Observation of the Λ0b→Λφdecay

Physics Letters B 759 (2016) 282–292 Contents lists available at ScienceDirect Physics Letters B www.elsevier.com/locate/physletb Observation of the Λ0 b→Λφ decay .The LHCb Collaboration a r t i c l e i n f o a b s t r a c t Article history: Received 10 March 2016 Received in revised form 13 May 2016 Accepted 24 May 2016 Available online 26 May 2016 Editor: W.-D. Schlatter The Λ0 b→Λφ decay is observed using data corresponding to an integrated luminosity of 3.0fb −1 recorded by the LHCb experiment. The decay proceeds at leading order via a b →sss loop transition and is therefore sensitive to the possible presence of particles beyond the Standard Model. A first observation is reported with a significance of 5.9standard deviations. The value of the branching fraction is measured to be (5.18 ±1.04 ±0.35+0.67 −0.62) ×10−6, where the first uncertainty is statistical, the second is systematic, and the third is related to external inputs. Triple-product asymmetries are measured to be consistent with zero. ©2016 The Author. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). Funded by SCOAP3. 1. Introduction In the Standard Model (SM), the flavour-changing neutral current decay Λ0 b→Λφ proceeds via a b →sss loop (penguin) process. A Feynman diagram of the gluonic penguin that contributes to this decay at leading order is displayed in Fig. 1. This transition has been the subject of theoretical and experimental interest in B0 sand B0decays, since possible beyond the SM particles in the loop could induce non-SM CP violation [1–3]. The process has been probed with decay-time-dependent methods in the B0 s→φφ and B0→K0 Sφdecay modes [4–7], which test for CP violation in the interference between mixing and decay. In addition, measurements of CP violation in the decay have been performed with the flavourspecific B0→K∗0φchannel [8]. The results to date are consistent with CP conservation in the b →sss process. Model-independently, non-SM physics contributions could appear differently in these decay modes, though many models contain strong correlations [9]. Measurements with Λ0 bbaryons offer the possibility to look for CP violation in the decay, both by studying CP asymmetries and by means of T-odd observables. These observables have been studied in greater detail for B0 sand B0meson decays than those for Λ0 bbaryons [4,8,10,11]. Proposed methods to study T-odd asymmetries of Λ0 bbaryons [12] exploit the polarisation structure of Λ0 b→ΛVdecays, where Vdenotes a vector resonance [12], and can be affected by the initial Λ0 bpolarisation if non-zero. An LHCb measurement of the initial polarisation in Λ0 b→J/ ψΛ decays has yielded a value consistent with zero, though polarisation at the level of 10% is possible given statistical uncertainties [13]. No SM prediction exists specifically for the T-odd asymmetries in Λ0 b→Λφ decays, though no large asymmetries are expected given the prediction of CP conservation in the decays of beauty mesons for the same transition. Measurements of CP asymmetries have Fig. 1. Feynman diagram contributing to the Λ0 b→Λφ decay. been performed by LHCb in an inclusive analysis of Λ0 b→Λhhdecays [14], where h(h)refers to a kaon or pion, with corresponding CP asymmetries measured to be consistent with zero. In this paper, a measurement of the Λ0 b→Λφ branching fraction is presented using the B0→K0 Sφdecay as a normalisation channel, which has a measured branching fraction of (7.3+0.7 −0.6) × 10−6[15]. The selection requirements used to isolate the Λ0 b→ Λφ decay with well-understood efficiencies reject suitable control channels for a ACP measurement. The Λ0 b→Λφ sample is then used to perform measurements of the T-odd triple-product asymmetries, which do not require a control channel. The results are based on pp collision data corresponding to an integrated luminosity of 1.0fb −1and 2.0fb −1collected by the LHCb experiment at centre-of-mass energies of √s=7TeV in 2011 and 8TeVin 2012, respectively. 2. Detector and simulation The LHCb detector [16,17] is a single-arm forward spectrometer covering the pseudorapidity range 2 <η<5, designed for the http://dx.doi.org/10.1016/j.physletb.2016.05.077 0370-2693/©2016 The Author. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). Funded by SCOAP3. The LHCb Collaboration / Physics Letters B 759 (2016) 282–292 283 study of particles containing bor cquarks. 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 4Tm, and three stations of siliconstrip 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 pTis 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. The online event selection is performed by a trigger, which consists of a hardware stage, based on information from the calorimeter and muon systems, followed by a software stage, which applies a full event reconstruction. At the hardware trigger stage, events are required to have a muon with high pTor a hadron, photon or electron with high transverse energy in the calorimeters. For hadrons, the transverse energy threshold is 3.5 GeV. In the subsequent software trigger, at least one charged particle must have a transverse momentum pT>1.7GeV/cand be inconsistent with originating from a PV. Finally, the tracks of two or more of the final-state particles are required to form a vertex that is significantly displaced from the PVs. The final state particles that are identified as kaons are required to have a combined invariant mass consistent with that of the φmeson. In the simulation, pp collisions are generated using Pythia8 [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] as described in Ref. [23]. The decays of Λ0 bbaryons are modelled according to a phase-space description. Differences in the efficiencies of protons and anti-protons, at the sub-percent level, are accounted for with the Geant4 implementation of the detector description. 3. Selection The Λ0 b→Λφ and B0→K0 Sφdecays are reconstructed through the Λ →pπ−, K0 S→π+π−and φ→K+K−final states, where the inclusion of charge conjugate processes is implied throughout the paper. Decays of Λ →pπ−and K0 S→π+π−are reconstructed in two different categories. The first category contains Λ (K0 S) hadrons that decay inside the vertex detector acceptance and the second contains Λ(K0 S) hadrons that decay outside. These categories are referred to as long and downstream, respectively. The high resolution of the vertex detector leads to enhanced momentum, vertex, and mass resolutions for candidates in the long category relative to downstream candidates. Boosted decision trees (BDTs) [24,25] are used to separate signal from background. Different BDTs are trained for decays where the daughter tracks of the Λ(K0 S) hadron are classified as long or downstream and according to whether the data was collected in 2011 (7 TeV) or 2012 (8 TeV), yielding eight separate BDTs in total. The set of input variables used to train the Λ0 b→Λφ (B0→K0 Sφ) BDTs consists of the Λ0 b(B0) vertex fit quality, pT, η, the difference in χ2of the PV reconstructed with and without the candidate (χ2 IP), the flight distance squared divided by the associated variance (χ2 FD), the angle between the momentum vector and the vector from the PV to the decay vertex, the Λ(K0 S) vertex fit quality, and the pTand ηof the φand the Λ(K0 S) hadrons. The minimum and maximum values of the pTand ηassociated to the final state particles are also included. In addition, the BDT trained on the long category uses the χ2 IP and χ2 FD of the Λ(K0 S) with respect to the associated PV. A PV is reconstructed by requiring a minimum of five good quality tracks that are consistent with originating from the same location within the luminous region. Before the BDTs are trained, initial loose requirements are imposed on the input variables. The BDTs are trained using simulated candidates for the signal and data sidebands for the background. For the training samples, the signal region is defined as being within 150 MeV/c2 of the known Λ0 b(B0) mass [26]. In addition, the K+K−invariant mass is required to be within 20 MeV/c2of the known φmass and the pπ−invariant mass is required to be within 15 MeV/c2 of the known Λmass [26]. The sidebands are defined to be within 500 MeV/c2of the known Λ0 b(B0) mass excluding the signal region. The figure of merit used to determine the requirement imposed on the Λ0 b→Λφ BDT output is defined as ε/(3/2 +Nbkg)[27], where εis the signal efficiency, and Nbkg is the number of background events. This figure of merit is optimised for detection at three standard deviations of decay modes not previously observed. The signal efficiency is obtained from simulated signal candidates and the number of background events is calculated from fits to the data sidebands interpolated to the signal region. This optimisation procedure is performed separately for each BDT. In contrast to the Λ0 b→Λφ BDTs, the optimum response requirement for the B0→K0 SφBDTs is chosen based on a figure of merit defined as Nsig/Nsig +Nbkg, where Nsig is the number of signal events, estimated from the BDT efficiency on simulated datasets normalised using the known branching fraction of the B0→K0 Sφdecay [15], and Nbkg is the expected number of background candidates in the signal region, extrapolated from the B0 sidebands. This figure of merit is chosen as the B0→K0 Sφbranching fraction is well measured and is optimised separately for each classifier. 4. Mass fit model For both the Λ0 b→Λφ and B0→K0 Sφdecay modes, a threedimensional fit is employed to determine the signal candidate yields. In the Λ0 b→Λφ case, the three dimensions are the pπ−K+K−, pπ−, and K+K−invariant masses, while in the fit to determine the B0→K0 Sφcandidate yield, the three dimensions are the π+π−K+K−, π+π−, and K+K−invariant masses. Four components are present in the B0→K0 Sφmass fit: the signal B0→K0 Sφcomponent, the B0→K0 SK+K−non-resonant contribution, a π+π−K+K−combinatorial component, along with a true K0 Scomponent combined with two random kaons. The B0→K0 SK+K−non-resonant component has been observed by the BaBar [28], Belle [6] and LHCb [29] Collaborations. This is separated from the signal decay through the different K+K−invariant mass line shapes. No significant partially reconstructed background, in which one or more of the final state particles are missed, is found in the B0mass region. Peaking backgrounds, from decays in which at least one of the final state particles has been misidentified, are suppressed by the narrow K+K−mass window around the φmeson and are treated as systematic uncertainties. The B0signal is modelled with the same modified Gaussian function as used in Ref. [30]. The modified Gaussian gives extra degrees of freedom to accommodate extended tails far from the mean. The φsignal is modelled with a relativistic Breit–Wigner 284 The LHCb Collaboration / Physics Letters B 759 (2016) 282–292 shape [31] convolved with a Gaussian resolution function. The K0 S signal is parametrised by the sum of two Gaussian functions with a common mean. Decays from real B0mesons to the K0 SK+K−final state in which the K+K−pair is non-resonant are described by the same B0and K0 Sline shapes as the signal, but with a phase-space factor to describe the non-resonant kaon pairs. The phase-space factor is given by the expression (m2−(2mK)2)/m2, where mis the K+K−invariant mass and mKis fixed to the value of the charged kaon mass. The use of a Flatté function [32] rather than a phasespace factor to describe a possible scalar component under the φ resonance is found to have a negligible effect on the results and is therefore not included. The combinatorial background is modelled by exponential functions in all three mass dimensions. A simultaneous fit to the long and downstream datasets is performed. The B0resolution, modified Gaussian tail parameters and resolutions and fractions of the K0 SGaussian functions are constrained to values obtained from a fit to simulated data, performed separately for long and downstream datasets. The total yield and fraction in the downstream dataset are left as free parameters for each component. The fit to the Λ0 b→Λφ channel uses the same fit model as the B0→K0 Sφcontrol channel: a modified Gaussian function is used to describe the Λ0 bmass shape, a double Gaussian model to describe the Λshape, and a relativistic Breit–Wigner convolved with a Gaussian resolution function to describe that of the φresonance. Due to the relatively unexplored mass spectra present in the Λ0 b→Λφ decay, the background contributions have been identified using the data sidebands. In the final fit, four components are present. These are the signal Λ0 b→Λφ component, the Λ0 b→ΛK+K−non-resonant component in which the K+K− dimension is described using the phase-space factor defined previously, combinatorial components with true φor Λresonances, and a component that has a combinatorial origin in all three mass dimensions. Combinatorial backgrounds are modelled by exponential functions in each fit dimension. As for the case of the B0→K0 Sφ fit, the total yield and fraction in the downstream dataset are left as free parameters for each component. In addition, the same parameters are constrained to simulated data as in the B0→K0 Sφfit. 5. Branching fraction measurement The Λ0 b→Λφ branching fraction is obtained from the relation B(Λ0 b→Λφ) = tot B0→K0 Sφ tot Λ0 b→Λφ ·fd fΛ0 b· NΛ0 b→Λφ NB0→K0 Sφ·B(B0→K0φ) 2 ·B(K0 S→π+π−) B(Λ →pπ−),(1) where tot denotes the combined efficiency of the candidate reconstruction, the offline selection, the trigger requirements, and the efficiency of detector acceptance; fd(Λ0 b)denotes the fraction of bquarks that hadronise to B0(Λ0 b) hadrons. The ratio is taken from the LHCb measured value fΛ0 b/fd=0.387 ±0.033 [33]. The extra factor 1/2in Eq. (1) accounts for the fact that only half of K0mesons will decay as K0 Smesons. The value of the B0→K0φ branching fraction is taken to be (7.3+0.7 −0.6) ×10−6[15], while the PDG values of the Λand K0 Sbranching fractions are used [26]. The reconstruction, selection and software trigger efficiencies, as well as the acceptance of the LHCb detector, are determined from simulated samples, using data-driven correction factors where necessary. The different interaction cross-sections of the final-state particles with the detector material are accounted for using simulated datasets. For the case of the hardware trigger, the efficiency of events triggered by the signal candidate is determined from control samples of D0→K−π+and Λ →pπ−decays. The efficiency of events triggered independently of the signal candidate is determined from simulation. The agreement between data and simulation for the distributions of the variables used in the BDT is verified with the B0→K0 Sφdata. Data-driven corrections for the reconstruction efficiency of tracks corresponding to the long category are obtained from J/ ψ samples using a tag-and-probe method [34]. This is applied after a separate weighting to ensure agreement in detector occupancy between data and simulation. For measurements of the relative branching fraction of Λ0 b→Λφ to B0→K0 Sφ, the final state differs by substituting the proton from the decay of the Λwith a pion. However, due to the differences in the kinematics of the pions from the Λand the K0 Sdecays, the distinct correction factors for both daughters of the Λand K0 Sare considered. In addition to the track reconstruction efficiency, the vertexing efficiency of longlived particles contains disagreement between data and simulation. The corresponding correction factors for the long and downstream datasets are determined separately from D0→φK0 Sdecays. The yields of the Λ0 b→Λφ signal and B0→K0 Sφcontrol mode are determined from simultaneous extended unbinned maximum likelihood fits to the respective datasets divided according to the data-taking period and also according to whether the Λ (K0 S)decay products are reconstructed as long or downstream tracks. Efficiencies are applied to each dataset individually. The projections of the fit result to Λ0 b→Λφ data are shown in Fig. 2. The fitted yields are 350 ±24 and 89 ±13 for the B0→K0 Sφand Λ0 b→Λφ decay modes, respectively. The statistical significance of the Λ0 b→Λφ decay, determined according to Wilks’ theorem [35] from the difference in the likelihood value of the fits with and without the Λ0 b→Λφ component, is found to be 6.5 standard deviations. With the systematic uncertainties discussed below included, the significance of the observed Λ0 b→Λφ decay yield is calculated to be 5.9 standard deviations. The projections of the fit result to the B0→K0 Sφdata are shown in Fig. 3. The fit is found to describe the data well in all three dimensions and a clear peak from the control mode is seen. The systematic contributions to the branching fraction uncertainty budget are summarised in Table 1. The largest contributions to the systematic uncertainties result from data-driven corrections applied to simulated data along with the mass model used to determine the signal yields. Signal mismodelling is accounted for using a one-dimensional kernel estimate for the description of the simulated mass distributions [36]. Background mismodelling is accounted for using a linear function. The kernel estimate is used in both the signal and control channels to describe the Λ0 b, B0, K0 S, and Λline shapes. In order to determine the systematic uncertainties, 1000 pseudoexperiments are generated with the alternative model and are subsequently fitted with the nominal model. The average difference between the generated and fitted yield values is taken as the systematic uncertainty. This leads to uncertainties of 3.0% and 0.6% for the signal and control mode yields, respectively. Systematic uncertainties associated with the efficiency corrections from simulated datasets are considered. The limited size of the simulated sample gives rise to an uncertainty of 2.2%. The main uncertainties in the tracking and vertexing correction factors arise from the limited size of the control sample, which leads to uncertainties of 0.5% and 2.6%, respectively. For the case of the trigger efficiency, uncertainties related to the software trigger cancel between the signal and control modes, as the software trigger decision is made only on the decay products of the φmeson. Un- The LHCb Collaboration / Physics Letters B 759 (2016) 282–292 285 Fig. 2. Fit projections to the pπ−K+K−invariant mass in the (a) long and (b) downstream datasets, the K+K−invariant mass in the (c) long and (d) downstream datasets, and the pπ−invariant mass in the (e) long and (f) downstream datasets. The total fit projection is given by the blue solid line. The blue and green dotted lines represent the φ+Λand pure combinatorial fit components, respectively. The red and magenta dashed lines represent the Λ0 b→Λφ signal and the Λ0 b→ΛK+K−non-resonant components, respectively. Black points represent the data. Data uncertainties are Poisson 68% confidence intervals. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.) certainties in the efficiency of the hardware trigger selections are estimated using data-driven methods, for which an uncertainty of 2.8% is applied. The BDTs used to select signal and control modes use the same input variables. Biases could exist if the simulation mismodels these variables differently for signal and control modes. In order to quantify this effect, the control mode is selected with the same classifier as the signal decay. The difference in the measured branching fraction is found to be 4.1%. The Λ0 b→Σ0(→Λγ)K+K−and Λ0 b→pK−φdecay modes are found to be the only significant peaking background contributions. However, for the case of the Λ0 b→pK−φdecay, the resulting candidates are reconstructed in the long dataset only. With the assumption that the branching fraction for this decay is the same size as for the signal, the contribution is <1% compared to the Λ0 b→Λφ decay and far from the Λ0 bsignal region, and is therefore ignored. In order to determine the shape in the pπ−K+K−spectrum of the Λ0 b→Σ0K+K−decay, a sample of Λ0 b→Σ0K+K−simulated events is used with a requirement that the K+K−invariant mass is within 30 MeV/c2of the nominal φmass. The inclusion of an additional fit component using the shape from simulation is found to have a small effect on the signal yield at the level of 0.1%, which is assigned as a systematic uncertainty. For the case of the B0→K0 Sφcontrol mode, no peaking background contributions have been identified. The branching fraction ratio is measured to be B(Λ0 b→Λφ) B(B0→K0 Sφ) fΛ0 b fB0=0.55 ±0.11 (stat) ±0.04 (syst). The use of the world average value of B(B0→K0 Sφ) =(3.65 +0.35 −0.30) × 10−6[15] gives the final result of B(Λ0 b→Λφ)/10−6=5.18 ±1.04 (stat) ±0.35 (syst) +0.50 −0.43 (B(B0→K0 Sφ)) ±0.44(fd/fΛ0 b). 6. Triple-product asymmetries The Λ0 b→Λφ decay is a spin-1/2to spin-1/2plus vector transition. Five angles are needed to describe this decay since Λ0 b baryons may potentially be produced with a transverse polarisation in proton–proton collisions [13], as shown in Fig. 4. The angle θis defined as the polar angle of the Λbaryon in the Λ0 brest frame with respect to the normal vector defined through ˆ n= p1× pΛ0 b | p1× pΛ0 b|,(2) where  p1is the momentum of an incoming proton and  pΛ0 bis the momentum of the Λ0 bbaryon. The angles θΛand Λare defined as the polar and azimuthal angles of the proton from the decay of the Λbaryon in the Λrest frame. The angles θφand φare defined as the polar and azimuthal angles of the K+meson in the rest frame of the φmeson. Triple-product asymmetries, which are odd under time-reversal, have been proposed by Leitner and Ajaltouni using the azimuthal angles ni, i ∈{Λ, φ}, defined as [12] 286 The LHCb Collaboration / Physics Letters B 759 (2016) 282–292 Fig. 3. Fit projections to the π+π−K+K−invariant mass in the (a) long and (b) downstream datasets, the K+K−invariant mass in the (c) long and (d) downstream datasets, and the π+π−invariant mass in the (e) long and (f) downstream datasets. The total fit projection is given by the blue solid line. The green and blue dotted lines represent the combinatorial and K0 S+random K+K−fit components, respectively. The red and magenta dashed lines represent the B0→K0 Sφsignal and the B0→K0 SK+K− non-resonant components, respectively. Black points represent the data. Data uncertainties are Poisson 68% confidence intervals. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.) Table 1 Systematic uncertainty contributions to the branching fraction ratio. Source Uncertainty (%) Mass model 3.0 Simulation sample size 2.2 Tracking efficiency 0.5 Vertex efficiency 2.6 Hardware trigger 2.8 Selection efficiency 4.1 Peaking background 0.1 Total 6.7 cosni= eY· ui,(3) sinni= eZ·( eY× ui), (4) where  ui= eZ׈ ni | eZ׈ ni|.(5) The basis { eX,  eY,  eZ}is defined in the Λ0 brest frame, in which  eZ is parallel to ˆ n,  eXis chosen to be parallel to the momentum of the incoming proton, and ˆ nΛ(φ) is the normal vector to the Λ(φ) decay plane, defined through ˆ nΛ= pp× pπ | pp× pπ|,(6) ˆ nφ= pK+× pK− | pK+× pK−|.(7) Asymmetries in cosniand sinni, where i ∈{Λ, φ}, are defined as Ac i=N+,c i−N−,c i N+,c i+N−,c i ,(8) As i=N+,s i−N−,s i N+,s i+N−,s i ,(9) where N+(−),c iand N+(−),s idenote the number of candidates for which the cosniand sinniobservables are positive (negative), respectively. The asymmetries Ac,s Λand Ac,s φare determined experimentally through a simultaneous unbinned maximum likelihood fit to datasets in which the relevant observables are positive and negative. The fit construction and observables are identical to that used for the branching fraction measurement. However, the yields for each dataset are parametrised in terms of the total yield, Nj, and the asymmetry, Aj, for fit component jas N+ j=Nj 2(1+Aj), (10) N− j=Nj 2(1−Aj). (11) Distributions of the sin n(Λ,φ) and cos n(Λ,φ) observables from Λ0 b→Λφ data have been extracted using the sPlot method [37] and are provided in Fig. 5. The numerical values of the fitted asymmetries are given in Table 2. Mismodelling of the mass components could lead to background contamination in the determination of the asymmetries. In The LHCb Collaboration / Physics Letters B 759 (2016) 282–292 287 Fig. 4. Decay angles for the Λ0 b→Λφ decay, where the angles are defined in the text. Fig. 5. Distributions of the angular observables: (a) sin nΛ,(b)cosnΛ,(c)sinnφ,(d)cosnφfrom weighted Λ0 b→Λφ data. Table 2 Asymmetries measured from Λ0 b→ Λφ data events. Asymmetry Fit value Ac Λ−0.22 ±0.12 As Λ0.13 ±0.12 Ac φ−0.01 ±0.12 As φ−0.07 ±0.12 the determination of the uncertainty related to the mass model, two contributions are considered. These are the line shape models and the background asymmetries. The effects of the line shapes are quantified using the same method as the branching fraction measurement, i.e. the generation of datasets with a one-dimensional kernel estimate of the simulation mass distributions in addition to modification of the background description. In the nominal fit, components that are not from the Λ0 b→Λφ signal have zero asymmetries. For background components this is justified due to the uncorrelated kinematics of the K+K−and pπ−systems. However, the non-resonant Λ0 b→ΛK+K−contribution could have non-zero asymmetries. The systematic uncertainty due to the assumption of zero background asymmetries is determined through comparing the nominal fit against the fit with all possible asymmetries allowed to vary freely. Efficiencies are found to be independent of the sinniand cosniobservables. The systematic uncertainty due to the angular acceptance is then taken from the statistical uncertainty in fits to the simulated datasets, after the application of an appropriate weighting to account for the differences between data and simulation. The resolutions of the angular observables are found from simulated events to be 32.3 mrad and 22.1 mrad for the nΛand nφangles, respectively. The uncertainty due to bin migration is then assigned assuming maximal asymmetry and leads to minor uncertainties of 0.007 for the nφangle and 0.010 for the nΛ 288 The LHCb Collaboration / Physics Letters B 759 (2016) 282–292 Table 3 Systematic uncertainty contributions to the triple-product asymmetries. Source Ac ΛAs ΛAc φAs φ Mass model 0.061 0.051 0.026 0.009 Angular acceptance 0.010 0.010 0.010 0.010 Angular resolution 0.008 0.008 0.005 0.005 Total 0.062 0.053 0.028 0.014 angle. Systematic contributions to the triple-product uncertainty budget are summarised in Table 3. 7. Summary A search for the Λ0 b→Λφ decay is presented based on a dataset of 3.0fb −1collected by the LHCb experiment in 2011 and 2012. The decay is observed for the first time with a significance of 5.9 standard deviations including systematic uncertainties. The branching fraction is found to be B(Λ0 b→Λφ)/10−6=5.18 ±1.04 (stat) ±0.35 (syst) +0.50 −0.43 (B(B0→K0 Sφ)) ±0.44(fd/fΛ0 b). Triple-product asymmetries are measured to be Ac Λ=−0.22 ±0.12 (stat) ±0.06 (syst), As Λ=0.13 ±0.12 (stat) ±0.05 (syst), Ac φ=−0.01 ±0.12 (stat) ±0.03 (syst), As φ=−0.07 ±0.12 (stat) ±0.01 (syst), and are consistent with zero. Data collected by the LHCb experiment in the forthcoming years will improve the statistical precision of these measurements and enable the dynamics of b →s transitions in beauty baryons to be probed in greater detail, which will greatly enhance the reach of searches for physics beyond the SM. Acknowledgements We express our gratitude to our colleagues in the CERN accelerator departments for the excellent performance of the LHC. We thank the technical and administrative staff at the LHCb institutes. We acknowledge support from CERN and from the national agencies: CAPES, CNPq, FAPERJ and FINEP (Brazil); NSFC (China); CNRS/IN2P3 (France); BMBF, DFG and MPG (Germany); INFN (Italy); FOM and NWO (The Netherlands); MNiSW and NCN (Poland); MEN/IFA (Romania); MinES and FANO (Russia); MinECo (Spain); SNSF and SER (Switzerland); NASU (Ukraine); STFC (United Kingdom); NSF (USA). We acknowledge the computing resources that are provided by CERN, IN2P3 (France), KIT and DESY (Germany), INFN (Italy), SURF (The Netherlands), PIC (Spain), GridPP (United Kingdom), RRCKI and Yandex LLC (Russia), CSCS (Switzerland), IFIN-HH (Romania), CBPF (Brazil), PL-GRID (Poland) and OSC (USA). We are indebted to the communities behind the multiple open source software packages on which we depend. Individual groups or members have received support from AvH Foundation (Germany), EPLANET, Marie Skłodowska-Curie Actions and ERC (European Union), Conseil Général de Haute-Savoie, Labex ENIGMASS and OCEVU, Région Auvergne (France), RFBR and Yandex LLC (Russia), GVA, XuntaGal and GENCAT (Spain), Herchel Smith Fund, The Royal Society, Royal Commission for the Exhibition of 1851 and the Leverhulme Trust (United Kingdom). References [1] Y. Shimizu, M. Tanimoto, K. Yamamoto, Supersymmetry contributions to CP violations in b →sand b →dtransitions taking account of new data, Phys. Rev. D 87 (2013) 056004, arXiv:1212.6486. [2] A. Datta, M. Imbeault, D. London, B →φK∗, B →φKSand new physics, Phys. Lett. B 671 (2009) 256, arXiv:0811.2957. [3] T. Moroi, CP violation in B0→φKSin SUSY GUT with right-handed neutrinos, Phys. Lett. B 493 (2000) 366, arXiv:hep-ph/0007328. [4] LHCb Collaboration, R. Aaij, et al., Measurement of CP violation in B0 s→φφ decays, Phys. Rev. D 90 (2014) 052011, arXiv:1407.2222. [5] LHCb Collaboration, R. Aaij, et al., First measurement of the CP-violating phase in B0 s→φφ decays, Phys. Rev. Lett. 110 (2013) 241802, arXiv:1303.7125. [6] Belle Collaboration, K. Abe, et al., Measurement of time-dependent CP-violating asymmetries in B0→φK0 S, K+K−K0 S, and ηK0 Sdecays, Phys. Rev. Lett. 91 (2003) 261602, arXiv:hep-ex/0308035. [7] BaBar Collaboration, J.P. Lees, et al., Study of CP violation in Dalitz-plot analyses of B0→K+K−K0 S, B+→K+K−K+, and B+→K0 SK0 SK+, Phys. Rev. D 85 (2012) 112010, arXiv:1201.5897. [8] LHCb Collaboration, R. Aaij, et al., Measurement of polarization amplitudes and CP asymmetries in B0→φK∗(892)0, J. High Energy Phys. 05 (2014) 069, arXiv:1403.2888. [9] M. Raidal, CP asymmetry in B →φKSdecays in left-right models and its implications on Bsdecays, Phys. Rev. Lett. 89 (2002) 231803, arXiv:hep-ph/0208091. [10] M. Gronau, J.L. Rosner, Triple product asymmetries in K, D(s)and B(s)decays, Phys. Rev. D 84 (2011) 096013, arXiv:1107.1232. [11] S.K. Patra, A. Kundu, CPT violation and triple-product correlations in B decays, Phys. Rev. D 87 (11) (2013) 116005, arXiv:1305.1417. [12] O. Leitner, Z.J. Ajaltouni, Testing CP and time reversal symmetries with Λb→ ΛV(1−)decays, Nucl. Phys. B, Proc. Suppl. 174 (2007) 169, arXiv:hep-ph/ 0610189. [13] LHCb Collaboration, R. Aaij, et al., Measurements of the 0 b→J/ψ decay amplitudes and the 0 bpolarisation in pp collisions at √s=7TeV, Phys. Lett. B 724 (2013) 27, arXiv:1302.5578. [14] LHCb Collaboration, R. Aaij, et al., Observations of Λ0 b→ΛK+π−and Λ0 b→ ΛK+K−decays and searches for other Λ0 band 0 bdecays to Λh+h−final states, J. High Energy Phys. (2016), http://dx.doi.org/10.1007/JHEP05(2016)081, arXiv:1603.00413. [15] Heavy Flavor Averaging Group, Y. Amhis, et al., Averages of b-hadron, c-hadron, and τ-lepton properties as of summer 2014, arXiv:1412.7515, updated results and plots available at http://www.slac.stanford.edu/xorg/hfag/. [16] LHCb Collaboration, A.A. Alves Jr., et al., The LHCb detector at the LHC, J. Instrum. 3 (2008) S08005. [17] LHCb Collaboration, R. Aaij, et al., LHCb detector performance, Int. J. Mod. Phys. A 30 (2015) 1530022, arXiv:1412.6352. [18] T. Sjöstrand, S. Mrenna, P. Skands, PYTHIA 6.4 physics and manual, J. High Energy Phys. 05 (2006) 026, arXiv:hep-ph/0603175; T. Sjöstrand, S. Mrenna, P. Skands, A brief introduction to PYTHIA 8.1, Comput. Phys. Commun. 178 (2008) 852, arXiv:0710.3820. [19] I. Belyaev, et al., Handling of the generation of primary events in Gauss, the LHCb simulation framework, J. Phys. Conf. Ser. 331 (2011) 032047. [20] D.J. Lange, The EvtGen particle decay simulation package, Nucl. Instrum. Methods Phys. Res., Sect. A 462 (2001) 152. [21] P. Golonka, Z. Was, PHOTOS Monte Carlo: a precision tool for QED corrections in Zand Wdecays, Eur. Phys. J. C 45 (2006) 97, arXiv:hep-ph/0506026. [22] Geant4 Collaboration, J. Allison, et al., Geant4 developments and applications, IEEE Trans. Nucl. Sci. 53 (2006) 270; Geant4 Collaboration, S. Agostinelli, et al., Geant4: a simulation toolkit, Nucl. Instrum. Methods Phys. Res., Sect. A 506 (2003) 250. [23] M. Clemencic, et al., The LHCb simulation application, Gauss: design, evolution and experience, J. Phys. Conf. Ser. 331 (2011) 032023. [24] L. Breiman, J.H. Friedman, R.A. Olshen, C.J. Stone, Classification and Regression Trees, Wadsworth international group, Belmont, California, USA, 1984. [25] R.E. Schapire, Y. Freund, A decision-theoretic generalization of on-line learning and an application to boosting, J. Comput. Syst. Sci. 55 (1997) 119. [26] Particle Data Group, K.A. Olive, et al., Review of particle physics, Chin. Phys. C 38 (2014) 090001. [27] G. Punzi, Sensitivity of searches for new signals and its optimization, in: L. Lyons, R. Mount, R. Reitmeyer (Eds.), Statistical Problems in Particle Physics, Astrophysics, and Cosmology, 2003, p. 79, arXiv:physics/0308063. [28] BaBar Collaboration, B. Aubert, et al., Measurement of CP asymmetries in B0→ φK0and B0→K+K−K0 Sdecays, Phys. Rev. D 71 (2005) 091102, arXiv:hep-ex/ 0502019. [29] LHCb Collaboration, R. Aaij, et al., Study of B0 (s)→K0 Sh+h− decays with first observation of B0 s→K0 SK±π∓and B0 s→K0 Sπ+π−, J. High Energy Phys. 10 (2013) 143, arXiv:1307.7648. [30] LHCb Collaboration, R. Aaij, et al., Observation of CP violation in B±→DK± decays, Phys. Lett. B 712 (2012) 203; The LHCb Collaboration / Physics Letters B 759 (2016) 282–292 289 LHCb Collaboration, R. Aaij, et al., Observation of CP violation in B±→DK± decays, Phys. Lett. B 713 (2012) 351, arXiv:1203.3662 (Erratum). [31] HERA-B Collaboration, I. Abt, et al., K∗0and φmeson production in proton– nucleus interactions at √s=41.6GeV, Eur. Phys. J. C 50 (2007) 315, arXiv: hep-ex/0606049. [32] S.M. Flatté, On the nature of 0+mesons, Phys. Lett. B 63 (1976) 228. [33] LHCb Collaboration, R. Aaij, et al., Study of the kinematic dependences of 0 b production in pp collisions and a measurement of the 0 b→+ cπ−branching fraction, J. High Energy Phys. 08 (2014) 143, arXiv:1405.6842. [34] LHCb Collaboration, R. Aaij, et al., Measurement of the track reconstruction efficiency at LHCb, J. Instrum. 10 (2015) P02007, arXiv:1408.1251. [35] S.S. Wilks, The large-sample distribution of the likelihood ratio for testing composite hypotheses, Ann. Math. Stat. 9 (1938) 60. [36] K.S. Cranmer, Kernel estimation in high-energy physics, Comput. Phys. Commun. 136 (2001) 198, arXiv:hep-ex/0011057. [37] M. Pivk, F.R. Le Diberder, sPlot: a statistical tool to unfold data distributions, Nucl. Instrum. Methods Phys. Res., Sect. A 555 (2005) 356, arXiv: physics/0402083. LHCb Collaboration R. Aaij 39, C. Abellán Beteta 41, B. Adeva 38, M. Adinolfi 47, Z. Ajaltouni 5, S. Akar 6, J. Albrecht 10, F. Alessio 39, M. Alexander 52, S. Ali 42, G. Alkhazov 31, P. Alvarez Cartelle 54, A.A. Alves Jr 58, S. Amato 2, S. Amerio 23, Y. Amhis 7, L. An 3,40, L. Anderlini 18, G. Andreassi 40, M. Andreotti 17,g, J.E. Andrews 59, R.B. Appleby 55, O. Aquines Gutierrez 11, F. Archilli 39, P. d’Argent 12, A. Artamonov36, M. Artuso 60, E. Aslanides 6, G. Auriemma 26,n, M. Baalouch 5, S. Bachmann 12, J.J. Back 49, A. Badalov37, C. Baesso 61, S. Baker 54, W. Baldini 17, R.J. Barlow 55, C. Barschel 39, S. Barsuk 7, W. Barter 39, V. Batozskaya 29, V. Battista 40, A. Bay40, L. Beaucourt 4, J. Beddow 52, F. Bedeschi 24, I. Bediaga 1, L.J. Bel 42, V. Bellee 40, N. Belloli 21,k, I. Belyaev 32, E. Ben-Haim 8, G. Bencivenni 19, S. Benson 39,∗, J. Benton 47, A. Berezhnoy 33, R. Bernet 41, A. Bertolin23, F. Betti 15, M.-O. Bettler 39, M. van Beuzekom 42, S. Bifani 46, P. Billoir 8, T. Bird 55, A. Birnkraut 10, A. Bizzeti 18,i, T. Blake 49, F. Blanc 40, J. Blouw 11, S. Blusk 60, V. Bocci 26, A. Bondar35, N. Bondar 31,39, W. Bonivento 16, A. Borgheresi 21,k, S. Borghi 55, M. Borisyak 67, M. Borsato 38, M. Boubdir 9, T.J.V. Bowcock 53, E. Bowen 41, C. Bozzi 17,39, S. Braun 12, M. Britsch 12, T. Britton 60, J. Brodzicka 55, E. Buchanan 47, C. Burr 55, A. Bursche2, J. Buytaert 39, S. Cadeddu 16, R. Calabrese 17,g, M. Calvi 21,k, M. Calvo Gomez 37,p, P. Campana 19, D. Campora Perez 39, L. Capriotti 55, A. Carbone15,e, G. Carboni 25,l, R. Cardinale 20,j, A. Cardini 16, P. Carniti 21,k, L. Carson 51, K. Carvalho Akiba 2, G. Casse 53, L. Cassina 21,k, L. Castillo Garcia 40, M. Cattaneo 39, Ch. Cauet 10, G. Cavallero 20, R. Cenci 24,t, M. Charles 8, Ph. Charpentier 39, G. Chatzikonstantinidis 46, M. Chefdeville 4, S. Chen 55, S.-F. Cheung 56, M. Chrzaszcz 41,27, X. Cid Vidal 39, G. Ciezarek 42, P.E.L. Clarke 51, M. Clemencic 39, H.V. Cliff 48, J. Closier 39, V. Coco 58, J. Cogan 6, E. Cogneras 5, V. Cogoni 16,f, L. Cojocariu 30, G. Collazuol 23,r, P. Collins 39, A. Comerma-Montells 12, A. Contu39, A. Cook 47, M. Coombes 47, S. Coquereau 8, G. Corti 39, M. Corvo 17,g, B. Couturier 39, G.A. Cowan 51, D.C. Craik 51, A. Crocombe 49, M. Cruz Torres 61, S. Cunliffe 54, R. Currie 54, C. D’Ambrosio 39, E. Dall’Occo 42, J. Dalseno 47, P.N.Y. David 42, A. Davis 58, O. De Aguiar Francisco 2, K. De Bruyn 6, S. De Capua 55, M. De Cian 12, J.M. De Miranda 1, L. De Paula 2, P. De Simone 19, C.-T. Dean 52, D. Decamp 4, M. Deckenhoff 10, L. Del Buono 8, N. Déléage 4, M. Demmer 10, D. Derkach 67, O. Deschamps 5, F. Dettori 39, B. Dey 22, A. Di Canto 39, F. Di Ruscio 25, H. Dijkstra 39, F. Dordei 39, M. Dorigo 40, A. Dosil Suárez 38, A. Dovbnya 44, K. Dreimanis 53, L. Dufour 42, G. Dujany 55, K. Dungs 39, P. Durante 39, R. Dzhelyadin 36, A. Dziurda 27, A. Dzyuba31, S. Easo 50,39, U. Egede 54, V. Egorychev 32, S. Eidelman 35, S. Eisenhardt 51, U. Eitschberger 10, R. Ekelhof 10, L. Eklund 52, I. El Rifai 5, Ch. Elsasser 41, S. Ely 60, S. Esen 12, H.M. Evans 48, T. Evans 56, A. Falabella 15, C. Färber 39, N. Farley 46, S. Farry 53, R. Fay 53, D. Fazzini 21,k, D. Ferguson 51, V. Fernandez Albor 38, F. Ferrari 15, F. Ferreira Rodrigues 1, M. Ferro-Luzzi 39, S. Filippov 34, M. Fiore 17,g, M. Fiorini 17,g, M. Firlej 28, C. Fitzpatrick 40, T. Fiutowski 28, F. Fleuret 7,b, K. Fohl 39, M. Fontana 16, F. Fontanelli 20,j, D.C. Forshaw 60, R. Forty 39, M. Frank 39, C. Frei 39, M. Frosini 18, J. Fu 22, E. Furfaro 25,l, A. Gallas Torreira 38, D. Galli 15,e, S. Gallorini 23, S. Gambetta 51, M. Gandelman 2, P. Gandini 56, Y. Gao 3, J. García Pardiñas 38, J. Garra Tico 48, L. Garrido 37, P.J. Garsed 48, D. Gascon 37, C. Gaspar 39, L. Gavardi 10, G. Gazzoni 5, D. Gerick 12, E. Gersabeck 12, M. Gersabeck 55, T. Gershon 49, Ph. Ghez 4, S. Gianì 40, V. Gibson 48, O.G. Girard 40, L. Giubega 30, V.V. Gligorov 39, C. Göbel 61, D. Golubkov 32, A. Golutvin 54,39, A. Gomes1,a, C. Gotti 21,k, M. Grabalosa Gándara 5, R. Graciani Diaz 37, L.A. Granado Cardoso 39, E. Graugés 37, E. Graverini 41, G. Graziani 18, A. Grecu30, P. Griffith 46, L. Grillo 12, O. Grünberg 65, B. Gui 60, E. Gushchin 34, Yu. Guz 36,39, T. Gys 39, T. Hadavizadeh 56, C. Hadjivasiliou 60, G. Haefeli 40, C. Haen 39, S.C. Haines 48, S. Hall 54, B. Hamilton 59, X. Han 12, S. Hansmann-Menzemer 12, N. Harnew 56, S.T. Harnew 47, J. Harrison 55, J. He 39, T. Head 40, A. Heister 9, K. Hennessy 53, P. Henrard 5, L. Henry 8, J.A. Hernando Morata 38, E. van Herwijnen 39, 290 The LHCb Collaboration / Physics Letters B 759 (2016) 282–292 M. Heß 65, A. Hicheur 2, D. Hill 56, M. Hoballah 5, C. Hombach 55, L. Hongming 40, W. Hulsbergen 42, T. Humair 54, M. Hushchyn 67, N. Hussain 56, D. Hutchcroft 53, M. Idzik 28, P. Ilten 57, R. Jacobsson 39, A. Jaeger 12, J. Jalocha 56, E. Jans 42, A. Jawahery59, M. John 56, D. Johnson 39, C.R. Jones 48, C. Joram 39, B. Jost 39, N. Jurik 60, S. Kandybei 44, W. Kanso 6, M. Karacson 39, T.M. Karbach 39,†, S. Karodia 52, M. Kecke 12, M. Kelsey 60, I.R. Kenyon 46, M. Kenzie 39, T. Ketel 43, E. Khairullin 67, B. Khanji 21,39,k, C. Khurewathanakul 40, T. Kirn 9, S. Klaver 55, K. Klimaszewski 29, M. Kolpin 12, I. Komarov 40, R.F. Koopman 43, P. Koppenburg 42,39, M. Kozeiha 5, L. Kravchuk 34, K. Kreplin 12, M. Kreps 49, P. Krokovny 35, F. Kruse 10, W. Krzemien 29, W. Kucewicz 27,o, M. Kucharczyk 27, V. Kudryavtsev 35, A.K. Kuonen40, K. Kurek 29, T. Kvaratskheliya 32, D. Lacarrere 39, G. Lafferty 55,39, A. Lai 16, D. Lambert 51, G. Lanfranchi 19, C. Langenbruch 49, B. Langhans 39, T. Latham 49, C. Lazzeroni 46, R. Le Gac 6, J. van Leerdam42, J.-P. Lees 4, R. Lefèvre 5, A. Leflat33,39, J. Lefrançois 7, E. Lemos Cid 38, O. Leroy 6, T. Lesiak 27, B. Leverington 12, Y. Li 7, T. Likhomanenko 67,66, R. Lindner 39, C. Linn 39, F. Lionetto 41, B. Liu 16, X. Liu 3, D. Loh 49, I. Longstaff 52, J.H. Lopes 2, D. Lucchesi 23,r, M. Lucio Martinez 38, H. Luo 51, A. Lupato 23, E. Luppi 17,g, O. Lupton 56, N. Lusardi 22, A. Lusiani24, X. Lyu 62, F. Machefert 7, F. Maciuc 30, O. Maev 31, K. Maguire 55, S. Malde 56, A. Malinin 66, G. Manca 7, G. Mancinelli 6, P. Manning 60, A. Mapelli 39, J. Maratas 5, J.F. Marchand 4, U. Marconi 15, C. Marin Benito 37, P. Marino 24,t, J. Marks 12, G. Martellotti 26, M. Martin 6, M. Martinelli 40, D. Martinez Santos 38, F. Martinez Vidal 68, D. Martins Tostes 2, L.M. Massacrier 7, A. Massafferri 1, R. Matev 39, A. Mathad49, Z. Mathe 39, C. Matteuzzi 21, A. Mauri 41, B. Maurin 40, A. Mazurov 46, M. McCann 54, J. McCarthy 46, A. McNab 55, R. McNulty 13, B. Meadows 58, F. Meier 10, M. Meissner 12, D. Melnychuk 29, M. Merk 42, A. Merli 22,u, E. Michielin 23, D.A. Milanes 64, M.-N. Minard 4, D.S. Mitzel 12, J. Molina Rodriguez 61, I.A. Monroy 64, S. Monteil 5, M. Morandin 23, P. Morawski 28, A. Mordà 6, M.J. Morello 24,t, J. Moron 28, A.B. Morris 51, R. Mountain 60, F. Muheim 51, D. Müller 55, J. Müller 10, K. Müller 41, V. Müller 10, M. Mussini 15, B. Muster 40, P. Naik 47, T. Nakada 40, R. Nandakumar 50, A. Nandi56, I. Nasteva 2, M. Needham 51, N. Neri 22, S. Neubert 12, N. Neufeld 39, M. Neuner 12, A.D. Nguyen 40, C. Nguyen-Mau 40,q, V. Niess 5, S. Nieswand 9, R. Niet 10, N. Nikitin 33, T. Nikodem 12, A. Novoselov 36, D.P. O’Hanlon 49, A. Oblakowska-Mucha28, V. Obraztsov 36, S. Ogilvy 52, O. Okhrimenko 45, R. Oldeman 16,48,f, C.J.G. Onderwater 69, B. Osorio Rodrigues 1, J.M. Otalora Goicochea 2, A. Otto 39, P. Owen 54, A. Oyanguren 68, A. Palano14,d, F. Palombo 22,u, M. Palutan 19, J. Panman 39, A. Papanestis 50, M. Pappagallo 52, L.L. Pappalardo 17,g, C. Pappenheimer 58, W. Parker 59, C. Parkes 55, G. Passaleva 18, G.D. Patel 53, M. Patel 54, C. Patrignani 20,j, A. Pearce55,50, A. Pellegrino 42, G. Penso 26,m, M. Pepe Altarelli 39, S. Perazzini 15,e, P. Perret 5, L. Pescatore 46, K. Petridis 47, A. Petrolini 20,j, M. Petruzzo 22, E. Picatoste Olloqui 37, B. Pietrzyk 4, M. Pikies 27, D. Pinci 26, A. Pistone20, A. Piucci 12, S. Playfer 51, M. Plo Casasus 38, T. Poikela 39, F. Polci 8, A. Poluektov 49,35, I. Polyakov 32, E. Polycarpo 2, A. Popov36, D. Popov 11,39, B. Popovici 30, C. Potterat 2, E. Price 47, J.D. Price 53, J. Prisciandaro 38, A. Pritchard 53, C. Prouve 47, V. Pugatch 45, A. Puig Navarro 40, G. Punzi 24,s, W. Qian 56, R. Quagliani 7,47, B. Rachwal 27, J.H. Rademacker 47, M. Rama 24, M. Ramos Pernas 38, M.S. Rangel 2, I. Raniuk 44, G. Raven 43, F. Redi 54, S. Reichert 55, A.C. dos Reis1, V. Renaudin 7, S. Ricciardi 50, S. Richards 47, M. Rihl 39, K. Rinnert 53,39, V. Rives Molina 37, P. Robbe 7, A.B. Rodrigues 1, E. Rodrigues 55, J.A. Rodriguez Lopez 64, P. Rodriguez Perez 55, A. Rogozhnikov 67, S. Roiser 39, V. Romanovsky 36, A. Romero Vidal38, J.W. Ronayne 13, M. Rotondo 23, T. Ruf 39, P. Ruiz Valls 68, J.J. Saborido Silva 38, N. Sagidova 31, B. Saitta 16,f, V. Salustino Guimaraes 2, C. Sanchez Mayordomo 68, B. Sanmartin Sedes 38, R. Santacesaria 26, C. Santamarina Rios 38, M. Santimaria 19, E. Santovetti 25,l, A. Sarti19,m, C. Satriano 26,n, A. Satta25, D.M. Saunders 47, D. Savrina 32,33, S. Schael 9, M. Schiller 39, H. Schindler 39, M. Schlupp 10, M. Schmelling 11, T. Schmelzer 10, B. Schmidt 39, O. Schneider 40, A. Schopper 39, M. Schubiger 40, M.-H. Schune 7, R. Schwemmer 39, B. Sciascia 19, A. Sciubba 26,m, A. Semennikov 32, A. Sergi 46, N. Serra 41, J. Serrano 6, L. Sestini 23, P. Seyfert 21, M. Shapkin 36, I. Shapoval 17,44,g, Y. Shcheglov 31, T. Shears 53, L. Shekhtman 35, V. Shevchenko 66, A. Shires 10, B.G. Siddi 17, R. Silva Coutinho 41, L. Silva de Oliveira 2, G. Simi 23,s, M. Sirendi 48, N. Skidmore 47, T. Skwarnicki 60, E. Smith 54, I.T. Smith 51, J. Smith 48, M. Smith 55, H. Snoek 42, M.D. Sokoloff 58, F.J.P. Soler 52, F. Soomro 40, D. Souza 47, B. Souza De Paula 2, B. Spaan 10, P. Spradlin 52, S. Sridharan 39, F. Stagni 39, M. Stahl 12, S. Stahl 39, S. Stefkova 54, O. Steinkamp 41, O. Stenyakin 36, S. Stevenson 56, S. Stoica 30, S. Stone 60, B. Storaci 41, S. Stracka 24,t,