included in the misidentified τh background estimation. Jets in QCD multijet events originate mainly from gluons, while in W + jet events they are predominantly from quarks. The quark Table 1 List of systematic uncertainties in the cross section measurement, and their combi- nation. Lepton reconstruction uncertainties are uncorrelated, while all other uncer- tainties are assumed 100% correlated. Source Uncertainty [%] eτh μτh Combined Experimental uncertainties: τh jet identification 6.0 6.0 6.0 τh misidentification background 4.3 4.3 4.3 τh energy scale 2.4 2.5 2.5 b-jet tagging, jet misidentification 1.6 1.6 1.6 jet energy scale, jet energy resolution, Emiss T 1.9 1.9 1.9 lepton reconstruction 0.8 0.6 0.5 other backgrounds 0.6 0.7 0.7 luminosity 2.6 2.6 2.6 Theoretical uncertainties: matrix element–parton shower matching 1.7 1.3 1.5 factorisation/renormalisation scale 2.9 2.9 2.9 generator 1.5 1.5 1.5 hadronisation 1.7 1.7 1.7 top-quark pT modelling 0.7 0.5 0.6 parton distribution functions 0.8 0.7 0.7 total systematic uncertainty 9.6 9.5 9.5 and gluon composition in the misidentified τh events lies between these two control samples. As wQCD < wW+jets, the actual Nmisid value is under- (over-) estimated by applying the wQCD (wW+jets) probability. We determine from data the rate for the misidentifi- cation of a jet to be identified as a τh, and from simulation the quark/gluon composition in the W + jet and multijet samples. From these quantities we derive the following combination: 〈 Nmisid〉 = SFW+jet × Nmisid W+jet + SFQCD × Nmisid QCD , (2) where the misidentification rates, extracted from the data control samples discussed above, are combined with the scale factors SFs determined from the set of equations describing the quark/gluon composition of the samples: SFQCD = 0.83 and SFW+jet = 0.17. The corresponding systematic uncertainty is obtained from Eq. (2) by weighting the relative deviations of Nmisid W+jet and Nmisid QCD from 〈Nmisid〉 with the related scale factors. This results in an uncer- tainty of 7% for both eτh and μτh channels. The efficiency of the OS requirement εOS is determined from simulated lepton + jet tt events and is applied in order to ob- tain the misidentified τh background after the final event selec- tion Nmisid OS , where Nmisid OS = εOS · Nmisid. We find values of εOS = 0.729 ± 0.002 (stat) ± 0.004 (syst) for the eτh selection and εOS = 0.731 ± 0.002 (stat) ± 0.003 (syst) for the μτh selection, where all sources of systematic uncertainty are accounted for in the mod- elling of the simulated tt lepton + jet events. 6. Systematic uncertainties Several sources of systematic uncertainty are considered and listed in Table 1. They are related both to the signal reconstruction efficiency, background determination, and luminosity measurement (Experimental uncertainties) and to the theoretical assumptions on the tt production (Theoretical uncertainties). In Table 1 and in what follows, relative values refer to the cross section uncertainty unless explicitly stated otherwise. 6.1. Experimental uncertainties Regarding the τh reconstruction, the uncertainty associated with the identification efficiency amounts to 6%, while the con- tribution relative to the τh jet energy scale is 2.4% (2.5%) for the eτh (μτh) channel, as estimated by varying the pT of the τh jet CMS Collaboration / Physics Letters B 739 (2014) 23–43 27 by 3% [39,40]. The uncertainty in the τh identification efficiency in- cludes the uncertainty in charge determination which is estimated to be smaller than 1%. The uncertainty related to the misidenti- fied τh background process, discussed in Section 5, is obtained by propagating the 7% uncertainty on 〈Nmisid〉 to the cross section de- termination and results in 4.3% for both channels. It also includes the uncertainty in the OS efficiency determination. The reconstruction of a light flavour jet as a b quark is defined as mistagging. The uncertainty due to b (mis)tagging is estimated to reflect the data-to-simulation scale factors and corresponding uncertainties for b-tagging and mistagging efficiencies [41]. When propagated to the cross section measurement, they amount to 1.6% for both eτh and μτh channels. The jet energy scale (JES) uncertainty is estimated [38] by vary- ing the jet energy within the pT- and η-dependent JES uncertain- ties per jet, and taking into account the uncertainty due to pileup and parton flavour. The jet energy resolution (JER) is estimated by smearing the jet energy in simulation within the η-dependent JER uncertainties per jet. The JES and JER uncertainties are propagated in order to estimate the uncertainty of the Emiss T scale. In addition, modelling of the Emiss T component, which is not clustered in jets, is also considered. The resulting uncertainty from propagating these effects to the cross section measurement is 1.9% for both the eτh and μτh channels. Uncertainties due to trigger, lepton identification, isolation, and lepton energy scale are calculated from independent samples with a “tag-and-probe” method [35,36], and yield 0.8% (0.6%) for the eτh (μτh) channel. An overall 0.6% (0.7%) uncertainty for the eτh (μτh) channel is due to other minor backgrounds, accounting for the uncertainties related to the theoretical cross sections, JES, and b-tagging in these simulated samples, and the � → τh (� = e, μ) misidentification in the Z/γ ∗ → �+�− and tt dilepton processes. Finally, the integrated luminosity is known with 2.6% accu- racy [44]. 6.2. Theoretical uncertainties The theoretical uncertainty due to the matrix element (ME) and parton shower (PS) matching is estimated by varying up and down by a factor of two the threshold between jet production at the ME level and via PS, and it results in 1.7% (1.3%) for the eτh (μτh) channel. The modelling uncertainty in the signal acceptance due to the factorisation and renormalisation scale choices is estimated by varying them simultaneously up and down by a factor of two from the nominal value equal to the Q 2 in the event, with an uncer- tainty of 2.9% found for both channels. The uncertainty due to the choice of the generator is estimated as the relative difference between the acceptances evaluated with MadGraph and powheg [24–26,45] after the full event selection and results in 1.5%. In a similar way, the uncertainty in the hadro- nisation scheme is evaluated from the relative differences between the acceptances from powheg + pythia and powheg + herwig samples, estimated prior to the b-tagging or τh jet requirement, resulting in a 1.7% uncertainty. We consider the uncertainty related to the top-quark pT scale modelling by varying the top-quark pT spectrum and evaluating the change in the signal acceptance, resulting in 0.6%, and the uncertainty related to the PDF variations following the PDF4LHC prescriptions [17], resulting in 0.7%. 7. Cross section measurement The number of expected signal and background events as well as the number of observed events after all selections are Table 2 Number of expected events for signal (assuming mtop = 172.5 GeV) and back- grounds. The background from misidentified τh is estimated from data, while the other backgrounds are estimated from simulation. Statistical and systematic uncer- tainties are shown. Source eτh μτh misidentified τh 1341 ± 3 ± 94 1653 ± 3 ± 116 tt → (�ν�)(�ν�)bb 55 ± 1 ± 3 68 ± 2 ± 4 Z/γ ∗ → ee,μμ 11 ± 5 ± 5 12 ± 5 ± 5 Z/γ ∗ → ττ 85 ± 14 ± 8 166 ± 20 ± 18 single top quark 104 ± 7 ± 9 133 ± 8 ± 10 dibosons 15 ± 1 ± 1 19 ± 1 ± 1 total expected background 1611 ± 17 ± 95 2051 ± 22 ± 118 expected signal yield 2134 ± 9 ± 170 2632 ± 11 ± 212 data 3779 4767 summarised in Table 2. The statistical and systematic uncertainties are also shown. The tt production cross section measured from τ dilepton events is σtt = (N − B)/(L · Atot), where N is the number of observed candidate events, B is the estimate of the background and L is the integrated luminosity. The total acceptance Atot is the product of the branching fractions, geometrical and kinematic acceptance, trigger, lepton identification, and the overall recon- struction efficiency. It is evaluated with respect to the inclusive tt sample. After the OS requirement and assuming a top-quark mass mtop = 172.5 GeV, we obtain: Atot(eτh) = 0.04333 ± 0.00017 (stat) ± 0.00300 (syst)%; Atot(μτh) = 0.05370 ± 0.00021 (stat) ± 0.00376 (syst)%. The statistical uncertainties are due to the limited number of sim- ulated events and the systematic uncertainties are estimated by accounting for all sources listed in Table 1. The statistical and sys- tematic uncertainties listed in Table 2 are propagated to the final cross section measurements: σtt(eτh) = 255 ± 4 (stat) ± 24 (syst) ± 7 (lumi) pb; σtt(μτh) = 258 ± 4 (stat) ± 24 (syst) ± 7 (lumi) pb. The BLUE method [46] is used to combine the cross section mea- surements in the eτh and μτh channels, yielding weights of 0.47 and 0.53, respectively. Lepton reconstruction uncertainties are un- correlated, while all other uncertainties are assumed 100% cor- related. With this method we obtain a combined result of σtt = 257 ± 3 (stat) ± 24 (syst) ± 7 (lumi) pb, in agreement with the NNLO expectation of 251.7 +6.3 −8.6 (scales) ± 6.5 (PDF) pb. Follow- ing the most recent conventions for the treatment of PDF and scale uncertainties the same calculation yields 252.9 +6.4 −8.6 (scale) ± 11.7 (PDF + αS) pb [15–19]. The dependence on the top-quark mass has been studied for the range 160–185 GeV and is well de- scribed by a linear variation. If we adjust our result to the current world average value of 173.3 GeV [47], we obtain a cross section that is lower by 3.1 pb. 8. Summary A measurement of the tt production cross section in the chan- nel tt → (�ν�)(τντ )bb is presented, where � is an electron or a muon, and the τ lepton is reconstructed through its hadronic decays. The data sample corresponds to an integrated luminosity of 19.6 fb−1 collected in proton–proton collisions at √ s = 8 TeV. Events are selected by requiring the presence of one isolated electron or muon, two or more jets (at least one of which is b-tagged), significant missing transverse energy, and one τ . The 28 CMS Collaboration / Physics Letters B 739 (2014) 23–43 largest background contribution is estimated from data and con- sists of tt events with one W boson decaying into jets, where one jet is misidentified as a τ . The measured cross section is σtt = 257 ± 3 (stat) ± 24 (syst) ± 7 (lumi) pb for a top-quark mass of 172.5 GeV. This measurement improves over previous results in this decay channel, and it is in good agreement with the standard model expectation and other measurements of the tt cross section at the same centre-of-mass energy. Acknowledgements We congratulate our colleagues in the CERN accelerator depart- ments for the excellent performance of the LHC and thank the technical and administrative staffs at CERN and at other CMS in- stitutes for their contributions to the success of the CMS effort. In addition, we gratefully acknowledge the computing centres and personnel of the Worldwide LHC Computing Grid for delivering so effectively the computing infrastructure essential to our analyses. Finally, we acknowledge the enduring support for the construc- tion and operation of the LHC and the CMS detector provided by the following funding agencies: BMWFW and FWF (Austria); FNRS and FWO (Belgium); CNPq, CAPES, FAPERJ, and FAPESP (Brazil); MES (Bulgaria); CERN; CAS, MOST, and NSFC (China); COLCIENCIAS (Colombia); MSES and CSF (Croatia); RPF (Cyprus); MoER, ERC IUT and ERDF (Estonia); Academy of Finland, MEC, and HIP (Finland); CEA and CNRS/IN2P3 (France); BMBF, DFG, and HGF (Germany); GSRT (Greece); OTKA and NIH (Hungary); DAE and DST (India); IPM (Iran); SFI (Ireland); INFN (Italy); NRF and WCU (Republic of Korea); LAS (Lithuania); MOE and UM (Malaysia); CINVESTAV, CONACYT, SEP, and UASLP-FAI (Mexico); MBIE (New Zealand); PAEC (Pakistan); MSHE and NSC (Poland); FCT (Portugal); JINR (Dubna); MON, RosAtom, RAS and RFBR (Russia); MESTD (Serbia); SEIDI and CPAN (Spain); Swiss Funding Agencies (Switzerland); MST (Taipei); ThEPCenter, IPST, STAR and NSTDA (Thailand); TUBITAK and TAEK (Turkey); NASU and SFFR (Ukraine); STFC (United Kingdom); DOE and NSF (USA). Individuals have received support from the Marie-Curie pro- gramme and the European Research Council and EPLANET (Eu- ropean Union); the Leventis Foundation; the Alfred P. Sloan Foundation; the Alexander von Humboldt-Stiftung; the Belgian Federal Science Policy Office; the Fonds pour la Formation à la Recherche dans l’Industrie et dans l’Agriculture (FRIA-Belgium); the Agentschap voor Innovatie door Wetenschap en Technologie (IWT- Belgium); the Ministry of Education, Youth and Sports (MEYS) of the Czech Republic; the Council of Science and Industrial Research, India; the HOMING PLUS programme of Foundation For Polish Science, cofinanced from European Union, Regional Development Fund; the Compagnia di San Paolo (Torino); the Consorzio per la Fisica (Trieste); MIUR project 20108T4XTM (Italy); the Thalis and Aristeia programmes cofinanced by EU–ESF and the Greek NSRF; and the National Priorities Research Program by Qatar National Re- search Fund. 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Tiko National Institute of Chemical Physics and Biophysics, Tallinn, Estonia P. Eerola, G. Fedi, M. Voutilainen Department of Physics, University of Helsinki, Helsinki, Finland J. Härkönen, V. Karimäki, R. Kinnunen, M.J. Kortelainen, T. Lampén, K. Lassila-Perini, S. Lehti, T. Lindén, P. Luukka, T. Mäenpää, T. Peltola, E. Tuominen, J. Tuominiemi, E. Tuovinen, L. Wendland Helsinki Institute of Physics, Helsinki, Finland T. Tuuva Lappeenranta University of Technology, Lappeenranta, Finland M. Besancon, F. Couderc, M. Dejardin, D. Denegri, B. Fabbro, J.L. Faure, C. Favaro, F. Ferri, S. Ganjour, A. Givernaud, P. Gras, G. Hamel de Monchenault, P. Jarry, E. Locci, J. Malcles, J. Rander, A. Rosowsky, M. Titov DSM/IRFU, CEA/Saclay, Gif-sur-Yvette, France S. Baffioni, F. Beaudette, P. Busson, C. Charlot, T. Dahms, M. Dalchenko, L. Dobrzynski, N. Filipovic, A. Florent, R. Granier de Cassagnac, L. Mastrolorenzo, P. Miné, C. Mironov, I.N. Naranjo, M. Nguyen, C. Ochando, P. Paganini, S. Regnard, R. 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Tropiano a,b a INFN Sezione di Firenze, Firenze, Italy b Università di Firenze, Firenze, Italy L. Benussi, S. Bianco, F. Fabbri, D. Piccolo INFN Laboratori Nazionali di Frascati, Frascati, Italy F. Ferro a, M. Lo Vetere a,b, E. Robutti a, S. Tosi a,b a INFN Sezione di Genova, Genova, Italy b Università di Genova, Genova, Italy M.E. Dinardo a,b, P. Dini a, S. Fiorendi a,b,2, S. Gennai a,2, R. Gerosa 2, A. Ghezzi a,b, P. Govoni a,b, M.T. Lucchini a,b,2, S. Malvezzi a, R.A. Manzoni a,b, A. Martelli a,b, B. Marzocchi, D. Menasce a, L. Moroni a, M. Paganoni a,b, S. Ragazzi a,b, N. Redaelli a, T. Tabarelli de Fatis a,b a INFN Sezione di Milano-Bicocca, Milano, Italy b Università di Milano-Bicocca, Milano, Italy S. Buontempo a, N. Cavallo a,c, S. Di Guida a,d,2, F. Fabozzi a,c, A.O.M. Iorio a,b, L. Lista a, S. Meola a,d,2, M. Merola a, P. Paolucci a,2 a INFN Sezione di Napoli, Napoli, Italy b Università di Napoli ‘Federico II’, Napoli, Italy c Università della Basilicata (Potenza), Napoli, Italy d Università G. Marconi (Roma), Napoli, Italy P. Azzi a, N. Bacchetta a, D. Bisello a,b, A. Branca a,b, R. Carlin a,b, P. Checchia a, M. Dall’Osso a,b, T. Dorigo a, M. Galanti a,b, F. Gasparini a,b, U. Gasparini a,b, P. Giubilato a,b, A. Gozzelino a, K. Kanishchev a,c, S. Lacaprara a, M. Margoni a,b, A.T. Meneguzzo a,b, M. Passaseo a, J. Pazzini a,b, N. Pozzobon a,b, P. Ronchese a,b, F. Simonetto a,b, E. Torassa a, M. Tosi a,b, P. Zotto a,b, A. Zucchetta a,b, G. Zumerle a,b a INFN Sezione di Padova, Padova, Italy b Università di Padova, Padova, Italy c Università di Trento (Trento), Padova, Italy M. Gabusi a,b, S.P. Ratti a,b, C. Riccardi a,b, P. Salvini a, P. Vitulo a,b a INFN Sezione di Pavia, Pavia, Italy b Università di Pavia, Pavia, Italy M. Biasini a,b, G.M. Bilei a, D. Ciangottini a,b, L. Fanò a,b, P. Lariccia a,b, G. 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Rahatlou a,b, C. Rovelli a, F. Santanastasio a,b, L. Soffi a,b,2, P. Traczyk a,b a INFN Sezione di Roma, Roma, Italy b Università di Roma, Roma, Italy CMS Collaboration / Physics Letters B 739 (2014) 23–43 35 N. Amapane a,b, R. Arcidiacono a,c, S. Argiro a,b,2, M. Arneodo a,c, R. Bellan a,b, C. Biino a, N. Cartiglia a, S. Casasso a,b,2, M. Costa a,b, A. Degano a,b, N. Demaria a, L. Finco a,b, C. Mariotti a, S. Maselli a, E. Migliore a,b, V. Monaco a,b, M. Musich a, M.M. Obertino a,c,2, G. Ortona a,b, L. Pacher a,b, N. Pastrone a, M. Pelliccioni a, G.L. Pinna Angioni a,b, A. Potenza a,b, A. Romero a,b, M. Ruspa a,c, R. Sacchi a,b, A. Solano a,b, A. Staiano a, U. Tamponi a a INFN Sezione di Torino, Torino, Italy b Università di Torino, Torino, Italy c Università del Piemonte Orientale (Novara), Torino, Italy S. Belforte a, V. Candelise a,b, M. Casarsa a, F. Cossutti a, G. Della Ricca a,b, B. Gobbo a, C. La Licata a,b, M. Marone a,b, D. Montanino a,b, A. Schizzi a,b,2, T. Umer a,b, A. Zanetti a a INFN Sezione di Trieste, Trieste, Italy b Università di Trieste, Trieste, Italy T.J. Kim Chonbuk National University, Chonju, Republic of Korea S. Chang, A. Kropivnitskaya, S.K. Nam Kangwon National University, Chunchon, Republic of Korea D.H. Kim, G.N. Kim, M.S. Kim, D.J. Kong, S. Lee, Y.D. Oh, H. Park, A. Sakharov, D.C. Son Kyungpook National University, Daegu, Republic of Korea J.Y. Kim, S. Song Chonnam National University, Institute for Universe and Elementary Particles, Kwangju, Republic of Korea S. Choi, D. Gyun, B. Hong, M. Jo, H. Kim, Y. Kim, B. Lee, K.S. Lee, S.K. Park, Y. Roh Korea University, Seoul, Republic of Korea M. Choi, J.H. Kim, I.C. Park, S. Park, G. Ryu, M.S. Ryu University of Seoul, Seoul, Republic of Korea Y. Choi, Y.K. Choi, J. Goh, D. Kim, E. Kwon, J. Lee, H. Seo, I. Yu Sungkyunkwan University, Suwon, Republic of Korea A. Juodagalvis Vilnius University, Vilnius, Lithuania J.R. Komaragiri, M.A.B. Md Ali National Centre for Particle Physics, Universiti Malaya, Kuala Lumpur, Malaysia H. Castilla-Valdez, E. De La Cruz-Burelo, I. Heredia-de La Cruz 28, R. Lopez-Fernandez, A. Sanchez-Hernandez Centro de Investigacion y de Estudios Avanzados del IPN, Mexico City, Mexico S. Carrillo Moreno, F. Vazquez Valencia Universidad Iberoamericana, Mexico City, Mexico I. Pedraza, H.A. Salazar Ibarguen Benemerita Universidad Autonoma de Puebla, Puebla, Mexico E. Casimiro Linares, A. Morelos Pineda Universidad Autónoma de San Luis Potosí, San Luis Potosí, Mexico 36 CMS Collaboration / Physics Letters B 739 (2014) 23–43 D. Krofcheck University of Auckland, Auckland, New Zealand P.H. Butler, S. Reucroft University of Canterbury, Christchurch, New Zealand A. Ahmad, M. Ahmad, Q. Hassan, H.R. Hoorani, S. Khalid, W.A. Khan, T. Khurshid, M.A. Shah, M. Shoaib National Centre for Physics, Quaid-I-Azam University, Islamabad, Pakistan H. Bialkowska, M. Bluj, B. Boimska, T. Frueboes, M. Górski, M. Kazana, K. Nawrocki, K. Romanowska-Rybinska, M. Szleper, P. Zalewski National Centre for Nuclear Research, Swierk, Poland G. Brona, K. Bunkowski, M. Cwiok, W. Dominik, K. Doroba, A. Kalinowski, M. Konecki, J. Krolikowski, M. Misiura, M. Olszewski, W. Wolszczak Institute of Experimental Physics, Faculty of Physics, University of Warsaw, Warsaw, Poland P. Bargassa, C. Beirão Da Cruz E Silva, P. Faccioli, P.G. Ferreira Parracho, M. Gallinaro, F. Nguyen, J. Rodrigues Antunes, J. Seixas, J. Varela, P. Vischia Laboratório de Instrumentação e Física Experimental de Partículas, Lisboa, Portugal I. Golutvin, V. Karjavin, V. Konoplyanikov, V. Korenkov, G. Kozlov, A. Lanev, A. Malakhov, V. Matveev 29, V.V. Mitsyn, P. Moisenz, V. Palichik, V. Perelygin, S. Shmatov, S. Shulha, N. Skatchkov, V. Smirnov, E. Tikhonenko, A. Zarubin Joint Institute for Nuclear Research, Dubna, Russia V. Golovtsov, Y. Ivanov, V. Kim 30, P. Levchenko, V. Murzin, V. Oreshkin, I. Smirnov, V. Sulimov, L. Uvarov, S. Vavilov, A. Vorobyev, An. Vorobyev Petersburg Nuclear Physics Institute, Gatchina (St. Petersburg), Russia Yu. Andreev, A. Dermenev, S. Gninenko, N. Golubev, M. Kirsanov, N. Krasnikov, A. Pashenkov, D. Tlisov, A. Toropin Institute for Nuclear Research, Moscow, Russia V. Epshteyn, V. Gavrilov, N. Lychkovskaya, V. Popov, G. Safronov, S. Semenov, A. Spiridonov, V. Stolin, E. Vlasov, A. Zhokin Institute for Theoretical and Experimental Physics, Moscow, Russia V. Andreev, M. Azarkin, I. Dremin, M. Kirakosyan, A. Leonidov, G. Mesyats, S.V. Rusakov, A. Vinogradov P.N. Lebedev Physical Institute, Moscow, Russia A. Belyaev, E. Boos, M. Dubinin 31, L. Dudko, A. Ershov, A. Gribushin, V. Klyukhin, O. Kodolova, I. Lokhtin, S. Obraztsov, M. Perfilov, S. Petrushanko, V. Savrin Skobeltsyn Institute of Nuclear Physics, Lomonosov Moscow State University, Moscow, Russia I. Azhgirey, I. Bayshev, S. Bitioukov, V. Kachanov, A. Kalinin, D. Konstantinov, V. Krychkine, V. Petrov, R. Ryutin, A. Sobol, L. 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Moran Universidad Autónoma de Madrid, Madrid, Spain H. Brun, J. Cuevas, J. Fernandez Menendez, S. Folgueras, I. Gonzalez Caballero, L. Lloret Iglesias Universidad de Oviedo, Oviedo, Spain J.A. Brochero Cifuentes, I.J. Cabrillo, A. Calderon, J. Duarte Campderros, M. Fernandez, G. Gomez, A. Graziano, A. Lopez Virto, J. Marco, R. Marco, C. Martinez Rivero, F. Matorras, F.J. Munoz Sanchez, J. Piedra Gomez, T. Rodrigo, A.Y. Rodríguez-Marrero, A. Ruiz-Jimeno, L. Scodellaro, I. Vila, R. Vilar Cortabitarte Instituto de Física de Cantabria (IFCA), CSIC–Universidad de Cantabria, Santander, Spain D. Abbaneo, E. Auffray, G. Auzinger, M. Bachtis, P. Baillon, A.H. Ball, D. Barney, A. Benaglia, J. Bendavid, L. Benhabib, J.F. Benitez, C. Bernet 7, G. Bianchi, P. Bloch, A. Bocci, A. Bonato, O. Bondu, C. Botta, H. Breuker, T. Camporesi, G. Cerminara, S. Colafranceschi 33, M. D’Alfonso, D. d’Enterria, A. Dabrowski, A. David, F. De Guio, A. De Roeck, S. De Visscher, M. Dobson, M. Dordevic, N. 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Langenegger, D. Renker, T. Rohe Paul Scherrer Institut, Villigen, Switzerland F. Bachmair, L. Bäni, L. Bianchini, P. Bortignon, M.A. Buchmann, B. Casal, N. Chanon, A. Deisher, G. Dissertori, M. Dittmar, M. Donegà, M. Dünser, P. Eller, C. Grab, D. Hits, W. Lustermann, B. Mangano, A.C. Marini, P. Martinez Ruiz del Arbol, D. Meister, N. Mohr, C. Nägeli 36, F. Nessi-Tedaldi, F. Pandolfi, F. Pauss, M. Peruzzi, M. Quittnat, L. Rebane, M. Rossini, A. Starodumov 37, M. Takahashi, K. Theofilatos, R. Wallny, H.A. Weber Institute for Particle Physics, ETH Zurich, Zurich, Switzerland C. Amsler 38, M.F. Canelli, V. Chiochia, A. De Cosa, A. Hinzmann, T. Hreus, B. Kilminster, C. Lange, B. Millan Mejias, J. Ngadiuba, P. Robmann, F.J. Ronga, S. Taroni, M. Verzetti, Y. Yang Universität Zürich, Zurich, Switzerland M. Cardaci, K.H. Chen, C. Ferro, C.M. Kuo, W. Lin, Y.J. Lu, R. Volpe, S.S. Yu National Central University, Chung-Li, Taiwan 38 CMS Collaboration / Physics Letters B 739 (2014) 23–43 P. 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Alexander, A. Chatterjee, J. Chu, S. Dittmer, N. Eggert, N. Mirman, G. Nicolas Kaufman, J.R. Patterson, A. Ryd, E. Salvati, L. Skinnari, W. Sun, W.D. Teo, J. Thom, J. Thompson, J. Tucker, Y. Weng, L. Winstrom, P. Wittich Cornell University, Ithaca, USA D. Winn Fairfield University, Fairfield, USA 40 CMS Collaboration / Physics Letters B 739 (2014) 23–43 S. Abdullin, M. Albrow, J. Anderson, G. Apollinari, L.A.T. Bauerdick, A. Beretvas, J. Berryhill, P.C. Bhat, K. Burkett, J.N. Butler, H.W.K. Cheung, F. Chlebana, S. Cihangir, V.D. Elvira, I. Fisk, J. Freeman, Y. Gao, E. Gottschalk, L. Gray, D. Green, S. Grünendahl, O. Gutsche, J. Hanlon, D. Hare, R.M. Harris, J. Hirschauer, B. Hooberman, S. Jindariani, M. Johnson, U. Joshi, K. Kaadze, B. Klima, B. Kreis, S. Kwan, J. Linacre, D. Lincoln, R. Lipton, T. Liu, J. Lykken, K. Maeshima, J.M. Marraffino, V.I. Martinez Outschoorn, S. Maruyama, D. Mason, P. McBride, K. Mishra, S. Mrenna, Y. Musienko 29, S. Nahn, C. Newman-Holmes, V. O’Dell, O. 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Adams, L. Apanasevich, V.E. Bazterra, D. Berry, R.R. Betts, I. Bucinskaite, R. Cavanaugh, O. Evdokimov, L. Gauthier, C.E. Gerber, D.J. Hofman, S. Khalatyan, P. Kurt, D.H. Moon, C. O’Brien, C. Silkworth, P. Turner, N. Varelas University of Illinois at Chicago (UIC), Chicago, USA E.A. Albayrak 47, B. Bilki 51, W. Clarida, K. Dilsiz, F. Duru, M. Haytmyradov, J.-P. Merlo, H. Mermerkaya 52, A. Mestvirishvili, A. Moeller, J. Nachtman, H. Ogul, Y. Onel, F. Ozok 47, A. Penzo, R. Rahmat, S. Sen, P. Tan, E. Tiras, J. Wetzel, T. Yetkin 53, K. Yi The University of Iowa, Iowa City, USA B.A. Barnett, B. Blumenfeld, S. Bolognesi, D. Fehling, A.V. Gritsan, P. Maksimovic, C. Martin, M. Swartz Johns Hopkins University, Baltimore, USA P. Baringer, A. Bean, G. Benelli, C. Bruner, J. Gray, R.P. Kenny III, M. Malek, M. Murray, D. Noonan, S. Sanders, J. Sekaric, R. Stringer, Q. Wang, J.S. Wood The University of Kansas, Lawrence, USA A.F. Barfuss, I. Chakaberia, A. Ivanov, S. Khalil, M. Makouski, Y. 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Turkewitz University of Minnesota, Minneapolis, USA J.G. Acosta, S. Oliveros University of Mississippi, Oxford, USA E. Avdeeva, K. Bloom, S. Bose, D.R. Claes, A. Dominguez, R. Gonzalez Suarez, J. Keller, D. Knowlton, I. Kravchenko, J. Lazo-Flores, S. Malik, F. Meier, G.R. Snow University of Nebraska-Lincoln, Lincoln, USA J. Dolen, A. Godshalk, I. Iashvili, A. Kharchilava, A. Kumar, S. Rappoccio State University of New York at Buffalo, Buffalo, USA G. Alverson, E. Barberis, D. Baumgartel, M. Chasco, J. Haley, A. Massironi, D.M. Morse, D. Nash, T. Orimoto, D. Trocino, R.J. Wang, D. Wood, J. Zhang Northeastern University, Boston, USA K.A. Hahn, A. Kubik, N. Mucia, N. Odell, B. Pollack, A. Pozdnyakov, M. Schmitt, S. Stoynev, K. Sung, M. Velasco, S. Won Northwestern University, Evanston, USA A. Brinkerhoff, K.M. Chan, A. Drozdetskiy, M. Hildreth, C. Jessop, D.J. Karmgard, N. Kellams, K. Lannon, W. Luo, S. Lynch, N. Marinelli, T. Pearson, M. Planer, R. Ruchti, N. Valls, M. Wayne, M. 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Bouhali 54, A. Castaneda Hernandez, R. Eusebi, W. Flanagan, J. Gilmore, T. Kamon 55, V. Khotilovich, V. Krutelyov, R. Montalvo, I. Osipenkov, Y. Pakhotin, A. Perloff, J. Roe, A. Rose, A. Safonov, T. Sakuma, I. Suarez, A. Tatarinov Texas A&M University, College Station, USA N. Akchurin, C. Cowden, J. Damgov, C. Dragoiu, P.R. Dudero, J. Faulkner, K. Kovitanggoon, S. Kunori, S.W. Lee, T. Libeiro, I. Volobouev Texas Tech University, Lubbock, USA E. Appelt, A.G. Delannoy, S. Greene, A. Gurrola, W. Johns, C. Maguire, Y. Mao, A. Melo, M. Sharma, P. Sheldon, B. Snook, S. Tuo, J. Velkovska Vanderbilt University, Nashville, USA M.W. Arenton, S. Boutle, B. Cox, B. Francis, J. Goodell, R. Hirosky, A. Ledovskoy, H. Li, C. Lin, C. Neu, J. Wood University of Virginia, Charlottesville, USA C. Clarke, R. Harr, P.E. Karchin, C. Kottachchi Kankanamge Don, P. Lamichhane, J. Sturdy Wayne State University, Detroit, USA D.A. Belknap, D. Carlsmith, M. Cepeda, S. Dasu, L. Dodd, S. Duric, E. Friis, R. Hall-Wilton, M. Herndon, A. Hervé, P. Klabbers, A. Lanaro, C. Lazaridis, A. Levine, R. Loveless, A. Mohapatra, I. Ojalvo, T. Perry, G.A. Pierro, G. Polese, I. Ross, T. Sarangi, A. Savin, W.H. Smith, C. Vuosalo, N. Woods University of Wisconsin, Madison, USA † Deceased. 1 Also at Vienna University of Technology, Vienna, Austria. 2 Also at CERN, European Organization for Nuclear Research, Geneva, Switzerland. 3 Also at Institut Pluridisciplinaire Hubert Curien, Université de Strasbourg, Université de Haute Alsace Mulhouse, CNRS/IN2P3, Strasbourg, France. 4 Also at National Institute of Chemical Physics and Biophysics, Tallinn, Estonia. 5 Also at Skobeltsyn Institute of Nuclear Physics, Lomonosov Moscow State University, Moscow, Russia. 6 Also at Universidade Estadual de Campinas, Campinas, Brazil. 7 Also at Laboratoire Leprince-Ringuet, Ecole Polytechnique, IN2P3–CNRS, Palaiseau, France. 8 Also at Joint Institute for Nuclear Research, Dubna, Russia. 9 Also at Suez University, Suez, Egypt. CMS Collaboration / Physics Letters B 739 (2014) 23–43 43 10 Also at Cairo University, Cairo, Egypt. 11 Also at Fayoum University, El-Fayoum, Egypt. 12 Also at British University in Egypt, Cairo, Egypt. 13 Now at Ain Shams University, Cairo, Egypt. 14 Also at Université de Haute Alsace, Mulhouse, France. 15 Also at Brandenburg University of Technology, Cottbus, Germany. 16 Also at Institute of Nuclear Research ATOMKI, Debrecen, Hungary. 17 Also at Eötvös Loránd University, Budapest, Hungary. 18 Also at University of Debrecen, Debrecen, Hungary. 19 Also at University of Visva-Bharati, Santiniketan, India. 20 Now at King Abdulaziz University, Jeddah, Saudi Arabia. 21 Also at University of Ruhuna, Matara, Sri Lanka. 22 Also at Isfahan University of Technology, Isfahan, Iran. 23 Also at Sharif University of Technology, Tehran, Iran. 24 Also at Plasma Physics Research Center, Science and Research Branch, Islamic Azad University, Tehran, Iran. 25 Also at Università degli Studi di Siena, Siena, Italy. 26 Also at Centre National de la Recherche Scientifique (CNRS) – IN2P3, Paris, France. 27 Also at Purdue University, West Lafayette, USA. 28 Also at Universidad Michoacana de San Nicolas de Hidalgo, Morelia, Mexico. 29 Also at Institute for Nuclear Research, Moscow, Russia. 30 Also at St. Petersburg State Polytechnical University, St. Petersburg, Russia. 31 Also at California Institute of Technology, Pasadena, USA. 32 Also at Faculty of Physics, University of Belgrade, Belgrade, Serbia. 33 Also at Facoltà Ingegneria, Università di Roma, Roma, Italy. 34 Also at Scuola Normale e Sezione dell’INFN, Pisa, Italy. 35 Also at University of Athens, Athens, Greece. 36 Also at Paul Scherrer Institut, Villigen, Switzerland. 37 Also at Institute for Theoretical and Experimental Physics, Moscow, Russia. 38 Also at Albert Einstein Center for Fundamental Physics, Bern, Switzerland. 39 Also at Gaziosmanpasa University, Tokat, Turkey. 40 Also at Adiyaman University, Adiyaman, Turkey. 41 Also at Cag University, Mersin, Turkey. 42 Also at Mersin University, Mersin, Turkey. 43 Also at Izmir Institute of Technology, Izmir, Turkey. 44 Also at Ozyegin University, Istanbul, Turkey. 45 Also at Marmara University, Istanbul, Turkey. 46 Also at Kafkas University, Kars, Turkey. 47 Also at Mimar Sinan University, Istanbul, Istanbul, Turkey. 48 Also at Rutherford Appleton Laboratory, Didcot, United Kingdom. 49 Also at School of Physics and Astronomy, University of Southampton, Southampton, United Kingdom. 50 Also at University of Belgrade, Faculty of Physics and Vinca Institute of Nuclear Sciences, Belgrade, Serbia. 51 Also at Argonne National Laboratory, Argonne, USA. 52 Also at Erzincan University, Erzincan, Turkey. 53 Also at Yildiz Technical University, Istanbul, Turkey. 54 Also at Texas A&M University at Qatar, Doha, Qatar. 55 Also at Kyungpook National University, Daegu, Republic of Korea. Measurement of the tt production cross section in pp collisions at √s=8 TeV in dilepton final states containing one τ lepton 1 Introduction 2 The CMS detector 3 Data and simulation samples 4 Event selection 5 Background estimate 6 Systematic uncertainties 6.1 Experimental uncertainties 6.2 Theoretical uncertainties 7 Cross section measurement 8 Summary Acknowledgements References CMS Collaboration