Measurement of the time-dependent CP asymmetries in B0s → J/ψK0S

JHEP06(2015)131 Published for SISSA by Springer Received:March 25, 2015 Accepted:May 27, 2015 Published:June 18, 2015 Measurement of the time-dependent CP asymmetries in B0 s→J/ψK0 S The LHCb collaboration E-mail: [email protected] Abstract: The first measurement of decay-time-dependent CP asymmetries in the decay B0 s→J/ψK0 S and an updated measurement of the ratio of branching fractions B ( B0 s→J/ψK0 S ) /B ( B0→J/ψK0 S ) are presented. The results are obtained using data corresponding to an integrated luminosity of 3.0 fb −1 of proton-proton collisions recorded with the LHCb detector at centre-of-mass energies of 7 and 8 TeV. The results on the CP asymmetries are A∆Γ(B0 s→J/ψK0 S) = 0.49 ±0.77 0.65 (stat) ±0.06(syst) , Cdir(B0 s→J/ψK0 S) = −0.28 ±0.41(stat) ±0.08(syst) , Smix(B0 s→J/ψK0 S) = −0.08 ±0.40(stat) ±0.08(syst) . The ratio B(B0 s→J/ψK0 S)/B(B0→J/ψK0 S) is measured to be 0.0431 ±0.0017(stat) ±0.0012(syst) ±0.0025(fs/fd), where the last uncertainty is due to the knowledge of the B0 s and B0 production fractions. Keywords: CP violation, Hadron-Hadron Scattering, Branching fraction, B physics, Flavor physics ArXiv ePrint: 1503.07055 Open Access, Copyright CERN, for the benefit of the LHCb Collaboration. Article funded by SCOAP3. doi:10.1007/JHEP06(2015)131 JHEP06(2015)131 Contents 1 Introduction 1 2 Detector and simulation 4 3 Event selection 4 3.1 Initial selection 4 3.2 Multivariate selection 5 4 Flavour tagging 6 5 Likelihood fit 8 5.1 Mass PDF 8 5.2 Decay time PDF 8 5.3 Likelihood fit 9 5.4 Fit results 10 6 Systematic uncertainties 11 7 Branching ratio measurement 12 8 Conclusion 13 The LHCb collaboration 17 1 Introduction In decays of neutral B mesons (where B stands for a B0 or B0 s meson) to a final state accessible to both B and B , the interference between the direct decay and the decay via oscillation leads to decay-time-dependent CP violation. Measurements of time-dependent CP asymmetries provide valuable tests of the flavour sector of the Standard Model (SM) and offer opportunities to search for signs of non-SM physics. A measurement of this asymmetry in the B0→J/ψK0 S decay mode allows for a determination of the effective CP phase [ 1 – 3 ] φeff d(B0→J/ψK0 S)≡φd+ ∆φd,(1.1) where φd is the relative phase of the B0 – B0 mixing amplitude and the tree-level decay process, and ∆ φd is a shift induced by the so-called penguin topologies, which are illustrated in figure 1. In the Standard Model, φd is equal to 2 β [ 4 ], where β≡arg ( −VcdV∗ cb/VtdV∗ tb ) is one of the angles of the unitarity triangle in the Cabibbo-Kobayashi-Maskawa (CKM) quark mixing matrix [ 5 , 6 ]. The latest average of the Belle and BaBar measurements – 1 – JHEP06(2015)131 reads sin φeff d = 0 . 665 ± 0 . 020 [ 7 ], while the recently updated analysis from LHCb reports sin φeff d= 0.729 ±0.035(stat) ±0.022(syst) [8]. Forthcoming data from the LHC and KEK e+e− super B factory will lead to an unprecedented precision on the phase φeff d . To translate this into an equally precise determination of the CKM phase β , it is essential to take into account the doubly Cabibbo-suppressed contributions from the penguin topologies, which lead to a value for ∆ φd that might be as large as O (1 ◦ ) [ 1 , 3 ]. By relying on approximate flavour symmetries, information on ∆ φd can be obtained from measurements of CP asymmetries in decays where the penguin topologies are enhanced. The B0 s→J/ψK0 S mode is the most promising candidate for this task [2,3,9]. Assuming no CP violation in mixing [ 7 ], the time-dependent CP asymmetry in B0 s→J/ψK0 Stakes the form aCP (t)≡Γ(B0 s(t)→J/ψK0 S)−Γ(B0 s(t)→J/ψK0 S) Γ(B0 s(t)→J/ψK0 S) + Γ(B0 s(t)→J/ψK0 S),(1.2) =Smix sin (∆mst)−Cdir cos (∆mst) cosh (∆Γst/2) + A∆Γ sinh (∆Γst/2) ,(1.3) where Γ( B0 s ( t ) →J/ψK0 S ) represents the time-dependent decay rate of the B0 s meson into the J/ψK0 S final state, and ∆ms≡mH−mL and ∆Γs≡ΓL−ΓH are, respectively, the mass and decay width difference between the heavy and light eigenstates of the B0 s meson system. The B0 s→J/ψK0 SCP observables are defined through the parameter λJ/ψ K0 S≡ −eiφsA(B0 s→J/ψK0 S) A(B0 s→J/ψK0 S)(1.4) in terms of the complex phase φs associated with the B0 s – B0 s mixing process and the ratio of time-independent transition amplitudes as A∆Γ ≡ −2Re[λJ/ψ K0 S] 1 + |λJ/ψ K0 S|2, Cdir ≡1−|λJ/ψ K0 S|2 1 + |λJ/ψ K0 S|2, Smix ≡2Im[λJ/ψ K0 S] 1 + |λJ/ψ K0 S|2,(1.5) where Cdir and Smix represent direct and mixing-induced CP violation, respectively. In the Standard Model φSM s≡2 arg(−VtsV∗ tb). A recent analysis [3] predicts A∆Γ B0 s→J/ψK0 S= 0.957 ±0.061 , Cdir B0 s→J/ψK0 S= 0.003 ±0.021 ,(1.6) Smix B0 s→J/ψK0 S= 0.29 ±0.20 . Similar expression for eqs. (1.3) and (1.5) are obtained for the B0→J/ψK0 S decay by replacing s↔d . The observable A∆Γ is not applicable in the measurement of B0→J/ψK0 S because it is assumed that ∆Γd= 0 [7]. This paper presents the first measurement of the time-dependent CP asymmetries in B0 s→J/ψK0 S decays, as well as an updated measurement of the ratio of time-integrated branching fractions B(B0 s→J/ψK0 S)/B(B0→J/ψK0 S) . This ratio was first measured by – 2 – JHEP06(2015)131 B0 (s) J/ψ K0 S W b d(s) d(s) c c s(d) B0 (s) J/ψ K0 S W b d(s)d(s) s(d) c c u, c, t Colour singlet exchange Figure 1 . Decay topologies contributing to the B0 (s)→J/ψK0 S channel: (left) tree diagram and (right) penguin diagram. the CDF collaboration [ 10 ], while the previously most precise measurement was reported by LHCb in ref. [ 11 ]. The analysis is performed with a data sample corresponding to an integrated luminosity of 3 . 0 fb−1 of proton-proton ( pp ) collisions, recorded by the LHCb experiment at centre-of-mass energies of 7 TeV and 8 TeV in 2011 and 2012, respectively. The analysis proceeds in two steps. The first step, described in detail in section 3, consists of a multivariate selection of B→J/ψK0 S candidates. In the second step a maximum likelihood fit is performed to the selected data. The fit model includes a prominent B0→J/ψK0 S component, which is used to improve the modelling of the B0 s→J/ψK0 S signal. In addition, the measurement of CP asymmetries associated with B0→J/ψK0 S decays offers a validation of the likelihood method’s implementation. However, the stringent event selection necessary to isolate the B0 s→J/ψK0 S candidates limits the precision on these two CP observables. Dedicated and more precise measurements of the B0→J/ψK0 SCP observables are therefore the subject of a separate publication [8]. For a time-dependent measurement of CP violation it is essential to determine the initial flavour of the B candidate, i.e. whether it contained a b or a b quark at production. The method to achieve this is called flavour tagging, and is discussed in section 4. The tagging information is combined with a description of the B mass and decay time distributions when performing the maximum likelihood fit, which is described in section 5. The three CP observables describing the B0 s→J/ψK0 S decays and two CP observables describing the B0→J/ψK0 S decays are obtained directly from the fit. The ratio of branching fractions [ 12 ] is derived from the ratio Rof fitted B0 s→J/ψK0 Sto B0→J/ψK0 Sevent yields as B(B0 s→J/ψK0 S) B(B0→J/ψK0 S)=R×fsel ×fd fs ,(1.7) where fsel is a correction factor for differences in selection efficiency between B0→J/ψK0 S and B0 s→J/ψK0 S decays, and fs/fd = 0 . 259 ± 0 . 015 [ 13 , 14 ] is the ratio of B0 s to B0 meson hadronisation fractions. The study of systematic effects on the ratio R and the CP observables is presented in section 6. The main results for the branching ratio measurement are reported in section 7and those for the CP observables in section 8. – 3 – JHEP06(2015)131 2 Detector and simulation The LHCb detector [ 15 , 16 ] is a single-arm forward spectrometer covering the pseudorapidity range 2 < η < 5, designed for the study of particles containing b or c quarks. The detector includes a high-precision tracking system consisting of a silicon-strip vertex detector surrounding the pp interaction region, a large-area silicon-strip detector located upstream of a dipole magnet with a bending power of about 4 Tm , and three stations of silicon-strip detectors and straw drift tubes placed downstream of the magnet. The tracking system provides a measurement of momentum, p , of charged particles with a relative uncertainty that varies from 0.5% at low momentum to 1.0% at 200 GeV/c . The minimum distance of a track to a primary vertex, the impact parameter, is measured with a resolution of (15 + 29 /pT ) µm , where pT is the component of the momentum transverse to the beam, in GeV/c . Different types of charged hadrons are distinguished using information from two ring-imaging Cherenkov detectors. Photons, electrons and hadrons are identified by a calorimeter system consisting of scintillating-pad and preshower detectors, an electromagnetic calorimeter and a hadronic calorimeter. Muons are identified by a system composed of alternating layers of iron and multiwire proportional chambers. In the simulation, pp collisions are generated using Pythia [ 17 , 18 ] with a specific LHCb configuration [ 19 ]. Decays of hadronic particles are described by EvtGen [ 20 ], in which final-state radiation is generated using Photos [ 21 ]. The interaction of the generated particles with the detector, and its response, are implemented using the Geant4 toolkit [22,23] as described in ref. [24]. 3 Event selection Candidate B→J/ψK0 S decays are considered in the J/ψ →µ+µ− and K0 S→π+π− final states. The event selection is based on an initial selection, followed by a two-stage multivariate analysis consisting of artificial neural network (NN) classifiers [25]. 3.1 Initial selection The online event selection is performed by a trigger, which consists of a hardware level, based on information from the calorimeter and muon systems, followed by a software level, which applies a full event reconstruction. The hardware trigger selects at least one muon with a transverse momentum pT> 1 . 48 (1 . 76) GeV/c or two muons with ppT(µ1)pT(µ2)> 1 . 3 (1 . 6) GeV/c in the 7 (8) TeV pp collisions. The software trigger consists of two stages. In the first stage, events are required to have either two oppositely charged muons with combined mass above 2 . 7 GeV/c2 , or at least one muon or one highpT charged particle ( pT> 1 . 8 GeV/c ) with an impact parameter larger than 100 µm with respect to all pp interaction vertices (PVs). In the second stage of the software trigger the tracks of two or more of the final-state particles are required to form a vertex that is significantly displaced from the PVs, and only events containing J/ψ →µ+µ−candidates are retained. In the offline selection, J/ψ candidates are selected by requiring two muon tracks to form a good quality vertex and have an invariant mass in the range [3030 , 3150] MeV/c2 . – 4 – JHEP06(2015)131 This interval corresponds to about eight times the µ+µ−mass resolution at the J/ψ mass and covers part of the J/ψ radiative tail. Decays of K0 S→π+π− are reconstructed in two different categories: the first involving K0 S mesons that decay early enough for the daughter pions to be reconstructed in the vertex detector; and the second containing K0 S that decay later such that track segments of the pions cannot be formed in the vertex detector. These categories are referred to as long and downstream, respectively. Long K0 S candidates have better mass, momentum and vertex resolution than those in the downstream category. The two pion tracks of the long (downstream) K0 S candidates are required to form a good quality vertex and their combined invariant mass must be within 35(64) MeV/c2 of the known K0 S mass [ 26 ]. To remove contamination from Λ →pπ− decays, the reconstructed mass of the long (downstream) K0 S candidates under the assumption that one of its daughter tracks is a proton is required to be more than 6(10) MeV/c2 away from the known Λ mass [ 26 ]. The K0 S decay vertex is required to be located downstream of the J/ψ decay vertex, i.e. it is required to have a positive flight distance. This removes approximately 50% of mis-reconstructed B0→J/ψK∗(892)0 background. The remaining B0→J/ψK∗(892)0 background is heavily suppressed by the first stage of the multivariate selection described below. Candidate B mesons are selected from combinations of J/ψ and K0 S candidates with mass mJ/ψ K0 S in the range [5180 , 5520] MeV/c2 and a decay time larger than 0 . 2 ps . The reconstructed mass and decay time are obtained from a kinematic fit [ 27 ] that constrains the masses of the µ+µ− and π+π− pairs to the known J/ψ and K0 S masses [ 26 ], respectively, and constrains the B candidate to originate from the PV. A good quality fit is required and the uncertainty on the B mass estimated by the kinematic fit must not exceed 30 MeV/c2 . In the case that the event has multiple PVs, a clear separation of the J/ψ decay vertex from any of the other PVs in the event is required, and all combinations of B candidates and PVs that pass the selection are considered. 3.2 Multivariate selection The first stage of the multivariate selection focuses on removing the mis-reconstructed B0→J/ψK∗(892)0 background that survives the requirement on the K0 S flight distance. It only affects the subsample of candidates for which the K0 S is reconstructed in the long category. The NN is trained on simulated B0→J/ψK0 S (signal) and B0→J/ψK∗(892)0 (background) data and only uses information associated with the reconstructed pions and K0 S candidate. This includes decay time, mass, momentum, impact parameter and particleidentification properties. The requirement on the NN classifier’s output is optimised to retain 99% of the original signal candidates in simulation, with a background rejection on simulated B0→J/ψK∗(892)0 candidates of 99.55%. This results in an estimated number of 18 ± 2 B0→J/ψK∗(892)0 candidates in the long K0 S data sample surviving this stage of the selection. Their yield is further reduced by the second NN classifier, and these candidates are therefore treated as combinatorial background in the remainder of the analysis. The second stage of the multivariate selection aims at reducing the combinatorial background to isolate the small B0 s→J/ψK0 S signal. In contrast to the first NN, it is trained entirely on data, using the B0→J/ψK0 S signal as a representative of the signal features of – 5 – JHEP06(2015)131 the B0 s→J/ψK0 S decay. Candidates for the training sample are those populating the mass ranges [5180 , 5340] MeV/c2 and [5390 , 5520] MeV/c2 , avoiding the B0 s signal region. The signal and background weights for the training of the second NN are determined using the sPlot technique [ 28 ] and obtained by performing an unbinned maximum likelihood fit to the B mass distribution of the candidates meeting the selection criteria on the first NN classifier’s output. The fit function is defined as the sum of a B0 signal component and a combinatorial background where the parametrisation of the individual components matches that of the likelihood method used for the full CP analysis and is described in more detail in section 5. Due to differences in the distributions of the input variables of the NN, as well as different signal-to-background ratios, the second stage of the multivariate selection is performed separately for the B candidate samples containing long and downstream K0 S candidates. The NN classifiers use information on the candidate’s kinematic properties, vertex and track quality, impact parameter, particle identification information from the RICH and muon detectors, as well as global event properties like track and PV multiplicities. The variables that are used in the second NN are chosen to avoid correlations with the reconstructed Bmass. Final selection requirements on the second stage NN classifier outputs are chosen to optimise the sensitivity to the B0 s signal using NS/√NS+NB as figure of merit, where NS and NB are respectively the expected number of signal and background events in a ± 30 MeV/c2 mass range around the B0 s peak. After applying the final requirement on the NN classifier output associated with the long (downstream) K0 S sample, the multivariate selection rejects, relative to the initial selection, 99.2% of the background in both samples while keeping 72.9% (58.3%) of the B0 signal. The lower selection efficiency on the downstream K0 S sample is due to the worse signal-to-background ratio after the initial selection, which requires a more stringent requirement on the NN classifier output. The resulting J/ψK0 S mass distributions are illustrated in figure 2. After applying the full selection, the long (downstream) B candidate can still be associated with more than one PV in about 1.5% (0.6%) of the events; in this case, one PVs is chosen at random. Likewise, about 0 . 24% (0.15%) of the selected events have multiple candidates sharing one or more tracks; in this case, one candidates is chosen at random. 4 Flavour tagging At the LHC, b quarks are predominantly produced in b¯ b pairs. When one of the two quarks hadronises to form the B meson decay of interest (“the signal B ”), the other b quark hadronises and decays independently. By exploiting this production mechanism, the signal B ’s initial flavour is identified by means of two classes of flavour-tagging algorithms. The opposite side (OS) taggers determine the flavour of the non-signal b -hadron [ 29 ] while the same side kaon (SSK) tagger exploits the fact that the additional s ( s ) quark produced in the fragmentation of a B0 s(B0 s) meson often forms a K+(K−) meson [30]. These algorithms provide tag decisions qOS and qSSK , which take the value +1 ( − 1) in case the signal candidate is tagged as a B ( B ) meson, and predictions ηOS and ηSSK for the – 6 – JHEP06(2015)131 ) 2 cEvents / ( 2 MeV/ 1 10 2 10 3 10 0 S KLong LHCb ) 2 c (MeV/ 0 S K ψ J/ m 5200 5300 5400 5500 Pull -5 0 5 ) 2 cEvents / ( 2 MeV/ 1 10 2 10 3 10 4 10 0 S KDownstream LHCb ) 2 c (MeV/ 0 S K ψ J/ m 5200 5300 5400 5500 Pull -5 0 5 Figure 2 . Mass distribution of B candidates at different stages of the event selection for the (left) long K0 S and (right) downstream K0 S sample. The data sample after initial selection (red, +), after the first neural net (green, × ) and after the second neural net (black, • ) are shown. Overlaid are projections of the fit described in section 5. Shown components are B0 s→J/ψK0 S (dark blue, dashed), B0→J/ψK0 S(red, dotted) and combinatorial background (turquoise, dash-dotted). probability of the tag to be incorrect. The latter is obtained using neural networks, which in the case of the OS taggers are trained on B+→J/ψK+ decays, while for the SSK tagger simulated B0 s→D− sπ+events are used. The mistag probability predicted by the tagging algorithms is calibrated in data to determine the true mistag probability ω , by using control samples of several flavour-specific B mesons decays. This calibration is performed individually for the OS and SSK tagging algorithms; for the latter, different calibration parameters are used to describe the B0 and B0 s mesons. For all events with both an OS and SSK tag decision, a combined tag decision and mistag probability is derived as described in ref. [29]. The figure of merit for the optimisation of a tagging algorithm is the effective tagging efficiency, εeff = εtag (1 − 2 ω ) 2 where εtag is the fraction of candidates with an assigned tag decision. In the long K0 S sample for the B0 s→J/ψK0 S mode, the OS and SSK taggers yield an εeff of (2 . 93 ± 0 . 06)% and (0 . 97 ± 0 . 12)%, respectively, while the sample with both an OS and SSK tagging response gives an εeff of (1 . 02 ± 0 . 10)%. In the respective downstream K0 S sample, the OS and SSK taggers yield an εeff of (2 . 74 ± 0 . 11)% and (1 . 45 ± 0 . 15)%, respectively, while the sample with both an OS and SSK tagging response gives an εeff of (0 . 48 ± 0 . 04)%. The combined εeff of all three overlapping samples for the B0 s→J/ψK0 S mode is measured to be (3 . 80 ± 0 . 18)% and (4 . 03 ± 0 . 16)% in the long and downstream K0 S sample, respectively. In the B0→J/ψK0 S mode, the main contribution is provided by the OS taggers, where the combined εeff is measured to be (2 . 60 ± 0 . 05)% and (2 . 63 ± 0 . 05)% in the long and downstream K0 S sample, respectively. Although the SSK tagging algorithm is – 7 – JHEP06(2015)131 Parameter Value Parameter Value ∆md0.510 ±0.003 ps−1[7] ∆ms17.757 ±0.021 ps−1[7] ∆Γd0 ps−1∆Γs0.081 ±0.006 ps−1[7] τBd1.520 ±0.004 ps [7]τBs1.509 ±0.004 ps [7] Table 1 . List of the observables describing the B0 and B0 s systems that are included as Gaussian constraints to the likelihood fit. specifically designed for B0 s mesons, a small, but non-vanishing effective tagging efficiency of (0 . 064 ± 0 . 009)% and (0 . 098 ± 0 . 013)% in the long and downstream K0 S sample, respectively, is also found for B0 mesons if the tag decision is reversed. This effect originates from same-side protons mis-identified as kaons, and kaons from the decay of K∗ (892) 0 mesons produced in correlation with the B0 . Both tagged particles have a charge opposite to those of kaons produced in correlation with the B0 s , and thus require the SSK tag decision to be inverted. Additionally, mis-identified pions carrying the same charge as the kaons correlated with the B0 s dilute the effect described above. The SSK tagging response for B0 candidates is studied on B0→J/ψ K∗(892)0candidates using both data and simulated events. 5 Likelihood fit The B0 s→J/ψK0 SCP observables are determined from an unbinned maximum likelihood fit. The data is fitted with a probability density function (PDF) defined as the sum of a B0 signal component, a B0 s signal component and a combinatorial background. In total it depends on seven observables. The PDF describes the reconstructed B mass ( mJ/ψ K0 S∈ [5180 , 5520] MeV/c2 ), the decay time ( t∈ [0 . 2 , 15] ps ), and tagging responses qOS and qSSK . Additionally, it also depends on the per-candidate decay time uncertainty estimate δt and mistag estimates ηOS and ηSSK . The long and downstream K0 S samples are modelled using separate PDFs but fitted simultaneously. The parameters common to both PDFs are the two B0→J/ψK0 S and three B0 s→J/ψK0 SCP observables, as well as the observables describing the B0and B0 ssystems that are listed in table 1. 5.1 Mass PDF The mass shapes of the B→J/ψK0 S modes in both data and simulation exhibit non- Gaussian tails on both sides of their signal peaks due to final-state radiation, the detector resolution and its dependence on the momenta of the final-state particles. Each signal shape is parametrised by a Hypatia function [ 31 ], whose tail parameters are taken from simulation. The B0 s component is constrained to have the same shape as the B0 PDF, but shifted by the B0 s – B0 mass difference, which is a free variable in the fit. 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Zhong3. 1Centro Brasileiro de Pesquisas F´ısicas (CBPF), Rio de Janeiro, Brazil 2Universidade Federal do Rio de Janeiro (UFRJ), Rio de Janeiro, Brazil 3Center for High Energy Physics, Tsinghua University, Beijing, China 4LAPP, Universit´e Savoie Mont-Blanc, CNRS/IN2P3, Annecy-Le-Vieux, France 5Clermont Universit´e, Universit´e Blaise Pascal, CNRS/IN2P3, LPC, Clermont-Ferrand, France 6CPPM, Aix-Marseille Universit´e, CNRS/IN2P3, Marseille, France 7LAL, Universit´e Paris-Sud, CNRS/IN2P3, Orsay, France 8LPNHE, Universit´e Pierre et Marie Curie, Universit´e Paris Diderot, CNRS/IN2P3, Paris, France 9Fakult¨at Physik, Technische Universit¨at Dortmund, Dortmund, Germany 10 Max-Planck-Institut f¨ur Kernphysik (MPIK), Heidelberg, Germany 11 Physikalisches Institut, Ruprecht-Karls-Universit¨at Heidelberg, Heidelberg, Germany 12 School of Physics, University College Dublin, Dublin, Ireland 13 Sezione INFN di Bari, Bari, Italy 14 Sezione INFN di Bologna, Bologna, Italy 15 Sezione INFN di Cagliari, Cagliari, Italy 16 Sezione INFN di Ferrara, Ferrara, Italy 17 Sezione INFN di Firenze, Firenze, Italy 18 Laboratori Nazionali dell’INFN di Frascati, Frascati, Italy 19 Sezione INFN di Genova, Genova, Italy 20 Sezione INFN di Milano Bicocca, Milano, Italy 21 Sezione INFN di Milano, Milano, Italy 22 Sezione INFN di Padova, Padova, Italy 23 Sezione INFN di Pisa, Pisa, Italy 24 Sezione INFN di Roma Tor Vergata, Roma, Italy 25 Sezione INFN di Roma La Sapienza, Roma, Italy 26 Henryk Niewodniczanski Institute of Nuclear Physics Polish Academy of Sciences, Krak´ow, Poland 27 AGH - University of Science and Technology, Faculty of Physics and Applied Computer Science, Krak´ow, Poland 28 National Center for Nuclear Research (NCBJ), Warsaw, Poland 29 Horia Hulubei National Institute of Physics and Nuclear Engineering, Bucharest-Magurele, Romania 30 Petersburg Nuclear Physics Institute (PNPI), Gatchina, Russia – 19 – JHEP06(2015)131 31 Institute of Theoretical and Experimental Physics (ITEP), Moscow, Russia 32 Institute of Nuclear Physics, Moscow State University (SINP MSU), Moscow, Russia 33 Institute for Nuclear Research of the Russian Academy of Sciences (INR RAN), Moscow, Russia 34 Budker Institute of Nuclear Physics (SB RAS) and Novosibirsk State University, Novosibirsk, Russia 35 Institute for High Energy Physics (IHEP), Protvino, Russia 36 Universitat de Barcelona, Barcelona, Spain 37 Universidad de Santiago de Compostela, Santiago de Compostela, Spain 38 European Organization for Nuclear Research (CERN), Geneva, Switzerland 39 Ecole Polytechnique F´ed´erale de Lausanne (EPFL), Lausanne, Switzerland 40 Physik-Institut, Universit¨at Z¨urich, Z¨urich, Switzerland 41 Nikhef National Institute for Subatomic Physics, Amsterdam, The Netherlands 42 Nikhef National Institute for Subatomic Physics and VU University Amsterdam, Amsterdam, The Netherlands 43 NSC Kharkiv Institute of Physics and Technology (NSC KIPT), Kharkiv, Ukraine 44 Institute for Nuclear Research of the National Academy of Sciences (KINR), Kyiv, Ukraine 45 University of Birmingham, Birmingham, United Kingdom 46 H.H. Wills Physics Laboratory, University of Bristol, Bristol, United Kingdom 47 Cavendish Laboratory, University of Cambridge, Cambridge, United Kingdom 48 Department of Physics, University of Warwick, Coventry, United Kingdom 49 STFC Rutherford Appleton Laboratory, Didcot, United Kingdom 50 School of Physics and Astronomy, University of Edinburgh, Edinburgh, United Kingdom 51 School of Physics and Astronomy, University of Glasgow, Glasgow, United Kingdom 52 Oliver Lodge Laboratory, University of Liverpool, Liverpool, United Kingdom 53 Imperial College London, London, United Kingdom 54 School of Physics and Astronomy, University of Manchester, Manchester, United Kingdom 55 Department of Physics, University of Oxford, Oxford, United Kingdom 56 Massachusetts Institute of Technology, Cambridge, MA, United States 57 University of Cincinnati, Cincinnati, OH, United States 58 University of Maryland, College Park, MD, United States 59 Syracuse University, Syracuse, NY, United States 60 Pontif´ıcia Universidade Cat´olica do Rio de Janeiro (PUC-Rio), Rio de Janeiro, Brazil, associated to 2 61 Institute of Particle Physics, Central China Normal University, Wuhan, Hubei, China, associated to 3 62 Departamento de Fisica , Universidad Nacional de Colombia, Bogota, Colombia, associated to 8 63 Institut f¨ur Physik, Universit¨at Rostock, Rostock, Germany, associated to 11 64 National Research Centre Kurchatov Institute, Moscow, Russia, associated to 31 65 Yandex School of Data Analysis, Moscow, Russia, associated to 31 66 Instituto de Fisica Corpuscular (IFIC), Universitat de Valencia-CSIC, Valencia, Spain, associated to 36 67 Van Swinderen Institute, University of Groningen, Groningen, The Netherlands, associated to 41 aUniversidade Federal do Triˆangulo Mineiro (UFTM), Uberaba-MG, Brazil bP.N. Lebedev Physical Institute, Russian Academy of Science (LPI RAS), Moscow, Russia cUniversit`a di Bari, Bari, Italy dUniversit`a di Bologna, Bologna, Italy eUniversit`a di Cagliari, Cagliari, Italy fUniversit`a di Ferrara, Ferrara, Italy gUniversit`a di Firenze, Firenze, Italy hUniversit`a di Urbino, Urbino, Italy iUniversit`a di Modena e Reggio Emilia, Modena, Italy jUniversit`a di Genova, Genova, Italy kUniversit`a di Milano Bicocca, Milano, Italy lUniversit`a di Roma Tor Vergata, Roma, Italy – 20 – JHEP06(2015)131 mUniversit`a di Roma La Sapienza, Roma, Italy nUniversit`a della Basilicata, Potenza, Italy oAGH - University of Science and Technology, Faculty of Computer Science, Electronics and Telecommunications, Krak´ow, Poland pLIFAELS, La Salle, Universitat Ramon Llull, Barcelona, Spain qHanoi University of Science, Hanoi, Viet Nam rUniversit`a di Padova, Padova, Italy sUniversit`a di Pisa, Pisa, Italy tScuola Normale Superiore, Pisa, Italy uUniversit`a degli Studi di Milano, Milano, Italy vPolitecnico di Milano, Milano, Italy – 21 –