Sampling 3 1.5 < |η| < 2.5 0.05 × 0.025
Table 3.1: Detailed granularity and pseudorapidity coverage oftheATLAS ECAL.
3.2.1 ECAL readout system
The LAr calorimeter (ECAL, HEC, and FCAL) has an uniform readout architecture, whose block diagram is shown in figure 3.8 [ 37 ]. The electric signals produced inthe cells are processed by electronic boards called ”front end boards” (FEB), which are mounted directly on thedetector cryostat. Each ofthe 1524 FEBs processes the signal from up to 128 channels. The original triangular-shaped pulse produced inthe electrodes is first amplified by a preamplifier array. The output ofthe preamplifier is then shaped and amplified again, splitting it into three gain scales (1-10-100). The signal is then sampled and stored during the LVL1 latency (up to 25 ns). When a positive LVL1 decision arrives, the signal is digitized in a 12 bits analog to digital converter (ADC), and the data sent via an optical link to the readout drivers electronic boards (ROD) [ 38 ].
equation ( 1 ) now corresponds to electrons that pass the loose requirements but fail the requirement on the
matching between track and cluster, instead of failing the full identification and isolation requirements. In addition, two alternative background-enriched samples are obtained using a tag-and-probe technique on the jet-triggered sample and on the sample triggered by the default analysis triggers, requiring the tag to fail certain aspects oftheelectron identification depending on the trigger. Furthermore, the event should have a missing transversemomentum smaller than 25 GeV, the probe needs to have the same charge as the tag and the invariant mass ofthe tag-and-probe pair needs to be outside theZ mass window from 71 to 111 GeV.
T values inthe range of 10 GeV to 40 GeV and
slightly lower above 40 GeV.
The Alpgen [ 19 ] and Sherpa [ 20 ] generators consider processes with up to five additional hard partons associ- ated withthe produced boson and give a good descrip- tion ofthe entire measured spectrum, up to large p Z T , with χ 2 /d.o.f. of 31.9/19 and 16.8/19, respectively. Here, the enhancement ofthecrosssection compared to the O(α 2 S ) prediction can be attributed to processes with large parton multiplicities [ 52 ], which correspond to tree-level diagrams of higher order inthe strong coupling. Sherpa v1.2.3 and Alpgen v2.13 are used, withthe latter being interfaced to Herwig v6.510 [ 16 ] for parton shower and fragmentation into particles, and to Jimmy v4.31 [ 32 ] to model underly- ing event contributions. For Alpgen, the CTEQ6L1 [ 53 ] PDF set is employed and the factorization scale is set to µ 2
Table 1: Signal and background processes withthe corresponding generators used for the nominal samples. If not specified, the order ofthecross-section calculation refers to the expansion inthe strong coupling constant (𝛼 S ). (★) The events were generated using the first PDF inthe NNPDF3.0NLO set and subsequently reweighted to the PDF4LHC15NLO set [ 38 ] using the internal algorithm in Powheg-Box v2. (†) The NNLO(QCD)+NLO(EW) cross-section calculation for the 𝑝 𝑝 → 𝑍 𝐻 process already includes the 𝑔𝑔 → 𝑍 𝐻 contribution. The 𝑞𝑞 → 𝑍 𝐻 process is normalised using thecross-section for the 𝑝 𝑝 → 𝑍 𝐻 process, after subtracting the 𝑔𝑔 → 𝑍 𝐻 contribution. An additional scale factor is applied to the 𝑞𝑞 → 𝑉 𝐻 processes as a function ofthetransversemomentumofthe vector boson, to account for electroweak (EW) corrections at NLO. This makes use ofthe 𝑉 𝐻 differentialcross-section computed with Hawk [ 39 , 40 ].
The measurements are compared with predictions from a variety of Monte Carlo generators. In general, 5-flavour number scheme (5FNS) calculations at NLO accuracy predict the inclusive cross-sections well, while inclusive 4-flavour number scheme (4FNS) LO calculations largely underestimate the data. Predictions ofZ bb at NLO accuracy agree with data only inthe two-b-jets case, and underestimate the data inthe case of events withat least one b-jet. Overall, Sherpa 5FNS (NLO), a 5FNS generator with matrix elements at NLO for up to two partons and matrix elements at LO for up to four partons, describes the various differential distributions within the experimental uncertainties. A significant discrepancy, common to all generators, is found for large values of m bb . The Sherpa Fusing 4FNS+5FNS (NLO) simulation, which combines 4FNS with 5FNS at NLO accuracy using a novel technique, agrees with Sherpa 5FNS (NLO), showing that in general atthe scales tested by this measurementthe effects of this merging are minor. A disagreement of about 20 30% is observed for large values ofthe leading b-jet transversemomentum, and for small angular separations between theZboson and the leading b-jet. The 5FNS simulation with matrix elements for up to four partons at LO, as implemented in MGaMC + Py8 (LO), describes the data within the experimental uncertainties in most cases. In some cases this simulation is even better than predictions from MGaMC + Py8 5FNS (NLO), which has matrix elements with only one parton at NLO. This indicates the importance of simulations with several partons inthe matrix element for a fair description ofthe data. The pure Z bb simulation at NLO inthe 4FNS, as generated by Sherpa and MGaMC, shows significant deviations from the data even inthe two-b-jets configuration, and this is more pronounced in MGaMC.
3 Data and simulation samples
The pp collision data sample used in this measurement was collected withtheATLASdetectorattheLHC during the 2015 and 2016 data-taking periods, corresponding to integrated luminosities of 3.3 fb −1 and 32.8 fb −1 , respectively, for a total of 36.1 fb −1 , after requiring that thedetector is fully operational. Events are considered if they were accepted by at least one ofthe single-muon or single-electron triggers [ 17 , 18 ]. Theelectron triggers select a cluster inthe calorimeter matched to a track. Electrons must then satisfy identification criteria based on a multivariate technique using a likelihood discriminant. In 2015, electrons had to satisfy a ‘medium’ identification requirement and have a transverse energy of E T > 24 GeV. In 2016, electrons had to satisfy a ‘tight’ identification together with an isolation criterion and have E T > 26 GeV. To avoid efficiency loss due to isolation at high E T , an additional trigger was used, selecting ‘medium’ electrons with E T > 60 GeV. Muons are triggered on by matching tracks reconstructed inthe muon spectrometer and inthe inner detector. In 2015, muons had to satisfy a ‘loose’ isolation requirement and have a transversemomentumof p T > 20 GeV. In 2016, the isolation criteria were tightened and the threshold increased to p T = 26 GeV. In both years, another muon trigger without any isolation requirement was used, selecting muons with p T > 50 GeV.
The systematic uncertainties on the multijet background are derived by varying the mass range and bin
width ofthe nominal fit, using the lepton transverse impact parameter d 0 as the fitting variable instead
ofthe invariant mass, using alternative simulation samples for the templates, allowing the normalisations ofthe non-multijet components to vary independently or within a wider range, and varying the lepton resolution and energy/momentum scales. In addition, given the multiple sources of multijet background intheelectronchannel, an alternative template is constructed by requiring that the electrons fail to meet an isolation criterion instead of failing to meet the nominal signal selection electron identification cri- terion.
In soft scattering events, one or two protons dissociate into a system of particles with low trans- verse momentum. They are dominant processes attheLHC and include diffraction, Multiple- Partonic Interactions (MPI), soft initial- and final-state radiation (ISR /FSR), and beam-beam remnants. These phenomena are grouped according to experimental trigger. For example, min- imum bias interactions are the processes that are selected with a loose trigger intended to select inelastic collisions with as little bias as possible. The Underlying Event (UE) is the collection of all the soft processes that accompany a high-transversemomentum interaction of interest. It is typically studied as a function ofthe highest-transversemomentum particle inthe event. On the other hand, inthe hard scattering events, like Drell-Yan processes, Higgs production, etc., high transversemomentum particles are produced. The rates and properties for the hard pro- cesses can be predicted with good precision using the perturbation theory, due to the asymptotic freedom property where quarks interact as free particles at large energy scales.
High transversemomentum (p T ), hadronically decaying,
electroweak-scale bosons have already been used in searches attheLHC [1–5], and are expected to play an increasingly signif- icant role as theLHC moves to higher centre-of-mass energies in 2015. Therefore it is important to study them directly. This Letter presents the observation of a high-p T Z → bb signal in a fully hadronic final state, and a measurementof its production crosssection. Themeasurement is compared to the next-to- leading-order (NLO) matrix element plus parton-shower pre- dictions of POWHEG [6–9] and aMC@NLO , where the parton-shower, hadronisation and underlying-event modelling are provided by Pythia-8.165  and Herwig++  respec- tively. This first measurementof a high-p T electroweak-scale bosonin an all-hadronic final state attheLHC demonstrates the validity of both the analysis techniques used and ofthe state-of-the-art NLO plus parton-shower particle-level predic- tions for electroweak-scale bosons decaying to bb. It is there- fore of great relevance for the search for the H → bb signal inthe (most sensitive) high Higgs boson p T range , as well as for searches for TeV-scale resonances decaying to bbbb via
Exclusive jet multiplicities attheLHC are expected to be described by means of two benchmark patterns, ‘staircase scaling’ with R (n+1)/n constant and ‘Poisson scaling’ with
R (n+1)/n inversely proportional to n [ 3 , 45 ], which provide limiting cases for certain kine-
matic conditions. While for high multiplicities a flat exclusive jet multiplicity ratio is derived from the non-abelian nature of QCD FSR, at low multiplicity the jet multiplicity ratio is flat due to the combined effect of a Poisson-distributed multiplicity distribution and parton density suppression [ 3 ]. The emission ofthe first parton should be suppressed more strongly than the subsequent parton emissions. The underlying Poisson scaling is expected to emerge after introducing large scale differences between the core process (Z (+1 jet)) and the p jet T ofthe second leading jet. Two selections are chosen to test the two benchmark scenarios: (a) the standard Z + jets selection and (b) events where the leading jet has a transversemomentumin excess of 150 GeV.
certainties inthe scale and resolution oftheelectron energy, muon momentum, jet energy and E miss T , as well as uncertainties inthe scale factors applied to the simulation in order to reproduce the trigger, re- construction, identification and isolation efficiencies measured in data. The uncertainties inthe jet energy scale are obtained from √ s = 13 TeV simulations and in situ measurements, similar to the ones described in Ref. [ 43 ]. The uncertainty inthe jet energy resolution is derived by extrapolating measurements in Run-1 data to √ s = 13 TeV. The uncertainty inthe E T miss is estimated by propagating the uncertainties inthetransverse momenta of hard physics objects and by applying momentum scale and resolution uncer- tainties to the track-based soft term. The uncertainty associated with pile-up modelling is ofthe order of 1% and can reach up to 2.9% inthe 0-jet bin ofthe unfolded jet multiplicity distribution. For the measure- ments ofthe W charge-dependent cross sections, an uncertainty arising from the charge misidentification of leptons is also considered. It a ffects only electrons and leads to an uncertainty of less than 0.05% inthe ratio of W + Z to W − Z integrated cross sections determined by combining the four decay channels. The dominant contribution among the experimental systematic uncertainties inthe eee and µee channels is due to the uncertainty intheelectron identification e fficiency, contributing at most 1.4% uncertainty to the integrated crosssection, while inthe eµµ and µµµ channels it originates from the muon reconstruction efficiency and is at most 1.1%. The systematic uncertainties inthe measured cross sections are determined by repeating the analysis after applying appropriate variations for each source of systematic uncertainty to the simulated samples.
an integrated luminosity of 20.3 fb −1 collected by theATLASdetectorattheLHC. Boosted hadronic- ally decaying top quarks with p T > 300 GeV are reconstructed within large-R jets and identified using
jet substructure techniques. The measured p T spectrum is extended in this analysis relative to previous
measurements. A particle-level cross-section is measured in a fiducial region that closely follows the event selection. Themeasurement uncertainty ranges from 13% to 29% and is generally dominated by the uncertainty on the jet energy scale of large-R jets. A parton-level cross-section is also reported, with larger systematic uncertainties due to its greater reliance on t¯t MC generators to correct the data. The measured cross-sections are compared to the predictions of several NLO and LO matrix-element generators normalized to NNLO+NNLL QCD calculations, and using various PDF sets. Previous measurements suggest that the top quark p T spectrum is well predicted at low p T by NLO and matrix-
be on the efficiency plateau for the respective triggers. The selection efficiency of electrons and muons in simulated events, as well as their energy and momentum scale and resolution, are adjusted to reproduce those observed inZ → `` events in data [ 29 – 31 ].
In order to reduce the large background from multijet production, lepton candidates are required to be isolated from neighbouring tracks within ∆R = 0.4 of their direction, as well as from other calorimeter energy depositions, corrected for pile-up contributions, within ∆R = 0.2. Inthe muon case, the sum oftransverse momenta of neighbouring tracks must be less than 2 GeV, while the sum ofthe calorimeter transverse energies must be less than 1 GeV. Intheelectron case, these requirements range between 1.35 GeV and 3.15 GeV depending on p T and η in order to yield a constant efficiency across momentum ranges and detector regions. Additionally, leptons are required to be consistent with originating from the PV. Their longitudinal impact parameter (|z 0 |) with respect to the PV must be smaller
We thank CERN for the very successful operation oftheLHC, as well as the support staff from our institutions without whom ATLAS could not be operated efficiently. We acknowledge the support of ANPCyT, Ar- gentina; YerPhI, Armenia; ARC, Australia; BMWF and FWF, Austria; ANAS, Azerbaijan; SSTC, Be- larus; 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; EPLANET and ERC, European Union; IN2P3-CNRS, CEA-DSM/IRFU, France; GNSF, 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; BRF and RCN, Norway; MNiSW, Poland; GRICES and FCT, Portugal; MERYS (MECTS), Romania; MES of Rus- sia and ROSATOM, Russian Federation; JINR; MSTD, Serbia; MSSR, Slovakia; ARRS and MVZT, Slovenia; DST/NRF, South Africa; MICINN, Spain; SRC and Wallenberg Foundation, Sweden; SER, SNSF and Can- tons of Bern and Geneva, Switzerland; NSC, Taiwan; TAEK, Turkey; STFC, the Royal Society and Lever- hulme Trust, United Kingdom; DOE and NSF, United States of America.
We thank CERN for the very successful operation oftheLHC, as well as the support staff from our institutions without whom ATLAS could not be operated efficiently.
We acknowledge the support of ANPCyT, Argentina; YerPhI, Armenia; ARC, Aus- tralia; BMWF and FWF, 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; EPLANET, ERC and NSRF, European Union; IN2P3-CNRS, CEA-DSM/IRFU, France; GNSF, Georgia; BMBF, DFG, HGF, MPG and AvH Foundation, Germany; GSRT and NSRF, Greece; ISF, MIN- ERVA, GIF, I-CORE and Benoziyo Center, Israel; INFN, Italy; MEXT and JSPS, Japan; CNRST, Morocco; FOM and NWO, Netherlands; BRF and RCN, Norway; MNiSW and NCN, Poland; GRICES and FCT, Portugal; MNE/IFA, Romania; MES of Russia and ROSATOM, Russian Federation; JINR; MSTD, Serbia; MSSR, Slovakia; ARRS and MIZŠ, Slovenia; DST/NRF, South Africa; MINECO, 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.
C. Hsu 146c , P.J. Hsu 82 , S.-C. Hsu 139 , D. Hu 35 , X. Hu 25 , Y. Huang 42 , Z. Hubacek 30 , F. Hubaut 84 , F. Huegging 21 , T.B. Huffman 119 , E.W. Hughes 35 , G. Hughes 71 ,
M. Huhtinen 30 , T.A. H¨ ulsing 82 , M. Hurwitz 15 , N. Huseynov 64,b , J. Huston 89 , J. Huth 57 , G. Iacobucci 49 , G. Iakovidis 10 , I. Ibragimov 142 , L. Iconomidou-Fayard 116 , E. Ideal 177 , P. Iengo 103a , O. Igonkina 106 , T. Iizawa 172 , Y. Ikegami 65 , K. Ikematsu 142 , M. Ikeno 65 , Y. Ilchenko 31,o , D. Iliadis 155 , N. Ilic 159 , Y. Inamaru 66 , T. Ince 100 , P. Ioannou 9 , M. Iodice 135a , K. Iordanidou 9 , V. Ippolito 57 , A. Irles Quiles 168 , C. Isaksson 167 , M. Ishino 67 , M. Ishitsuka 158 , R. Ishmukhametov 110 , C. Issever 119 , S. Istin 19a , J.M. Iturbe Ponce 83 , R. Iuppa 134a,134b , J. Ivarsson 80 , W. Iwanski 39 , H. Iwasaki 65 , J.M. Izen 41 , V. Izzo 103a , B. Jackson 121 , M. Jackson 73 , P. Jackson 1 , M.R. Jaekel 30 , V. Jain 2 , K. Jakobs 48 , S. Jakobsen 30 , T. Jakoubek 126 , J. Jakubek 127 , D.O. Jamin 152 , D.K. Jana 78 , E. Jansen 77 , H. Jansen 30 , J. Janssen 21 , M. Janus 171 , G. Jarlskog 80 , N. Javadov 64,b , T. Jav˚ urek 48 , L. Jeanty 15 , J. Jejelava 51a ,p , G.-Y. Jeng 151 , D. Jennens 87 , P. Jenni 48,q , J. Jentzsch 43 , C. Jeske 171 , S. J´ez´equel 5 , H. Ji 174 , J. Jia 149 , Y. Jiang 33b , M. Jimenez Belenguer 42 , S. Jin 33a , A. Jinaru 26a , O. Jinnouchi 158 , M.D. Joergensen 36 , K.E. Johansson 147a,147b , P. Johansson 140 , K.A. Johns 7 , K. Jon-And 147a,147b , G. Jones 171 , R.W.L. Jones 71 , T.J. Jones 73 , J. Jongmanns 58a , P.M. Jorge 125a,125b , K.D. Joshi 83 , J. Jovicevic 148 , X. Ju 174 , C.A. Jung 43 , R.M. Jungst 30 , P. Jussel 61 , A. Juste Rozas 12,n , M. Kaci 168 , A. Kaczmarska 39 , M. Kado 116 , H. Kagan 110 , M. Kagan 144 , E. Kajomovitz 45 , C.W. Kalderon 119 , S. Kama 40 , A. Kamenshchikov 129 , N. Kanaya 156 , M. Kaneda 30 , S. Kaneti 28 , V.A. Kantserov 97 , J. Kanzaki 65 , B. Kaplan 109 , A. Kapliy 31 , D. Kar 53 , K. Karakostas 10 , N. Karastathis 10 , M. Karnevskiy 82 , S.N. Karpov 64 , Z.M. Karpova 64 , K. Karthik 109 , V. Kartvelishvili 71 , A.N. Karyukhin 129 , L. Kashif 174 , G. Kasieczka 58b , R.D. Kass 110 , A. Kastanas 14 , Y. Kataoka 156 , A. Katre 49 , J. Katzy 42 , V. Kaushik 7 , K. Kawagoe 69 , T. Kawamoto 156 , G. Kawamura 54 , S. Kazama 156 , V.F. Kazanin 108 , M.Y. Kazarinov 64 , R. Keeler 170 , R. Kehoe 40 , M. Keil 54 , J.S. Keller 42 , J.J. Kempster 76 , H. Keoshkerian 5 , O. Kepka 126 , B.P. Kerˇsevan 74 , S. Kersten 176 , K. Kessoku 156 ,
MSMT CR, MPO CR and VSC CR, Czech Republic; DNRF, DNSRC and Lundbeck Foundation, Den- mark; IN2P3-CNRS, CEA-DSM/IRFU, France; GNSF, Georgia; BMBF, HGF, and MPG, Germany; GSRT, Greece; RGC, Hong Kong SAR, China; ISF, I-CORE and Benoziyo Center, Israel; INFN, Italy; MEXT and JSPS, Japan; CNRST, Morocco; FOM and NWO, Netherlands; RCN, Norway; MNiSW and NCN, Poland; FCT, Portugal; MNE/IFA, Romania; MES of Russia and NRC KI, Russian Feder- ation; JINR; MESTD, Serbia; MSSR, Slovakia; ARRS and MIZŠ, Slovenia; DST /NRF, South Africa; MINECO, Spain; SRC and Wallenberg Foundation, Sweden; SERI, SNSF and Cantons of Bern and Geneva, Switzerland; MOST, Taiwan; TAEK, Turkey; STFC, United Kingdom; DOE and NSF, United States of America. In addition, individual groups and members have received support from BCKDF, the Canada Council, CANARIE, CRC, Compute Canada, FQRNT, and the Ontario Innovation Trust, Canada; EPLANET, ERC, FP7, Horizon 2020 and Marie Skłodowska-Curie Actions, European Union; Investisse- ments d’Avenir Labex and Idex, ANR, Region Auvergne and Fondation Partager le Savoir, France; DFG and AvH Foundation, Germany; Herakleitos, Thales and Aristeia programmes co-financed by EU-ESF and the Greek NSRF; BSF, GIF and Minerva, Israel; BRF, Norway; the Royal Society and Leverhulme Trust, United Kingdom.
The systematic uncertainty due to the dependence ofthe unfolding on the prior signal distribution, as obtained from MC simulations, is evaluated through a data-driven ‘closure test’. The simulated signal sample is reweighted at particle level such that the distribution ofthe fully simulated reconstructed jet mass more closely matches the observed data. Pseudo-data from the reweighted signal MC sample are then unfolded using the response matrix from the original unweighted signal MC sample, and the unfolded result is compared withthe reweighted particle-level distribution. Differences observed in this comparison are taken as systematic uncertainties inthe unfolding, and are referred to as unfolding non-closure uncertainties inthe following. The uncertainty due to the dependence on the number of unfolding iteration steps is negligible. The statistical uncertainties inthe signal MC sample, used to build the response matrix, and background templates are also considered.