Search for new phenomena in dijet events using 37 fb(-1) of pp collision data collected at root s=13 TeV with the ATLAS detector

Search for new phenomena in dijet events using 37 fb−1of pp collision data collected at ffiffi s p=13 TeV with the ATLAS detector M. Aaboud et al.* (ATLAS Collaboration) (Received 28 March 2017; published 28 September 2017) Dijet events are studied in the proton-proton collision data set recorded at ffiffiffi s p¼13 TeV with the ATLAS detector at the Large Hadron Collider in 2015 and 2016, corresponding to integrated luminosities of 3.5fb−1and 33.5fb−1respectively. Invariant mass and angular distributions are compared to background predictions and no significant deviation is observed. For resonance searches, a new method for fitting the background component of the invariant mass distribution is employed. The data set is then used to set upper limits at a 95% confidence level on a range of new physics scenarios. Excited quarks with masses below 6.0 TeV are excluded, and limits are set on quantum black holes, heavy W0bosons, W bosons, and a range of masses and couplings in a Z0dark matter mediator model. Model-independent limits on signals with a Gaussian shape are also set, using a new approach allowing factorization of physics and detector effects. From the angular distributions, a scale of new physics in contact interaction models is excluded for scenarios with either constructive or destructive interference. These results represent a substantial improvement over those obtained previously with lower integrated luminosity. DOI: 10.1103/PhysRevD.96.052004 I. INTRODUCTION The Large Hadron Collider (LHC) [1] at CERN has been colliding protons at a center-of-mass energy of ffiffiffi s p¼ 13 TeV since 2015. With the completion of the 2016 physics run, the total integrated luminosity of run-2 data at 13 TeV now exceeds that of the total run-1 data set by more than 10 fb−1. When combined with the increase in parton luminosity [2] at high energy scales, due to the raising of the center-of-mass energy from 8 to 13 TeV, this very large data set provides an exceptional opportunity to search for new phenomena. New particles directly produced in proton-proton (pp) collisions must interact with the constituent partons of the proton and, consequently, can produce partons when they decay. Such partonic final states dominate in many models of new phenomena beyond the Standard Model (BSM) which are accessible at the LHC. The partons shower and hadronize, creating collimated jets of particles carrying approximately the four-momenta of the partons. The production rates for BSM signals decaying to two-jet (dijet) final states can be large, allowing such signals to be probed through searches for anomalous dijet production at masses constituting significant fractions of the total hadron collision energy. In the Standard Model (SM), hadronic collision production of jet pairs primarily results from 2→2parton scattering processes via strong interactions described by quantum chromodynamics (QCD). Particles emerge from these collisions as jets with high transverse momentum (pT) with respect to the incoming partons. A smooth and monotonically decreasing distribution for the dijet invariant mass, mjj, is predicted by QCD [3]. The presence of a new resonant state decaying to two jets may introduce an excess in this distribution, localized near the mass of this resonance. Furthermore, in QCD most dijet production occurs in the forward direction at small angles θ, defined as the polar angle with respect to the direction of the initial partons in the dijet center-of-mass frame,1due to t-channel poles in the cross sections for the dominant scattering processes. Many theories of BSM physics predict additional dijet production with a more isotropic signature, and thus a significant population of jets produced at large θ [3,4]. The search reported in this paper exploits these generic features of BSM signals in an analysis of the dijet mass and angular distributions. Following a modelnonspecific search for deviations from the SM in both types of distributions, limits are set on the masses of excited quarks, quantum black holes, W0and Z0bosons, and excited chiral Wbosons, on contact interactions scales, and on generic Gaussian-shaped signal production. Results from prior investigations of dijet distributions with lower-energy hadron collisions at the S¯ ppS[5–7], the Tevatron [8,9], and the LHC at ffiffiffi s p¼7–8TeV [10–21] *Full author list given at the end of the article. Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI. 1Since, experimentally, the two partons cannot be distinguished, θis always taken between 0 and π=2. PHYSICAL REVIEW D 96, 052004 (2017) 2470-0010=2017=96(5)=052004(26) 052004-1 © 2017 CERN, for the ATLAS Collaboration were found to be in agreement with QCD predictions. Recent searches at 13 TeV [22–24] included extensions of the analysis to di-b-jet final states [25] and to lower masses [24,26], and observed no significant deviations from the Standard Model. This paper presents an analysis of the full 2015 and 2016 data sets recorded by the ATLAS detector at the LHC, corresponding to 37.0fb−1of pp collision data at ffiffiffi s p¼13 TeV. II. ATLAS DETECTOR The ATLAS experiment [27,28] at the LHC is a multipurpose particle detector with a forward-backward symmetric cylindrical geometry with layers of tracking, calorimeter, and muon detectors over nearly the entire solid angle around the pp collision point.2The directions and energies of high-pThadronic jets are measured using silicon tracking detectors and a transition radiation straw-tube tracker, hadronic and electromagnetic calorimeters, and a muon spectrometer. Hadronic energy measurements are provided by a calorimeter with scintillator active layers and steel absorber material for the pseudorapidity range jηj<1.7, while electromagnetic (EM) energy measurements are provided by a calorimeter with liquid argon (LAr) active material and lead absorber material covering the pseudorapidity range jηj<3.2. The endcap and forward regions, extending up to jηj¼4.9, are instrumented with LAr calorimeters for both EM and hadronic energy measurements. The lower-level trigger is implemented in hardware and uses a subset of the detector information to reduce the accepted rate to 100 kHz. This is followed by a software-based high-level trigger that reduces the rate of events recorded to 1 kHz [29]. III. EVENT SELECTION Groups of contiguous calorimeter cells (topological clusters) are formed based on the significance of local energy deposits over calorimeter noise [30,31]. Topological clusters are grouped into jets using the anti-ktalgorithm [32,33] with radius parameter R¼0.4. Jet four-momenta are computed by summing over the topological clusters that constitute each jet, treating the energy of each cluster as resulting from a four-momentum with zero mass. Jets with pTabove 20 GeV are reconstructed with an efficiency of nearly 100%. Jet calibrations derived from simulation are used to correct the jet energies and directions to those of particle-level jets from the hard-scatter interaction clustered with the same algorithm and parameters.3This calibration procedure [35–40] is followed by a residual calibration accounting for the differences between data and simulation, beginning with a correction to the relative response for forward jets (jηj>0.8) with respect to central jets (jηj<0.8). Using this method and other in situ techniques where a jet to be calibrated is balanced against a wellcalibrated reference object [41,42], analysis of jet data at 13 TeV corrects the jet response and contributes to the uncertainty estimates up to jet pTvalues of 2.3 TeV, beyond which the calibration is frozen. The total jet energy scale uncertainty is 1% for central jets with pTof 500 GeVand grows to 3% for jets with pTof 2 TeV, at which point, due to the limited size of the event sample available for the in situ studies, an uncertainty is derived from alternative methods using the single-particle response measurements described in Ref. [43]. Uncertainty in the jet energy resolution has a negligible impact on the analysis. The dijet mass resolution is 2.4% and 2.0% for dijet masses of 2 and 5 TeV, respectively, derived at 13 TeV from the simulation of QCD processes as in Ref. [23]. Collision events are recorded using a trigger that requires at least one jet reconstructed by the high-level trigger with a pTgreater than 380 GeV, the lowest-pTsingle-jet trigger that saves all events that activate it. Events containing at least two jets are selected for offline analysis if the pTof the leading (subleading) jet is greater than 440 (60) GeV. This requirement ensures a trigger efficiency of at least 99.5% for collisions that enter into the analysis. Events are discarded from the search if any jets with pT>60 GeV are compatible with noncollision background or calorimeter noise [44]. IV. MONTE CARLO SIMULATION Monte Carlo (MC) events from multijet production described by QCD are generated with PYTHIA 8.186 [45] using the A14 [46] set of tuned parameters for the underlying event and the leading-order NNPDF2.3 [47] parton distribution functions (PDFs). The renormalization and factorization scales are set to the average pTof the two leading anti-kt,R¼0.4truth jets. Detector effects are simulated using GEANT 4[48] within the ATLAS software infrastructure [49]. The same software used to reconstruct data is also used to reconstruct simulated events. The simulated events are used to provide a background estimate for the dijet angular distributions, to test the data-based background estimate used for the mjj distribution, and to provide qualitative comparisons to kinematic distributions in data. 2ATLAS uses a right-handed coordinate system with its origin at the nominal interaction point (IP) in the center of the detector and the zaxis along the beam line. The xaxis points from the IP to the center of the LHC ring, and the y-axis points upwards. Cylindrical coordinates ðr; ϕÞare used in the transverse plane, ϕ being the azimuthal angle around the zaxis. The pseudorapidity is defined in terms of the polar angle θas η¼−ln tanðθ=2Þ.Itis equivalent to the rapidity for massless particles. 3The “particle level”jets are built from stable particles defined by having a proper mean decay length of cτ>10 mm. Particles from interactions other than the hard scattering, as well as muons and neutrinos, are not included in this definition. More information about the particle definition can be found in Ref. [34]. M. AABOUD et al. PHYSICAL REVIEW D 96, 052004 (2017) 052004-2 PYTHIA calculations use matrix elements that are at leading order in the QCD coupling constant, with simulation of higher-order contributions partially covered by the parton shower modeling. They also include modeling of hadronization effects. The distributions of events predicted by PYTHIA are reweighted to next-to-leading-order (NLO) predictions of NLOJET ++ [50–52] using massand angledependent correction factors defined as in Ref. [21]. The correction factors modify the shape of the angular distributions at the level of 15% at high values of mjj and low rapidity separation between the leading and subleading jets. The correction is 5% or less for the highest values of rapidity separation. The PYTHIA predictions also omit electroweak effects. These are included as additional massand angle-dependent correction factors [53] that differ from unity by up to 3% in the mjj >3.4TeV region. The PYTHIA distributions corrected for NLO and electroweak effects are compared to the angular and mjj distributions in data and are found to be in good agreement within experimental uncertainties. Signal samples are generated as described in Sec. VII for a range of benchmark models: excited quarks (q)[54,55], new heavy vector bosons (W0,Z0)[56–58], excited chiral bosons (W)[59,60], quantum black holes (QBH) [61–63] and contact interactions [64,65]. After these signals are simulated, most of the samples are reconstructed using the same framework as used for QCD processes, though a small fraction of the samples employ a simplified parametrization of the detector as described in Ref. [66] for improved processing time. No difference between full simulation and this fast simulation is observed in the relevant variables for this analysis. V. RESONANCE SEARCH The mjj distribution formed from the two leading jets in selected events is analyzed for evidence of contributions from resonant BSM phenomena. The rapidity of an outgoing parton is y¼1=2ln ½ðEþpzÞ=ðE−pzÞ, where Eis its energy and pzis the component of its momentum along the zaxis. The rapidity difference y¼ðy1−y2Þ=2is defined between the two leading jets and is invariant under Lorentz boosts along the zaxis. A requirement of jyj<0.6 reduces the background from QCD processes. This nominal selection is used for the model-independent search phase, to set limits on generically shaped signals (discussed in Sec. VII), and to constrain the q, QBH, W0and Z0 benchmark models, all of whose distributions peak at y¼0. A second signal region with a wider selection of jyj<1.2is also defined, optimized for signals produced at more forward angles. The Wbenchmark model, whose distribution peaks at jyj>1.0, is constrained using this selection. Due to the requirements on yand pTthe selection is fully efficient only for mjj >1.1TeV (1.7 TeV for the jyj<1.2selection). Therefore, the analysis is performed above this mass threshold. Bin widths are chosen to approximate the mjj resolution and therefore widen as the mass increases, from about 130 GeV at the lowest mjj values to about 180 GeV at the highest. They differ slightly between the jyj<0.6and jyj<1.2selections as the resolution also differs. Figure 1shows the observed mjj distribution for events passing the two yselections, overlaid with examples of the signals described in Sec. VII. The background estimate is illustrated by the solid red line and is derived from the sliding-window fitting method described below. The largest value of mjj detected is 8.12 TeV . Prior dijet searches found that expressions of the form fðzÞ¼p1ð1−zÞp2zp3zp4log z;ð1Þ where z¼mjj= ffiffiffi s pand the piare parameters, describe dijet mass distributions observed at lower collision energies. Some past searches required fewer terms in Eq. (1), such as by setting p4¼0, but more parameters are ultimately required to describe the distribution as integrated luminosity increases [23]. Searches at CDF, as well as at ATLAS and CMS at both ffiffiffi s p¼8and ffiffiffi s p¼13 TeV, previously found Eq. (1) to fit the observed spectrum [8,10,15,16,19,24]. This parametrization also provides a good description of simulated QCD samples. With increasing luminosity and the corresponding extension of the mjj range and decrease in statistical uncertainties, a single global fit to the entire spectrum using Eq. (1) cannot necessarily be relied upon. Since the global fit is still viable for this analysis, it presented an opportunity to develop new methods for addressing the background estimate. For the resonance search in this paper, a new sliding-window fitting technique is used, fitting only restricted regions of the spectrum and therefore retaining more flexibility. The limited range of the sliding-window fit allows the use of a three-parameter fit function, while the global fit requires a nonzero p4. The sliding-window fit produces search and limit results compatible with those from the global fit used in previous analyses. The reliability of this new background fitting method in presence of a signal has also been checked. Tests performed for the full range of signal widths considered in this paper have shown good linearity between the injected and extracted signal. The background for the invariant mass spectrum is constructed bin-by-bin by performing a likelihood fit to the data in each window and using the fit value in the central bin of the window for the background description. At the low end of the spectrum the window is compressed down depending on the number of available bins. When it is below 60% of the nominal window size, the values for the center bin and all bins below it are taken from the fit at this window. The values from the full set of windows are then joined to create the background for the full mass range. The window size is chosen to be the widest in which the three-parameter version of Eq. (1) describes the data well in SEARCH FOR NEW PHENOMENA IN DIJET EVENTS …PHYSICAL REVIEW D 96, 052004 (2017) 052004-3 each window of the fit, considering different metrics for the fit goodness. The nominal window size covers approximately half of the total number of bins seen in Fig. 1, wide enough for all the considered benchmark signals to fit within an individual window. The uncertainty due to the values of the parameters in Eq. (1) is estimated by repeating the sliding-window fitting procedure on pseudodata drawn via Poisson fluctuations from the nominal background prediction, that is, the fit result in data. The uncertainty in each mjj bin is taken to be the root mean square of the fit results for all pseudoexperiments in that bin. To estimate an uncertainty due to the choice of background parametrization, an additional sliding-window fit using Eq. (1) with p4≠0is compared to the nominal ansatz, and the average difference between the two fit results across a set of pseudodata is taken as an uncertainty. This background prediction for the mjj distribution does not involve simulated collisions and is therefore not affected by uncertainties such as those due to MC modeling and statistics. The BUMPHUNTER algorithm quantifies the statistical significance of any localized excess in the mjj distribution [67,68]. The algorithm compares the binned mjj distribution of the data to the fitted background estimate, considering contiguous mass intervals in all possible locations, from a width of two bins to a width of half of the distribution. For each interval in the scan, it computes the significance of any excess found. The algorithm identifies the interval 4326–4595 GeV, indicated by the two vertical lines in Fig. 1, as the most discrepant interval in the jyj<0.6signal region. The global significance of this outcome is evaluated using the ensemble of possible outcomes across all intervals scanned, by applying the algorithm to pseudodata samples drawn randomly from the background fit. Without including systematic uncertainties, the probability that fluctuations of the background model would produce an excess at least as significant as the one observed in the data anywhere in the distribution (the BUMPHUNTER probability) is 0.63. Thus, there is no evidence of a localized contribution to the mass distribution from BSM phenomena. Similarly, the search in the second signal region with jyj<1.2shows no significant deviation from the smooth background parametrization, with the same interval identified as the most discrepant and a BUMPHUNTER probability of 0.83. VI. ANGULAR ANALYSIS Differences between the rapidities of two jets are invariant under Lorentz boosts along the zaxis, hence the following function of the rapidity difference 2y, FIG. 1. The reconstructed dijet mass distribution mjj (filled points) is shown for events with pT>440 (60) GeV for the leading (subleading) jet. The spectrum with jyj<0.6is shown in (a) for events above mjj ¼1.1TeV while the selection with jyj<1.2is shown in (b) for events above mjj ¼1.7TeV. The solid line depicts the background prediction from the sliding-window fit. Predictions for benchmark signals are normalized to a cross section large enough to make the shapes distinguishable above the data. The vertical lines indicate the most discrepant interval identified by the BUMPHUNTER algorithm, for which the p-value is stated in the figure. The middle panel shows the bin-by-bin significances of the data-fit differences, considering only statistical uncertainties. The lower panel shows the relative differences between the data and the prediction of PYTHIA 8simulation of QCD processes, corrected for NLO and electroweak effects, and is shown purely for comparison. The shaded band denotes the experimental uncertainty in the jet energy scale calibration. M. AABOUD et al. PHYSICAL REVIEW D 96, 052004 (2017) 052004-4 χ¼e2jyj∼1þcosθ 1−cos θ; is the same in the detector frame as in the partonic centerof-mass frame. The variable χis constructed such that, in the limit of massless parton scattering and when only t-channel scattering contributes to the partonic cross section, the angular distribution dN=dχis approximately independent of χ[69]. In the center-of-mass frame, the two partons have rapidity y. A momentum imbalance between the two incident partons boosts the center-of-mass frame of the collision with respect to the laboratory frame along the z direction by yB¼lnðxi=xjÞ¼ðy1þy2Þ=2; where yBis the rapidity of the boosted center-of-mass frame, xiand xjare the fractions of the proton momentum (Bjorken x) carried by each incident parton, and y1and y2 are the rapidities of the outgoing partons in the detector frame. The measured shapes of the observed dN=dχ distributions differ from the parton-level distributions because the observed ones convolve the parton-level distributions with nonuniform parton momentum distributions in xiand xj, and also contain some admixture of nont-channel processes. Restricting the range of the two-parton invariant mass and placing an upper bound on yBreduces these differences. The dN=dχ(angular) distributions of events with jyj<1.7and jyBj<1.1are analyzed for contributions from BSM signals. The data with mjj <2.5TeV are discarded to remove trigger inefficiencies which otherwise arise due to the loosened yselection compared to the resonance analysis. The data set is then analyzed by fitting to it a PYTHIA MC sample acting as an SM template as explained below. This sample is simulated as described in Sec. IV, including the aforementioned corrections. Figure 2 shows the angular distributions of the data in different mjj ranges starting from 3.4 TeV, the SM prediction for the shape of the angular distributions after it is fit to data, and examples of the signals described in Sec. VII. In the statistical analysis, MC simulation is normalized to data; in Fig. 2both the MC simulation and the data are normalized to unit integral in each mjj range for clarity of display. Theoretical uncertainties in simulations of the angular distributions from QCD processes are estimated as described in Ref. [23].4The effect of varying the choice of PDF sets on the multijet prediction is estimated using NLOJET ++ with three different PDF sets: CT10 [70], MSTW2008 [71] and NNPDF2.3 [47]. As the choice of PDF mainly affects the total cross section rather than the shape of the χdistributions, these uncertainties are negligible (<1%) in this analysis. The uncertainty due to the choice of renormalization and factorization scales is estimated using NLOJET ++ by varying each one independently up and down by a factor of 2. The resulting uncertainties, taken as the variations in the normalized χdistributions, depend on both mjj and χand rise to 12% (8%) for the renormalization (factorization) scale, at the smallest χ values and high mjj values. The statistical uncertainty in the simulated NLO corrections is less than 1%. The dominant experimental uncertainty in the predictions of the χdistributions is the jet energy scale uncertainty, with an impact of at most 15% at high mjj values, for the raw distribution before the fit is performed. The uncertainty in the jet energy resolution has negligible impact. The theoretical uncertainties and the total uncertainties are displayed as shaded bands around the prediction in Fig. 2, where theoretical uncertainties can be seen to dominate. The compatibility of the χdistribution in data with the SM prediction and with the BSM signals discussed in χ 12 3 4 5 6 7 10 20 30 < 3.7 TeV jj 3.4 < m χ 12 3 4 5 6 7 10 20 0.03 0.04 0.05 < 4.0 TeV jj 3.7 < m χ < 4.3 TeV jj 4.0 < m χ 0.03 0.04 0.05 < 4.6 TeV jj 4.3 < m < 4.9 TeV jj 4.6 < m χ 0.04 0.06 < 5.4 TeV jj 4.9 < m Data SM =22 TeVΛ1,−= LL ηCI =15 TeVΛ1,+= LL ηCI Theoretical uncert. Total uncertainty χ χ1/N dN/d 0.04 0.06 > 5.4 TeV jj m ATLAS -1 =13 TeV, 37.0 fbs FIG. 2. Reconstructed distributions of the dijet angular variable χin different regions of the dijet invariant mass mjj for events with jyj<1.7,jyBj<1.1, and pT>440 (60) GeV for the leading (subleading) jet. The data (points), PYTHIA predictions with NLO and electroweak corrections applied (solid lines), and examples of the contact interaction (CI) signals discussed in the text (dashed lines) are shown. The theoretical uncertainties and the total theoretical and experimental uncertainties in the predictions are displayed as shaded bands around the SM prediction. The SM background prediction and corresponding systematic uncertainty bands are extracted from the best-fit to the data. Data and predictions are normalized to unity in each mjj bin. 4Uncertainties in electroweak corrections are not yet available and so are not included. SEARCH FOR NEW PHENOMENA IN DIJET EVENTS …PHYSICAL REVIEW D 96, 052004 (2017) 052004-5 Sec. VII is tested using a combined fit in seven coarse mjj bins covering mjj >3.4TeV as shown in Fig. 2. The range mjj <3.4TeV provides no sensitivity to the studied benchmark models in ranges which are not yet excluded. A profile likelihood fit is performed, using as templates the dN=dχdistributions in each mjj bin for data and QCD MC events. The likelihood function includes nuisance parameters corresponding to the systematic uncertainties described above, treated as correlated across bins. The MC simulation is normalized to the data separately in each mjj bin, making this a shape-only comparison. All systematic uncertainties are treated as correlated in mjj; where this assumption is less secure, such as for the choice of MC event generator tune, other correlation models are tested and the differences are found to be inconsequential. The fit to the data is strongly constrained by the lowest mjj bins, which have good statistical precision as well as negligible contributions from possible BSM signals, providing constraints of between 20% and 40% on the uncertainties in the higher mjj bins. The CLb, or confidence level for the background-only hypothesis, comparing data to SM predictions is 0.06. Thus no significant deviation of the data from the background-only hypothesis is observed. Limits on the production of BSM signals are set using the CLs method [72,73], which takes the CLbvalue into account and thereby avoids setting overly strong limits in light of the rather low observed p-value. VII. BENCHMARK SIGNALS The data are used to constrain several of the many BSM models that predict dijet excesses. Excited quarks, quantum black holes, and W0,W, and Z0bosons would produce peaks in the mjj distribution. Contact interactions would introduce smooth changes in the high-mass tail of the mjj distribution that could be detected in the analysis of the χ distributions. The signal models are simulated using the parton-level event generators indicated below, in an identical manner to QCD processes, using the same PDFs and parameters for nonperturbative effects, except where noted otherwise. The renormalization and factorization scales are set to the average pTof the two leading jets. The efficiency for all signal models is close to unity, henceforth acceptance times efficiency is referred to as acceptance. For all models, acceptance is computed from all events which pass the analysis selection, including distribution tails caused by the sharp rise of PDFs at low Bjorken x. If extra spatial dimensions exist, the fundamental scale of gravity could be lowered to a few TeV and the LHC could produce quantum black holes at or above this scale [4,61,62,74–77]. High-multiplicity final states from thermalizing black holes are explored at ffiffiffi s p¼13 TeV by ATLAS in Refs. [78,79] and by CMS in Ref. [80]. This analysis explores QBH that would be produced at or above the fundamental scale of gravity MDand decay into a few particles rather than the high-multiplicity final states characteristic of thermalizing black holes [61–63,81]. These would appear in the mjj distribution as an excess localized near the threshold mass for quantum black hole production, Mth. Here, production and decay to two jets is simulated using the BLACKMAX event generator [63] assuming an Arkani-Hamed–Dimopoulous–Dvali (ADD) scenario [82,83] with MD¼Mth and a number of extra dimensions n¼6, as in Ref. [19]. In this model, the branching ratio to dijets is greater than 96%. The PDFs used are CTEQ6L1 [84]. The QBH signals peak slightly above their threshold values and have negligible low-mass tails. The reconstructed signal peaks have width-to-mass ratios of approximately 10%. The acceptance of the resonance search selection for quantum black holes is approximately 53% across all studied masses. Excited quarks are predicted in models of compositeness and are a typical benchmark for quark-gluon resonances used in many past dijet searches [8,10,12,22,23]. The q model is simulated with PYTHIA 8.186, assuming spin-1=2 excited quarks with coupling constants the same as for SM quarks; no interference with the SM is simulated. Only the decay of the excited quark to a gluon and an upor downtype quark is simulated; this corresponds to a branching ratio of 85%. Before parton shower effects are taken into account, the intrinsic width of the qsignals is comparable to the detector resolution. After showering, a radiative tail is present that increases in strength for higher qmasses, an effect augmented by the impact of PDFs decreasing towards higher masses. The resonance search selection acceptance for a qwith a mass of 4 TeV is 58% . Additional spin-1 W0and Z0bosons often arise in the symmetry breaking of extended gauge theories. A W0 model with axial-vector SM couplings and a corresponding branching ratio to quarks of 75% is considered [85]. Events are simulated with PYTHIA 8.205 and decays are restricted to quark-antiquark pairs with all three quark-flavor doublets included. A leptophobic Z0model is also simulated, with matrix elements calculated in MADGRAPH 5_ AMC @ NLO v2.2.3 [86] and parton showering performed in PYTHIA 8.210. The Z0model assumes axial-vector couplings to all SM quarks and to a Dirac fermion dark matter candidate. Final states with top quarks are not simulated, and the acceptance for these is assumed to be zero and is taken into account for the branching ratio and normalization of simulated data. The model considered follows a scenario [58] where the Z0branching ratio to dark matter is negligible, hence the dijet production rate and resonance width depend only on the coupling to quarks, gq, and the mass of the resonance mZ0. Before parton shower effects are considered, the intrinsic width of the Z0signal ranges from 0.05% of the mass of a 1.5 TeV Z0with gq¼0.1to 10% of the mass of a 3.5 TeV Z0with gq¼0.5. The W0signal has an intrinsic width similar to a Z0of coupling gq¼0.3at M. AABOUD et al. PHYSICAL REVIEW D 96, 052004 (2017) 052004-6 every mass point considered. For coupling values of gq¼0.6and above, the intrinsic width of the Z0for the mass range of interest increases to 15% and beyond, resulting in a very wide peak and in a loss of sensitivity in the resonance search, which is therefore limited to gq≤0.5. No interference with the SM is simulated for either the W0or the Z0model. The resonance search selection acceptance for a mass of 3 TeV is 40% for the W0model and 47% for the Z0model with gq¼0.2. Because of the large radiative tails of the W0signals, the acceptance for this model increases to a maximum at approximately 2.5 TeV and decreases to values smaller than 20% for masses above 6.0 TeV. An excited Wboson is generated through a simplified model [87] in the CalcHEP 3.6 event generator [88],in combination with the NNPDF2.3 NLO PDF set and PYTHIA 8.210 for the simulation of nonperturbative effects. The mixing angle in this model (ϕX) is set to zero, producing leptophobic decays of the Wthat are limited to all SM quarks. The angular distribution of the Wdiffers from that of the other signals under study, peaking at yvalues above 1. Therefore, this benchmark model is constrained using the alternative signal region with jyj<1.2. The acceptance for the leptophobic Wsignal with this selection increases from 33% around 2 TeV to nearly 60% for the highest masses examined. Results are also provided as limits on the cross section times acceptance times branching ratio to two jets, σ×A× BR, of a hypothetical signal modeled as a Gaussian peak in the particle-level mjj distribution. When limits are set on Gaussian signal models that can contribute to the reconstructed mjj spectrum (e.g. as in Ref. [19]), the description of the corresponding distribution folds together the actual physical signal and detector effects (acceptance and resolution). Here a model is defined at particle level, within a fiducial region. This model is then folded with the effects of the detector response, described through an MCbased transfer matrix that relates the particle level and reconstructed observables. The transfer matrix accounts for bin-to-bin migrations due to resolution effects, as well as for the fractions of events passing the selection only at particle or reconstruction level. In order to avoid large simulation-based extrapolations, the fiducial selection at particlelevelmatchestheone appliedatreconstructionlevel. Limits on a given signal model can be interpreted from the phenomenological point of view at particle level, without need for further information about the detector response. For sufficiently narrow resonances, these results may be used to set limits in BSM models beyond those considered explicitly in this paper. The predicted signals should be compared at particle level, after applying the resonance selection, with the limit that corresponds most closely to the width of the Gaussian contribution predicted by the model. Since a Gaussian signal shape is assumed in determining the limits, any long tails in the mjj distribution should not be included in the model under study. A procedure similar to the one detailed in Appendix A.1 of Ref. [19] can be followed, after applying the nonperturbative corrections and performing the fiducial selection at particle level, without applying any further detector smearing as it is already accounted for in the folding procedure. The folding procedure applied for the various signal samples discussed above, using transfer matrices based on either the same or different samples, yields reconstructed distributions compatible with the ones from full simulation. The limits on narrow signals at particle level, folded with the detector effects, are similar to the ones obtained for a Gaussian signal at reconstruction level having a width equal to the one expected from detector resolution.5For resonance widths comparable to the resolution, differences up to about 20% are observed between the results of the two limit-setting approaches. The folding method yields results at particle level, accounting also for the mass dependence of the resolution within the range of the resonance, hence its relevance for providing results that are easy to interpret. For large signal widths, the effect of the detector resolution on the global width is smaller and the difference between the results of the two limit-setting approaches is reduced. For all signals described above, the following systematic uncertainties are included in the limit setting: jet energy scale, acceptance uncertainties associated to the choice of PDF, and luminosity. The jet energy uncertainty ranges from 1.5% at the lowest masses to 3% for masses above 4.5 TeV. On average, the PDF uncertainty affects the angular distributions by 1%. The uncertainty in the combined 2015 þ2016 integrated luminosity is 3.2%. It is derived, following a methodology similar to that detailed in Ref. [89], from a preliminary calibration of the luminosity scale using x–ybeam-separation scans performed in August 2015 and May 2016. The dijet angular distributions can also be modified by new mediating particles with a mass much higher than that which can be probed directly. A four-fermion effective field theory (contact interaction) characterized by a single energy scale Λcan be used to describe these effects: Lqq ¼2π Λ2½ηLLð¯ qLγμqLÞð¯ qLγμqLÞþηRRð¯ qRγμqRÞð¯ qRγμqRÞ þ2ηRLð¯ qRγμqRÞð¯ qLγμqLÞ;ð2Þ where the quark fields have left-handed (L) and righthanded (R) chiral projections and the coefficients ηLL, ηRR, and ηRL activate various interactions. Contact interactions with a nonzero left-chiral color-singlet coupling (ηLL ¼1,ηRL ¼ηRR ¼0) are simulated using PYTHIA 5Differences of about 4% between these limits are seen, due to non-Gaussian tails of the resolution which are taken into account by the folding matrix, but are not accounted for in the case of the Gaussian signal at reconstruction level. SEARCH FOR NEW PHENOMENA IN DIJET EVENTS …PHYSICAL REVIEW D 96, 052004 (2017) 052004-7 8.816. This type of coupling is chosen because its angular distributions are representative of those of other BSM models (e.g. Z0and others studied here by the resonance search). Interference of the signal model with the SM process q¯ q→q¯ qis included. Events are simulated for both constructive and destructive interference with Λ¼7TeV. From this sample, the angular distributions for other values of Λare obtained using the fact that the interference term is proportional to 1=Λ2and the pure contact-interaction cross section is proportional to 1=Λ4. The PYTHIA signal prediction is reweighted to the NLO cross sections provided by CIJET [90]. Uncertainties in the prediction of the angular distributions for contact interaction signals are obtained in the same manner as for QCD processes, including JES and PDF uncertainties (as discussed in Sec. VI). VIII. RESULTS Starting from the mjj distribution obtained with the resonance search selection, a Bayesian method [16] is applied to the data and simulation of signals at a series of discrete masses to set 95% credibility-level (C.L.) upper limits on the cross section times acceptance for the signals described above. The method uses a constant prior for the signal cross section and Gaussian priors for nuisance parameters corresponding to systematic uncertainties in the signal and background distributions. The expected limits are calculated using pseudoexperiments generated from the maximum-likelihood values of the background uncertainties in the sliding-window background model and accounting for the full set of systematic uncertainties in both the signal and background models. The limit is interpolated logarithmically between the discrete masses TABLE I. Summary of the analysis selection criteria for the three considered signal regions. pleading Tpsubleading TjyjjyBjmjj Resonance >0.44 TeV >0.06 TeV <0.6 >1.1TeV W>0.44 TeV >0.06 TeV <1.2 >1.7TeV Angular >0.44 TeV >0.06 TeV <1.7<1.1>2.5TeV FIG. 3. The 95% C.L. upper limits obtained from the dijet invariant mass (mjj) distribution on cross section times acceptance times branching ratio to two jets, σ×A× BR, for the models described in the text. Clockwise from top left: q, quantum black holes with n¼6generated with BLACKMAX ,W0, and Wwhere the first three use the nominal selection and the last uses the widened jyj<1.2 selection. The numerical values of the observed and expected limits are summarized in Table II. M. AABOUD et al. PHYSICAL REVIEW D 96, 052004 (2017) 052004-8 to create continuous exclusion curves. No uncertainty in the theoretical cross section for the signals is assessed. The various selection criteria for the different signal regions are summarized in Table I. The mass limits for each of the models are shown in Figs. 3and 4and Table II. Figure 5shows limits on the Gaussian contributions to the particle-level mjj distribution obtained for a mean mass mGand five different widths, from a narrow width to a width of 15% of mG. The expected limit and the corresponding 1σand 2σbands are also indicated for a narrow-width resonance. Limits are set only when mGis within 1.1–6.5 TeV and separated by at least the width of the Gaussian resonance from the beginning of this range. Resonances with effective cross sections exceeding values ranging from approximately 20–50 fb for masses of 2 TeV to 0.2–0.5 fb for masses above 6 TeV are excluded. As the width increases, the expected signal contribution is distributed across more bins. Therefore, wider signals are less affected by statistical fluctuations of the data in a single bin than narrower signals. Starting from the χdistributions obtained with the angular selection, the CLsmethod is used to set limits on potential contributions from contact interactions, using the background predicted by the SM simulation as the null hypothesis. The asymptotic approximation [91] of a profile likelihood ratio is used to set 95% C.L. limits. For each value of Λand each ηLL tested, a combined fit is performed on the seven mjj regions of Fig. 2, using the procedure described in Sec. VI. The maximum-likelihood values of the nuisance parameters do not differ significantly from the expectations. The bounds on contact interactions thus obtained are shown in Fig. 6and in Table II. In the case of destructive interference, the expected event yield including the signal may be lower than that for the backgroundalone prediction. The kinematic regions where this occurs depend on both Λand mjj. An observed excess in the data then produces a weaker limit below a given Λvalue, and a stronger one above that Λvalue, in combination with information from the mjj spectrum in the fit. The same approach is used to set limits on the resonant benchmark signals described in Sec. VII, as a consistency [TeV] Z' m 1.5 2 2.5 3 3.5 q g 0.05 0.1 0.15 0.2 0.25 0.3 -1 = 13 TeV, 37.0 fbs ATLAS Observed 95% CL upper limit Expected 95% CL upper limit FIG. 4. The 95% C.L. exclusion limits for the Z0model described in the text, as a function of the coupling to quarks, gq, and the mass, mZ0, obtained from the dijet invariant mass mjj distribution. For a given mass, the cross sections rise with gq, and thus the upper left unfilled area is excluded, as indicated by the direction of the hatched band. The exclusion applies up to gq¼0.5, in the sensitivity range of the method as explained in the text. Points were simulated with 0.5 TeV spacing in mass and spacing as fine as 0.05 in gq. A smooth curve is drawn between points by interpolating in g2 qfollowed by an interpolation in mZ0. TABLE II. The 95% C.L. lower limits on the masses of ADD quantum black holes ( BLACKMAX event generator), W0and W bosons, excited quarks, and Z0bosons for selected coupling values from the resonance search, as well as on the scale of contact interactions for constructive (ηLL ¼−1) and destructive (ηLL ¼þ1) interference from the angular analysis. Where an additional range is listed, masses within the range are also excluded. Full limits on the Z0model are provided in Fig. 4. 95% C.L. exclusion limit Model Observed Expected Quantum black hole 8.9 TeV 8.9 TeV W03.6 TeV 3.7 TeV W3.4 TeV 3.6 TeV 3.77 TeV—3.85 TeV Excited quark 6.0 TeV 5.8 TeV Z0(gq¼0.1) 2.1 TeV 2.1 TeV Z0(gq¼0.2) 2.9 TeV 3.3 TeV Contact interaction (ηLL ¼−1) 21.8 TeV 28.3 TeV. Contact interaction (ηLL ¼þ1)13.1 TeV 15.0 TeV 17.4 TeV—29.5 TeV [TeV] G m 246 BR [pb]×A×σ 4− 10 3− 10 2− 10 1− 10 1 ATLAS -1 =13 TeV, 37.0 fbs |y*| < 0.6 = 0 G /m G σExp. 95% CL upper limit for σ 2 ± and σ 1 ±Expected Obs. 95% CL upper limit for: = 0.15 G /m G σ = 0.10 G /m G σ = 0.07 G /m G σ = 0.03 G /m G σ = 0 G /m G σ FIG. 5. The 95% C.L. upper limits obtained from the dijet invariant mass mjj distribution on cross section times acceptance times branching ratio to two jets, σ×A× BR, for a hypothetical signal with a cross section σGthat produces a Gaussian contribution to the particle-level mjj distribution, as a function of the mean of the Gaussian mass distribution mG. Observed limits are obtained for five different widths, from a narrow width to 15% of mG. The expected limit and the corresponding 1σand 2σbands are also indicated for a narrow-width resonance. SEARCH FOR NEW PHENOMENA IN DIJET EVENTS …PHYSICAL REVIEW D 96, 052004 (2017) 052004-9 S. Grinstein,13,v Ph. Gris,37 J.-F. Grivaz,119 S. Groh,86 E. Gross,175 J. Grosse-Knetter,57 G. C. Grossi,82 Z. J. Grout,81 A. Grummer,107 L. Guan,92 W. Guan,176 J. Guenther,65 F. Guescini,163a D. Guest,166 O. Gueta,155 B. Gui,113 E. Guido,53a,53b T. Guillemin,5S. Guindon,2U. Gul,56 C. Gumpert,32 J. Guo,36c W. Guo,92 Y. Guo,36a R. Gupta,43 S. Gupta,122 G. 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Marti-Garcia,170 C. B. Martin,113 T. A. Martin,173 V. J. Martin,49 B. Martin dit Latour,15 M. Martinez,13,v V. I. Martinez Outschoorn,169 S. Martin-Haugh,133 V. S. Martoiu,28b A. C. Martyniuk,81 A. Marzin,32 L. Masetti,86 T. Mashimo,157 R. Mashinistov,98 J. Masik,87 A. L. Maslennikov,111,d L. Massa,135a,135b P. Mastrandrea,5 A. Mastroberardino,40a,40b T. Masubuchi,157 P. Mättig,178 J. Maurer,28b S. J. Maxfield,77 D. A. Maximov,111,d R. Mazini,153 I. Maznas,156 S. M. Mazza,94a,94b N. C. Mc Fadden,107 G. Mc Goldrick,161 S. P. Mc Kee,92 A. McCarn,92 R. L. McCarthy,150 T. G. McCarthy,103 L. I. McClymont,81 E. F. McDonald,91 J. A. Mcfayden,81 G. Mchedlidze,57 S. J. McMahon,133 P. C. McNamara,91 R. A. McPherson,172,p S. Meehan,140 T. J. Megy,51 S. Mehlhase,102 A. Mehta,77 T. Meideck,58 K. Meier,60a B. Meirose,44 D. Melini,170,ii B. R. Mellado Garcia,147c J. D. Mellenthin,57 M. Melo,146a F. Meloni,18 S. B. Menary,87 L. Meng,77 X. T. Meng,92 A. Mengarelli,22a,22b S. Menke,103 E. Meoni,40a,40b S. Mergelmeyer,17 P. Mermod,52 L. Merola,106a,106b C. Meroni,94a F. S. Merritt,33 A. Messina,134a,134b J. Metcalfe,6A. S. Mete,166 C. Meyer,124 J-P. Meyer,138 J. Meyer,109 H. Meyer Zu Theenhausen,60a F. Miano,151 R. P. Middleton,133 S. Miglioranzi,53a,53b L. Mijović,49 SEARCH FOR NEW PHENOMENA IN DIJET EVENTS …PHYSICAL REVIEW D 96, 052004 (2017) 052004-17 G. Mikenberg,175 M. Mikestikova,129 M. Mikuž,78 M. Milesi,91 A. Milic,161 D. W. Miller,33 C. Mills,49 A. Milov,175 D. A. Milstead,148a,148b A. A. Minaenko,132 Y. Minami,157 I. A. Minashvili,68 A. I. Mincer,112 B. Mindur,41a M. Mineev,68 Y. Minegishi,157 Y. Ming,176 L. M. Mir,13 K. P. Mistry,124 T. Mitani,174 J. Mitrevski,102 V. A. Mitsou,170 A. Miucci,18 P. S. Miyagawa,141 A. Mizukami,69 J. U. Mjörnmark,84 T. Mkrtchyan,180 M. Mlynarikova,131 T. Moa,148a,148b K. Mochizuki,97 P. Mogg,51 S. Mohapatra,38 S. Molander,148a,148b R. Moles-Valls,23 R. Monden,71 M. C. Mondragon,93 K. Mönig,45 J. Monk,39 E. Monnier,88 A. Montalbano,150 J. Montejo Berlingen,32 F. Monticelli,74 S. Monzani,94a,94b R. W. Moore,3N. Morange,119 D. Moreno,21 M. Moreno Llácer,32 P. Morettini,53a S. Morgenstern,32 D. Mori,144 T. Mori,157 M. Morii,59 M. Morinaga,157 V. Morisbak,121 A. K. Morley,152 G. Mornacchi,32 J. D. Morris,79 L. Morvaj,150 P. Moschovakos,10 M. Mosidze,54b H. J. Moss,141 J. Moss,145,jj K. Motohashi,159 R. Mount,145 E. Mountricha,27 E. J. W. Moyse,89 S. Muanza,88 R. D. Mudd,19 F. Mueller,103 J. Mueller,127 R. S. P. Mueller,102 D. Muenstermann,75 P. Mullen,56 G. A. Mullier,18 F. J. Munoz Sanchez,87 W. J. Murray,173,133 H. Musheghyan,181 M. Muškinja,78 A. G. Myagkov,132,kk M. Myska,130 B. P. Nachman,16 O. Nackenhorst,52 K. Nagai,122 R. Nagai,69,dd K. Nagano,69 Y. Nagasaka,61 K. Nagata,164 M. Nagel,51 E. Nagy,88 A. M. Nairz,32 Y. Nakahama,105 K. Nakamura,69 T. Nakamura,157 I. Nakano,114 R. F. Naranjo Garcia,45 R. Narayan,11 D. I. Narrias Villar,60a I. Naryshkin,125 T. Naumann,45 G. Navarro,21 R. Nayyar,7H. A. Neal,92 P. Yu. Nechaeva,98 T. J. Neep,138 A. Negri,123a,123b M. Negrini,22a S. Nektarijevic,108 C. Nellist,119 A. Nelson,166 M. E. Nelson,122 S. Nemecek,129 P. Nemethy,112 M. Nessi,32,ll M. S. Neubauer,169 M. Neumann,178 P. R. Newman,19 T. Y. Ng,62c T. Nguyen Manh,97 R. B. Nickerson,122 R. Nicolaidou,138 J. Nielsen,139 V. Nikolaenko,132,kk I. Nikolic-Audit,83 K. Nikolopoulos,19 J. K. Nilsen,121 P. Nilsson,27 Y. Ninomiya,157 A. Nisati,134a N. Nishu,35c R. Nisius,103 I. Nitsche,46 T. Nobe,157 Y. Noguchi,71 M. Nomachi,120 I. Nomidis,31 M. A. Nomura,27 T. Nooney,79 M. Nordberg,32 N. Norjoharuddeen,122 O. Novgorodova,47 S. Nowak,103 M. Nozaki,69 L. Nozka,117 K. Ntekas,166 E. Nurse,81 F. Nuti,91 K. O’connor,25 D. C. O’Neil,144 A. A. O’Rourke,45 V. O’Shea,56 F. G. Oakham,31,e H. Oberlack,103 T. Obermann,23 J. Ocariz,83 A. Ochi,70 I. Ochoa,38 J. P. Ochoa-Ricoux,34a S. Oda,73 S. Odaka,69 H. Ogren,64 A. Oh,87 S. H. Oh,48 C. C. Ohm,16 H. Ohman,168 H. Oide,53a,53b H. Okawa,164 Y. Okumura,157 T. Okuyama,69 A. Olariu,28b L. F. Oleiro Seabra,128a S. A. Olivares Pino,49 D. Oliveira Damazio,27 A. Olszewski,42 J. Olszowska,42 A. Onofre,128a,128e K. Onogi,105 P. U. E. Onyisi,11,z M. J. Oreglia,33 Y. Oren,155 D. Orestano,136a,136b N. Orlando,62b R. S. Orr,161 B. Osculati,53a,53b,a R. Ospanov,36a G. Otero y Garzon,29 H. Otono,73 M. Ouchrif,137d F. Ould-Saada,121 A. Ouraou,138 K. P. Oussoren,109 Q. Ouyang,35a M. Owen,56 R. E. Owen,19 V. E. Ozcan,20a N. Ozturk,8K. Pachal,144 A. Pacheco Pages,13 L. Pacheco Rodriguez,138 C. Padilla Aranda,13 S. Pagan Griso,16 M. Paganini,179 F. Paige,27 G. Palacino,64 S. Palazzo,40a,40b S. Palestini,32 M. Palka,41b D. Pallin,37 E. St. Panagiotopoulou,10 I. Panagoulias,10 C. E. Pandini,83 J. G. Panduro Vazquez,80 P. Pani,32 S. Panitkin,27 D. Pantea,28b L. Paolozzi,52 Th. D. Papadopoulou,10 K. Papageorgiou,9A. Paramonov,6 D. Paredes Hernandez,179 A. J. Parker,75 M. A. Parker,30 K. A. Parker,45 F. Parodi,53a,53b J. A. Parsons,38 U. Parzefall,51 V. R. Pascuzzi,161 J. M. Pasner,139 E. Pasqualucci,134a S. Passaggio,53a Fr. Pastore,80 S. Pataraia,178 J. R. Pater,87 T. Pauly,32 B. Pearson,103 S. Pedraza Lopez,170 R. Pedro,128a,128b S. V. Peleganchuk,111,d O. Penc,129 C. Peng,35a H. Peng,36a J. Penwell,64 B. S. Peralva,26b M. M. Perego,138 D. V. Perepelitsa,27 L. Perini,94a,94b H. Pernegger,32 S. Perrella,106a,106b R. Peschke,45 V. D. Peshekhonov,68,a K. Peters,45 R. F. Y. Peters,87 B. A. Petersen,32 T. C. Petersen,39 E. Petit,58 A. Petridis,1C. Petridou,156 P. Petroff,119 E. Petrolo,134a M. Petrov,122 F. Petrucci,136a,136b N. E. Pettersson,89 A. Peyaud,138 R. Pezoa,34b F. H. Phillips,93 P. W. Phillips,133 G. Piacquadio,150 E. Pianori,173 A. Picazio,89 E. Piccaro,79 M. A. Pickering,122 R. Piegaia,29 J. E. Pilcher,33 A. D. Pilkington,87 A. W. J. Pin,87 M. Pinamonti,135a,135b J. L. Pinfold,3H. Pirumov,45 M. Pitt,175 L. Plazak,146a M.-A. Pleier,27 V. Pleskot,86 E. Plotnikova,68 D. Pluth,67 P. Podberezko,111 R. Poettgen,148a,148b R. Poggi,123a,123b L. Poggioli,119 D. Pohl,23 G. Polesello,123a A. Poley,45 A. Policicchio,40a,40b R. Polifka,32 A. Polini,22a C. S. Pollard,56 V. Polychronakos,27 K. Pommès,32 D. Ponomarenko,100 L. Pontecorvo,134a B. G. Pope,93 G. A. Popeneciu,28d A. Poppleton,32 S. Pospisil,130 K. Potamianos,16 I. N. Potrap,68 C. J. Potter,30 G. Poulard,32 T. Poulsen,84 J. Poveda,32 M. E. Pozo Astigarraga,32 P. Pralavorio,88 A. Pranko,16 S. Prell,67 D. Price,87 L. E. Price,6M. Primavera,76a S. Prince,90 N. Proklova,100 K. Prokofiev,62c F. Prokoshin,34b S. Protopopescu,27 J. Proudfoot,6M. Przybycien,41a A. Puri,169 P. Puzo,119 J. Qian,92 G. Qin,56 Y. Qin,87 A. Quadt,57 M. Queitsch-Maitland,45 D. Quilty,56 S. Raddum,121 V. Radeka,27 V. Radescu,122 S. K. Radhakrishnan,150 P. Radloff,118 P. Rados,91 F. Ragusa,94a,94b G. Rahal,182 J. A. Raine,87 S. Rajagopalan,27 C. Rangel-Smith,168 T. Rashid,119 S. Raspopov,5M. G. Ratti,94a,94b D. M. Rauch,45 F. Rauscher,102 S. Rave,86 I. Ravinovich,175 J. H. Rawling,87 M. Raymond,32 A. L. Read,121 N. P. Readioff,58 M. Reale,76a,76b D. M. Rebuzzi,123a,123b A. Redelbach,177 G. Redlinger,27 R. Reece,139 R. G. Reed,147c K. Reeves,44 L. Rehnisch,17 J. Reichert,124 A. Reiss,86 M. AABOUD et al. PHYSICAL REVIEW D 96, 052004 (2017) 052004-18 C. Rembser,32 H. Ren,35a M. Rescigno,134a S. Resconi,94a E. D. Resseguie,124 S. Rettie,171 E. Reynolds,19 O. L. Rezanova,111,d P. Reznicek,131 R. Rezvani,97 R. Richter,103 S. Richter,81 E. Richter-Was,41b O. Ricken,23 M. Ridel,83 P. Rieck,103 C. J. Riegel,178 J. Rieger,57 O. Rifki,115 M. Rijssenbeek,150 A. Rimoldi,123a,123b M. Rimoldi,18 L. Rinaldi,22a G. Ripellino,149 B. Ristić,32 E. Ritsch,32 I. Riu,13 F. Rizatdinova,116 E. Rizvi,79 C. Rizzi,13 R. T. Roberts,87 S. H. Robertson,90,p A. Robichaud-Veronneau,90 D. Robinson,30 J. E. M. Robinson,45 A. Robson,56 E. Rocco,86 C. Roda,126a,126b Y. Rodina,88,mm S. Rodriguez Bosca,170 A. Rodriguez Perez,13 D. Rodriguez Rodriguez,170 S. Roe,32 C. S. Rogan,59 O. Røhne,121 J. Roloff,59 A. Romaniouk,100 M. Romano,22a,22b S. M. Romano Saez,37 E. Romero Adam,170 N. Rompotis,77 M. Ronzani,51 L. Roos,83 S. Rosati,134a K. Rosbach,51 P. Rose,139 N.-A. Rosien,57 E. Rossi,106a,106b L. P. Rossi,53a J. H. N. Rosten,30 R. Rosten,140 M. Rotaru,28b I. Roth,175 J. Rothberg,140 D. Rousseau,119 A. Rozanov,88 Y. Rozen,154 X. Ruan,147c F. Rubbo,145 F. Rühr,51 A. Ruiz-Martinez,31 Z. Rurikova,51 N. A. Rusakovich,68 H. L. Russell,90 J. P. Rutherfoord,7N. Ruthmann,32 Y. F. Ryabov,125 M. Rybar,169 G. Rybkin,119 S. Ryu,6A. Ryzhov,132 G. F. Rzehorz,57 A. F. Saavedra,152 G. Sabato,109 S. Sacerdoti,29 H. F-W. Sadrozinski,139 R. Sadykov,68 F. Safai Tehrani,134a P. Saha,110 M. Sahinsoy,60a M. Saimpert,45 M. Saito,157 T. Saito,157 H. Sakamoto,157 Y. Sakurai,174 G. Salamanna,136a,136b J. E. Salazar Loyola,34b D. Salek,109 P. H. Sales De Bruin,168 D. Salihagic,103 A. Salnikov,145 J. Salt,170 D. Salvatore,40a,40b F. Salvatore,151 A. Salvucci,62a,62b,62c A. Salzburger,32 D. Sammel,51 D. Sampsonidis,156 D. Sampsonidou,156 J. Sánchez,170 V. Sanchez Martinez,170 A. Sanchez Pineda,167a,167c H. Sandaker,121 R. L. Sandbach,79 C. O. Sander,45 M. Sandhoff,178 C. Sandoval,21 D. P. C. Sankey,133 M. Sannino,53a,53b Y. Sano,105 A. Sansoni,50 C. Santoni,37 R. Santonico,135a,135b H. Santos,128a I. Santoyo Castillo,151 A. Sapronov,68 J. G. Saraiva,128a,128d B. Sarrazin,23 O. Sasaki,69 K. Sato,164 E. Sauvan,5 G. Savage,80 P. Savard,161,e N. Savic,103 C. Sawyer,133 L. Sawyer,82,u J. Saxon,33 C. Sbarra,22a A. Sbrizzi,22a,22b T. Scanlon,81 D. A. Scannicchio,166 M. Scarcella,152 V. Scarfone,40a,40b J. Schaarschmidt,140 P. Schacht,103 B. M. Schachtner,102 D. Schaefer,32 L. Schaefer,124 R. Schaefer,45 J. Schaeffer,86 S. Schaepe,23 S. Schaetzel,60b U. Schäfer,86 A. C. Schaffer,119 D. Schaile,102 R. D. Schamberger,150 V. Scharf,60a V. A. Schegelsky,125 D. Scheirich,131 M. Schernau,166 C. Schiavi,53a,53b S. Schier,139 L. K. Schildgen,23 C. Schillo,51 M. Schioppa,40a,40b S. Schlenker,32 K. R. Schmidt-Sommerfeld,103 K. Schmieden,32 C. Schmitt,86 S. Schmitt,45 S. Schmitz,86 U. Schnoor,51 L. Schoeffel,138 A. Schoening,60b B. D. Schoenrock,93 E. Schopf,23 M. Schott,86 J. F. P. Schouwenberg,108 J. Schovancova,181 S. Schramm,52 N. Schuh,86 A. Schulte,86 M. J. Schultens,23 H.-C. Schultz-Coulon,60a H. Schulz,17 M. Schumacher,51 B. A. Schumm,139 Ph. Schune,138 A. Schwartzman,145 T. A. Schwarz,92 H. Schweiger,87 Ph. Schwemling,138 R. Schwienhorst,93 J. Schwindling,138 A. Sciandra,23 G. Sciolla,25 F. Scuri,126a,126b F. Scutti,91 J. Searcy,92 P. Seema,23 S. C. Seidel,107 A. Seiden,139 J. M. Seixas,26a G. Sekhniaidze,106a K. Sekhon,92 S. J. Sekula,43 N. Semprini-Cesari,22a,22b S. Senkin,37 C. Serfon,121 L. Serin,119 L. Serkin,167a,167b M. Sessa,136a,136b R. Seuster,172 H. Severini,115 T. Sfiligoj,78 F. Sforza,32 A. Sfyrla,52 E. Shabalina,57 N. W. Shaikh,148a,148b L. Y. Shan,35a R. Shang,169 J. T. Shank,24 M. Shapiro,16 P. B. Shatalov,99 K. Shaw,167a,167b S. M. Shaw,87 A. 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Zwalinski32 (ATLAS Collaboration) 1Department of Physics, University of Adelaide, Adelaide, Australia 2Physics Department, SUNY Albany, Albany New York, USA 3Department of Physics, University of Alberta, Edmonton Alberta, Canada 4aDepartment of Physics, Ankara University, Ankara, Turkey 4bIstanbul Aydin University, Istanbul, Turkey 4cDivision of Physics, TOBB University of Economics and Technology, Ankara, Turkey 5LAPP, CNRS/IN2P3 and Université Savoie Mont Blanc, Annecy-le-Vieux, France 6High Energy Physics Division, Argonne National Laboratory, Argonne Illinois, USA 7Department of Physics, University of Arizona, Tucson Arizona, USA 8Department of Physics, The University of Texas at Arlington, Arlington Texas, USA 9Physics Department, National and Kapodistrian University of Athens, Athens, Greece 10Physics Department, National Technical University of Athens, Zografou, Greece 11Department of Physics, The University of Texas at Austin, Austin Texas, USA 12Institute of Physics, Azerbaijan Academy of Sciences, Baku, Azerbaijan 13Institut de Física d’Altes Energies (IFAE), The Barcelona Institute of Science and Technology, Barcelona, Spain 14Institute of Physics, University of Belgrade, Belgrade, Serbia 15Department for Physics and Technology, University of Bergen, Bergen, Norway 16Physics Division, Lawrence Berkeley National Laboratory and University of California, Berkeley California, USA 17Department of Physics, Humboldt University, Berlin, Germany 18Albert Einstein Center for Fundamental Physics and Laboratory for High Energy Physics, University of Bern, Bern, Switzerland 19School of Physics and Astronomy, University of Birmingham, Birmingham, United Kingdom 20aDepartment of Physics, Bogazici University, Istanbul, Turkey 20bDepartment of Physics Engineering, Gaziantep University, Gaziantep, Turkey 20dIstanbul Bilgi University, Faculty of Engineering and Natural Sciences, Istanbul, Turkey 20eBahcesehir University, Faculty of Engineering and Natural Sciences, Istanbul, Turkey 21Centro de Investigaciones, Universidad Antonio Narino, Bogota, Colombia 22aINFN Sezione di Bologna, Italy 22bDipartimento di Fisica e Astronomia, Università di Bologna, Bologna, Italy 23Physikalisches Institut, University of Bonn, Bonn, Germany 24Department of Physics, Boston University, Boston Massachusetts, USA 25Department of Physics, Brandeis University, Waltham Massachusetts, USA 26aUniversidade Federal do Rio De Janeiro COPPE/EE/IF, Rio de Janeiro, Brazil 26bElectrical Circuits Department, Federal University of Juiz de Fora (UFJF), Juiz de Fora, Brazil 26cFederal University of Sao Joao del Rei (UFSJ), Sao Joao del Rei, Brazil 26dInstituto de Fisica, Universidade de Sao Paulo, Sao Paulo, Brazil 27Physics Department, Brookhaven National Laboratory, Upton New York, USA 28aTransilvania University of Brasov, Brasov, Romania 28bHoria Hulubei National Institute of Physics and Nuclear Engineering, Bucharest, Romania 28cDepartment of Physics, Alexandru Ioan Cuza University of Iasi, Iasi, Romania 28dNational Institute for Research and Development of Isotopic and Molecular Technologies, Physics Department, Cluj Napoca, Romania SEARCH FOR NEW PHENOMENA IN DIJET EVENTS …PHYSICAL REVIEW D 96, 052004 (2017) 052004-21 28eUniversity Politehnica Bucharest, Bucharest, Romania 28fWest University in Timisoara, Timisoara, Romania 29Departamento de Física, Universidad de Buenos Aires, Buenos Aires, Argentina 30Cavendish Laboratory, University of Cambridge, Cambridge, United Kingdom 31Department of Physics, Carleton University, Ottawa Ontario, Canada 32CERN, Geneva, Switzerland 33Enrico Fermi Institute, University of Chicago, Chicago Illinois, USA 34aDepartamento de Física, Pontificia Universidad Católica de Chile, Santiago, Chile 34bDepartamento de Física, Universidad Técnica Federico Santa María, Valparaíso, Chile 35aInstitute of High Energy Physics, Chinese Academy of Sciences, Beijing, China 35bDepartment of Physics, Nanjing University, Jiangsu, China 35cPhysics Department, Tsinghua University, Beijing, China 36aDepartment of Modern Physics and State Key Laboratory of Particle Detection and Electronics, University of Science and Technology of China, Anhui, China 36bSchool of Physics, Shandong University, Shandong, China 36cDepartment of Physics and Astronomy, Key Laboratory for Particle Physics, Astrophysics and Cosmology, Ministry of Education; Shanghai Key Laboratory for Particle Physics and Cosmology, Shanghai Jiao Tong University, Shanghai(also at PKU-CHEP);, China 37Université Clermont Auvergne, CNRS/IN2P3, LPC, Clermont-Ferrand, France 38Nevis Laboratory, Columbia University, Irvington New York, USA 39Niels Bohr Institute, University of Copenhagen, Kobenhavn, Denmark 40aINFN Gruppo Collegato di Cosenza, Laboratori Nazionali di Frascati, Italy 40bDipartimento di Fisica, Università della Calabria, Rende, Italy 41aAGH University of Science and Technology, Faculty of Physics and Applied Computer Science, Krakow, Poland 41bMarian Smoluchowski Institute of Physics, Jagiellonian University, Krakow, Poland 42Institute of Nuclear Physics Polish Academy of Sciences, Krakow, Poland 43Physics Department, Southern Methodist University, Dallas Texas, USA 44Physics Department, University of Texas at Dallas, Richardson Texas, USA 45DESY, Hamburg and Zeuthen, Germany 46Lehrstuhl für Experimentelle Physik IV, Technische Universität Dortmund, Dortmund, Germany 47Institut für Kernund Teilchenphysik, Technische Universität Dresden, Dresden, Germany 48Department of Physics, Duke University, Durham North Carolina, USA 49SUPA - School of Physics and Astronomy, University of Edinburgh, Edinburgh, United Kingdom 50INFN e Laboratori Nazionali di Frascati, Frascati, Italy 51Fakultät für Mathematik und Physik, Albert-Ludwigs-Universität, Freiburg, Germany 52Departement de Physique Nucleaire et Corpusculaire, Université de Genève, Geneva, Switzerland 53aINFN Sezione di Genova, Italy 53bDipartimento di Fisica, Università di Genova, Genova, Italy 54aE. Andronikashvili Institute of Physics, Iv. Javakhishvili Tbilisi State University, Tbilisi, Georgia 54bHigh Energy Physics Institute, Tbilisi State University, Tbilisi, Georgia 55II Physikalisches Institut, Justus-Liebig-Universität Giessen, Giessen, Germany 56SUPA - School of Physics and Astronomy, University of Glasgow, Glasgow, United Kingdom 57II Physikalisches Institut, Georg-August-Universität, Göttingen, Germany 58Laboratoire de Physique Subatomique et de Cosmologie, Université Grenoble-Alpes, CNRS/IN2P3, Grenoble, France 59Laboratory for Particle Physics and Cosmology, Harvard University, Cambridge Massachusetts, USA 60aKirchhoff-Institut für Physik, Ruprecht-Karls-Universität Heidelberg, Heidelberg, Germany 60bPhysikalisches Institut, Ruprecht-Karls-Universität Heidelberg, Heidelberg, Germany 60cZITI Institut für technische Informatik, Ruprecht-Karls-Universität Heidelberg, Mannheim, Germany 61Faculty of Applied Information Science, Hiroshima Institute of Technology, Hiroshima, Japan 62aDepartment of Physics, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong, China 62bDepartment of Physics, The University of Hong Kong, Hong Kong, China 62cDepartment of Physics and Institute for Advanced Study, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China 63Department of Physics, National Tsing Hua University, Taiwan, Taiwan 64Department of Physics, Indiana University, Bloomington Indiana, USA 65Institut für Astround Teilchenphysik, Leopold-Franzens-Universität, Innsbruck, Austria 66University of Iowa, Iowa City Iowa, USA 67Department of Physics and Astronomy, Iowa State University, Ames Iowa, USA M. AABOUD et al. PHYSICAL REVIEW D 96, 052004 (2017) 052004-22 68Joint Institute for Nuclear Research, JINR Dubna, Dubna, Russia 69KEK, High Energy Accelerator Research Organization, Tsukuba, Japan 70Graduate School of Science, Kobe University, Kobe, Japan 71Faculty of Science, Kyoto University, Kyoto, Japan 72Kyoto University of Education, Kyoto, Japan 73Research Center for Advanced Particle Physics and Department of Physics, Kyushu University, Fukuoka, Japan 74Instituto de Física La Plata, Universidad Nacional de La Plata and CONICET, La Plata, Argentina 75Physics Department, Lancaster University, Lancaster, United Kingdom 76aINFN Sezione di Lecce, Italy 76bDipartimento di Matematica e Fisica, Università del Salento, Lecce, Italy 77Oliver Lodge Laboratory, University of Liverpool, Liverpool, United Kingdom 78Department of Experimental Particle Physics, Jožef Stefan Institute and Department of Physics, University of Ljubljana, Ljubljana, Slovenia 79School of Physics and Astronomy, Queen Mary University of London, London, United Kingdom 80Department of Physics, Royal Holloway University of London, Surrey, United Kingdom 81Department of Physics and Astronomy, University College London, London, United Kingdom 82Louisiana Tech University, Ruston Louisiana, USA 83Laboratoire de Physique Nucléaire et de Hautes Energies, UPMC and Université Paris-Diderot and CNRS/IN2P3, Paris, France 84Fysiska institutionen, Lunds universitet, Lund, Sweden 85Departamento de Fisica Teorica C-15, Universidad Autonoma de Madrid, Madrid, Spain 86Institut für Physik, Universität Mainz, Mainz, Germany 87School of Physics and Astronomy, University of Manchester, Manchester, United Kingdom 88CPPM, Aix-Marseille Université and CNRS/IN2P3, Marseille, France 89Department of Physics, University of Massachusetts, Amherst Massachusetts, USA 90Department of Physics, McGill University, Montreal Quebec, Canada 91School of Physics, University of Melbourne, Victoria, Australia 92Department of Physics, The University of Michigan, Ann Arbor Michigan, USA 93Department of Physics and Astronomy, Michigan State University, East Lansing Michigan, USA 94aINFN Sezione di Milano, Italy 94bDipartimento di Fisica, Università di Milano, Milano, Italy 95B.I. Stepanov Institute of Physics, National Academy of Sciences of Belarus, Minsk, Republic of Belarus 96Research Institute for Nuclear Problems of Byelorussian State University, Minsk, Republic of Belarus 97Group of Particle Physics, University of Montreal, Montreal Quebec, Canada 98P.N. Lebedev Physical Institute of the Russian Academy of Sciences, Moscow, Russia 99Institute for Theoretical and Experimental Physics (ITEP), Moscow, Russia 100National Research Nuclear University MEPhI, Moscow, Russia 101D.V. Skobeltsyn Institute of Nuclear Physics, M.V. Lomonosov Moscow State University, Moscow, Russia 102Fakultät für Physik, Ludwig-Maximilians-Universität München, München, Germany 103Max-Planck-Institut für Physik (Werner-Heisenberg-Institut), München, Germany 104Nagasaki Institute of Applied Science, Nagasaki, Japan 105Graduate School of Science and Kobayashi-Maskawa Institute, Nagoya University, Nagoya, Japan 106aINFN Sezione di Napoli, Italy 106bDipartimento di Fisica, Università di Napoli, Napoli, Italy 107Department of Physics and Astronomy, University of New Mexico, Albuquerque New Mexico, USA 108Institute for Mathematics, Astrophysics and Particle Physics, Radboud University Nijmegen/Nikhef, Nijmegen, Netherlands 109Nikhef National Institute for Subatomic Physics and University of Amsterdam, Amsterdam, Netherlands 110Department of Physics, Northern Illinois University, DeKalb Illinois, USA 111Budker Institute of Nuclear Physics, SB RAS, Novosibirsk, Russia 112Department of Physics, New York University, New York New York, USA 113Ohio State University, Columbus Ohio, USA 114Faculty of Science, Okayama University, Okayama, Japan 115Homer L. Dodge Department of Physics and Astronomy, University of Oklahoma, Norman Oklahoma, USA 116Department of Physics, Oklahoma State University, Stillwater Oklahoma, USA 117Palacký University, RCPTM, Olomouc, Czech Republic 118Center for High Energy Physics, University of Oregon, Eugene Oregon, USA SEARCH FOR NEW PHENOMENA IN DIJET EVENTS …PHYSICAL REVIEW D 96, 052004 (2017) 052004-23 119LAL, Univ. Paris-Sud, CNRS/IN2P3, Université Paris-Saclay, Orsay, France 120Graduate School of Science, Osaka University, Osaka, Japan 121Department of Physics, University of Oslo, Oslo, Norway 122Department of Physics, Oxford University, Oxford, United Kingdom 123aINFN Sezione di Pavia, Italy 123bDipartimento di Fisica, Università di Pavia, Pavia, Italy 124Department of Physics, University of Pennsylvania, Philadelphia Pennsylvania, USA 125National Research Centre “Kurchatov Institute”B.P.Konstantinov Petersburg Nuclear Physics Institute, Saint Petersburg, Russia 126aINFN Sezione di Pisa, Italy 126bDipartimento di Fisica E. Fermi, Università di Pisa, Pisa, Italy 127Department of Physics and Astronomy, University of Pittsburgh, Pittsburgh Pennsylvania, USA 128aLaboratório de Instrumentação e Física Experimental de Partículas - LIP, Lisboa, Portugal 128bFaculdade de Ciências, Universidade de Lisboa, Lisboa, Portugal 128cDepartment of Physics, University of Coimbra, Coimbra, Portugal 128dCentro de Física Nuclear da Universidade de Lisboa, Lisboa, Portugal 128eDepartamento de Fisica, Universidade do Minho, Braga, Portugal 128fDepartamento de Fisica Teorica y del Cosmos and CAFPE, Universidad de Granada, Granada, Portugal 128gDep Fisica and CEFITEC of Faculdade de Ciencias e Tecnologia, Universidade Nova de Lisboa, Caparica, Portugal 129Institute of Physics, Academy of Sciences of the Czech Republic, Praha, Czech Republic 130Czech Technical University in Prague, Praha, Czech Republic 131Charles University, Faculty of Mathematics and Physics, Prague, Czech Republic 132State Research Center Institute for High Energy Physics (Protvino), NRC KI, Russia 133Particle Physics Department, Rutherford Appleton Laboratory, Didcot, United Kingdom 134aINFN Sezione di Roma, Italy 134bDipartimento di Fisica, Sapienza Università di Roma, Roma, Italy 135aINFN Sezione di Roma Tor Vergata, Italy 135bDipartimento di Fisica, Università di Roma Tor Vergata, Roma, Italy 136aINFN Sezione di Roma Tre, Italy 136bDipartimento di Matematica e Fisica, Università Roma Tre, Roma, Italy 137aFaculté des Sciences Ain Chock, Réseau Universitaire de Physique des Hautes Energies - Université Hassan II, Casablanca, Morocco 137bCentre National de l’Energie des Sciences Techniques Nucleaires, Rabat, Morocco 137cFaculté des Sciences Semlalia, Université Cadi Ayyad, LPHEA-Marrakech, Morocco 137dFaculté des Sciences, Université Mohamed Premier and LPTPM, Oujda, Morocco 137eFaculté des sciences, Université Mohammed V, Rabat, Morocco 138DSM/IRFU (Institut de Recherches sur les Lois Fondamentales de l’Univers), CEA Saclay (Commissariat à l’Energie Atomique et aux Energies Alternatives), Gif-sur-Yvette, France 139Santa Cruz Institute for Particle Physics, University of California Santa Cruz, Santa Cruz California, USA 140Department of Physics, University of Washington, Seattle Washington, USA 141Department of Physics and Astronomy, University of Sheffield, Sheffield, United Kingdom 142Department of Physics, Shinshu University, Nagano, Japan 143Department Physik, Universität Siegen, Siegen, Germany 144Department of Physics, Simon Fraser University, Burnaby British Columbia, Canada 145SLAC National Accelerator Laboratory, Stanford California, USA 146aFaculty of Mathematics, Physics & Informatics, Comenius University, Bratislava, Slovak Republic 146bDepartment of Subnuclear Physics, Institute of Experimental Physics of the Slovak Academy of Sciences, Kosice, Slovak Republic 147aDepartment of Physics, University of Cape Town, Cape Town, South Africa 147bDepartment of Physics, University of Johannesburg, Johannesburg, South Africa 147cSchool of Physics, University of the Witwatersrand, Johannesburg, South Africa 148aDepartment of Physics, Stockholm University, Sweden 148bThe Oskar Klein Centre, Stockholm, Sweden 149Physics Department, Royal Institute of Technology, Stockholm, Sweden 150Departments of Physics & Astronomy and Chemistry, Stony Brook University, Stony Brook New York, USA 151Department of Physics and Astronomy, University of Sussex, Brighton, United Kingdom M. AABOUD et al. PHYSICAL REVIEW D 96, 052004 (2017) 052004-24 152School of Physics, University of Sydney, Sydney, Australia 153Institute of Physics, Academia Sinica, Taipei, Taiwan 154Department of Physics, Technion: Israel Institute of Technology, Haifa, Israel 155Raymond and Beverly Sackler School of Physics and Astronomy, Tel Aviv University, Tel Aviv, Israel 156Department of Physics, Aristotle University of Thessaloniki, Thessaloniki, Greece 157International Center for Elementary Particle Physics and Department of Physics, The University of Tokyo, Tokyo, Japan 158Graduate School of Science and Technology, Tokyo Metropolitan University, Tokyo, Japan 159Department of Physics, Tokyo Institute of Technology, Tokyo, Japan 160Tomsk State University, Tomsk, Russia 161Department of Physics, University of Toronto, Toronto Ontario, Canada 162aINFN-TIFPA, Italy 162bUniversity of Trento, Trento, Italy 163aTRIUMF, Vancouver British Columbia, Canada 163bDepartment of Physics and Astronomy, York University, Toronto Ontario, Canada 164Faculty of Pure and Applied Sciences, and Center for Integrated Research in Fundamental Science and Engineering, University of Tsukuba, Tsukuba, Japan 165Department of Physics and Astronomy, Tufts University, Medford Massachusetts, USA 166Department of Physics and Astronomy, University of California Irvine, Irvine California, USA 167aINFN Gruppo Collegato di Udine, Sezione di Trieste, Udine, Italy 167bICTP, Trieste, Italy 167cDipartimento di Chimica, Fisica e Ambiente, Università di Udine, Udine, Italy 168Department of Physics and Astronomy, University of Uppsala, Uppsala, Sweden 169Department of Physics, University of Illinois, Urbana Illinois, USA 170Instituto de Fisica Corpuscular (IFIC), Centro Mixto Universidad de Valencia - CSIC, Spain 171Department of Physics, University of British Columbia, Vancouver British Columbia, Canada 172Department of Physics and Astronomy, University of Victoria, Victoria British Columbia, Canada 173Department of Physics, University of Warwick, Coventry, United Kingdom 174Waseda University, Tokyo, Japan 175Department of Particle Physics, The Weizmann Institute of Science, Rehovot, Israel 176Department of Physics, University of Wisconsin, Madison Wisconsin, USA 177Fakultät für Physik und Astronomie, Julius-Maximilians-Universität, Würzburg, Germany 178Fakultät für Mathematik und Naturwissenschaften, Fachgruppe Physik, Bergische Universität Wuppertal, Wuppertal, Germany 179Department of Physics, Yale University, New Haven Connecticut, USA 180Yerevan Physics Institute, Yerevan, Armenia 181CH-1211 Geneva 23, Switzerland 182Centre de Calcul de l’Institut National de Physique Nucléaire et de Physique des Particules (IN2P3), Villeurbanne, France 183Academia Sinica Grid Computing, Institute of Physics, Academia Sinica, Taipei, Taiwan aDeceased. bAlso at Department of Physics, King’s College London, London, United Kingdom. cAlso at Institute of Physics, Azerbaijan Academy of Sciences, Baku, Azerbaijan. dAlso at Novosibirsk State University, Novosibirsk, Russia. eAlso at TRIUMF, Vancouver British Columbia, Canada. fAlso at Department of Physics & Astronomy, University of Louisville, Louisville, Kentucky, USA. gAlso at Physics Department, An-Najah National University, Nablus, Palestine. hAlso at Department of Physics, California State University, Fresno California, USA. iAlso at Department of Physics, University of Fribourg, Fribourg, Switzerland. jAlso at II Physikalisches Institut, Georg-August-Universität, Göttingen, Germany. kAlso at Departament de Fisica de la Universitat Autonoma de Barcelona, Barcelona, Spain. lAlso at Departamento de Fisica e Astronomia, Faculdade de Ciencias, Universidade do Porto, Portugal. mAlso at Tomsk State University, Tomsk, Russia. nAlso at The Collaborative Innovation Center of Quantum Matter (CICQM), Beijing, China. oAlso at Universita di Napoli Parthenope, Napoli, Italy. pAlso at Institute of Particle Physics (IPP), Canada. qAlso at Horia Hulubei National Institute of Physics and Nuclear Engineering, Bucharest, Romania. rAlso at Department of Physics, St. Petersburg State Polytechnical University, St. Petersburg, Russia. sAlso at Borough of Manhattan Community College, City University of New York, New York City, USA. SEARCH FOR NEW PHENOMENA IN DIJET EVENTS …PHYSICAL REVIEW D 96, 052004 (2017) 052004-25