arXiv:1109.4725v2 [hep-ex] 5 Dec 2011
Search for New Phenomena in t ¯ t Events with Large Missing Transverse Momentum in Proton-Proton Collisions at √ s = 7 TeV with the ATLAS Detector
ATLAS Collaboration (Dated: November 8, 2018)
A search for new phenomena in t¯tevents with large missing transverse momentum in proton- proton collisions at a center-of-mass energy of 7 TeV is presented. The measurement is based on 1.04 fb−1 of data collected with the ATLAS detector at the LHC. The search is carried out in the single-lepton channel, characterized by an isolated lepton of high transverse momentum, four or more jets and large missing transverse momentum. Contributions to this final state may arise from a number of Standard Model extensions. The results are interpreted in terms of a model where new top-quark partners are pair-produced and each decay to an on-shell top (or antitop) quark and a long-lived undetected neutral particle. The data are found to be consistent with Standard Model expectations. A limit at 95% confidence level is set excluding a cross-section times branching ratio of 1.1 pb for a top-partner mass of 420 GeV and a neutral particle mass less than 10 GeV.
In a model of exotic fourth generation quarks, top-partner masses are excluded up to 420 GeV and neutral particle masses up to 140 GeV.
The top quark holds great promise as a probe for new phenomena at the TeV scale. It has the strongest cou- pling to the Standard Model Higgs boson, and as a conse- quence it is the main contributor to the quadratic diver- gence in the Higgs mass. Thus, assuming the “natural- ness” hypothesis of effective quantum field theory, light top partners (with masses below about 1 TeV) should correspond to one of the most robust predictions of solu- tions to the hierarchy problem.
In this letter, a search is presented for pair-produced exotic top partnersT T, each decaying to a top quark and a stable, neutral weakly-interacting particle A0, which in some models may be its own antiparticle. The final state for such a process (T T → ttA0A0) is identical to tt, though with a larger amount of missing transverse momentum (ETmiss) from the undetectedA0 pair. In su- persymmetry models with R-parity conservation, T is identified with the stop squark and A0 with the light- est supersymmetric particle, the neutralino (χ0) [1] or the gravitino ( ˜G) [2]. The tt+ETmiss [3] signature ap- pears in a general set of dark matter motivated mod- els, as well as in other Standard Model (SM) extensions, such as the above-mentioned supersymmetry models, lit- tle Higgs models withT-parity conservation [4–6], models of universal extra dimensions (UED) with Kaluza-Klein parity [7], models in which baryon and lepton number conservation arises from gauge symmetries [8] or models with third generation scalar leptoquarks. Many of these models provide a mechanism for electroweak symmetry breaking and predict dark matter candidates, which can be identified indirectly through their large ETmiss signa- ture.
The search is performed in thet¯tsingle-lepton channel where oneW boson produced by the top pair decays to a lepton-neutrino pair (W →ℓν, includingτ decays toe or µ) and the otherW boson decays to a pair of quarks (W → qq′), resulting in a final state with an isolated lepton of high transverse momentum, four or more jets
and largeETmiss. The observed yield in this signal region is compared with the SM expectation. In the absence of signal an upper limit on the cross-section times branch- ing ratio BR(T T →ttA0A0) is quoted. In the model of exotic fourth generation up-type quarks [9] theT T pro- duction cross-section is predicted to be approximately six times higher than for stop squarks with a similar mass [3], due to the multiple spin states of two T’s compared to scalar stops. For this model the cross-section limits are converted to an exclusion curve in the T vs A0 mass parameter space. A search for these exotic top-quark partners was performed in proton-antiproton collisions at√
s= 1.96 TeV by the CDF Collaboration [10]. The data were found to be consistent with SM expectations.
A 95% confidence level limit was set excluding a top- partner mass of 360 GeV for a neutral particle mass less than 100 GeV. A recent update by CDF in the all-jets channel excludes top-partner masses up to 400 GeV [11].
The ATLAS detector [12] consists of an inner detec- tor tracking system (ID) surrounded by a superconduct- ing solenoid providing a 2 T magnetic field, electromag- netic and hadronic calorimeters, and a muon spectrome- ter (MS). The ID consists of pixel and silicon microstrip detectors inside a transition radiation tracker which pro- vide tracking in the region|η|<2.5 [13]. The electromag- netic calorimeter is a lead/liquid-argon (LAr) detector in the barrel (|η|< 1.475) and endcap (1.375< |η|<3.2) regions. Hadron calorimetry is based on two different detector technologies. The barrel (|η| < 0.8) and ex- tended barrel (0.8<|η|<1.7) calorimeters are composed of scintillator/steel, while the hadronic endcap calorime- ters (1.5 < |η| < 3.2) are copper/LAr. The forward calorimeters (3.1 < |η| < 4.9) are instrumented with copper/LAr and tungsten/LAr, providing electromag- netic and hadronic energy measurements, respectively.
The MS consists of three large superconducting toroids with 24 coils, a system of trigger chambers, and preci- sion tracking chambers which provide muon momentum
measurements up to|η|of 2.7.
The analysis is based on data recorded by the AT- LAS detector in 2011 using 1.04 fb−1 of integrated lu- minosity. The data were collected using electron and muon triggers. Requirements that ensure the quality of beam conditions, detector performance and data are im- posed. Monte Carlo (MC) event samples with full AT- LAS detector simulation [14] based on theGeant4pro- gram [15] and corrected for all known detector effects are used to model the signal process and most of the back- grounds. The multi-jet background is modeled using data control samples rather than the simulation. The back- ground sources are separated into four main categories according to their importance: dilepton tt (where both W bosons decay to a lepton-neutrino pair: W → ℓν);
single-lepton tt and W+jets; multi-jet production; and other electroweak processes, such as diboson production, single top, and Z+jets. The tt and single top samples are produced with MC@NLO [16], while the W+jets and Z+jets samples are generated with Alpgen [17].
Herwig[18] is used to simulate the parton shower and fragmentation, andJimmy[19] is used for the underlying event simulation. The diboson background is simulated using Herwig. The tt cross-section is normalized to approximate next-to-next-to-leading order (NNLO) cal- culations [20], the inclusive W+jets and Z+jets cross- sections are normalized to NNLO predictions [21], and the cross-sections of the other backgrounds are normal- ized to NLO predictions [22]. Additional corrections to the MC predictions are extracted from the data, as de- scribed below.
Electron and muon candidates are selected as for other recent ATLAS top quark studies using the single-lepton signature [23]. Jets are reconstructed using the anti- kt [24] algorithm with the distance parameter R = 0.4.
To take into account the differences in calorimeter re- sponse to electrons and hadrons, apT- andη-dependent factor, derived from simulated events and validated with data, is applied to each jet to provide an average energy scale correction [25] corresponding to the energies of the reconstructed particles.
In the calorimeter, the energy deposited by particles is reconstructed in three-dimensional clusters. These clus- ters are calibrated according to the associated recon- structed high-pT object. The energy of these clusters is summed vectorially, and projections of this sum in the transverse plane correspond to the negative of theETmiss components [26]. Clusters not associated with any high- pT object and muons reconstructed in the MS are also included in theETmisscalculation.
Events are selected with exactly one isolated electron or muon that passes the following kinematic selection cri- teria. Electrons are required to satisfy ET > 25 GeV and|η|<2.47. Electrons in the region between the bar- rel and the endcap electromagnetic calorimeters (1.37<
|η| <1.52) are removed. Muon candidates are required to satisfy pT > 20 GeV and |η| < 2.5. These selected leptons lie in the efficiency plateau of the single-lepton
triggers. Only events with four or more reconstructed jets with pT > 25 GeV and |η| < 2.5 are selected.
To reduce the single-leptont¯t and W+jets background, events are required to have ETmiss > 100 GeV and mT
>150 GeV, wheremTis the transverse mass of the lepton andETmiss [27]. Events with either a second lepton can- didate withpT>15 GeV or a track withpT>12 GeV, with no other tracks withpT>3 GeV within ∆R= 0.4 (∆R ≡ p
∆η2+ ∆φ2), are rejected in order to reduce the contribution from tt dilepton events. In particular the isolated track veto is useful in reducing single-prong hadronicτ decays intt dilepton events. A summary of the background estimates and a comparison with the ob- served number of selected events passing all selection cri- teria are shown in Table I. A total yield of 101 ± 16 events is expected from SM sources, and 105 events are observed in data. The background composition is similar in the electron and muon channels.
TABLE I: Summary of expected SM yields including statisti- cal and systematic uncertainties compared with the observed number of events in the signal region.
Source Number of events Dileptontt 62±15 Single-leptontt/W+jets 33.1±3.8
Multi-jet 1.2±1.2 Single top 3.5±0.8 Z+jets 0.9±0.3 Dibosons 0.9±0.2 Total 101±16
Data 105
The dominant background arises fromttdilepton final states in which one of the leptons is not reconstructed, is outside the detector acceptance, or is a τ lepton. In all such cases, thettdecay products include two high-pT
neutrinos, resulting in largeETmissandmT tails. In MC, the second lepton veto removes 45% of the dileptonttand 10% of the single-leptonttin the signal region. The veto performance is validated in the data in several control regions both enhanced and depleted in dileptontt. Based on the data-MC agreement in these control regions a 10%
uncertainty is assigned to the veto efficiencies modeled in MC simulation.
The next largest background comes from single-lepton sources, including W+jets and tt with one leptonic W decay. Both the normalization and the shape of themT
distribution for this combined background are extracted from the data. First, the yield of the single-lepton back- ground estimated from simulation is normalized in the control region 60 GeV< mT<90 GeV to the data which gives a correction of (−5±3)%. Next, the shape of the mTdistribution in MC is compared with data in various signal-depleted control regions, where events satisfy the signal event selection but have fewer than four jets. In these control samples events with identifiedb-jets, based on lifetimeb-tagging [23], are rejected in order to reduce
the dileptont¯tbackground, such that these control sam- ples are dominated byW+jets events; the corresponding loss of single-lepton t¯t from this b-jet veto is accounted for in the systematic uncertainties. A comparison be- tween data and MC in this control region shows that MC systematically underestimates the tails of themT distri- bution above 150 GeV, and a shape correction is derived that results in a (15±10)% increase of the expected yield in the signal region.
The multi-jet background is extracted from the data using techniques similar to those described in Ref. [23].
The techniques exploit the fact that the lepton isolation efficiency is different in signal and multi-jet events. In both lepton channels the contribution to the signal region is consistent with zero.
The contributions from single top, diboson produc- tion (W W, W Z, and ZZ), and Z+jets are estimated using MC simulation, normalized to the theoretical cross- section and total integrated luminosity.
The background yields estimated from MC simulated events are affected by systematic uncertainties related to the modeling of detector performance, reconstruction and object identification. The largest of these uncertainties are from the jet energy scale [25] (approximately 5-7%
on the jet pT, including a contribution from pileup ef- fects, leading to an 11% uncertainty on the background event yield), and from the performance of the second lepton veto in dileptontt(10%). Other uncertainties in- clude those on the lepton momentum scales and trigger and reconstruction efficiencies. Lepton momentum scales and resolutions are determined from fits to the Z-mass peak. Trigger and reconstruction efficiencies are evalu- ated using tag-and-probe measurements in Z → e+e− or Z → µ+µ− events. To evaluate the effect of lepton momentum and jet energy scale uncertainties, theETmiss andmTare recalculated for each uncertainty on selected objects. Other small uncertainties affecting theETmisscal- culation are due to multipleppinteractions, jets withpT
below 20 GeV, and calorimeter clusters that are not asso- ciated to a selected object [28]. Additionally, theoretical cross-section uncertainties from choice of scales and par- ton distribution functions are considered for these back- ground sources, as are the effects of using alternative MC generators, shower models, and initial- and final-state ra- diation tunings [23]. Finally, the 3.7% uncertainty on the integrated luminosity [29] is applied to each background source.
The systematic uncertainties applied to data-driven backgrounds are determined from the data. The dom- inant uncertainty for single-lepton backgrounds is due to the (15±10)% shape correction, and is derived from the variation in the measured correction in different control regions and from uncertainties in theb-tagging efficiency.
The uncertainty on the single-lepton normalization of (−5±3)% includes equal contributions from limited data statistics in theW mass region and expected differences between the W+jets and single-lepton tt contributions to the signal and control regions. A 100% systematic
uncertainty is assigned to the small estimated multi-jet yield.
The expected and observed event yields are consistent within statistical and systematic uncertainties. There- fore, the results are interpreted as a limit on the possible non-SM contribution to the selected sample. A model in- volving pair-production of heavy quark-like objects (T T), each forced to decay to a top quark and a scalar neutral A0, is chosen to establish these limits.
MadGraph[30] is used to simulate the signal process with the parton distribution function set CTEQ6L1 [31], andPythia [32] is used to simulate the parton shower and fragmentation. A grid ofT and A0 masses is gen- erated with 300 GeV ≤m(T) ≤450 GeV and 10 GeV
≤m(A0)≤ 150 GeV. Each sample is normalized to the cross-section calculated at approximate NNLO in QCD usingHathor[33], ranging from 8.0 pb for a T mass of 300 GeV to 0.66 pb for aT mass of 450 GeV. Using this grid of signal samples, the efficiency times acceptance for theT T signal model is parametrized as a function of the T and A0 masses to generate the expected signal event yield for any pair of masses. The combined acceptance times signal selection efficiency varies between 3 and 5%
for smallA0 masses and decreases to between 2 and 4%
for largerA0 masses.
All common systematic uncertainties for MC-based backgrounds are applied to this signal model. These in- clude the uncertainties on the jet energy scale, lepton reconstruction efficiencies and scales, integrated luminos- ity and the dilepton veto efficiency. Overall, the system- atic uncertainty on the signal acceptance times efficiency varies between 11 and 14%, and is largest for those sam- ples with aT-A0mass difference closest to the top quark mass. The theoretical uncertainties on the signal cross- section vary between 10 to 15% and originate mainly from the choice of scales (mT/2< µR=µF <2mT, whereµR
andµF are the renormalisation and factorization scale) and parton distribution functions.
The ETmiss and mT distributions for data are shown in Fig. 1 and compared with the background and signal predictions. There is no significant evidence of an ex- cess over the SM prediction, and the kinematics are well modeled.
From the observed event yield and the predicted sig- nal and background event yields after all cuts, a frequen- tist confidence interval on the signal hypothesis is cal- culated for various assumedT andA0 masses, assuming Gaussian systematic uncertainties. Correlations between signal and background uncertainties are included. Fig- ure 2 shows the region of parameter space excluded at the 95% confidence level. As the mass difference between the T and A0 approaches the top quark mass, the A0 con- tributes less momentum to theETmiss, and signal becomes indistinguishable from SMtt. Assuming aT T→ttA0A0
branching ratio of 100%, signal points withT mass up to 420 GeV are excluded at the 95% confidence level for an A0 mass below 10 GeV, as are signal points with 330 GeV < m(T) < 390 GeV for an A0 mass below
[GeV]
mT
150 200 250 300 350 400
Events/25 GeV
0 20 40 60 80 100 120
[GeV]
mT
150 200 250 300 350 400
Events/25 GeV
0 20 40 60 80 100
120 Data, s=7 TeV
W + jets t t
Other Backgrounds Background Uncertainty
)=100 GeV m(T)=360 GeV, m(A0
)=100 GeV m(T)=440 GeV, m(A0
L dt=1.04 fb-1
∫
150 200 250 300 350 400
0 20 40 60 80 100
120 ATLAS (a)
[GeV]
miss
ET
100 150 200 250 300 350 400
Events/30 GeV
0 10 20 30 40 50 60 70 80 90
[GeV]
miss
ET
100 150 200 250 300 350 400
Events/30 GeV
0 10 20 30 40 50 60 70 80
90 Data, s=7 TeV
W + jets t t
Other Backgrounds Background Uncertainty
)=100 GeV m(T)=360 GeV, m(A0
)=100 GeV m(T)=440 GeV, m(A0
L dt=1.04 fb-1
∫
100 150 200 250 300 350 400
0 10 20 30 40 50 60 70 80
90 ATLAS (b)
FIG. 1: (a) Transverse mass of the lepton and missing energy and (b)ETmiss after applying all selection criteria except the cut on the variable shown. MC background contributions are stacked on top of each other and normalized according to the data-driven corrections discussed in the text. The lines with the arrows indicate the selection criteria that define the signal region (mT>150 GeV andETmiss>100 GeV). ‘Other Back- grounds’ includes both multi-jet backgrounds and Z+jets, single top and diboson production. Expectations from two signal mass points are stacked separately on top of the SM background. The last bin includes the overflow.
140 GeV. Figure 3 shows the cross-section times branch- ing ratio excluded at the 95% confidence level versus T mass, for an A0 mass of 10 GeV. A cross-section times branching ratio of 1.1 (1.9) pb is excluded at the 95% con- fidence level for a T mass of 420 (370) GeV and an A0
mass of 10 (140) GeV. The estimated acceptance times efficiency for spin-12 T T models is consistent within sys- tematic uncertainties with that for scalar models, such as pair production of stop squarks (with a ttχ0χ0 final state) or third-generation leptoquarks (with attντντ fi- nal state). The cross-section limits presented in Fig. 3 are therefore approximately valid for such models, although the predicted cross-section is typically below the current sensitivity.
T Mass [GeV]
300 350 400 450 500 550 600
Mass [GeV]0A
50 100 150 200
T Mass [GeV]
300 350 400 450 500 550 600
Mass [GeV]0A
50 100 150 200
σ)
±1 Expected Limit ( Obs. Limit (Theory Unc.) CDF Exclusion
0)
0A A t
→t T BR(T
× σ Excl.
ATLAS L dt=1.04 fb-1
∫
s=7 TeV) < m(t)
0
m(T)-m(A
1.5pb 1pb 2pb 3pb
FIG. 2: Excluded region (under the curve) at the 95% confi- dence level as a function ofT andA0masses, compared with the CDF exclusion [10, 11]. Theoretical uncertainties on the T T cross-section are not included in the limit, but the effect of these uncertainties is shown. The gray contours show the excluded cross-section times branching ratio as a function of the two masses.
T Mass [GeV]
300 320 340 360 380 400 420 440 ) [pb]0A0 Att→T BR(T×σ
10-1
1 10
Mass = 10 GeV A0
σ)
±1 Expected Limit ( Observed Limit
σ
±1 Theory T NNLO Spin-1/2 T
σ
±1 Theory T NLO Scalar T
ATLAS L dt=1.04 fb-1
∫
s=7 TeVFIG. 3: Cross-section times branching ratio excluded at the 95% confidence level versusTmass for anA0mass of 10 GeV.
Theoretical predictions for both spin-12 and scalarT pair pro- duction are also shown.
In summary, in 1.04 fb−1 of data in ppcollisions at a center-of-mass energy of 7 TeV, there is no evidence of an excess of events with largeETmissin a sample dominated byttevents. Using a model of pair-produced quark-like objects decaying to a top quark and a heavy neutral par- ticle, a limit is established excluding masses of these top partners up to 420 GeV and stable weakly-interacting particle masses up to 140 GeV (see Fig. 2). In particular, a cross-section times branching ratio of 1.1 pb is excluded at the 95% confidence level for m(T) = 420 GeV and m(A0) = 10 GeV. The cross-section limits are approxi- mately valid for a number of models of new phenomena.
We thank CERN for the very successful operation of the LHC, as well as the support staff from our in- stitutions without whom ATLAS could not be oper- ated efficiently. We acknowledge the support of AN- PCyT, Argentina; YerPhI, Armenia; ARC, Australia;
BMWF, Austria; ANAS, Azerbaijan; SSTC, Belarus;
CNPq and FAPESP, Brazil; NSERC, NRC and CFI, Canada; CERN; CONICYT, Chile; CAS, MOST and NSFC, China; COLCIENCIAS, Colombia; MSMT CR, MPO CR and VSC CR, Czech Republic; DNRF, DNSRC and Lundbeck Foundation, Denmark; ARTEMIS, Euro- pean Union; IN2P3-CNRS, CEA-DSM/IRFU, France;
GNAS, Georgia; BMBF, DFG, HGF, MPG and AvH Foundation, Germany; GSRT, Greece; ISF, MINERVA, GIF, DIP and Benoziyo Center, Israel; INFN, Italy;
MEXT and JSPS, Japan; CNRST, Morocco; FOM and NWO, Netherlands; RCN, Norway; MNiSW, Poland;
GRICES and FCT, Portugal; MERYS (MECTS), Ro- mania; MES of Russia and ROSATOM, Russian Federa- tion; JINR; MSTD, Serbia; MSSR, Slovakia; ARRS and MVZT, Slovenia; DST/NRF, South Africa; MICINN, Spain; SRC and Wallenberg Foundation, Sweden; SER, SNSF and Cantons of Bern and Geneva, Switzerland;
NSC, Taiwan; TAEK, Turkey; STFC, the Royal Society and Leverhulme Trust, United Kingdom; DOE and NSF, United States of America. The crucial computing sup- port from all WLCG partners is acknowledged gratefully, in particular from CERN and the ATLAS Tier-1 facili- ties at TRIUMF (Canada), NDGF (Denmark, Norway, Sweden), CC-IN2P3 (France), KIT/GridKA (Germany), INFN-CNAF (Italy), NL-T1 (Netherlands), PIC (Spain), ASGC (Taiwan), RAL (UK) and BNL (USA) and in the Tier-2 facilities worldwide.
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The ATLAS Collaboration
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B. ˚Asman146a,146b, L. Asquith5, K. Assamagan24, A. Astbury169, A. Astvatsatourov52, G. Atoian175, B. Aubert4, E. Auge115, K. Augsten127,
M. Aurousseau145a, N. Austin73, G. Avolio163, R. Avramidou9, D. Axen168, C. Ay54, G. Azuelos93,e, Y. Azuma155, M.A. Baak29, G. Baccaglioni89a, C. Bacci134a,134b, A.M. Bach14, H. Bachacou136, K. Bachas29, G. Bachy29, M. Backes49, M. Backhaus20, E. Badescu25a, P. Bagnaia132a,132b, S. Bahinipati2, Y. Bai32a, D.C. Bailey158, T. Bain158, J.T. Baines129, O.K. Baker175, M.D. Baker24, S. Baker77, E. Banas38, P. Banerjee93, Sw. Banerjee172, D. Banfi29,
A. Bangert137, V. Bansal169, H.S. Bansil17, L. Barak171, S.P. Baranov94, A. Barashkou65, A. Barbaro Galtieri14, T. Barber27, E.L. Barberio86, D. Barberis50a,50b, M. Barbero20, D.Y. Bardin65, T. Barillari99, M. Barisonzi174, T. Barklow143, N. Barlow27, B.M. Barnett129, R.M. Barnett14, A. Baroncelli134a, G. Barone49, A.J. Barr118, F. Barreiro80, J. Barreiro Guimar˜aes da Costa57, P. Barrillon115, R. Bartoldus143, A.E. Barton71, D. Bartsch20, V. Bartsch149,
R.L. Bates53, L. Batkova144a, J.R. Batley27, A. Battaglia16, M. Battistin29, G. Battistoni89a,
F. Bauer136, H.S. Bawa143,f, B. Beare158, T. Beau78, P.H. Beauchemin118, R. Beccherle50a, P. Bechtle41, H.P. Beck16, M. Beckingham138, K.H. Becks174, A.J. Beddall18c, A. Beddall18c, S. Bedikian175, V.A. Bednyakov65, C.P. Bee83, M. Begel24,
S. Behar Harpaz152, P.K. Behera63, M. Beimforde99, C. Belanger-Champagne85, P.J. Bell49, W.H. Bell49, G. Bella153, L. Bellagamba19a, F. Bellina29,
M. Bellomo29, A. Belloni57, O. Beloborodova107, K. Belotskiy96, O. Beltramello29, S. Ben Ami152, O. Benary153, D. Benchekroun135a, C. Benchouk83, M. Bendel81, N. Benekos165, Y. Benhammou153, D.P. Benjamin44, M. Benoit115, J.R. Bensinger22, K. Benslama130, S. Bentvelsen105, D. Berge29,
E. Bergeaas Kuutmann41, N. Berger4, F. Berghaus169, E. Berglund49, J. Beringer14, K. Bernardet83,
P. Bernat77, R. Bernhard48, C. Bernius24, T. Berry76, A. Bertin19a,19b, F. Bertinelli29, F. Bertolucci122a,122b, M.I. Besana89a,89b, N. Besson136, S. Bethke99,
W. Bhimji45, R.M. Bianchi29, M. Bianco72a,72b, O. Biebel98, S.P. Bieniek77, K. Bierwagen54, J. Biesiada14, M. Biglietti134a,134b, H. Bilokon47, M. Bindi19a,19b, S. Binet115, A. Bingul18c, C. Bini132a,132b, C. Biscarat177, U. Bitenc48, K.M. Black21, R.E. Blair5, J.-B. Blanchard115,
G. Blanchot29, T. Blazek144a, C. Blocker22, J. Blocki38, A. Blondel49, W. Blum81, U. Blumenschein54,
G.J. Bobbink105, V.B. Bobrovnikov107, S.S. Bocchetta79, A. Bocci44, C.R. Boddy118, M. Boehler41, J. Boek174, N. Boelaert35, S. B¨oser77, J.A. Bogaerts29, A. Bogdanchikov107, A. Bogouch90,∗, C. Bohm146a, V. Boisvert76, T. Bold37, V. Boldea25a, N.M. Bolnet136, M. Bona75, V.G. Bondarenko96, M. Bondioli163, M. Boonekamp136, G. Boorman76, C.N. Booth139, S. Bordoni78, C. Borer16, A. Borisov128, G. Borissov71, I. Borjanovic12a, S. Borroni87, K. Bos105, D. Boscherini19a, M. Bosman11, H. Boterenbrood105, D. Botterill129, J. Bouchami93, J. Boudreau123, E.V. Bouhova-Thacker71, C. Bourdarios115,
N. Bousson83, A. Boveia30, J. Boyd29, I.R. Boyko65, N.I. Bozhko128, I. Bozovic-Jelisavcic12b, J. Bracinik17, A. Braem29, P. Branchini134a, G.W. Brandenburg57, A. Brandt7, G. Brandt15, O. Brandt54, U. Bratzler156, B. Brau84, J.E. Brau114, H.M. Braun174, B. Brelier158, J. Bremer29, R. Brenner166, S. Bressler152,
D. Breton115, D. Britton53, F.M. Brochu27, I. Brock20, R. Brock88, T.J. Brodbeck71, E. Brodet153,
F. Broggi89a, C. Bromberg88, G. Brooijmans34, W.K. Brooks31b, G. Brown82, H. Brown7, P.A. Bruckman de Renstrom38, D. Bruncko144b, R. Bruneliere48, S. Brunet61, A. Bruni19a, G. Bruni19a, M. Bruschi19a, T. Buanes13, F. Bucci49, J. Buchanan118, N.J. Buchanan2, P. Buchholz141, R.M. Buckingham118, A.G. Buckley45, S.I. Buda25a, I.A. Budagov65,
B. Budick108, V. B¨uscher81, L. Bugge117, D. Buira-Clark118, O. Bulekov96, M. Bunse42,
T. Buran117, H. Burckhart29, S. Burdin73, T. Burgess13, S. Burke129, E. Busato33, P. Bussey53, C.P. Buszello166,
F. Butin29, B. Butler143, J.M. Butler21, C.M. Buttar53, J.M. Butterworth77, W. Buttinger27, S. Cabrera Urb´an167, D. Caforio19a,19b, O. Cakir3a, P. Calafiura14, G. Calderini78, P. Calfayan98, R. Calkins106,
L.P. Caloba23a, R. Caloi132a,132b, D. Calvet33, S. Calvet33, R. Camacho Toro33, P. Camarri133a,133b, M. Cambiaghi119a,119b, D. Cameron117, S. Campana29, M. Campanelli77, V. Canale102a,102b, F. Canelli30,g, A. Canepa159a, J. Cantero80, L. Capasso102a,102b, M.D.M. Capeans Garrido29, I. Caprini25a, M. Caprini25a, D. Capriotti99, M. Capua36a,36b, R. Caputo148, R. Cardarelli133a, T. Carli29, G. Carlino102a, L. Carminati89a,89b, B. Caron159a, S. Caron48, G.D. Carrillo Montoya172, A.A. Carter75, J.R. Carter27, J. Carvalho124a,h, D. Casadei108, M.P. Casado11, M. Cascella122a,122b, C. Caso50a,50b,∗, A.M. Castaneda Hernandez172,
E. Castaneda-Miranda172, V. Castillo Gimenez167, N.F. Castro124a, G. Cataldi72a, F. Cataneo29, A. Catinaccio29, J.R. Catmore71, A. Cattai29, G. Cattani133a,133b, S. Caughron88, D. Cauz164a,164c, P. Cavalleri78, D. Cavalli89a, M. Cavalli-Sforza11, V. Cavasinni122a,122b, F. Ceradini134a,134b,
A.S. Cerqueira23a, A. Cerri29, L. Cerrito75, F. Cerutti47, S.A. Cetin18b, F. Cevenini102a,102b, A. Chafaq135a, D. Chakraborty106, K. Chan2, B. Chapleau85, J.D. Chapman27, J.W. Chapman87, E. Chareyre78, D.G. Charlton17, V. Chavda82, C.A. Chavez Barajas29, S. Cheatham85, S. Chekanov5, S.V. Chekulaev159a, G.A. Chelkov65, M.A. Chelstowska104, C. Chen64, H. Chen24, S. Chen32c, T. Chen32c, X. Chen172, S. Cheng32a, A. Cheplakov65, V.F. Chepurnov65, R. Cherkaoui El Moursli135e, V. Chernyatin24, E. Cheu6, S.L. Cheung158, L. Chevalier136,
G. Chiefari102a,102b, L. Chikovani51a, J.T. Childers58a, A. Chilingarov71, G. Chiodini72a, M.V. Chizhov65, G. Choudalakis30, S. Chouridou137, I.A. Christidi77, A. Christov48, D. Chromek-Burckhart29, M.L. Chu151, J. Chudoba125, G. Ciapetti132a,132b, K. Ciba37, A.K. Ciftci3a, R. Ciftci3a, D. Cinca33, V. Cindro74, M.D. Ciobotaru163, C. Ciocca19a,19b, A. Ciocio14, M. Cirilli87, M. Ciubancan25a, A. Clark49, P.J. Clark45, W. Cleland123, J.C. Clemens83, B. Clement55,
C. Clement146a,146b, R.W. Clifft129, Y. Coadou83, M. Cobal164a,164c, A. Coccaro50a,50b, J. Cochran64, P. Coe118, J.G. Cogan143, J. Coggeshall165,
E. Cogneras177, C.D. Cojocaru28, J. Colas4, A.P. Colijn105, C. Collard115, N.J. Collins17,
C. Collins-Tooth53, J. Collot55, G. Colon84, P. Conde Mui˜no124a, E. Coniavitis118, M.C. Conidi11,
M. Consonni104, V. Consorti48, S. Constantinescu25a, C. Conta119a,119b, F. Conventi102a,i, J. Cook29, M. Cooke14, B.D. Cooper77, A.M. Cooper-Sarkar118, N.J. Cooper-Smith76, K. Copic34, T. Cornelissen50a,50b, M. Corradi19a, F. Corriveau85,j, A. Cortes-Gonzalez165, G. Cortiana99, G. Costa89a, M.J. Costa167,
D. Costanzo139, T. Costin30, D. Cˆot´e29, L. Courneyea169, G. Cowan76, C. Cowden27,
B.E. Cox82, K. Cranmer108, F. Crescioli122a,122b, M. Cristinziani20, G. Crosetti36a,36b, R. Crupi72a,72b, S. Cr´ep´e-Renaudin55, C.-M. Cuciuc25a,
C. Cuenca Almenar175, T. Cuhadar Donszelmann139, M. Curatolo47, C.J. Curtis17, P. Cwetanski61, H. Czirr141, Z. Czyczula175, S. D’Auria53, M. D’Onofrio73, A. D’Orazio132a,132b,
P.V.M. Da Silva23a, C. Da Via82, W. Dabrowski37, T. Dai87, C. Dallapiccola84, M. Dam35,
M. Dameri50a,50b, D.S. Damiani137, H.O. Danielsson29, D. Dannheim99, V. Dao49, G. Darbo50a, G.L. Darlea25b, C. Daum105, J.P. Dauvergne29, W. Davey86,
T. Davidek126, N. Davidson86, R. Davidson71, E. Davies118,c, M. Davies93, A.R. Davison77, Y. Davygora58a, E. Dawe142, I. Dawson139, J.W. Dawson5,∗, R.K. Daya39, K. De7, R. de Asmundis102a, S. De Castro19a,19b, P.E. De Castro Faria Salgado24, S. De Cecco78, J. de Graat98, N. De Groot104, P. de Jong105, C. De La Taille115, H. De la Torre80,
B. De Lotto164a,164c, L. De Mora71, L. De Nooij105, D. De Pedis132a, A. De Salvo132a, U. De Sanctis164a,164c, A. De Santo149, J.B. De Vivie De Regie115, S. Dean77, R. Debbe24, D.V. Dedovich65, J. Degenhardt120, M. Dehchar118, C. Del Papa164a,164c, J. Del Peso80, T. Del Prete122a,122b, M. Deliyergiyev74,
A. Dell’Acqua29, L. Dell’Asta89a,89b,
M. Della Pietra102a,i, D. della Volpe102a,102b, M. Delmastro29, P. Delpierre83, N. Delruelle29, P.A. Delsart55, C. Deluca148, S. Demers175, M. Demichev65, B. Demirkoz11,k, J. Deng163, S.P. Denisov128, D. Derendarz38, J.E. Derkaoui135d, F. Derue78, P. Dervan73, K. Desch20, E. Devetak148, P.O. Deviveiros158, A. Dewhurst129, B. DeWilde148, S. Dhaliwal158, R. Dhullipudi24,l, A. Di Ciaccio133a,133b, L. Di Ciaccio4, A. Di Girolamo29, B. Di Girolamo29, S. Di Luise134a,134b, A. Di Mattia88, B. Di Micco29, R. Di Nardo133a,133b, A. Di Simone133a,133b, R. Di Sipio19a,19b, M.A. Diaz31a, F. Diblen18c, E.B. Diehl87, J. Dietrich41, T.A. Dietzsch58a, S. Diglio115, K. Dindar Yagci39, J. Dingfelder20, C. Dionisi132a,132b, P. Dita25a, S. Dita25a, F. Dittus29, F. Djama83, T. Djobava51b, M.A.B. do Vale23a, A. Do Valle Wemans124a, T.K.O. Doan4, M. Dobbs85, R. Dobinson29,∗, D. Dobos29, E. Dobson29,
M. Dobson163, J. Dodd34, C. Doglioni118, T. Doherty53, Y. Doi66,∗, J. Dolejsi126, I. Dolenc74, Z. Dolezal126, B.A. Dolgoshein96,∗, T. Dohmae155, M. Donadelli23d, M. Donega120, J. Donini55, J. Dopke29, A. Doria102a, A. Dos Anjos172, M. Dosil11, A. Dotti122a,122b, M.T. Dova70, J.D. Dowell17, A.D. Doxiadis105, A.T. Doyle53, Z. Drasal126, J. Drees174,
N. Dressnandt120, H. Drevermann29, C. Driouichi35, M. Dris9, J. Dubbert99, T. Dubbs137, S. Dube14, E. Duchovni171, G. Duckeck98, A. Dudarev29, F. Dudziak64, M. D¨uhrssen29, I.P. Duerdoth82, L. Duflot115, M-A. Dufour85, M. Dunford29, H. Duran Yildiz3b, R. Duxfield139, M. Dwuznik37,
![FIG. 2: Excluded region (under the curve) at the 95% confi- confi-dence level as a function of T and A 0 masses, compared with the CDF exclusion [10, 11]](https://thumb-eu.123doks.com/thumbv2/1library_info/4025081.1542046/4.918.89.441.83.607/excluded-region-curve-confi-function-masses-compared-exclusion.webp)
