Observation of a diffractive contribution to dijet production in
proton-proton collisions at
p
ffiffiffi
s
¼ 7 TeV
S. Chatrchyan et al.* (CMS Collaboration)
(Received 9 September 2012; published 8 January 2013)
The cross section for dijet production in proton-proton collisions at pffiffiffis¼ 7 TeV is presented as a function of ~, a variable that approximates the fractional momentum loss of the scattered proton in single-diffractive events. The analysis is based on an integrated luminosity of 2:7 nb1collected with the CMS detector at the LHC at low instantaneous luminosities, and uses events with jet transverse momentum of at least 20 GeV. The dijet cross section results are compared to the predictions of diffractive and non-diffractive models. The low- ~ data show a significant contribution from diffractive dijet production, observed for the first time at the LHC. The associated rapidity gap survival probability is estimated.
DOI:10.1103/PhysRevD.87.012006 PACS numbers: 13.87.Ce, 12.38.Qk, 12.40.Nn
I. INTRODUCTION
A significant fraction of the total inelastic proton-proton cross section at high energies is attributed to diffractive processes, characterized by the presence of a large rapidity region y with no hadrons, usually called ‘‘rapidity gap’’ [rapidity is defined as y¼ ð1=2Þ ln½ðE þ pZÞ=ðE pZÞ, where E and pZare the energy and longitudinal momen-tum of the final-state particle, respectively]. Diffractive scattering is described in the framework of Regge theory as mediated by a strongly interacting color-singlet ex-change with the vacuum quantum numbers, the so-called ‘‘pomeron trajectory’’ [1]. Diffractive events with a hard parton-parton scattering are especially interesting because they can be studied in terms of perturbative quantum chromodynamics (pQCD). In diffractive events the proton emitting the pomeron either remains intact, losing only a few percent of its momentum, or is found in a low mass excited state. In addition, since the vacuum quantum numbers are exchanged, no particles are produced in a large rapidity range adjacent to the scattered proton (or its dissociation products).
Diffraction with a hard scale has been studied in proton-antiproton (pp) and electron-proton (ep) collisions at CERN [2], Fermilab [3–6], and DESY [7–10]. Such hard diffractive processes can be described in terms of the convolution of diffractive parton distribution functions (dPDFs) and hard scattering cross sections, which are calculable in pQCD. In this approach, the pomeron is treated as a color-singlet combination of partons with the vacuum quantum numbers. The dPDFs have been deter-mined by the HERA experiments [7,9] by means of QCD
fits to inclusive diffractive deep inelastic scattering data, and have been successfully used to describe different hard diffractive processes in ep collisions. This success is based on the factorization theorem for diffractive electron-proton interactions, and on the validity of the QCD evolution equations for the dPDFs [11–13]. However, in hard dif-fractive hadron-hadron collisions factorization does not hold because of soft scatterings between the spectator partons, leading to the suppression of the observed diffrac-tive cross section. The suppression is quantified by the so-called ‘‘rapidity gap survival probability’’ [14], which is a nonperturbative quantity with large theoretical uncertain-ties [15–18]. It was measured to be about 10% in diffrac-tive dijet production in pp collisions at the Tevatron [5].
This paper presents a study of dijet production in proton-proton (pp) collisions at a center-of-mass energy ofpffiffiffis¼ 7 TeV. The data were collected with the Compact Muon Solenoid (CMS) detector at the LHC in 2010 and correspond to an integrated luminosity of 2:7 nb1. The cross section for production of dijets is presented as a function of ~, a variable that approximates the fractional momentum loss of the proton, for events in which both jets have transverse momenta pj1;j2T > 20 GeV and jet axes in the pseudorapidity range jj1;j2j < 4:4. Pseudorapidity is
defined as ¼ ln½tanð=2Þ, where is the polar angle relative to the anticlockwise proton beam direction, and is equal to the rapidity in the limit of a massless particle. The measurements are compared to the predictions of nondif-fractive (ND) and difnondif-fractive models, and the rapidity gap survival probability is estimated.
The paper is organized as follows: in Sec. II a brief description of the CMS detector is provided. The defini-tions of the kinematic variables are introduced in Sec.III. The event selection is explained in Sec. IV. Section V
describes the main features of the Monte Carlo (MC) generators used in this analysis. The cross section deter-mination for dijets as a function of ~ and the systematic uncertainties of the measurements are discussed in Sec.VI.
*Full author list given at the end of the article.
Published by the American Physical Society under the terms of the Creative Commons Attribution 3.0 License. Further distri-bution of this work must maintain attridistri-bution to the author(s) and the published article’s title, journal citation, and DOI.
The results are presented in Sec.VII, and the summary is given in Sec.VIII.
II. EXPERIMENTAL SETUP
A detailed description of the CMS detector can be found elsewhere [19]. The central feature of the CMS apparatus is a superconducting solenoid, of 6 m internal diameter. Within the field volume are the silicon pixel and strip tracker, the crystal electromagnetic calorimeter (ECAL) and the brass-scintillator hadronic calorimeter (HCAL). The tracker measures charged particles within the pseudor-apidity rangejnj < 2:4. ECAL and HCAL provide cover-age in pseudorapidity up tojnj < 3 in the barrel region and two endcap regions. The HCAL, when combined with the ECAL, measures jets with an energy resolution E=E 100%=pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiEðGeVÞ 5%. The calorimeter cells are grouped in projective towers, of granularity ¼ 0:087 0:087 at central rapidities and 0:175 0:175 at forward rapidities, where is the azimuthal angle in radians. In addition to the barrel and endcap detectors, CMS has extensive forward calorimetry. The forward part of the hadron calorimeter, HF, consists of steel absorbers and embedded radiation-hard quartz fibers, which provide a fast collection of Cherenkov light. The pseudorapidity coverage of the HF is 2:9 < jj < 5:2. In the current analysis only the range 3:0 < jj < 4:9 was used, thus restricting the data to a region of well-understood recon-struction efficiency. The first level of the CMS trigger system, composed of custom hardware processors, uses information from the calorimeters and muon detectors to select the most interesting events in a fixed time interval of less than 4 s. The High Level Trigger processor farm further decreases the event rate from around 100 kHz to around 300 Hz, before data storage.
III. KINEMATICS AND CROSS SECTIONS Diffractive dijet production (Fig.1) is characterized by the presence of a high-momentum proton (or a system Y with the same quantum numbers as the proton) with frac-tional momentum loss smaller than a few percent and a system X, which contains high-pT jets and is separated from the proton by a large rapidity gap, with y 3 or 4 units. The kinematics of this reaction is described by the masses of the systems X and Y, MX and MY, and the
squared four-momentum transfer t at the proton vertex. For the events selected in this analysis both MX and MY are much smaller thanpffiffiffis.
The cross section for single-diffractive (SD) dijet pro-duction (i.e. when the forward-going system Y is a proton) is usually expressed in terms of the variable ¼ M2
X=s,
which approximates the fractional momentum loss of the scattered proton. Under the assumption of QCD factoriza-tion, the cross section can be written as
d ddt¼ X Z dx1dx2d^tfð; tÞfPðx1; Þfpðx2; Þd ^ð^s; ^tÞ d^t ; (1) where the sum is over all parton flavors. The variables x1;2 are the parton momentum fractions in the pomeron and proton, the scale at which the PDFs are evaluated is indi-cated with , and ^ð^s; ^tÞ is the hard-scattering subprocess cross section, which is a function of the partonic center-of-mass energy squared ^s and momentum transfer squared ^t. The function fpðx2; Þ is the inclusive PDF of the proton that breaks up, while the dPDF of the surviving proton is written as fdiffð; t; x1; Þ ¼ fð; tÞfPðx1; Þ, where fð; tÞ is the so-called pomeron flux and fPðx1; Þ is the
pomeron structure function. The cross section dependence on and t is driven by the pomeron flux, usually parame-trized according to Regge theory as
fð; tÞ ¼ e
Bt
2PðtÞ1; (2)
where PðtÞ is the pomeron trajectory and B is the slope parameter. This ansatz is consistent with the HERA ep data [7–9], but is known not to hold between the ep and the Tevatron (pp) data [3–6], where an extra suppression (gap survival probability) factor is needed.
In this analysis is approximated by the variables ~þ (system X going in the z direction) and ~ (system X going in the þz direction) defined at the level of stable particles as ~ ¼ P ðEi pi zÞ ffiffiffi s p ; (3) where Eiand pi
zare the energy and longitudinal
momen-tum of the ith final-state particle with 1 < < 4:9 for ~
þ and4:9 < < þ1 for ~. In the region of low ~, this variable is a good approximation of for single-diffractive events. This is illustrated for single-single-diffractive dijet events simulated byPYTHIA8[20] in Fig.2, where the correlations between the values of and ~þ, determined at
p p
t
jet jet Y X IPFIG. 1. Schematic diagram of diffractive dijet production. The diagram shows the example of the gg! jet process; the qq and gq initial states also contribute.
generated and reconstructed (see Sec. IV) levels, are shown. The mass of the forward-going system Y, which includes all particles with > 4:9 (or < 4:9), was also estimated with thePYTHIA8generator; the mass is limited by the pseudorapidity range and is typically smaller than 30–40 GeV, with average5 GeV.
IV. EVENT SELECTION
The data were collected with the CMS detector in 2010 at low luminosities. The average number of extra pp inter-actions for any given event (the so-called pileup interac-tions) in the data is 0.09. The low number of pileup interactions simplified the extraction of the diffractive signal, since the particles produced in such interactions may fill the rapidity gap and hence reduce the visible diffractive cross section. However, the requirement of low pileup limits the available data sample since only a small amount of low-luminosity runs was collected.
At the trigger level events were selected by requiring at least one jet with uncorrected transverse-momentum greater than 6 GeV. The efficiency of the trigger, estimated using a minimum-bias data sample, was found to be greater than 95% for the dijet events considered in this analysis.
Offline, the jets were reconstructed with the anti-kT
inclusive jet finding algorithm [21] with a distance parame-ter of 0.5. The jet clusparame-tering algorithm was used to recon-struct jets from particle-flow (PF) objects [22], which are particle candidates obtained by combining the information of the tracking system and of the calorimeters in an optimal way. The reconstructed jet momenta were fully corrected to the level of stable particles (with lifetime such that c > 10 mm, hereafter referred to as ‘‘particle level’’), by means of a procedure partially based on MC simulation and partially on data [23].
The quantities ~þ and ~ were reconstructed using Eq. (3) from the energies and longitudinal momenta of all PF objects measured in thejj < 4:9 range. For charged PF objects (jj < 2:4, the region covered by the tracker) a minimum transverse momentum of 0.2 GeV was required. In the forward region, 3:0 < jj < 4:9, particu-larly relevant for this analysis, PF candidates were selected with energy greater than 4 GeV. A constant scale factor C¼ 1:45 0:04, determined from the MC simulation by comparing the generated and reconstructed values of ~, is applied to the measured ~. The error on the correction factor C is estimated by changing the MC models used to evaluate it. The value of C reflects the fact that not all final-state particles are detected because of the limited accep-tance and imperfect response of the detector. It also takes into account the inefficiency of PF object reconstruction. In practice, C acts as a scale calibration for ~; it depends only slightly on the value of ~and on the MC generator used. This dependence, of the order of a few percent, is included in the systematic uncertainty. The resolution of
~
, in the region of the present measurement, is 25%, and practically independent of ~.
Events were selected offline by applying the following requirements:
(i) the jets should pass the standard CMS quality criteria [23];
(ii) events should have at least two jets, each with transverse momentum, corrected to particle level, greater than 20 GeV. This requirement ensures high trigger efficiency;
(iii) the axes of the two leading jets (jets were ordered in pTwith the first, leading jet having the highest pT) should be in the pseudorapidity region jj1;j2j < 4:4 so that the reconstructed jets are fully
contained in the detector;
(iv) a primary vertex should be within a longitudinal distancejzj < 24 cm of the center of CMS; (v) beam-scraping events, in which long horizontal
sec-tions of the pixel tracker are hit by charged particles
) + ξ∼ ( 10 log -4 -3.5 -3 -2.5 -2 -1.5 -1 -0.5 0 )ξ ( 10 log -4 -3.5 -3 -2.5 -2 -1.5 -1 -0.5 0 1 10 2 10 3 10 4 10 CMS simulation ) + ξ∼ ( 10 log -4 -3.5 -3 -2.5 -2 -1.5 -1 -0.5 0 )ξ( 10 log -4 -3.5 -3 -2.5 -2 -1.5 -1 -0.5 0 1 10 2 10 CMS simulation
FIG. 2 (color online). The generated versus generated (top panel) and reconstructed (bottom panel) ~þ correlations for single-diffractive dijet events simulated byPYTHIA8; events in the bottom panel are those passing the selection described in Sec.IV.
traveling parallel to the beam, were rejected with a special algorithm [24];
(vi) to enhance the diffractive contribution, the require-ments max< 3 (min>3) were also applied.
Here max (min) is the pseudorapidity of the most forward (backward) PF object. The max (min) selection together with the pseudorapidity coverage of the detector,jj < 4:9, is equivalent to imposing a pseudorapidity gap of at least 1.9 units, with no PF objects with energy greater than 4 GeV in the HF calorimeter.
The number of selected events before the max (min) requirement is 277 953. The number of events passing also the max< 3 (min>3) selection is 804 (774); of these,
222 (220) have ~þ< 0:01 ( ~< 0:01). The differential cross section for dijet production was calculated separately as a function of ~þ and ~. The final results were aver-aged, and the average is presented as a function of ~.
The max, minrequirements reject most pileup interac-tions. The remaining pileup background was estimated with minimum-bias MC samples (PYTHIA6Z1 andPYTHIA8; see next section) and was found to be less than 2%.
V. MONTE CARLO SIMULATION
The simulation of ND dijet events was performed with the PYTHIA6 (version 6.422) [25] and PYTHIA8 (version 8.135) [20] generators; the events were generated in PYTHIA6 with tunes Z2 [26] and D6T [27], and in PYTHIA8with tune 1 [20]. The more recent PYTHIA8tune 4C [28] yields similar results as the tune 1 used here. Minimum-bias events were generated with PYTHIA6 tune Z1 [26] and withPYTHIA8tune 1.
Diffractive dijet events were simulated with thePOMPYT [29],POMWIG[30], andPYTHIA8generators. ThePYTHIA8 generator can simulate inclusive, nondiffractive as well as diffractive dijet events; separate samples were produced for the two processes. The modeling of diffractive events in these generators is based on the Ingelman and Schlein approach [31], which considers the diffractive reaction as a two-step process: one proton emits a pomeron with frac-tional momentum and then the pomeron interacts with the other proton. All three diffractive generators were used with dPDFs from the same fit to diffractive deep inelastic scattering data (H1 fit B [7]). The parametrization of the pomeron flux inPOMPYTandPOMWIGis also based on the
QCD fits to the HERA data [7], while it is different in PYTHIA8 [32]. This leads to different predictions for the diffractive cross sections calculated by PYTHIA8 and POMPYTor POMWIG (notably in their normalization). The effect of the rapidity gap survival probability is not simu-lated in any of the three diffractive generators.
The main difference between POMPYTand POMWIG is thatPOMPYTuses thePYTHIAframework whilePOMWIGis based on HERWIG [33]. Both programs generate single-diffractive dissociation. InPYTHIA8double-diffractive dis-sociation (DD), in which both protons dissociate, is also included. The contribution from central diffractive disso-ciation, in which both protons stay intact, was estimated with POMWIG. It amounts to1% of the diffractive con-tribution in the ~ region used in the analysis and was neglected. Only pomeron exchange was assumed; the Reggeon exchange contribution in the region ~ < 0:01 was estimated with POMPYT and was found to be less than 2%, and less than 1% in the lowest ~ bin used in the analysis.
The diffractive component of the dijet cross section was also computed at next-to-leading (NLO) accuracy with the POWHEG[34] framework using the CTEQ6M PDF for the proton that breaks up and H1 fit B for the dPDF. The parton shower and hadronization were carried out with PYTHIA8 (tune 1).
The generators used are listed in TableIalong with some of their features. All generated events were processed through the simulation of the CMS detector, based on GEANT4 [35] and reconstructed in the same manner as the data. All samples were generated without pileup. The mea-surements were corrected for detector acceptance and reso-lution with a suitable combination of nondiffractive (PYTHIA6 Z2) and diffractive (POMPYT) models (see Sec.VA).
Figure3shows the comparison between the uncorrected data and detector-level MC simulations for the recon-structed pTdistributions of the leading and second-leading jets with axes in the range jj1;j2j < 4:4. The simulated
distributions are normalized to the number of events in the corresponding distributions for the data. The data and MC simulations are in agreement, for both PYTHIA6 Z2 and PYTHIA8tune 1.
Figure4presents the comparison between data and MC simulations for the reconstructed (detector-level) pseudor-apidity distributions of the leading and second-leading jets.
TABLE I. Monte Carlo generators used in this work with details on their model ingredients.
Model PDF dPDF Parameter tune Process
PYTHIA6 CTEQ6L1 none Z2, D6T Nondiffractive jets
PYTHIA8 CTEQ5L H1 fit B Tune 1 Diffractive plus nondiffractive jets
POMPYT CTEQ6L1 H1 fit B PYTHIA6D6T Diffractive jets only
POMWIG CTEQ6L1 H1 fit B HERWIG Diffractive jets only
50 100 150 200 250 300 ) -1 (GeV T dN/dp -2 10 -1 10 1 10 2 10 3 10 4 10 DATA PYTHIA6 Z2 ND PYTHIA8 tune 1 ND | < 4.4 j1,j2 η , | 2 jet 1 jet → , pp -1 = 7 TeV, L = 2.7 nb s CMS, (GeV) j1 T p 50 100 150 200 250 300 ratio 1 2 3 DATA/PYTHIA6 Z2 DATA/PYTHIA8 T 50 100 150 200 250 300 ) -1 (GeV T dN/dp -2 10 -1 10 1 10 2 10 3 10 4 10 DATA PYTHIA6 Z2 ND PYTHIA8 tune 1 ND | < 4.4 j1,j2 η , | 2 jet 1 jet → , pp -1 = 7 TeV, L = 2.7 nb s CMS, (GeV) j2 T p 50 100 150 200 250 300 ratio 1 2 3 DATA/PYTHIA6 Z2 DATA/PYTHIA8
FIG. 3 (color online). Reconstructed transverse-momentum distributions of the leading (left panel) and second-leading (right panel) jets (black dots) compared to detector-level MC simulations (histograms) generated with two nondiffractive models (PYTHIA6Z2 and PYTHIA8tune 1). The error bars indicate the statistical uncertainty. The MC distributions are normalized to the number of events in the corresponding distributions for the data. The ratios of the data and MC distributions are also shown.
j1 η -5 -4 -3 -2 -1 0 1 2 3 4 5 η dN/d 0 10000 20000 30000 40000 50000 60000 DATA PYTHIA6 Z2 ND PYTHIA8 tune 1 ND > 20 GeV j1,j2 T , p 2 jet 1 jet → , pp -1 = 7 TeV, L = 2.7 nb s CMS, j2 η -5 -4 -3 -2 -1 0 1 2 3 4 5 η dN/d 0 10000 20000 30000 40000 50000 60000 DATA PYTHIA6 Z2 ND PYTHIA8 tune 1 ND > 20 GeV j1,j2 T , p 2 jet 1 jet → , pp -1 = 7 TeV, L = 2.7 nb s CMS,
FIG. 4 (color online). Reconstructed pseudorapidity dis-tributions of the leading (top panel) and second-leading (bottom panel) jets (black dots) compared to detector-level MC simulations (histograms) generated with two nondiffractive models (PYTHIA6 Z2 andPYTHIA8 tune 1). The statistical un-certainties are smaller than the data points. The MC distributions are normalized to the number of events in the corresponding distributions for the data.
1 j η -5 -4 -3 -2 -1 0 1 2 3 4 5 η dN/d 0 20 40 60 80 100 120 140 160 180 200 220 DATA PYTHIA6 Z2 ND + POMPYT (x0.23) SD PYTHIA6 Z2 ND POMPYT (x0.23) SD < 3 max η > 20 GeV, j1,j2 T , p 2 jet 1 jet → , pp -1 = 7 TeV, L = 2.7 nb s CMS, 2 j η -5 -4 -3 -2 -1 0 1 2 3 4 5 η dN/d 0 20 40 60 80 100 120 140 160 180 200 220 DATA PYTHIA6 Z2 ND + POMPYT (x0.23) SD PYTHIA6 Z2 ND POMPYT (x0.23) SD < 3 max η > 20 GeV, j1,j2 T , p 2 jet 1 jet → , pp -1 = 7 TeV, L = 2.7 nb s CMS,
FIG. 5 (color online). Reconstructed pseudorapidity distribu-tions of the leading (top panel) and second-leading (bottom panel) jets after the max< 3 selection (black dots) compared to three detector-level MC simulations (histograms). Events with the min>3 condition are also included in the figure with j1;j2! j1;j2. The error bars indicate the statistical uncer-tainty. The predictions of the nondiffractive (PYTHIA6Z2) and diffractive (POMPYT, scaled by the value quoted in the legend) contributions and their sum are also shown. The sum of the predictions of the two MC simulations is normalized to the number of events in the corresponding distributions for the data. OBSERVATION OF A DIFFRACTIVE CONTRIBUTION TO . . . PHYSICAL REVIEW D 87, 012006 (2013)
Also here, the MC distributions are normalized to the number of events in the data. Data are better described byPYTHIA6tune Z2 than byPYTHIA8tune 1.
The pseudorapidity distributions of the two leading jets for events selected with the max< 3 requirement are presented in Fig. 5. Events with the min>3 condition are also included in Fig. 5 with j1;j2!
j1;j2. The pseudorapidity gap condition enhances the
diffractive component in the data, and selects events with the jets mainly in the hemisphere opposite to that of the gap. A combination of PYTHIA6 Z2 and POMPYT events reproduces the data reasonably well; the relative normal-ization of the models is optimized with the procedure described in Sec.VA.
A. Reconstructed ~ distributions and determination of the relativePOMPYTandPYTHIA6normalization The reconstructed ~ distribution is shown in Fig. 6
before the max, minselections. Here again, the shape of the distribution can be described by the combination of diffractive and nondiffractive MC models. The best com-bination was obtained by minimizing the difference between the ~ distributions of the data and of the sum of nondiffractive and diffractive models. The relative contri-bution of diffractive dijet production and the overall nor-malization of the sum were found in this fit, and the diffractive contribution was scaled accordingly. The over-all normalization of the fit result is not relevant. The effect of the calorimeter energy scale uncertainty, estimated by varying by10% the energy of all PF objects not associ-ated with the leading jets, is shown by the band. The solid
line in Fig.6(a)indicates the result of the fit, according to which the diffractive dijet cross section predicted by POMPYTshould be multiplied by a factor’ 0:23 to match the data. The uncertainty of this correction factor was estimated by changing the fitting procedure and was found to be20%. Figure6(b)presents the same data compared to PYTHIA6D6T þPOMPYT; here the fit requires the POMPYTnormalization to be scaled by a factor of’ 0:17.
Figure6(c) compares the data to PYTHIA8tune 1; both the single-diffractive and the double-diffractive compo-nents are added to the nondiffractive part, all simulated byPYTHIA8. The result of the fit is very different from that forPOMWIGandPYTHIA6: the normalization of the diffrac-tive components of PYTHIA8 needs to be multiplied by a factor ’ 2:5 to match the data. This large difference is a consequence of the different implementation of the pom-eron flux inPYTHIA8andPOMPYT.
In all three cases, after normalization, the shape of the reconstructed ~ distribution in the data is de-scribed satisfactorily by the MC models (PYTHIA6Z2 þ POMPYT resulting in the best description). However, the predicted nondiffractive component in the lowest ~ bin varies from about 0.1% for PYTHIA6 D6T to as much as 10%–20% forPYTHIA6Z2 andPYTHIA8.
The effect of the max< 3 (min>3) requirement is
illustrated in Fig.7, where the reconstructed ~ distributions with and without the max< 3 (min>3) condition are
compared to MC simulations. These pseudorapidity gap selections reject events at high values of ~. The region of low ~, where the diffractive contribution dominates, is only marginally affected. The data and MC simulations are in
ξ ∼ dN/d 3 10 4 10 5 10 6 10 ξ ∼ -3 10 10-2 10-1 DATA PYTHIA6 Z2 ND + POMPYT (x0.23) SD PYTHIA6 Z2 ND POMPYT (x0.23) SD (a) ξ ∼ -3 10 10-2 10-1 DATA PYTHIA6 D6T ND + POMPYT (x0.17) SD PYTHIA6 D6T ND POMPYT (x0.17) SD (b) ξ ∼ -3 10 10-2 10-1 DATA
PYTHIA8 tune 1 ND + PYTHIA8 SD+DD (x2.5) PYTHIA8 tune 1 ND PYTHIA8 SD+DD (x2.5) (c) > 20 GeV j1,j2 T | < 4.4, p j1,j2 η , | 2 jet 1 jet → , pp -1 = 7 TeV, L = 2.7 nb s CMS,
FIG. 6 (color online). Reconstructed ~ distribution compared to detector-level MC predictions with and without diffractive dijet production. The predictions of (a)PYTHIA6 Z2 þPOMPYT, (b)PYTHIA6 D6T þPOMPYT, and (c)PYTHIA8tune 1 are shown (in all the cases the relative diffractive contributions from the MC simulation are scaled by the values given in the legend). The error bars indicate the statistical uncertainty; the band represents the calorimeter energy scale uncertainty. The sum of the predictions of the two MC simulations is normalized to the number of events in the corresponding distributions for the data.
agreement at low ~. The relative normalization ofPYTHIA6 Z2 andPOMPYTin the figure is the same as in Fig.6.
VI. CROSS SECTION DETERMINATION AND SYSTEMATIC UNCERTAINTIES The differential cross section for dijet production as a function of ~ is evaluated as djj d ~ ¼ Ni jj L Ai ~i; (4) where Ni
jjis the measured number of dijet events in the ith
~
bin, Aiis the correction factor defined as the number of
reconstructed MC events in that bin divided by the number of generated events in the same bin, ~iis the bin width, L is the integrated luminosity, and is the trigger efficiency. The factors Ai include the effects of the geometrical
ac-ceptance of the apparatus, and that of all the selections listed in Sec. IV, as well as the unfolding corrections to account for the finite resolution of the reconstructed vari-ables used in the analysis. Various unfolding techniques (bin-by-bin, singular value decomposition [36] and Bayesian [37]) yield consistent results and the bin-by-bin correction was kept. In addition, the measured number of events, Njji, is corrected for the effect of pileup. This correction takes into account the probability of single pp interactions, evaluated on a run-by-run basis, as well as the probability that pileup interactions do not destroy the visible gap, estimated with the minimum-bias MC samples (PYTHIA6Z1 andPYTHIA8tune 1); the average correction is
1.07. The cross section is measured for dijets with the axes in the pseudorapidity range jj1;j2j < 4:4 and pj1;j2
T >
20 GeV in the ~ bins 0:0003 < ~ < 0:002, 0:002 < ~ < 0:0045, and 0:0045 < ~ < 0:01. The cross section results for ~þand ~are averaged, yielding the cross section as a function of ~.
The systematic uncertainties are estimated by varying the selection criteria and by modifying the analysis proce-dure as follows:
(1) The uncertainty on the jet energy scale varies between 2% and 9% depending on the jet pT and [23]. It decreases with the jet pTand is typically higher at high . The energy of the reconstructed jets is varied accordingly.
(2) The effect of the uncertainty on the jet energy resolution is studied by changing the resolution by up to10% in the central region (jj < 2:3) and by up to20% in the forward regions (jj > 2:3) [23]. (3) The systematic uncertainty related to the ~ reconstruction is determined as follows: (i) the effect of the calorimeter energy scale uncertainty is estimated by varying the energy of all PF objects not associated with the leading jets by 10%; (ii) the pT threshold for tracks is increased from 200 to 250 MeV; (iii) the correction factor C is varied by3%, i.e. by its uncertainty (as discussed in Sec.IV).
(4) The uncertainty on the correction factor Aiin Eq. (4)
is estimated by changing the MC models used to evaluate it. In addition, the relative fraction of dif-fraction is changed by20%, i.e. by the uncertainty of the scaling factors obtained in the fits discussed in Sec.VA.
(5) The sensitivity to pileup is studied by restricting the analysis to events with only one reconstructed vertex.
(6) The sensitivity to the jet reconstruction procedure is studied by repeating the analysis with jets recon-structed only with calorimetric information instead of particle-flow objects. This affects the results by 4% at most.
(7) The difference in the results obtained for the cross section as a function of ~þ and ~ is found to be less than 11% and is included in the systematic uncertainty.
(8) The uncertainty on the trigger efficiency is esti-mated from the comparison of the turn-on curves as a function of the jet pTin the minimum-bias data and the MC simulation. The resulting uncertainty is 3%.
(9) The uncertainty on the integrated luminosity is esti-mated to be 4% [38,39].
The total systematic uncertainty is calculated as the qua-dratic sum of the individual contributions. The resulting
ξ ∼ -3 10 10-2 10-1 ξ ∼ dN/d 3 10 4 10 5 10 DATA > -3) min η <3 (or max η DATA PYTHIA6 Z2 ND + POMPYT (x0.23) SD > -3) min η < 3 (or max η PYTHIA6 Z2 ND + POMPYT (x0.23) SD > 20 GeV j1,j2 T | < 4.4, p j1,j2 η , | 2 jet 1 jet → , pp -1 = 7 TeV, L=2.7 nb s CMS,
FIG. 7 (color online). Reconstructed ~ distributions with (open symbols) and without (closed symbols) the max< 3 (or min>3) condition are compared to detector-level MC predictions including diffractive dijet production (PYTHIA6Z2 þ POMPYT). The error bars indicate the statistical uncertainty; the band represents the calorimeter energy scale uncertainty. The relative diffractive dijet contribution from the MC simula-tion has been scaled by the factor 0.23. The sum of the pre-dictions of the two MC simulations is normalized to the number of events in the corresponding distributions for the data.
uncertainty of the cross section measurement is 30%, dominated by the jet energy scale. The effect of each systematic check on the cross section uncertainty is given in TableII.
VII. RESULTS
TableIIIand Fig.8present the differential cross section for dijet production as a function of ~. The data are com-pared to the predictions of nondiffractive (PYTHIA6Z2 and PYTHIA8tune 1) and diffractive (POMPYTSD,POMWIGSD, PYTHIA8SD þ DD, andPOWHEG) models. The normaliza-tion of the predicnormaliza-tions is absolute, unlike in Fig.6.
The following conclusions can be drawn from Fig.8: (i) The generators PYTHIA6 Z2 and PYTHIA8 tune 1,
without hard diffraction, cannot by themselves describe the low- ~ data, especially in the first bin, 0:0003 < ~ < 0:002.
(ii) It was noted already in Sec.VAthat the contribution of SD MC models, e.g.POMWIGandPOMPYT, is needed to describe the low- ~ data, reflecting the presence of hard diffractive events in this region. However, these MC models predict more events than are observed, by a factor of about 5 in the lowest ~ bin.
(iii) The ratio of the measured cross section to that expected from the POMPYT and POMWIG simula-tions is 0:21 0:07 in the first ~ bin, where the nondiffractive contribution is small. This ratio can
be taken as an upper limit of the rapidity gap survival probability (not simulated by the event generators considered). This is an upper limit because the measured cross section includes a con-tribution from proton-dissociative events in which the scattered proton is excited into a low mass state, which escapes undetected in the forward region; the dPDFs also include a proton-dissociative con-tribution. If the amount of proton-dissociative events in the data is assumed to be 41%, as esti-mated at particle level withPYTHIA8, and that in the dPDFs is taken to be 23% [7], then this upper limit can be turned into an estimate of the rapidity gap survival probability of 0:12 0:05.
(iv) POMPYT and POMWIG are LO MC generators. If POWHEG is used to predict the diffractive cross section at NLO in the first ~ bin andPYTHIA8tune 1 is used for hadronization, the ratio between data and predictions becomes 0:14 0:05. With the assumptions just discussed on the proton-dissociative contribution, the rapidity gap survival probability becomes 0:08 0:04.
(v) Figure 8 also shows that the normalization of the SD þ DDPYTHIA8prediction disagrees with that of POMPYTandPOMWIG, and would have to be scaled up by a factor of about 2 to match the data. This is a consequence of the different modeling of diffraction in these generators: while they all use the same H1 dPDFs, the parametrization of the pomeron flux in PYTHIA8is different—and, notably, not the one used in the H1 fit. Because of this, PYTHIA8 (version 8.135) cannot be used to extract the rapidity gap survival probability.
While the rapidity gap survival probability measured at the Tevatron [5,6] is close to that found in the present analysis, the two measurements cannot be directly com-pared because of the different kinematic regions they cover: 0:035 < < 0:095 for the CDF data and 0:0003 <
~
< 0:002 for the present CMS data. This difference is
TABLE II. Contributions to the systematic uncertainty on the dijet cross section in the three lowest ~ bins considered. The total systematic uncertainty calculated as the quadratic sum of the individual contributions is given in the last row.
Uncertainty source 0:0003 < ~ < 0:002 0:002 < ~ < 0:0045 0:0045 < ~ < 0:01
1. Jet energy scale (þ 26; 19)% (þ 21; 20)% (þ 28; 16)%
2. Jet energy resolution (þ 6; 4)% (þ 4; 3)% (þ 3; 2)%
3. PF energy, pT threshold, C (þ 7; 15)% (þ 14; 8)% (þ 12; 11)%
4. MC model uncertainty (þ 5; 3)% (þ 2; 14)% (þ 3; 1)%
5. One-vertex selection (þ 6; 0)% (þ 0; 1)% (þ 1; 0)%
6. Jet objects (Calorimeter, PF) (þ 0; 4)% (þ 0; 4)% (þ 2; 4)%
7. ~þ, ~difference 8% 8% 11%
8. Trigger efficiency 3% 3% 3%
9. Luminosity 4% 4% 4%
Total error (þ 30; 26)% (þ 27; 29)% (þ 33; 23)%
TABLE III. Differential cross section for inclusive dijet pro-duction as a function of ~ for jets with pj1;j2T > 20 GeV and jet axes in the pseudorapidity rangejj1;j2j < 4:4.
~
bin djj=d ~ (b)
0:0003 < ~ < 0:002 5:0 0:9ðstatÞþ1:51:3ðsystÞ
0:002 < ~ < 0:0045 8:2 0:9ðstatÞþ2:22:4ðsystÞ 0:0045 < ~ < 0:01 13:5 0:9ðstatÞþ4:53:1ðsystÞ
relevant because the rapidity gap survival probability depends on the parton momentum x and is expected to increase with decreasing x (and hence ): from about 0.05 at x¼ 101 to about 0.3 for x¼ 103 according to Ref. [40].
VIII. SUMMARY
The differential cross section for dijet production as a function of ~, a variable that approximates the fractional momentum loss of the proton in single-diffractive pro-cesses, has been measured with the CMS detector for events with at least two jets with pj1;j2T > 20 GeV in the pseudorapidity regionjj1;j2j < 4:4. The results are
com-pared to diffractive (POMPYT,POMWIG, andPYTHIA8SD þ DD) and nondiffractive (PYTHIA6 Z2, D6T, and PYTHIA8 tune 1) MC models. The low- ~ data show a significant contribution from diffractive dijet production, observed for the first time at the LHC. The associated rapidity gap survival probability is estimated. Leading-order diffractive generators (POMPYTand POMWIG), based on dPDFs from the HERA experiments, overestimate the measured cross section and their normalization needs to be scaled down by a factor of5. This factor can be interpreted as the effect
of the rapidity gap survival probability. The results are also compared with NLO predictions. The rapidity gap survival probability, estimated from the comparison of the cross section measured in the first bin, 0:0003 < ~ < 0:002, with LO and NLO diffractive MC models, ranges from 0:08 0:04 (NLO) to 0:12 0:05 (LO).
ACKNOWLEDGMENTS
We would like to thank M. Diehl, M. G. Ryskin, and L. Trentadue for their valuable suggestions. We congratulate our colleagues in the CERN accelerator departments for the excellent performance of the LHC machine. We thank the technical and administrative staff at CERN and other CMS institutes, and acknowledge support from BMWF and FWF (Austria); FNRS and FWO (Belgium); CNPq, CAPES, FAPERJ, and FAPESP (Brazil); MES (Bulgaria); CERN; CAS, MoST, and NSFC (China); COLCIENCIAS (Colombia); MSES (Croatia); RPF (Cyprus); MoER, SF0690030s09 and ERDF (Estonia); Academy of Finland, MEC, and HIP (Finland); CEA and CNRS/ IN2P3 (France); BMBF, DFG, and HGF (Germany); GSRT (Greece); OTKA and NKTH (Hungary); DAE and DST (India); IPM (Iran); SFI (Ireland); INFN (Italy); NRF and WCU (Korea); LAS (Lithuania); CINVESTAV, CONACYT, SEP, and UASLP-FAI (Mexico); MSI (New Zealand); PAEC (Pakistan); MSHE and NSC (Poland); FCT (Portugal); JINR (Armenia, Belarus, Georgia, Ukraine, Uzbekistan); MON, RosAtom, RAS and RFBR (Russia); MSTD (Serbia); SEIDI and CPAN (Spain); Swiss Funding Agencies (Switzerland); NSC (Taipei); ThEP, IPST and NECTEC (Thailand); TUBITAK and TAEK (Turkey); NASU (Ukraine); STFC (United Kingdom); DOE and NSF (USA). Individuals have received support from the Marie-Curie programme and the European Research Council (European Union); the Leventis Foundation; the A. P. Sloan Foundation; the Alexander von Humboldt Foundation; the Austrian Science Fund (FWF); the Belgian Federal Science Policy Office; the Fonds pour la Formation a` 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 Czech Republic; the Council of Science and Industrial Research, India; the Compagnia di San Paolo (Torino); and the HOMING PLUS programme of the Foundation for Polish Science, cofinanced by the European Union, Regional Development Fund.
ξ ∼ -3 10 -2 10 b)µ (ξ∼ /djj σ d -1 10 1 10 DATA PYTHIA6 Z2 ND PYTHIA8 tune1 ND POMPYT CTEQ6L1 & H1 Fit B POMWIG CTEQ6L1 & H1 Fit B PYTHIA8 SD+DD
POWHEG+PYTHIA8 CTEQ6M & H1 Fit B > 20 GeV j1,j2 T | < 4.4, p j1,j2 η , | 2 jet 1 jet → , pp -1 =7 TeV, L = 2.7 nb s CMS,
FIG. 8 (color online). The differential cross section for inclu-sive dijet production as a function of ~ for jets with axes in the rangejj1;j2j < 4:4 and pj1;j2
T > 20 GeV. The points are plotted at
the center of the bins. The error bars indicate the statistical uncertainty and the band represents the systematic uncertainties added in quadrature. The predictions of nondiffractive (PYTHIA6 Z2 andPYTHIA8tune 1) and diffractive (POMPYTSD,POMWIGSD andPYTHIA8SD þ DD) MC generators are also shown, along with that of the NLO calculation based onPOWHEG(first bin only). The predictions ofPOMPYTandPOMWIGin the first ~ bin are identical.
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K. H. Hoffmann,39S. Honc,39I. Katkov,39,qJ. R. Komaragiri,39P. Lobelle Pardo,39D. Martschei,39S. Mueller,39
Th. Mu¨ller,39M. Niegel,39A. Nu¨rnberg,39O. Oberst,39A. Oehler,39J. Ott,39G. Quast,39K. Rabbertz,39F. Ratnikov,39 N. Ratnikova,39S. Ro¨cker,39A. Scheurer,39F.-P. Schilling,39G. Schott,39H. J. Simonis,39F. M. Stober,39 D. Troendle,39R. Ulrich,39J. Wagner-Kuhr,39S. Wayand,39T. Weiler,39M. Zeise,39G. Daskalakis,40T. Geralis,40
S. Kesisoglou,40A. Kyriakis,40D. Loukas,40I. Manolakos,40A. Markou,40C. Markou,40C. Mavrommatis,40 E. Ntomari,40L. Gouskos,41T. J. Mertzimekis,41A. Panagiotou,41N. Saoulidou,41I. Evangelou,42C. Foudas,42,f P. Kokkas,42N. Manthos,42I. Papadopoulos,42V. Patras,42G. Bencze,43C. Hajdu,43,fP. Hidas,43D. Horvath,43,s F. Sikler,43V. Veszpremi,43G. Vesztergombi,43,tN. Beni,44S. Czellar,44J. Molnar,44J. Palinkas,44Z. Szillasi,44 J. Karancsi,45P. Raics,45Z. L. Trocsanyi,45B. Ujvari,45M. Bansal,46S. B. Beri,46V. Bhatnagar,46N. Dhingra,46 R. Gupta,46M. Kaur,46M. Z. Mehta,46N. Nishu,46L. K. Saini,46A. Sharma,46J. B. Singh,46Ashok Kumar,47
Arun Kumar,47S. Ahuja,47A. Bhardwaj,47B. C. Choudhary,47S. Malhotra,47M. Naimuddin,47K. Ranjan,47 V. Sharma,47R. K. Shivpuri,47S. Banerjee,48S. Bhattacharya,48S. Dutta,48B. Gomber,48Sa. Jain,48Sh. Jain,48 R. Khurana,48S. Sarkar,48M. Sharan,48A. Abdulsalam,49R. K. Choudhury,49D. Dutta,49S. Kailas,49V. Kumar,49
P. Mehta,49A. K. Mohanty,49,fL. M. Pant,49P. Shukla,49T. Aziz,50S. Ganguly,50M. Guchait,50,uM. Maity,50,v G. Majumder,50K. Mazumdar,50G. B. Mohanty,50B. Parida,50K. Sudhakar,50N. Wickramage,50S. Banerjee,51
S. Dugad,51H. Arfaei,52H. Bakhshiansohi,52,wS. M. Etesami,52,xA. Fahim,52,wM. Hashemi,52H. Hesari,52 A. Jafari,52,wM. Khakzad,52M. Mohammadi Najafabadi,52S. Paktinat Mehdiabadi,52B. Safarzadeh,52,y M. Zeinali,52,xM. Abbrescia,53a,53bL. Barbone,53a,53bC. Calabria,53a,53b,fS. S. Chhibra,53a,53bA. Colaleo,53a
D. Creanza,53a,53cN. De Filippis,53a,53c,fM. De Palma,53a,53bL. Fiore,53aG. Iaselli,53a,53cL. Lusito,53a,53b G. Maggi,53a,53cM. Maggi,53aB. Marangelli,53a,53bS. My,53a,53cS. Nuzzo,53a,53bN. Pacifico,53a,53bA. Pompili,53a,53b
G. Pugliese,53a,53cG. Selvaggi,53a,53bL. Silvestris,53aG. Singh,53a,53bR. Venditti,53aG. Zito,53aG. Abbiendi,54a A. C. Benvenuti,54aD. Bonacorsi,54a,54bS. Braibant-Giacomelli,54a,54bL. Brigliadori,54a,54bP. Capiluppi,54a,54b
A. Castro,54a,54bF. R. Cavallo,54aM. Cuffiani,54a,54bG. M. Dallavalle,54aF. Fabbri,54aA. Fanfani,54a,54b D. Fasanella,54a,54b,fP. Giacomelli,54aC. Grandi,54aL. Guiducci,54a,54bS. Marcellini,54aG. Masetti,54a M. Meneghelli,54a,54b,fA. Montanari,54aF. L. Navarria,54a,54bF. Odorici,54aA. Perrotta,54aF. Primavera,54a,54b A. M. Rossi,54a,54bT. Rovelli,54a,54bG. P. Siroli,54a,54bR. Travaglini,54a,54bS. Albergo,55a,55bG. Cappello,55a,55b
M. Chiorboli,55a,55bS. Costa,55a,55bR. Potenza,55a,55bA. Tricomi,55a,55bC. Tuve,55a,55bG. Barbagli,56a V. Ciulli,56a,56bC. Civinini,56aR. D’Alessandro,56a,56bE. Focardi,56a,56bS. Frosali,56a,56bE. Gallo,56aS. Gonzi,56a,56b
M. Meschini,56aS. Paoletti,56aG. Sguazzoni,56aA. Tropiano,56a,fL. Benussi,57S. Bianco,57S. Colafranceschi,57,z F. Fabbri,57D. Piccolo,57P. Fabbricatore,58aR. Musenich,58aS. Tosi,58a,58bA. Benaglia,59a,59b,fF. De Guio,59a,59b
L. Di Matteo,59a,59b,fS. Fiorendi,59a,59bS. Gennai,59a,fA. Ghezzi,59a,59bS. Malvezzi,59aR. A. Manzoni,59a,59b A. Martelli,59a,59bA. Massironi,59a,59b,fD. Menasce,59aL. Moroni,59aM. Paganoni,59a,59bD. Pedrini,59a S. Ragazzi,59a,59bN. Redaelli,59aS. Sala,59aT. Tabarelli de Fatis,59a,59bS. Buontempo,60aC. A. Carrillo Montoya,60a
N. Cavallo,60a,aaA. De Cosa,60a,60b,fO. Dogangun,60a,60bF. Fabozzi,60a,aaA. O. M. Iorio,60aL. Lista,60a S. Meola,60a,bbM. Merola,60a,60bP. Paolucci,60a,fP. Azzi,61aN. Bacchetta,61a,fP. Bellan,61a,61bD. Bisello,61a,61b
A. Branca,61a,fR. Carlin,61a,61bP. Checchia,61aT. Dorigo,61aF. Gasparini,61a,61bA. Gozzelino,61a
K. Kanishchev,61a,61cS. Lacaprara,61aI. Lazzizzera,61a,61cM. Margoni,61a,61bA. T. Meneguzzo,61a,61bJ. Pazzini,61a N. Pozzobon,61a,61bP. Ronchese,61a,61bF. Simonetto,61a,61bE. Torassa,61aM. Tosi,61a,61bA. Triossi,61a S. Vanini,61a,61bP. Zotto,61a,61bG. Zumerle,61a,61bM. Gabusi,62a,62bS. P. Ratti,62a,62bC. Riccardi,62a,62b P. Torre,62a,62bP. Vitulo,62a,62bM. Biasini,63a,63bG. M. Bilei,63aL. Fano`,63a,63bP. Lariccia,63a,63bA. Lucaroni,63a,63b,f
G. Mantovani,63a,63bM. Menichelli,63aA. Nappi,63a,63bF. Romeo,63a,63bA. Saha,63aA. Santocchia,63a,63b A. Spiezia,63a,63bS. Taroni,63a,63b,fP. Azzurri,64a,64cG. Bagliesi,64aT. Boccali,64aG. Broccolo,64a,64cR. Castaldi,64a
R. T. D’Agnolo,64a,64cR. Dell’Orso,64aF. Fiori,64a,64b,fL. Foa`,64a,64cA. Giassi,64aA. Kraan,64aF. Ligabue,64a,64c T. Lomtadze,64aL. Martini,64a,ccA. Messineo,64a,64bF. Palla,64aA. Rizzi,64a,64bA. T. Serban,64a,ddP. Spagnolo,64a P. Squillacioti,64a,fR. Tenchini,64aG. Tonelli,64a,64b,fA. Venturi,64a,fP. G. Verdini,64aL. Barone,65a,65bF. Cavallari,65a D. Del Re,65a,65b,fM. Diemoz,65aC. Fanelli,65aM. Grassi,65a,65b,fE. Longo,65a,65bP. Meridiani,65a,fF. Micheli,65a,65b
S. Nourbakhsh,65a,65bG. Organtini,65a,65bR. Paramatti,65aS. Rahatlou,65a,65bM. Sigamani,65aL. Soffi,65a,65b N. Amapane,66a,66bR. Arcidiacono,66a,66cS. Argiro,66a,66bM. Arneodo,66a,66cC. Biino,66aN. Cartiglia,66a M. Costa,66a,66bN. Demaria,66aC. Mariotti,66a,fS. Maselli,66aE. Migliore,66a,66bV. Monaco,66a,66bM. Musich,66a,f
M. M. Obertino,66a,66cN. Pastrone,66aM. Pelliccioni,66aA. Potenza,66a,66bA. Romero,66a,66bM. Ruspa,66a,66c R. Sacchi,66a,66bA. Solano,66a,66bA. Staiano,66aA. Vilela Pereira,66aS. Belforte,67aV. Candelise,67a,67bF. Cossutti,67a
S. G. Heo,68T. Y. Kim,68S. K. Nam,68S. Chang,69D. H. Kim,69G. N. Kim,69D. J. Kong,69H. Park,69S. R. Ro,69 D. C. Son,69T. Son,69J. Y. Kim,70Zero J. Kim,70S. Song,70S. Choi,71D. Gyun,71B. Hong,71M. Jo,71H. Kim,71 T. J. Kim,71K. S. Lee,71D. H. Moon,71S. K. Park,71M. Choi,72J. H. Kim,72C. Park,72I. C. Park,72S. Park,72 G. Ryu,72Y. Cho,73Y. Choi,73Y. K. Choi,73J. Goh,73M. S. Kim,73E. Kwon,73B. Lee,73J. Lee,73S. Lee,73H. Seo,73
I. Yu,73M. J. Bilinskas,74I. Grigelionis,74M. Janulis,74A. Juodagalvis,74H. Castilla-Valdez,75
E. De La Cruz-Burelo,75I. Heredia-de La Cruz,75R. Lopez-Fernandez,75R. Magan˜a Villalba,75J. Martı´nez-Ortega,75 A. Sa´nchez-Herna´ndez,75L. M. Villasenor-Cendejas,75S. Carrillo Moreno,76F. Vazquez Valencia,76 H. A. Salazar Ibarguen,77E. Casimiro Linares,78A. Morelos Pineda,78M. A. Reyes-Santos,78D. Krofcheck,79
A. J. Bell,80P. H. Butler,80R. Doesburg,80S. Reucroft,80H. Silverwood,80M. Ahmad,81M. H. Ansari,81 M. I. Asghar,81H. R. Hoorani,81S. Khalid,81W. A. Khan,81T. Khurshid,81S. Qazi,81M. A. Shah,81M. Shoaib,81
H. Bialkowska,82B. Boimska,82T. Frueboes,82R. Gokieli,82M. Go´rski,82M. Kazana,82K. Nawrocki,82 K. Romanowska-Rybinska,82M. Szleper,82G. Wrochna,82P. Zalewski,82G. Brona,83K. Bunkowski,83M. Cwiok,83
W. Dominik,83K. Doroba,83A. Kalinowski,83M. Konecki,83J. Krolikowski,83N. Almeida,84P. Bargassa,84 A. David,84P. Faccioli,84P. G. Ferreira Parracho,84M. Gallinaro,84J. Seixas,84J. Varela,84P. Vischia,84 I. Belotelov,85P. Bunin,85M. Gavrilenko,85I. Golutvin,85I. Gorbunov,85A. Kamenev,85V. Karjavin,85G. Kozlov,85 A. Lanev,85A. Malakhov,85P. Moisenz,85V. Palichik,85V. Perelygin,85S. Shmatov,85V. Smirnov,85A. Volodko,85 A. Zarubin,85S. Evstyukhin,86V. Golovtsov,86Y. Ivanov,86V. Kim,86P. Levchenko,86V. Murzin,86V. Oreshkin,86 I. Smirnov,86V. Sulimov,86L. Uvarov,86S. Vavilov,86A. Vorobyev,86An. Vorobyev,86Yu. Andreev,87A. Dermenev,87
S. Gninenko,87N. Golubev,87M. Kirsanov,87N. Krasnikov,87V. Matveev,87A. Pashenkov,87D. Tlisov,87 A. Toropin,87V. Epshteyn,88M. Erofeeva,88V. Gavrilov,88M. Kossov,88,fN. Lychkovskaya,88V. Popov,88 G. Safronov,88S. Semenov,88V. Stolin,88E. Vlasov,88A. Zhokin,88A. Belyaev,89E. Boos,89M. Dubinin,89,e A. Ershov,89A. Gribushin,89L. Khein,89V. Klyukhin,89O. Kodolova,89I. Lokhtin,89A. Markina,89S. Obraztsov,89
M. Perfilov,89S. Petrushanko,89A. Popov,89A. Proskuryakov,89L. Sarycheva,89,aV. Savrin,89V. Andreev,90 M. Azarkin,90I. Dremin,90M. Kirakosyan,90A. Leonidov,90G. Mesyats,90S. V. Rusakov,90A. Vinogradov,90
I. Azhgirey,91I. Bayshev,91S. Bitioukov,91V. Grishin,91,fV. Kachanov,91D. Konstantinov,91A. Korablev,91 V. Krychkine,91V. Petrov,91R. Ryutin,91A. Sobol,91L. Tourtchanovitch,91S. Troshin,91N. Tyurin,91A. Uzunian,91
A. Volkov,91P. Adzic,92,eeM. Djordjevic,92M. Ekmedzic,92D. Krpic,92,eeJ. Milosevic,92M. Aguilar-Benitez,93 J. Alcaraz Maestre,93P. Arce,93C. Battilana,93E. Calvo,93M. Cerrada,93M. Chamizo Llatas,93N. Colino,93 B. De La Cruz,93A. Delgado Peris,93D. Domı´nguez Va´zquez,93C. Fernandez Bedoya,93J. P. Ferna´ndez Ramos,93 A. Ferrando,93J. Flix,93M. C. Fouz,93P. Garcia-Abia,93O. Gonzalez Lopez,93S. Goy Lopez,93J. M. Hernandez,93 M. I. Josa,93G. Merino,93J. Puerta Pelayo,93A. Quintario Olmeda,93I. Redondo,93L. Romero,93J. Santaolalla,93
M. S. Soares,93C. Willmott,93C. Albajar,94G. Codispoti,94J. F. de Troco´niz,94H. Brun,95J. Cuevas,95 J. Fernandez Menendez,95S. Folgueras,95I. Gonzalez Caballero,95L. Lloret Iglesias,95J. Piedra Gomez,95 J. A. Brochero Cifuentes,96I. J. Cabrillo,96A. Calderon,96S. H. Chuang,96J. Duarte Campderros,96M. Felcini,96,ff
M. Fernandez,96G. Gomez,96J. Gonzalez Sanchez,96A. Graziano,96C. Jorda,96A. Lopez Virto,96J. Marco,96 R. Marco,96C. Martinez Rivero,96F. Matorras,96F. J. Munoz Sanchez,96T. Rodrigo,96A. Y. Rodrı´guez-Marrero,96
A. Ruiz-Jimeno,96L. Scodellaro,96M. Sobron Sanudo,96I. Vila,96R. Vilar Cortabitarte,96D. Abbaneo,97 E. Auffray,97G. Auzinger,97P. Baillon,97A. H. Ball,97D. Barney,97J. F. Benitez,97C. Bernet,97,gG. Bianchi,97 P. Bloch,97A. Bocci,97A. Bonato,97C. Botta,97H. Breuker,97T. Camporesi,97G. Cerminara,97T. Christiansen,97
J. A. Coarasa Perez,97D. D’Enterria,97A. Dabrowski,97A. De Roeck,97S. Di Guida,97M. Dobson,97 N. Dupont-Sagorin,97A. Elliott-Peisert,97B. Frisch,97W. Funk,97G. Georgiou,97M. Giffels,97D. Gigi,97K. Gill,97
D. Giordano,97M. Giunta,97F. Glege,97R. Gomez-Reino Garrido,97P. Govoni,97S. Gowdy,97R. Guida,97 M. Hansen,97P. Harris,97C. Hartl,97J. Harvey,97B. Hegner,97A. Hinzmann,97V. Innocente,97P. Janot,97 K. Kaadze,97E. Karavakis,97K. Kousouris,97P. Lecoq,97Y.-J. Lee,97P. Lenzi,97C. Lourenc¸o,97T. Ma¨ki,97 M. Malberti,97L. Malgeri,97M. Mannelli,97L. Masetti,97F. Meijers,97S. Mersi,97E. Meschi,97R. Moser,97 M. U. Mozer,97M. Mulders,97P. Musella,97E. Nesvold,97T. Orimoto,97L. Orsini,97E. Palencia Cortezon,97
E. Perez,97L. Perrozzi,97A. Petrilli,97A. Pfeiffer,97M. Pierini,97M. Pimia¨,97D. Piparo,97G. Polese,97 L. Quertenmont,97A. Racz,97W. Reece,97J. Rodrigues Antunes,97G. Rolandi,97,ggC. Rovelli,97,hhM. Rovere,97 H. Sakulin,97F. Santanastasio,97C. Scha¨fer,97C. Schwick,97I. Segoni,97S. Sekmen,97A. Sharma,97P. Siegrist,97
P. Silva,97M. Simon,97P. Sphicas,97,iiD. Spiga,97A. Tsirou,97G. I. Veres,97,tJ. R. Vlimant,97H. K. Wo¨hri,97 S. D. Worm,97,jjW. D. Zeuner,97W. Bertl,98K. Deiters,98W. Erdmann,98K. Gabathuler,98R. Horisberger,98
Q. Ingram,98H. C. Kaestli,98S. Ko¨nig,98D. Kotlinski,98U. Langenegger,98F. Meier,98D. Renker,98T. Rohe,98 J. Sibille,98,kkL. Ba¨ni,99P. Bortignon,99M. A. Buchmann,99B. Casal,99N. Chanon,99A. Deisher,99G. Dissertori,99
M. Dittmar,99M. Donega`,99M. Du¨nser,99J. Eugster,99K. Freudenreich,99C. Grab,99D. Hits,99P. Lecomte,99 W. Lustermann,99A. C. Marini,99P. Martinez Ruiz del Arbol,99N. Mohr,99F. Moortgat,99C. Na¨geli,99,llP. Nef,99
F. Nessi-Tedaldi,99F. Pandolfi,99L. Pape,99F. Pauss,99M. Peruzzi,99F. J. Ronga,99M. Rossini,99L. Sala,99 A. K. Sanchez,99A. Starodumov,99,mmB. Stieger,99M. Takahashi,99L. Tauscher,99,aA. Thea,99K. Theofilatos,99 D. Treille,99C. Urscheler,99R. Wallny,99H. A. Weber,99L. Wehrli,99C. Amsler,100V. Chiochia,100S. De Visscher,100 C. Favaro,100M. Ivova Rikova,100B. Millan Mejias,100P. Otiougova,100P. Robmann,100H. Snoek,100S. Tupputi,100
M. Verzetti,100Y. H. Chang,101K. H. Chen,101C. M. Kuo,101S. W. Li,101W. Lin,101Z. K. Liu,101Y. J. Lu,101 D. Mekterovic,101A. P. Singh,101R. Volpe,101S. S. Yu,101P. Bartalini,102P. Chang,102Y. H. Chang,102 Y. W. Chang,102Y. Chao,102K. F. Chen,102C. Dietz,102U. Grundler,102W.-S. Hou,102Y. Hsiung,102K. Y. Kao,102
Y. J. Lei,102R.-S. Lu,102D. Majumder,102E. Petrakou,102X. Shi,102J. G. Shiu,102Y. M. Tzeng,102X. Wan,102 M. Wang,102A. Adiguzel,103M. N. Bakirci,103,nnS. Cerci,103,ooC. Dozen,103I. Dumanoglu,103E. Eskut,103
S. Girgis,103G. Gokbulut,103E. Gurpinar,103I. Hos,103E. E. Kangal,103T. Karaman,103G. Karapinar,103,pp A. Kayis Topaksu,103G. Onengut,103K. Ozdemir,103S. Ozturk,103,qqA. Polatoz,103K. Sogut,103,rr D. Sunar Cerci,103,ooB. Tali,103,ooH. Topakli,103,nnL. N. Vergili,103M. Vergili,103I. V. Akin,104T. Aliev,104 B. Bilin,104S. Bilmis,104M. Deniz,104H. Gamsizkan,104A. M. Guler,104K. Ocalan,104A. Ozpineci,104M. Serin,104 R. Sever,104U. E. Surat,104M. Yalvac,104E. Yildirim,104M. Zeyrek,104E. Gu¨lmez,105B. Isildak,105,ssM. Kaya,105,tt O. Kaya,105,ttS. Ozkorucuklu,105,uuN. Sonmez,105,vvK. Cankocak,106L. Levchuk,107F. Bostock,108J. J. Brooke,108
E. Clement,108D. Cussans,108H. Flacher,108R. Frazier,108J. Goldstein,108M. Grimes,108G. P. Heath,108 H. F. Heath,108L. Kreczko,108S. Metson,108D. M. Newbold,108,jjK. Nirunpong,108A. Poll,108S. Senkin,108 V. J. Smith,108T. Williams,108L. Basso,109,wwK. W. Bell,109A. Belyaev,109,wwC. Brew,109R. M. Brown,109 D. J. A. Cockerill,109J. A. Coughlan,109K. Harder,109S. Harper,109J. Jackson,109B. W. Kennedy,109E. Olaiya,109
D. Petyt,109B. C. Radburn-Smith,109C. H. Shepherd-Themistocleous,109I. R. Tomalin,109W. J. Womersley,109 R. Bainbridge,110G. Ball,110R. Beuselinck,110O. Buchmuller,110D. Colling,110N. Cripps,110M. Cutajar,110
P. Dauncey,110G. Davies,110M. Della Negra,110W. Ferguson,110J. Fulcher,110D. Futyan,110A. Gilbert,110 A. Guneratne Bryer,110G. Hall,110Z. Hatherell,110J. Hays,110G. Iles,110M. Jarvis,110G. Karapostoli,110L. Lyons,110 A.-M. Magnan,110J. Marrouche,110B. Mathias,110R. Nandi,110J. Nash,110A. Nikitenko,110,mmA. Papageorgiou,110
J. Pela,110,fM. Pesaresi,110K. Petridis,110M. Pioppi,110,xxD. M. Raymond,110S. Rogerson,110A. Rose,110 M. J. Ryan,110C. Seez,110P. Sharp,110,aA. Sparrow,110M. Stoye,110A. Tapper,110M. Vazquez Acosta,110 T. Virdee,110S. Wakefield,110N. Wardle,110T. Whyntie,110M. Chadwick,111J. E. Cole,111P. R. Hobson,111 A. Khan,111P. Kyberd,111D. Leggat,111D. Leslie,111W. Martin,111I. D. Reid,111P. Symonds,111L. Teodorescu,111
M. Turner,111K. Hatakeyama,112H. Liu,112T. Scarborough,112O. Charaf,113C. Henderson,113P. Rumerio,113 A. Avetisyan,114T. Bose,114C. Fantasia,114A. Heister,114J. St. John,114P. Lawson,114D. Lazic,114J. Rohlf,114
D. Sperka,114L. Sulak,114J. Alimena,115S. Bhattacharya,115D. Cutts,115A. Ferapontov,115U. Heintz,115 S. Jabeen,115G. Kukartsev,115E. Laird,115G. Landsberg,115M. Luk,115M. Narain,115D. Nguyen,115M. Segala,115 T. Sinthuprasith,115T. Speer,115K. V. Tsang,115R. Breedon,116G. Breto,116M. Calderon De La Barca Sanchez,116 S. Chauhan,116M. Chertok,116J. Conway,116R. Conway,116P. T. Cox,116J. Dolen,116R. Erbacher,116M. Gardner,116 R. Houtz,116W. Ko,116A. Kopecky,116R. Lander,116T. Miceli,116D. Pellett,116F. Ricci-tam,116B. Rutherford,116
M. Searle,116J. Smith,116M. Squires,116M. Tripathi,116R. Vasquez Sierra,116V. Andreev,117D. Cline,117 R. Cousins,117J. Duris,117S. Erhan,117P. Everaerts,117C. Farrell,117J. Hauser,117M. Ignatenko,117C. Jarvis,117
C. Plager,117G. Rakness,117P. Schlein,117,aP. Traczyk,117V. Valuev,117M. Weber,117J. Babb,118R. Clare,118 M. E. Dinardo,118J. Ellison,118J. W. Gary,118F. Giordano,118G. Hanson,118G. Y. Jeng,118,yyH. Liu,118 O. R. Long,118A. Luthra,118H. Nguyen,118S. Paramesvaran,118J. Sturdy,118S. Sumowidagdo,118R. Wilken,118
S. Wimpenny,118W. Andrews,119J. G. Branson,119G. B. Cerati,119S. Cittolin,119D. Evans,119F. Golf,119 A. Holzner,119R. Kelley,119M. Lebourgeois,119J. Letts,119I. Macneill,119B. Mangano,119S. Padhi,119C. Palmer,119
G. Petrucciani,119M. Pieri,119M. Sani,119V. Sharma,119S. Simon,119E. Sudano,119M. Tadel,119Y. Tu,119 A. Vartak,119S. Wasserbaech,119,zzF. Wu¨rthwein,119A. Yagil,119J. Yoo,119D. Barge,120R. Bellan,120 C. Campagnari,120M. D’Alfonso,120T. Danielson,120K. Flowers,120P. Geffert,120J. Incandela,120C. Justus,120
P. Kalavase,120S. A. Koay,120D. Kovalskyi,120V. Krutelyov,120S. Lowette,120N. Mccoll,120V. Pavlunin,120 F. Rebassoo,120J. Ribnik,120J. Richman,120R. Rossin,120D. Stuart,120W. To,120C. West,120A. Apresyan,121
A. Bornheim,121Y. Chen,121E. Di Marco,121J. Duarte,121M. Gataullin,121Y. Ma,121A. Mott,121H. B. Newman,121 C. Rogan,121M. Spiropulu,121,eV. Timciuc,121J. Veverka,121R. Wilkinson,121Y. Yang,121R. Y. Zhu,121B. Akgun,122 V. Azzolini,122R. Carroll,122T. Ferguson,122Y. Iiyama,122D. W. Jang,122Y. F. Liu,122M. Paulini,122H. Vogel,122
I. Vorobiev,122J. P. Cumalat,123B. R. Drell,123C. J. Edelmaier,123W. T. Ford,123A. Gaz,123B. Heyburn,123 E. Luiggi Lopez,123J. G. Smith,123K. Stenson,123K. A. Ulmer,123S. R. Wagner,123J. Alexander,124A. Chatterjee,124
N. Eggert,124L. K. Gibbons,124B. Heltsley,124A. Khukhunaishvili,124B. Kreis,124N. Mirman,124 G. Nicolas Kaufman,124J. R. Patterson,124A. Ryd,124E. Salvati,124W. Sun,124W. D. Teo,124J. Thom,124 J. Thompson,124J. Tucker,124J. Vaughan,124Y. Weng,124L. Winstrom,124P. Wittich,124D. Winn,125S. Abdullin,126
M. Albrow,126J. Anderson,126L. A. T. Bauerdick,126A. Beretvas,126J. Berryhill,126P. C. Bhat,126I. Bloch,126 K. Burkett,126J. N. Butler,126V. Chetluru,126H. W. K. Cheung,126F. Chlebana,126V. D. Elvira,126I. Fisk,126
J. Freeman,126Y. Gao,126D. Green,126O. Gutsche,126J. Hanlon,126R. M. Harris,126J. Hirschauer,126 B. Hooberman,126S. Jindariani,126M. Johnson,126U. Joshi,126B. Kilminster,126B. Klima,126S. Kunori,126 S. Kwan,126C. Leonidopoulos,126J. Linacre,126D. Lincoln,126R. Lipton,126J. Lykken,126K. Maeshima,126 J. M. Marraffino,126S. Maruyama,126D. Mason,126P. McBride,126K. Mishra,126S. Mrenna,126Y. Musienko,126,aaa
C. Newman-Holmes,126V. O’Dell,126O. Prokofyev,126E. Sexton-Kennedy,126S. Sharma,126W. J. Spalding,126 L. Spiegel,126P. Tan,126L. Taylor,126S. Tkaczyk,126N. V. Tran,126L. Uplegger,126E. W. Vaandering,126R. Vidal,126
J. Whitmore,126W. Wu,126F. Yang,126F. Yumiceva,126J. C. Yun,126D. Acosta,127P. Avery,127D. Bourilkov,127 M. Chen,127T. Cheng,127S. Das,127M. De Gruttola,127G. P. Di Giovanni,127D. Dobur,127A. Drozdetskiy,127
R. D. Field,127M. Fisher,127Y. Fu,127I. K. Furic,127J. Gartner,127J. Hugon,127B. Kim,127J. Konigsberg,127 A. Korytov,127A. Kropivnitskaya,127T. Kypreos,127J. F. Low,127K. Matchev,127P. Milenovic,127,bbb G. Mitselmakher,127L. Muniz,127R. Remington,127A. Rinkevicius,127P. Sellers,127N. Skhirtladze,127 M. Snowball,127J. Yelton,127M. Zakaria,127V. Gaultney,128S. Hewamanage,128L. M. Lebolo,128S. Linn,128
P. Markowitz,128G. Martinez,128J. L. Rodriguez,128T. Adams,129A. Askew,129J. Bochenek,129J. Chen,129 B. Diamond,129S. V. Gleyzer,129J. Haas,129S. Hagopian,129V. Hagopian,129M. Jenkins,129K. F. Johnson,129
H. Prosper,129V. Veeraraghavan,129M. Weinberg,129M. M. Baarmand,130B. Dorney,130M. Hohlmann,130 H. Kalakhety,130I. Vodopiyanov,130M. R. Adams,131I. M. Anghel,131L. Apanasevich,131Y. Bai,131V. E. Bazterra,131
R. R. Betts,131I. Bucinskaite,131J. Callner,131R. Cavanaugh,131C. Dragoiu,131O. Evdokimov,131L. Gauthier,131 C. E. Gerber,131D. J. Hofman,131S. Khalatyan,131F. Lacroix,131M. Malek,131C. O’Brien,131C. Silkworth,131 D. Strom,131N. Varelas,131U. Akgun,132E. A. Albayrak,132B. Bilki,132,cccW. Clarida,132F. Duru,132S. Griffiths,132
J.-P. Merlo,132H. Mermerkaya,132,dddA. Mestvirishvili,132A. Moeller,132J. Nachtman,132C. R. Newsom,132 E. Norbeck,132Y. Onel,132F. Ozok,132S. Sen,132E. Tiras,132J. Wetzel,132T. Yetkin,132K. Yi,132B. A. Barnett,133
B. Blumenfeld,133S. Bolognesi,133D. Fehling,133G. Giurgiu,133A. V. Gritsan,133Z. J. Guo,133G. Hu,133 P. Maksimovic,133S. Rappoccio,133M. Swartz,133A. Whitbeck,133P. Baringer,134A. Bean,134G. Benelli,134
O. Grachov,134R. P. Kenny Iii,134M. Murray,134D. Noonan,134S. Sanders,134R. Stringer,134G. Tinti,134 J. S. Wood,134V. Zhukova,134A. F. Barfuss,135T. Bolton,135I. Chakaberia,135A. Ivanov,135S. Khalil,135 M. Makouski,135Y. Maravin,135S. Shrestha,135I. Svintradze,135J. Gronberg,136D. Lange,136D. Wright,136 A. Baden,137M. Boutemeur,137B. Calvert,137S. C. Eno,137J. A. Gomez,137N. J. Hadley,137R. G. Kellogg,137 M. Kirn,137T. Kolberg,137Y. Lu,137M. Marionneau,137A. C. Mignerey,137K. Pedro,137A. Peterman,137A. Skuja,137 J. Temple,137M. B. Tonjes,137S. C. Tonwar,137E. Twedt,137A. Apyan,138G. Bauer,138J. Bendavid,138W. Busza,138 E. Butz,138I. A. Cali,138M. Chan,138V. Dutta,138G. Gomez Ceballos,138M. Goncharov,138K. A. Hahn,138Y. Kim,138 M. Klute,138K. Krajczar,138,eeeW. Li,138P. D. Luckey,138T. Ma,138S. Nahn,138C. Paus,138D. Ralph,138C. Roland,138
G. Roland,138M. Rudolph,138G. S. F. Stephans,138F. Sto¨ckli,138K. Sumorok,138K. Sung,138D. Velicanu,138 E. A. Wenger,138R. Wolf,138B. Wyslouch,138S. Xie,138M. Yang,138Y. Yilmaz,138A. S. Yoon,138M. Zanetti,138
S. I. Cooper,139B. Dahmes,139A. De Benedetti,139G. Franzoni,139A. Gude,139S. C. Kao,139K. Klapoetke,139 Y. Kubota,139J. Mans,139N. Pastika,139R. Rusack,139M. Sasseville,139A. Singovsky,139N. Tambe,139 J. Turkewitz,139L. M. Cremaldi,140R. Kroeger,140L. Perera,140R. Rahmat,140D. A. Sanders,140E. Avdeeva,141 K. Bloom,141S. Bose,141J. Butt,141D. R. Claes,141A. Dominguez,141M. Eads,141J. Keller,141I. Kravchenko,141
J. Lazo-Flores,141H. Malbouisson,141S. Malik,141G. R. Snow,141U. Baur,142A. Godshalk,142I. Iashvili,142 S. Jain,142A. Kharchilava,142A. Kumar,142S. P. Shipkowski,142K. Smith,142G. Alverson,143E. Barberis,143 D. Baumgartel,143M. Chasco,143J. Haley,143D. Nash,143D. Trocino,143D. Wood,143J. Zhang,143A. Anastassov,144
A. Kubik,144N. Mucia,144N. Odell,144R. A. Ofierzynski,144B. Pollack,144A. Pozdnyakov,144M. Schmitt,144
