Measurement of the production cross section of a W boson in association with two b jets in pp collisions at \(\sqrt{s} = 8{\,\mathrm{{TeV}}} \)

The European Physical Journal C, Feb 2017

The production cross section of a W boson in association with two b jets is measured using a sample of proton–proton collisions at \(\sqrt{s} = 8{\,\mathrm{{TeV}}} \) collected by the CMS experiment at the CERN LHC. The data sample corresponds to an integrated luminosity of 19.8\(\,\text {fb}^\text {-1}\). The W bosons are reconstructed via their leptonic decays, \(\mathrm {W}\rightarrow \ell \nu \), where \(\ell =\mu \) or \(\mathrm {e}\). The fiducial region studied contains exactly one lepton with transverse momentum \(p_{\mathrm {T}} ^{\ell }>30\,\mathrm{{GeV}} \) and pseudorapidity \(|\eta ^{\ell } |<2.1\), with exactly two b jets with \(p_{\mathrm {T}} >25\,\mathrm{{GeV}} \) and \(|\eta |<2.4\) and no other jets with \(p_{\mathrm {T}} >25\,\mathrm{{GeV}} \) and \(|\eta |<4.7\). The cross section is measured to be \(\sigma ( {{\mathrm {p}\mathrm {p}}} \rightarrow {\mathrm {W}} (\ell \nu )\)+\({\mathrm{{b}}\mathrm{{\overline{b}}}})= 0.64 \pm 0.03{\,\mathrm{{(stat)}}} \pm 0.10{\,\mathrm{{(syst)}}} \pm 0.06{\,\mathrm{{(theo)}}} \pm 0.02{\,\mathrm{{(lumi)}}}\,{\text {pb}}\), in agreement with standard model predictions.

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Measurement of the production cross section of a W boson in association with two b jets in pp collisions at \(\sqrt{s} = 8{\,\mathrm{{TeV}}} \)

Eur. Phys. J. C Measurement of the production cross section of a W boson in association with two b jets in pp collisions at √s = 8 TeV CMS Collaboration 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 22 24 25 26 29 30 31 32 34 35 36 37 38 39 40 43 44 45 47 48 49 50 51 53 54 55 57 58 59 60 61 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 0 CERN , 1211 Geneva 23 , Switzerland 1 State Key Laboratory of Nuclear Physics and Technology, Peking University , Beijing, China Y. Ban, G. Chen, Q. Li, S. Liu, Y. Mao, S. J. Qian, D. Wang, Z. Xu 2 Beihang University , Beijing , China W. Fang 3 University of Sofia , Sofia, Bulgaria A. Dimitrov, I. Glushkov, L. Litov, B. Pavlov, P. Petkov 4 Universidad San Francisco de Quito , Quito , Ecuador E. Carrera Jarrin 5 Charles University , Prague , Czech Republic M. Finger 6 University of Cyprus , Nicosia, Cyprus A. Attikis, G. Mavromanolakis, J. Mousa, C. Nicolaou, F. Ptochos, P. A. Razis, H. Rykaczewski 7 Institute Rudjer Boskovic , Zagreb , Croatia V. Brigljevic, D. Ferencek, K. Kadija, S. Micanovic, L. Sudic, T. Susa 8 Faculty of Science, University of Split , Split , Croatia Z. Antunovic, M. Kovac 9 Helsinki Institute of Physics , Helsinki, Finland J. Härkönen, V. Karimäki, R. Kinnunen, T. Lampén, K. Lassila-Perini, S. Lehti, T. Lindén, P. Luukka, T. Peltola, J. Tuominiemi, E. Tuovinen, L. Wendland 10 Department of Physics, University of Helsinki , Helsinki , Finland P. Eerola, J. Pekkanen, M. Voutilainen 11 National Institute of Chemical Physics and Biophysics , Tallinn , Estonia B. Calpas, M. Kadastik, M. Murumaa, L. Perrini, M. Raidal, A. Tiko, C. Veelken 12 III. Physikalisches Institut A, RWTH Aachen University , Aachen, Germany M. Brodski, E. Dietz-Laursonn, D. Duchardt, M. Endres, M. Erdmann, S. Erdweg, T. Esch, R. Fischer, A. Güth, M. Hamer, T. Hebbeker, C. Heidemann, K. Hoepfner, S. Knutzen, M. Merschmeyer, A. Meyer, P. Millet, S. Mukherjee, M. Olschewski, K. Padeken, T. Pook, M. Radziej, H. Reithler, M. Rieger, F. Scheuch, L. Sonnenschein, D. Teyssier, S. Thüer 13 Tbilisi State University , Tbilisi , Georgia I. Bagaturia 14 Georgian Technical University , Tbilisi , Georgia T. Toriashvili 15 Lappeenranta University of Technology , Lappeenranta , Finland J. Talvitie, T. Tuuva 16 Wigner Research Centre for Physics , Budapest, Hungary G. Bencze, C. Hajdu, P. Hidas, D. Horvath 17 University of Ioánnina , Ioannina , Greece I. Evangelou, G. Flouris, C. Foudas, P. Kokkas, N. Loukas, N. Manthos, I. Papadopoulos, E. Paradas 18 University of Hamburg , Hamburg, Germany V. Blobel, M. Centis Vignali, A. R. Draeger, T. Dreyer, E. Garutti, K. Goebel, D. Gonzalez, J. Haller, M. Hoffmann, A. Junkes, R. Klanner, R. Kogler, N. Kovalchuk, T. Lapsien, T. Lenz, I. Marchesini, D. Marconi, M. Meyer, M. Niedziela, D. Nowatschin, J. Ott, F. Pantaleo 19 , A. Makovec , J. Molnar, Z. Szillasi 20 , P. Mal, K. Mandal, A. Nayak 21 , D. K. Sahoo, N. Sahoo , S. K. Swain 22 Panjab University , Chandigarh , India S. Bansal, S. B. Beri, V. Bhatnagar, R. Chawla , U. Bhawandeep, A. K. Kalsi , A. Kaur, M. Kaur, R. Kumar, A. Mehta, M. Mittal, J. B. Singh, G. Walia 23 , T. Peiffer , A. Perieanu, J. Poehlsen, C. Sander, C. Scharf, P. Schleper, A. Schmidt, S. Schumann, J. Schwandt, H. Stadie, G. Steinbrück, F. M. Stober, M. Stöver, H. Tholen, D. Troendle, E. Usai, L. Vanelderen, A. Vanhoefer, B. Vormwald 24 National Institute of Science Education and Research , Bhubaneswar , India S. Bahinipati, S. Choudhury 25 University of Debrecen , Debrecen , Hungary M. Bartók 26 Institute of Nuclear Research ATOMKI , Debrecen , Hungary N. Beni, S. Czellar, J. Karancsi 27 , P. Raics, Z. L. Trocsanyi, B. Ujvari 28 , F. Sikler, V. Veszpremi, G. Vesztergombi 29 Indian Institute of Science Education and Research (IISER) , Pune , India S. Chauhan, S. Dube, V. Hegde, A. Kapoor, K. Kothekar, A. Rane, S. Sharma 30 Tata Institute of Fundamental Research-B , Mumbai , India S. Banerjee, S. Bhowmik 31 Tata Institute of Fundamental Research-A, Mumbai, India T. Aziz , S. Dugad, G. Kole, B. Mahakud, S. Mitra, G. B. Mohanty, B. Parida, N. Sur, B. Sutar 32 University College Dublin , Dublin, Ireland M. Felcini, M. Grunewald 33 , M. Khakzad , M. Mohammadi Najafabadi, M. Naseri, S. Paktinat Mehdiabadi, F. Rezaei Hosseinabadi, B. Safarzadeh 34 Institute for Research in Fundamental Sciences (IPM) , Tehran , Iran H. Behnamian, S. Chenarani 35 Vilnius University , Vilnius , Lithuania V. Dudenas, A. Juodagalvis, J. Vaitkus 36 Sungkyunkwan University , Suwon, Korea Y. Choi, J. Goh, C. Hwang, J. Lee, I. Yu 37 University of Seoul , Seoul , Korea M. Choi, H. Kim, H. Kim, J. H. Kim, J. S. H. Lee, I. C. Park, G. Ryu, M. S. Ryu 38 Institute of Experimental Physics, Faculty of Physics, University of Warsaw , Warsaw , Poland K. Bunkowski, A. Byszuk 39 National Centre for Nuclear Research , Swierk , Poland H. Bialkowska , M. Bluj, B. Boimska, T. Frueboes, M. Górski, M. Kazana, K. Nawrocki, K. Romanowska-Rybinska, M. Szleper, P. Zalewski 40 National Centre for Physics, Quaid-I-Azam University , Islamabad, Pakistan A. Ahmad, M. Ahmad, Q. Hassan, H. R. Hoorani, W. A. Khan, M. A. Shah, M. Shoaib, M. Waqas 41 , L. Dudko, A. Gribushin, V. Klyukhin , O. Kodolova, I. Lokhtin, I. Miagkov, S. Obraztsov, M. Perfilov, V. Savrin, A. Snigirev 42 , K. Doroba , A. Kalinowski, M. Konecki, J. Krolikowski, M. Misiura, M. Olszewski, M. Walczak 43 Novosibirsk State University (NSU) , Novosibirsk , Russia V. Blinov 44 Skobeltsyn Institute of Nuclear Physics, Lomonosov Moscow State University , Moscow, Russia A. Baskakov, A. Belyaev, E. Boos, V. Bunichev, M. Dubinin 45 P.N. Lebedev Physical Institute , Moscow , Russia V. Andreev, M. Azarkin 46 , S. V. Rusakov, A. Terkulov 47 Moscow Institute of Physics and Technology , Dolgoprudny , Russia A. Bylinkin 48 Institute for Nuclear Research , Moscow , Russia Yu. Andreev , A. Dermenev, S. Gninenko, N. Golubev, A. Karneyeu, M. Kirsanov, N. Krasnikov, A. 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Woodard The production cross section of a W boson in association with two b jets is measured using a sample of proton-proton collisions at √s = 8 TeV collected by the CMS experiment at the CERN LHC. The data sample corresponds to an integrated luminosity of 19.8 fb−1. The W bosons are reconstructed via their leptonic decays, W → ν, where = μ or e. The fiducial region studied contains exactly one lepton with transverse momentum pT > 30 GeV and pseudorapidity |η | < 2.1, with exactly two b jets with pT > 25 GeV and |η| < 2.4 and no other jets with pT > 25 GeV and |η| < 4.7. The cross section is measured to be σ (pp → W( ν)+bb) = 0.64 ± 0.03 (stat) ± 0.10 (syst) ± 0.06 (theo) ± 0.02 (lumi) pb, in agreement with standard model predictions. - The measurement of W or Z boson production in association with b quarks in proton–proton collisions provides important input for refinement of calculations in perturbative quantum chromodynamics and is also relevant for searches and measurements. In particular, these processes constitute a background to the experimental measurement of a standard model (SM) Higgs boson in which the Higgs boson decays into a bb pair in association with a vector boson. The discovery by the ATLAS and CMS Collaborations at the CERN LHC of a neutral boson with a mass of about 125 GeV [1–4] motivates further studies to establish the nature of the boson and determine its coupling to bottom quarks. Furthermore, different models based on extensions of the Higgs sector are being compared with LHC data using final states composed of leptons and b jets. In this context, a better understanding of the b hadron production mechanism and the kinematic properties of associated jets is required to refine the background predictions and increase the sensitivity to new physics. Throughout this paper,hadronic showers originating from bottom or antibottom quarks are referred to as b jets, and b-tagged jets are the reconstructed objects either in simulation or data that have been identified as such. The production of W [5,6] or Z [7–11] bosons in association with b jets has been measured at the LHC using pp collisions at √s = 7 TeV using data samples corresponding to an integrated luminosity of up to 5 fb−1, and at the Fermilab Tevatron [12,13] using proton–antiproton collisions at √s = 1.96 TeV. This analysis extends previous measurements of the W+bb production cross section [5] and uses data at √s = 8 TeV collected with the CMS detector, corresponding to an integrated luminosity of 19.8 fb−1 [14]. Whereas the previous CMS analysis used only the muon decay channel, this analysis uses both muon and electron decay modes. 2 CMS detector The central feature of the CMS apparatus is a superconducting solenoid of 6 m internal diameter, providing a magnetic field of 3.8 T. Within the solenoid volume are a silicon pixel and strip tracker, a lead tungstate crystal electromagnetic calorimeter (ECAL), and a brass and scintillator hadron calorimeter (HCAL), each composed of a barrel and two endcap sections. Muons are measured in gas-ionization detectors embedded in the steel flux-return yoke outside the solenoid. Extensive forward calorimetry complements the coverage provided by the barrel and endcap detectors. A more detailed description of the CMS detector, together with a definition of the coordinate system used and the relevant kinematic variables, can be found in Ref. [15]. 3 Event selection and reconstruction The W → μνμ (W → eνe) events are selected using single-muon (single-electron) triggers that require a loosely isolated muon (electron) with transverse momentum pT > 24 (27) GeV and pseudorapidity |η| < 2.1 (2.5). Individual particles emerging from each collision are reconstructed with the particle-flow (PF) technique [16, 17]. This approach uses the information from all subdetectors to identify and reconstruct individual particle candidates in the event, classifying them into mutually exclusive categories: charged and neutral hadrons, photons, electrons, and muons. Muons are reconstructed by combining the information from the tracker and the muon spectrometer [18, 19]. Electrons are reconstructed by combining the information from the tracker and the calorimeter [20]. Both the muon and the electron candidates are required to have pT > 30 GeV and |η| < 2.1 to ensure that the triggers are fully efficient. They are also required to originate from the primary vertex of the event, chosen as the vertex with the highest pT2 of the charged particles associated with it. Furthermore, the leptons must be isolated, where the isolation variable is defined as I = ETneutral − 0.5 tex. Small corrections to the relative and absolute jet energy calibrations of the detector are applied as a function of the pT and η of the jet [27]. The combined secondary vertex (CSV) b tagging algorithm [28, 29] exploits the long lifetime and relatively large mass of b hadrons to provide b jet identification. The CSV algorithm combines information about impact parameter significance, secondary vertex kinematic properties, and jet kinematic properties in a likelihood-ratio discriminator. The identification of b jets (b tagging) is made by imposing a minimum threshold on the CSV discriminator value. In this analysis, b-tagged jets are required to pass a threshold with an efficiency of 40% in the signal phase space and a misidentification probability of 0.1% (1%) for light (charm) jets. Jets are corrected for the difference in efficiency between data and simulation using scale factors dependent on the pT of the jet. 4 Simulated samples After all selection requirements detailed in Sect. 5 are applied, the contributing background processes to the overall yield are the associated production of a massive vector boson and jets (V+jets where V = W or Z), as well as production of diboson (W+W−, WZ, ZZ), tt, single top quark, γ +jets, and QCD multijet events. These background contributions are estimated from simulation, except for the QCD background, which is estimated from data as described in Sect. 5. Simulated samples of V+jets, γ +jets and tt + jets are generated at tree-level with MadGraph 5.1 [30, 31] using the CTEQ6L [32] parton distribution function (PDF) set. These samples are interfaced with pythia 6.4 [33] for hadronization using the Z2* tune for the underlying event. The most recent pythia Z2* tune is derived from the Z1 tune [34], which uses the CTEQ5L PDF set, whereas Z2* adopts CTEQ6L [32]. The kT-MLM [35, 36] matching scheme is used. For the signal distributions, the shapes are taken from a dedicated high-statistics generated sample of exclusive W+bb. The normalization is obtained from the W+bb component of an inclusive W+jets sample by separating the W+jets simulated sample into three subsamples labeled as W+bb, W+cc, and W+udscg, which are defined below. If an event contains a bottom jet from the matrix element or parton shower, it is categorized as W+bb. A bottom quark at generator level requires the presence of a bottom hadron within a cone of radius Δ R = 0.4 with respect to the jet axis. The jets are constructed using generator-level information using all stable particles in the event, excluding neutrinos. Jets with a distance smaller than Δ R = 0.5 with respect to a lepton are removed from the event. If an event does not contain any b jet, but an even, nonzero number of charm jets, again from the matrix element or parton shower, it is categorized as W+cc. The remaining events are categorized as W+udscg. The energy of the selected leptons at the generator level is corrected for final-state radiation by summing the four-momenta of all the photons generated within a cone of radius ΔR = 0.1 around the lepton. Leptons that do not originate from the primary vertex are not considered for selection. Single top quark event samples are generated at nextto-leading order (NLO) with powheg 2.0 [37–40] using the CTEQ6M PDF set. Hadronization is performed using pythia 6.4 with the Z2* tune. Diboson samples are generated and hadronized with pythia 6.4 at leading order (LO) using the CTEQ6L PDF set and the Z2* tune. The cross sections for the V+jets processes are normalized using the predictions for inclusive W and Z boson production from fewz 3.1 [41,42] evaluated at next-to-next-to-leading order (NNLO). The cross section for γ +jets is evaluated at LO using MadGraph with the CTEQ6L PDF set. Single top quark and diboson production cross sections are normalized to the NLO cross section predictions from mcfm 7.0 [43, 44] using the MSTW2008 NLO PDF set [45]. The tt cross section used is 241.5 ± 8.5 pb, and was determined from data collected by the ATLAS and CMS experiments [46–48] at the LHC at √s = 8 TeV. For all simulated processes, the detector response is simulated using a detailed description of the CMS detector based on Geant4 [49]. The reconstruction of simulated events is performed with the same algorithms used for the data. Events induced by additional simultaneous pp interactions are simulated using events generated with pythia 6. During the 2012 data taking, the average pileup rate was 21 interactions per bunch crossing; the simulated number of pileup interactions has been reweighted to match this distribution in the data. 5 Analysis strategy The W+bb yield is estimated using a binned maximumlikelihood fit to the MT distribution in the signal event sample. With the exception of the multijet processes, the distributions and normalizations of all background contributions in the fit are taken from simulation. Consequently, it is important to verify that the simulation describes the data. The dominant background in the signal event sample arises from the tt process. Therefore, the data and simulation are compared in two tt-dominated control samples: one characterized by a pair of opposite flavor leptons (ttmultilepton), and the other by the presence of three or more jets (tt-multijet). The simulation is reweighted to describe the data in the control regions and then is used to predict the MT distributions in the signal region. The signal region contains a muon (electron) with pT > 30 GeV, |η| < 2.1, and satisfying I < 0.12 (0.10). Exactly two b-tagged jets with pT > 25 GeV and |η| < 2.4 are also required. Events with additional leptons with pT > 10 GeV and |η| < 2.4 or a third jet with pT > 25 GeV and |η| < 4.7 are rejected. The tt-multijet sample is obtained using the same selection criteria as for the signal event sample, but requiring at least three jets in the event with pT > 25 GeV and |η| < 2.4 instead of vetoing events that have more than two jets. The tt-multilepton sample uses similar selection criteria as the signal event sample; however, the lepton requirement is modified. The event must contain two isolated leptons of different flavor, each with pT > 30 GeV and |η| < 2.1. In the tt-multilepton sample, the MT variable is calculated with respect to the electron in the electron channel and the muon in the muon channel. The QCD background distributions in the MT variable are estimated from data using event samples that pass all signal requirements, but requiring the muon (electron) is not isolated, I > 0.20 (0.15). The resulting distributions are corrected for the presence of all other backgrounds, as estimated from simulation. Their contribution is less than 1% of the QCD background rate. The QCD background normalization is adjusted in order to describe the number of data events at MT < 20 GeV, after subtracting the non-QCD backgrounds obtained from simulation. In the fiducial regions used in this analysis, no correlation is observed between I and MT in multijet events simulated with pythia 6, so the use of an inverted isolation requirement to obtain the QCD background distribution is possible. However, this is not the case for the ΔR distance between the two b-tagged jets, ΔR(b, b), or the lepton pT. The shape of the QCD distribution for these variables is therefore taken from an MT < 30 GeV sideband and validated against QCD multijet simulation. The normalization of the QCD background in these variables is set to the final normalization resulting from the fit to the MT variable, which was derived using the inverted isolation requirement. The normalizations and distributions of the simulated backgrounds are allowed to vary in the fit within the uncertainties listed in Table 1 as described in Sect. 6. The uncorrelated normalization uncertainties are uncertainties in the cross section of the given sample. Two major parameters in the simulations significantly affect the normalization of the simulated distributions: the b tagging efficiency and the jet energy scale (JES). The control samples as well as the signal event samples show similar sensitivity to the b tagging efficiency, and its adjustment affects all the regions in a correlated manner. Because tt production may have more than two jets in the final state, the rejection of events with a third jet makes it sensitive to JES. The effect on the leading jets is moderate, but JES variations lead to significant migration of jets into and out of the veto region. The tt-multijet sample, since it has no veto on a third jet, is less sensitive to JES variations than the tt-multilepton sample. The variation in the JES changes the W+bb yield in the signal region by less than 1%. The fit procedure consists of three consecutive steps in which the simulated distributions in two control samples and the event sample are fit to data using the MT variable, which is chosen because it has a well-known shape for W+jets production that allows for reliable signal extraction. First, the fit is performed using the tt-multijet sample. It results in a correction of the b tagging efficiency, measured separately in the muon and electron channels and then combined. The simulation is corrected using this result and the corrected simulated samples are fit to the data in the tt-multilepton sample. The result of the second step is used to adjust JES and as a result of this procedure, the simulation is expected to better describe the tt contribution. The final step is to extract the number of W+bb events from the fit to MT in the signal event sample. Similar results can be obtained by performing a simultaneous fit of the signal and the two control regions. We find that the b tagging efficiency correction and JES correction have opposite effects on the distributions and thus compensate for each other in a simultaneous fit, reducing its precision. Separating these effects in steps provides better understanding of underlying uncertainties and therefore more precise results. 6 Systematic uncertainties The main sources of the systematic uncertainties are listed in Table 1. The size of the variation is shown for each source, together with its effect on the measured cross section. These are included in the fit. Some of the uncertainties affect only the normalization in the respective contributions. These include the uncertainties in the theoretical cross section for a given sample, which are uncorrelated between samples and are included as log-normal constraints on the rate. The uncertainty due to the b tagging efficiency and the uncertainty due to the JES are observed to only affect the normalizations of the samples in the MT variable. The uncertainties that affect both the normalization and the shape of the MT distributions are listed in the table under “Shape” and are incorporated into the fit via binned distributions, which are obtained by varying the source of the given uncertainty and reprocessing the simulated sample. Such uncertainties in the template are interpolated quadratically. As a conservative estimate of the uncertainty in QCD multijet background, a 50% uncertainty has been considered. This results in an uncertainty of 2–3% in the measured cross section. The b tagging efficiency and JES rescaling uncertainties are taken from their respective fits. The renormalization and factorization scales respectively are set at μR = μF = mW, and the uncertainties on this choice are estimated from the change in acceptance found by varying μR and μF up and down by a factor of two. The PDF uncertainties are estimated from the change in acceptance found by varying the PDF set following the LHAPDF/PDF4LHC prescription [50–53], considering PDF sets from the CTEQ, MSTW, NNPDF, and HERA Collaborations. 7 Results The fit in the tt-multijet sample is used to obtain b tagging efficiency rescaling factors separately for the muon and electron channels in order to better describe the b tagging efficiency in the simulation as described in Sect. 5. The results of the fit are presented in the two plots at the top of Fig. 1. The central values of the b tagging efficiency rescaling factors, 1.12 ± 0.08 (muon channel) and 1.16 ± 0.08 (electron channel), are averaged to 1.14±0.08 with the combined uncertainty, dominated by systematics, taken as the maximum of the uncertainties for the individual lepton channels. The simulation is reweighted accordingly for the next fit, and the uncertainty in this fit sets the one standard deviation bound on the b tagging efficiency rescaling factor in subsequent fits. A fit to the tt-multilepton sample adjusts the JES, as described in Sect. 5. As a result, the simulated MT distributions change normalization. The best fit results in changing the normalization by approximately 3.4% from its central value, which corresponds to 1.3 standard deviations in JES. The middle plots in Fig. 1 show the results of the fits in the ttmultilepton sample for the muon (left) and the electron (right) channels. The JES is therefore shifted by 1.3 standard deviations in the simulation with the uncertainty taken from the fit. Thus the simulation is tuned to describe the tt control samples and is used to extract the signal yield in the signal region. The results of the fit in the W+bb signal region are shown in the bottom of Fig. 1. All background contributions are allowed to vary in the fit within their uncertainties, while the W+bb normalization remains a free parameter of the fit. The correlations across all simulated samples are taken into account as shown in Table 1. Based on the fits the number of events of each type in the signal event sample is given in Table 2. Events coming from the production of a Higgs boson in association with a vector boson constitute a negligible fraction of the overall event yield and are not considered. Distributions for variables other than those being directly fit are also produced by applying the results from the three fits to the simulated samples. Distributions of Δ R(b, b) and pT combining both lepton flavors are presented in Fig. 2. The angular separation between the b jets is seen to be well modeled, and the pT distribution shows an agreement within 10% for pT < 100 GeV, with a slightly falling trend in the ratio of data and simulation. The cross section is calculated as Nrdeactoanstructed (NrMecConstructed/NgMenCerated) L Nrdeactoanstructed A L 20 40 60 80 100 120 140 160 180 200 220 Transverse mass [GeV] 20 40 60 80 100 120 140 160 180 200 220 Transverse mass [GeV] 20 40 60 80 100 120 140 160 180 200 220 Transverse mass [GeV] 20 40 60 80 100 120 140 160 180 200 220 Transverse mass [GeV] 0 1.3 C1.2 M1.1 / 1 a0.9 t 0.8 aD0.70 0 1.3 C1.2 M1.1 / 1 a0.9 t 0.8 aD0.70 0 1.3 C1.2 M1.1 / 1 a0.9 t 0.8 aD0.70 20 40 60 80 100 120 140 160 180 200 220 Transverse mass [GeV] 20 40 60 80 100 120 140 160 180 200 220 Transverse mass [GeV] Fig. 1 The transverse mass distributions (upper) in the tt-multijet phase space after fitting to obtain the b tagging efficiency rescale factors, (middle) in the tt-multilepton sample after fitting to find the appropriate JES, and (lower) in the W+bb signal sample after fitting simultaneously muon and electron decay channels. The lepton channels are shown separately with the muon sample on the left and the electron sample on the right. The last bin contains overflow events. The shaded area represents the total uncertainty in the simulation after the fit 0 1.3 C1.2 M1.1 / 1 a0.9 t 0.8 aD0.70 0 1.3 C1.2 M1.1 / 1 a0.9 t 0.8 aD0.70 0 1.3 C1.2 M1.1 / 1 a0.9 t 0.8 aD0.70 Table 1 The main sources of systematic uncertainty in the W+bb signal event sample. The column labeled “Variation” indicates the bounds on the normalization change of a given sample due to a variation of the uncertainty by one standard deviation. The last column indicates the contribution of the given systematic to the overall uncertainty in the measured cross section. The uncertainty labeled “b tag eff rescaling” is the uncertainty associated with the rescaling of the b tagging efficiency. UES refers to the scale of energy deposits not clustered into jets, and MES and EES refer to the muon and electron energy scales. The uncertainty labeled as “Id/Iso/Trg” is the uncertainty associated with the efficiency of the lepton identification, isolation, and trigger. The uncertainties in the integrated luminosity [14] and in the acceptance due to PDF uncertainties and scale choices are not included in the fit, and are treated separately Table 2 Initial and final yields obtained in the W+bb signal region. The uncertainties in the signal strength represent the total uncertainty of the fit where L is the integrated luminosity, Nrdeactoanstructed is the number of observed signal events, NrMecConstructed is the number of expected signal events from simulation reconstructed in the fiducial region, NgMenCerated is the number of generated events in the fiducial region, A and are the acceptance and efficiency, α is the measured signal strength in the given lepton channel, and σgen is the simulated fiducial cross section of the signal sample. The signal strength is the scale factor in the W+bb cross section predicted by the fit, after factoring out contributions to the overall change in normalization due to systematic effects which are correlated across samples. In this analysis, the fiducial cross section is calculated as follows: MadGraph is used to compute the W+bb cross section with fiducial selections applied. Then a k-factor for inclusive W production is applied that is obtained from the ratio of the inclusive W cross section calculated with fewz 3.1 (at NNLO using the five-flavour CTEQ6M PDF set) and to that with MadGraph. The product A is 10 to 15% and results from the combined effects of the efficiency of the lepton identification requirements (80%) and b tagging efficiency (40% per jet) and has an uncertainty of 10%, arising from scale and PDF choices as indicated in the bottom of Table 1. The W+bb cross section is measured within a fiducial volume, which is defined by requiring leptons with pT > 30 GeV and |η| < 2.1 and exactly two b-tagged jets of pT > 25 GeV and |η| < 2.4. The measured cross sections are presented in Table 3. The combination of the muon and electron measurements is done using a simultaneous fit to both channels, taking into account correlations across samples. 0 1.3 C1.2 /aM01..911 t 0.8 aD0.07.5 V eG2500 5 / s tn2000 e v E 1500 100 120 140 160 180 200 Lepton transverse momentum [GeV] Fig. 2 Distributions of ΔR(b, b) and pT after applying the results from the fits to the simulation. The QCD background shape is taken from an MT < 30 GeV sideband and the muon and electron channels have been combined in these distributions. The last bin contains overflow events and the shaded area represents the total uncertainty in the simulation after the fit The measured cross sections are compared to theoretical predictions from mcfm 7.0 [43, 44] with the MSTW2008 PDF set, as well as from MadGraph 5 interfaced with pythia 6 in the four- and five-flavour schemes and MadGraph 5 with pythia 8 [54] in the four-flavour scheme. In the four- and five-flavour approaches, the four and five lightest quark flavours are used in the proton PDF sets. In the fiveflavour scheme, the PDF set CTEQ6L is used and interfaced with pythia 6 using the Z2* tune. The two four-flavour samples are produced using an NNLO PDF set interfaced with 19.8 fb-1 (8 TeV) 19.8 fb-1 (8 TeV) Table 3 Measured cross sections in the muon, electron, and combined lepton channels. The systematic uncertainty (syst) includes the contributions from all rows in Table 1 that have an entry in the “Variation” column, and the theoretical uncertainty (theo) includes the combination of the uncertainties associated with the choice of μR, μF, and PDF pythia version 6 using the CTEQ6L tune in one sample, and version 8 using the CUETP8M1 tune [55] in the other. Comparisons between the results of calculations performed under different assumptions provide important feedback on the validity of the techniques employed. Differences in predictions arising from the modelling of b quarks as massive or massless are possible, as are variations in predictions arising from the use of different showering packages (pythia 6 vs. pythia 8) or matrix element generators (MadGraph vs. mcfm 7.0). In the phase space explored here, these predictions are all very close in their central value and agree with each other well within their respective uncertainties. The mcfm 7.0 cross section calculation is performed at the level of parton jets and thus requires a hadronization correction. The multiplicative hadronization correction factor 0.81 ± 0.07 is calculated using the MadGraph + pythia 6 sample and agrees well with the factor 0.84 ± 0.03 calculated in the 7 TeV Z+b analysis [8]. The correction factor is obtained for jets computed excluding neutrinos from the particle list because such jets are closer in kinematics to particle jets at the detector level. The uncertainty reflects both the limited statistics of the MadGraph + pythia 6 sample as well as a comparison with the MadGraph + pythia 8 sample. The mcfm 7.0 and four-flavour MadGraph predictions do not take into account W+bb production where the bb system is produced in a different partonic level interaction than the one which produced the W boson, albeit in the same collision. Simulations of MadGraph + pythia events that include double parton interactions (DPI) reproduce the W+jets data [56]. Therefore a MadGraph + pythia 8 sample of a W boson produced in association with a bb pair coming from DPI is generated to study the effect on the fiducial cross section. Using this dedicated sample, an additive correction σDPI is estimated to be 0.06 ± 0.06 pb, where the uncertainty is conservatively assigned to be 100% of the value. The resulting cross section predictions in the fiducial phase space at the hadron level, including the estimated MCFM (x Hadronization) 0.51 ± 0.02 PDF ± 0.06 DPI pb MadGraph5 + Pythia6 4F 0.49 ± 0.02 PDF ± 0.06 DPI pb MadGraph5 + Pythia8 4F 0.50 ± 0.03 PDF ± 0.06 DPI pb Fig. 3 Comparison between the measured W( ν) + bb cross section and various QCD predictions. The orange band indicates the uncertainty in the given sample associated with PDF choice and the yellow band represents the uncertainty associated with DPI. The labels 4F and 5F refer to the four- and five-flavour PDF schemes. In the case of the MadGraph + pythia 6 (5F) sample, the effects of DPI are already included in the generated samples so the DPI correction is not needed. The measured cross section is also shown with the total uncertainty in black and the luminosity, statistical, theoretical, and systematic uncertainties indicated hadronization and DPI corrections as needed, are compared in Fig. 3 with the measured value. Within one standard deviation the predictions agree with the measured cross section. 8 Summary The cross section for the production of a W boson in association with two b jets was measured using a sample of proton–proton collisions at √s = 8 TeV collected by the CMS experiment. The data sample corresponds to an integrated luminosity of 19.8 fb−1. The W bosons were reconstructed via their leptonic decays, W → ν, where = μ or e. The fiducial region studied contains exactly one lepton with transverse momentum pT > 30 GeV and pseudorapidity |η | < 2.1, with exactly two b jets with pT > 25 GeV and |η| < 2.4 and no other jets with pT > 25 GeV and |η| < 4.7. The cross section is σ (pp → W( ν) + bb) = 0.64±0.03 (stat)±0.10 (syst)±0.06 (theo)±0.02 (lumi) pb, in agreement with standard model predictions. Acknowledgements We congratulate our colleagues in the CERN accelerator departments for the excellent performance of the LHC and thank the technical and administrative staffs at CERN and at other CMS institutes for their contributions to the success of the CMS effort. In addition, we gratefully acknowledge the computing centers CMS 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 construction 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); SENESCYT (Ecuador); 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); MSIP and NRF (Republic of Korea); LAS (Lithuania); MOE and UM (Malaysia); BUAP, CINVESTAV, CONACYT, LNS, SEP, and UASLPFAI (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 (UK); DOE and NSF (USA). Individuals have received support from the Marie-Curie program and the European Research Council and EPLANET (European Union); the Leventis Foundation; the A. P. Sloan Foundation; the Alexander von Humboldt Foundation; 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 program of the Foundation for Polish Science, cofinanced from European Union, Regional Development Fund, the Mobility Plus program of the Ministry of Science and Higher Education, the National Science Center (Poland), contracts Harmonia 2014/14/M/ST2/00428, Opus 2013/11/B/ST2/04202, 2014/13/B/ST2/02543 and 2014/15/B/ST2/03998, Sonata-bis 2012/07/ E/ST2/01406; the Thalis and Aristeia programs cofinanced by EU-ESF and the Greek NSRF; the National Priorities Research Program by Qatar National Research Fund; the Programa Clarín-COFUND del Principado de Asturias; the Rachadapisek Sompot Fund for Postdoctoral Fellowship, Chulalongkorn University and the Chulalongkorn Academic into Its 2nd Century Project Advancement Project (Thailand); and the Welch Foundation, contract C-1845. Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecomm ons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. Funded by SCOAP3. Yerevan Physics Institute, Yerevan, Armenia V. Khachatryan, A. M. Sirunyan, A. Tumasyan Institut für Hochenergiephysik der OeAW, Wien, Austria W. Adam, E. Asilar, T. Bergauer, J. Brandstetter, E. Brondolin, M. Dragicevic, J. Erö, M. Flechl, M. Friedl, R. Frühwirth1, V. M. Ghete, C. Hartl, N. Hörmann, J. Hrubec, M. Jeitler1, A. König, I. Krätschmer, D. Liko, T. Matsushita, I. Mikulec, D. Rabady, N. Rad, B. Rahbaran, H. Rohringer, J. Schieck1, J. Strauss, W. Treberer-Treberspurg, W. Waltenberger, C.-E. Wulz1 National Centre for Particle and High Energy Physics, Minsk, Belarus V. Mossolov, N. Shumeiko, J. Suarez Gonzalez Universiteit Antwerpen, Antwerpen, Belgium S. Alderweireldt, E. A. De Wolf, X. Janssen, J. Lauwers, M. Van De Klundert, H. Van Haevermaet, P. Van Mechelen, N. Van Remortel, A. Van Spilbeeck Vrije Universiteit Brussel, Brussel, Belgium S. Abu Zeid, F. Blekman, J. D’Hondt, N. Daci, I. De Bruyn, K. Deroover, N. Heracleous, S. Lowette, S. Moortgat, L. Moreels, A. Olbrechts, Q. Python, S. Tavernier, W. Van Doninck, P. Van Mulders, I. Van Parijs Centro Brasileiro de Pesquisas Fisicas, Rio de Janeiro, Brazil W. L. Aldá Júnior, F. L. Alves, G. A. Alves, L. Brito, C. Hensel, A. Moraes, M. E. Pol, P. Rebello Teles Universidade do Estado do Rio de Janeiro, Rio de Janeiro, Brazil E. Belchior Batista Das Chagas, W. Carvalho, J. Chinellato4, A. Custódio, E. M. Da Costa, G. G. Da Silveira, D. De Jesus Damiao, C. De Oliveira Martins, S. Fonseca De Souza, L. M. Huertas Guativa, H. 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Mahrous11 Institut Pluridisciplinaire Hubert Curien, Université de Strasbourg, Université de Haute Alsace Mulhouse, CNRS/IN2P3 Strasbourg, France J.-L. Agram12, J. Andrea, A. Aubin, D. Bloch, J.-M. Brom, M. Buttignol, E. C. Chabert, N. Chanon, C. Collard, E. Conte12, X. Coubez, J.-C. Fontaine12, D. Gelé, U. Goerlach, A.-C. Le Bihan, J. A. Merlin13, K. Skovpen, P. Van Hove Centre de Calcul de l’Institut National de Physique Nucleaire et de Physique des Particules, CNRS/IN2P3 Villeurbanne, France S. Gadrat Institut de Physique Nucléaire de Lyon, Université de Lyon, Université Claude Bernard Lyon 1, CNRS-IN2P3, Villeurbanne, France S. Beauceron, C. Bernet, G. Boudoul, E. Bouvier, C. A. Carrillo Montoya, R. Chierici, D. Contardo, B. Courbon, P. Depasse, H. El Mamouni, J. Fan, J. Fay, S. Gascon, M. Gouzevitch, G. Grenier, B. Ille, F. Lagarde, I. B. Laktineh, M. Lethuillier, L. Mirabito, A. L. Pequegnot, S. Perries, A. Popov14, D. Sabes, V. Sordini, M. Vander Donckt, P. Verdier, S. Viret Institute of Nuclear and Particle Physics (INPP), NCSR Demokritos, Aghia Paraskevi, Greece G. Anagnostou, G. Daskalakis, T. Geralis, V. A. Giakoumopoulou, A. Kyriakis, D. Loukas, I. Topsis-Giotis National and Kapodistrian University of Athens, Athens, Greece A. Agapitos, S. Kesisoglou, A. Panagiotou, N. Saoulidou, E. Tziaferi MTA-ELTE Lendület CMS Particle and Nuclear Physics Group, Eötvös Loránd University, Budapest, Hungary N. Filipovic Saha Institute of Nuclear Physics, Kolkata, India R. Bhattacharya, S. Bhattacharya, K. Chatterjee, S. Dey, S. Dutt, S. Dutta, S. Ghosh, N. Majumdar, A. Modak, K. Mondal, S. Mukhopadhyay, S. Nandan, A. Purohit, A. Roy, D. Roy, S. Roy Chowdhury, S. Sarkar, M. Sharan, S. Thakur INFN Sezione di Baria , Università di Barib, Politecnico di Baric, Bari, Italy M. Abbresciaa ,b, C. Calabriaa ,b, C. Caputoa ,b, A. Colaleoa , D. Creanzaa ,c, L. Cristellaa ,b, N. De Filippisa ,c, M. De Palmaa ,b, L. Fiorea , G. Iasellia ,c, G. Maggia ,c, M. Maggia , G. 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Ciullia ,b, C. Civininia , R. D’Alessandroa ,b, E. Focardia ,b, V. Goria ,b, P. Lenzia ,b, M. Meschinia , S. Paolettia , G. Sguazzonia , L. Viliania ,b,13 INFN Laboratori Nazionali di Frascati, Frascati, Italy L. Benussi, S. Bianco, F. Fabbri, D. Piccolo, F. Primavera13 INFN Sezione di Genovaa , Università di Genovab, Genoa, Italy V. Calvellia ,b, F. Ferroa , M. Lo Veterea ,b, M. R. Mongea ,b, E. Robuttia , S. Tosia ,b INFN Sezione di Milano-Bicoccaa , Università di Milano-Bicoccab, Milan, Italy L. Brianza13, M. E. Dinardoa ,b, S. Fiorendia ,b, S. Gennaia , A. Ghezzia ,b, P. Govonia ,b, S. Malvezzia , R. A. Manzonia ,b,13, B. Marzocchia ,b, D. Menascea , L. Moronia , M. Paganonia ,b, D. Pedrinia , S. Pigazzini, S. Ragazzia ,b, T. Tabarelli de Fatisa ,b INFN Sezione di Napolia , Università di Napoli, ’Federico II’b Napoli, Italy, Università della Basilicatac, Potenza, Italy, Università G. Marconid , Rome, Italy S. Buontempoa , N. Cavalloa ,c, G. De Nardo, S. Di Guidaa ,d ,13, M. Espositoa ,b, F. Fabozzia ,c, A. O. M. Iorioa ,b, G. Lanzaa , L. Listaa , S. Meolaa ,d ,13, P. Paoluccia ,13, C. Sciaccaa ,b, F. Thyssen INFN Sezione di Padovaa , Università di Padovab, Padova, Italy, Università di Trentoc, Trento, Italy P. Azzia ,13, N. Bacchettaa , L. Benatoa ,b, D. Biselloa ,b, A. Bolettia ,b, R. Carlina ,b, A. Carvalho Antunes De Oliveiraa ,b, P. Checchiaa , M. Dall’Ossoa ,b, P. De Castro Manzanoa , T. Dorigoa , U. Dossellia , F. Gasparinia ,b, U. Gasparinia ,b, F. Gonellaa , A. Gozzelinoa , S. Lacapraraa , A. T. Meneguzzoa ,b, J. Pazzinia ,b,13, M. Pegoraroa , N. Pozzobona ,b, P. Ronchesea ,b, M. Sgaravattoa , F. Simonettoa ,b, E. Torassaa , A. Zucchettaa ,b, G. Zumerlea ,b INFN Sezione di Paviaa , Università di Paviab, Pavia, Italy A. Braghieria , A. Magnania ,b, P. Montagnaa ,b, S. P. Rattia ,b, V. Rea , C. Riccardia ,b, P. Salvinia , I. Vaia ,b, P. Vituloa ,b INFN Sezione di Perugiaa , Università di Perugiab, Perugia, Italy L. Alunni Solestizia ,b, G. M. 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Santanastasioa ,b INFN Sezione di Triestea , Università di Triesteb, Trieste, Italy S. Belfortea , M. Casarsaa , F. Cossuttia , G. Della Riccaa ,b, C. La Licataa ,b, A. Schizzia ,b, A. Zanettia Kyungpook National University, Taegu, Korea D. H. Kim, G. N. Kim, M. S. Kim, S. Lee, S. W. Lee, Y. D. Oh, S. Sekmen, D. C. Son, Y. C. Yang Hanyang University, Seoul, Korea J. A. Brochero Cifuentes, T. J. Kim Centro de Investigacion y de Estudios Avanzados del IPN, Mexico City, Mexico H. Castilla-Valdez, E. De La Cruz-Burelo, I. Heredia De La Cruz34, A. Hernandez-Almada, R. Lopez-Fernandez, R. Magaña Villalba, J. Mejia Guisao, A. Sanchez-Hernandez Universidad Iberoamericana, Mexico City, Mexico S. Carrillo Moreno, C. Oropeza Barrera, F. Vazquez Valencia Benemerita Universidad Autonoma de Puebla, Puebla, Mexico S. Carpinteyro, I. Pedraza, H. A. Salazar Ibarguen, C. Uribe Estrada Universidad Autónoma de San Luis Potosí, San Luis Potosí, Mexico A. 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Xia 1: Also at Vienna University of Technology, Vienna, Austria 2: Also at State Key Laboratory of Nuclear Physics and Technology, Peking University, Beijing, China 3: Also at Institut Pluridisciplinaire Hubert Curien, Université de Strasbourg, Université de Haute Alsace Mulhouse, CNRS/IN2P3, Strasbourg, France 4: Also at Universidade Estadual de Campinas, Campinas, Brazil 5: Also at Université Libre de Bruxelles, Bruxelles, Belgium 6: Also at Deutsches Elektronen-Synchrotron, Hamburg, Germany 7: Also at Joint Institute for Nuclear Research, Dubna, Russia 8: Also at Suez University, Suez, Egypt 9: Now at British University in Egypt, Cairo, Egypt 10: Also at Ain Shams University, Cairo, Egypt 11: Now at Helwan University, Cairo, Egypt 12: Also at Université de Haute Alsace, Mulhouse, France 13: Also at CERN, European Organization for Nuclear Research, Geneva, Switzerland 14: Also at Skobeltsyn Institute of Nuclear Physics, Lomonosov Moscow State University, Moscow, Russia 15: Also at Tbilisi State University, Tbilisi, Georgia 16: Also at Ilia State University, Tbilisi, Georgia 17: Also at RWTH Aachen University, III. Physikalisches Institut A, Aachen, Germany 18: Also at University of Hamburg, Hamburg, Germany 19: Also at Brandenburg University of Technology, Cottbus, Germany 20: Also at Institute of Nuclear Research ATOMKI, Debrecen, Hungary 21: Also at MTA-ELTE Lendület CMS Particle and Nuclear Physics Group, Eötvös Loránd University, Budapest, Hungary 22: Also at University of Debrecen, Debrecen, Hungary 23: Also at Indian Institute of Science Education and Research, Bhopal, India 24: Also at Institute of Physics, Bhubaneswar, India 25: Also at University of Visva-Bharati, Santiniketan, India 26: Also at University of Ruhuna, Matara, Sri Lanka 27: Also at Isfahan University of Technology, Isfahan, Iran 28: Also at University of Tehran, Department of Engineering Science, Tehran, Iran 29: Also at Plasma Physics Research Center, Science and Research Branch, Islamic Azad University, Tehran, Iran 30: Also at Università degli Studi di Siena, Siena, Italy 31: Also at Purdue University, West Lafayette, USA 32: Also at International Islamic University of Malaysia, Kuala Lumpur, Malaysia 33: Also at Malaysian Nuclear Agency, MOSTI, Kajang, Malaysia 34: Also at Consejo Nacional de Ciencia y Tecnología, Mexico city, Mexico 35: Also at Warsaw University of Technology, Institute of Electronic Systems, Warsaw, Poland 36: Also at Institute for Nuclear Research, Moscow, Russia 37: Now at National Research Nuclear University ’Moscow Engineering Physics Institute’ (MEPhI), Moscow, Russia 38: Also at Institute of Nuclear Physics of the Uzbekistan Academy of Sciences, Tashkent, Uzbekistan 39: Also at St. Petersburg State Polytechnical University, St. Petersburg, Russia 40: Also at University of Florida, Gainesville, USA 41: Also at P.N. Lebedev Physical Institute, Moscow, Russia 42: Also at California Institute of Technology, Pasadena, USA 43: Also at Budker Institute of Nuclear Physics, Novosibirsk, Russia 44: Also at Faculty of Physics, University of Belgrade, Belgrade, Serbia 45: Also at INFN Sezione di Roma; Università di Roma, Rome, Italy 46: Also at Scuola Normale e Sezione dell’INFN, Pisa, Italy 47: Also at National and Kapodistrian University of Athens, Athens, Greece 48: Also at Riga Technical University, Riga, Latvia 49: Also at Institute for Theoretical and Experimental Physics, Moscow, Russia 50: Also at Albert Einstein Center for Fundamental Physics, Bern, Switzerland 51: Also at Gaziosmanpasa University, Tokat, Turkey 52: Also at Mersin University, Mersin, Turkey 53: Also at Cag University, Mersin, Turkey 54: Also at Piri Reis University, Istanbul, Turkey 55: Also at Adiyaman University, Adiyaman, Turkey 56: Also at Ozyegin University, Istanbul, Turkey 57: Also at Izmir Institute of Technology, Izmir, Turkey 58: Also at Marmara University, Istanbul, Turkey 59: Also at Kafkas University, Kars, Turkey 60: Also at Istanbul Bilgi University, Istanbul, Turkey 61: Also at Yildiz Technical University, Istanbul, Turkey 62: Also at Hacettepe University, Ankara, Turkey 63: Also at Rutherford Appleton Laboratory, Didcot, UK 64: Also at School of Physics and Astronomy, University of Southampton, Southampton, UK 65: Also at Instituto de Astrofísica de Canarias, La Laguna, Spain 66: Also at Utah Valley University, Orem, USA 67: Also at University of Belgrade, Faculty of Physics and Vinca Institute of Nuclear Sciences, Belgrade, Serbia 68: Also at Facoltà Ingegneria, Università di Roma, Rome, Italy 69: Also at Argonne National Laboratory, Argonne, USA 70: Also at Erzincan University, Erzincan, Turkey 71: Also at Mimar Sinan University, Istanbul, Istanbul, Turkey 72: Also at Texas A&M University at Qatar, Doha, Qatar 73: Also at Kyungpook National University, Taegu, Korea 1. 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V. Khachatryan, A. M. Sirunyan, A. Tumasyan, W. Adam. Measurement of the production cross section of a W boson in association with two b jets in pp collisions at \(\sqrt{s} = 8{\,\mathrm{{TeV}}} \), The European Physical Journal C, 2017, 92, DOI: 10.1140/epjc/s10052-016-4573-z