dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements. We thank the fishery observers and field workers, who collected sighting data and photographs from fishing vessels and from land. The fishery observation program is conducted by the Terres australes et antarctiques françaises (TAAF) and the réserve naturelle nationale des Terres australes françaises, and field work at Possession Island is conducted by the n°109 program with the  Institut Polaire Français (IPEV). We are grateful to  Charlotte Chazeau  from the  Muséum National d'Histoire Naturelle (Paris)  for extracting data from the 'Pecheker' database. Special thanks to  Lyndsay Clavareau  and  Martin Marzloff  ( Ifremer, Bretagne ) for their support at the beginning of the project. We also thank the reviewers for their insightful comments and  Yonina Eizenberg  for proofreading the manuscript.
Does this new particle also couple to the other known fundamental particles as expected, i. e. the quarks and charged leptons? What is the exact mass value and width of this resonance? Can we directly measure the shape of the Higgs field potential discussed in Section 2, e. g. via Higgs boson pair production? ATLAS and CMS discovered this new particle with a data sample of about 10 fb -1 each  52. An additional 15 fb -1 was collected by the end of 2012 by both experiments in run 1. From 2015 to 2018, the LHC run 2, the experiments collected 139 fb -1 each at a higher center of mass energy: 13 TeV. In the next few years the LHC will deliver for each experiment ≈ 150 fb -1 in run 3 (2022-2024), and then the accelerator and both experiments will be upgraded for the high luminosity phase to collect a total of 3-4 ab -1 each.
Many analyses are presently still being finalized on the collected data set but several full run 2 results have been completed and show an emerging picture that we discuss next. In the collisions for this data set about 7 million Higgs bosons have been produced so the LHC can be considered as the first Higgs factory, even though only a fraction can be identified and used to study the properties of the particle. Analyses of data from the LHC go in parallel with advances in precision theoretical calculations for the Standard Model production and decay rates as well as modelling of the backgrounds  [53] [54] [55] [56]. In the Standard Model all the couplings of the Higgs boson to fermions and vector bosons were known as a function of the Higgs boson's mass before the discovery of the Higgs boson.
A direct measurement can be made through the associated production of the Higgs boson with a pair of tt quarks. The topologies of events are complex and typically contain many jets, among which two at least originate from b-quarks, electrons 5/25 or muons, and the decay products of the Higgs boson itself, see Fig. 2  (left). The first direct observation of the top Yukawa coupling of the Higgs boson was achieved only with a large but partial run 2 dataset and using all Higgs boson decay channels bb, τ + τ -, WW * , ZZ * and γγ by ATLAS and CMS  63, 64. The respective signal strengths for ttH production were found to be 1. 32 ± 0. 39 (ATLAS) and 1. 26 ± 0. 31 (CMS), in accord with the Standard Model expectation. With the entire dataset, the diphoton channel alone provides an unambiguous observation of the pp → ttH production process  65, 66  , see Fig.
The signal resides on a large background tail: the Z boson to muon pair cross section is 5 orders of magnitude larger than the expected signal. The hunt for the H → µ -µ + decays started early on. Any observed signal would have unexpected with the initial LHC  luminosity since for a true Higgs particle this decay is expected to be strongly suppressed compared to, e. g. , the Higgs boson to τ -τ * decay, and would have been evidence that the newly found particle was not the Standard Model Higgs boson! And indeed, no evidence for this decay was found in the run 1 data. The full set of the run 2 data was recently used by ATLAS and CMS to search for this channel. The large background of mostly Drell-Yan di-muon production required the experiments to use sophisticated tools such as machine learning algorithms to extract a significant signal.
79. covariant derivative  [5] [6] [7]. These relations are demonstrated by the data in a dramatic way in Fig. 5  (right). These are the fruits of the first 10 years of data taking and careful analysis at the LHC! The other landmark result of this first phase of the LHC is the fact that so far no new phenomena beyond those predicted by the Standard Model have been observed. This has led the experiments and the theory communities to perform combined interpretations of all measurements in Higgs, electroweak and top physics in a framework where the Standard Model is considered as an effective field theory  55, [85] [86] [87] [88] [89]. With the full LHC dataset, the precision on the couplings of the Higgs boson is expected to reach between 1% and 2% for the ones to gauge bosons and between 2% and 4% for couplings to the charged fermions of the third generation.
This method assumes that the running of the Higgs boson couplings do not deviate significantly from those expected from the Standard Model. CMS extracts with 80. 2 fb -1 of data a central value of the width to be constrained to 3. 2± 2. 8 2. 2 MeV at 68% CL, and a range constrained between [0. 08,9. 16] MeV at 95% CL  91. The ATLAS experiment reports an upper limit of 14. 4 MeV, based on 36 fb -1 of data  92. It is quite interesting that new results show now also a lower limit of the allowed range, but the measurement is presently still statistics limited. Since the ultimate LHC data sample will have an 20-fold higher statistics, this method is most promising to experimentally verify the Higgs boson's width in the next 15 years. Note however that there is a model dependent assumption using this method that no extra new particles contribute to the off-shell mass rate, i.
This can be turned into limits on dark matter searches as shown  95, 96  in Fig. 6. The comparison is performed in the context of Higgs portal models  97. The translation of the H → invisible result into a weak interacting massive particle-nucleon scattering cross section σ WIMP-N relies on an effective field theory approach under the assumption that invisible Higgs boson decays to a pair of WIMPs is kinematically possible and that the WIMP is a scalar or a fermion  [98] [99] [100]. The excluded σ WIMP-N values range down to e. g. 2 × 10 -46 cm 2 in the fermion WIMP scenario, probing a new exclusion region for masses below 10 GeV. W en extrapolated to the full LHC dataset, including the high luminosity phase, a projected sensitivity of 2. 5% should be reached.
It is one of the deepest questions of the Standard Model and may provide a portal to new physics beyond the Standard Model. Experimentally this can be addressed via the measurement of the production of multiple Higgs bosons. With the run 2 dataset, a major effort to perform complete analyses in a number of final state channels, combining a variety of decay modes for each Higgs boson γγ, bb, τ + τ -,WW * seeking the highest sensitivity to the di-Higgs boson production was made  107, 108. The final state that was immediately recognized for providing an optimal compromise between the number of events produced and a decent signal-to-background ratio, was (H → bb)(H → γγ), but adding additional channels can substantially enhance the sensitivity. The net HH production receives contributions from processes that involve Higgs boson self-interactions as well as other interactions that are not sensitive to λ.
The measurement of the kinematics of the HH system   96  is crucial to disentangle the different contributions and infer information on λ 109. Individual experiment combinations with a partial run 2 dataset show that limits below 10 times the Standard Model expected rate can be set on the HH production cross section. An analysis with the full run 2 dataset from CMS in the bbγγ channel allows one to exclude values of the trilinear coupling smaller than -3. 3 and larger than 8. 5 times the Standard Model expectation  110. With the full run 2 dataset, the ATLAS experiment performed a search for the HH → b bb b final state in the electroweak vector boson fusion production mode  111  , this process has a cross section smaller by approximately one order of magnitude, but is sensitive to HHVV (c 2V ) coupling and has set a limit of -1.
Acknowledgements None of the results presented in this review would have been possible without the diligent efforts of all our colleagues from the  LHC accelerator group , the ATLAS and CMS experiments, the computing divisions, the theoretical community and many more, whom all took part in this fantastic adventure at the energy frontier. Specifically we like to thank  Maria Cepeda ,  Fred Jegerlehner  and  Janina Krzysiak  for sharing their insight and useful discussions in the preparation of this manuscript.
detector The ATLAS Collaboration Jet energy scale and resolution measurements with their associated uncertainties are reported for jets using 36-81 fb -1 of proton-proton collision data with a centre-of-mass energy of √  = 13 TeV collected by the ATLAS detector at the LHC. Jets are reconstructed using two different input types: topo-clusters formed from energy deposits in calorimeter cells, as well as an algorithmic combination of charged-particle tracks with those topo-clusters, referred to as the ATLAS particle-flow reconstruction method. The anti-  jet algorithm with radius parameter  = 0. 4 is the primary jet definition used for both jet types. This result presents new jet energy scale and resolution measurements in the high pile-up conditions of late LHC Run 2 as well as a full calibration of particle-flow jets in ATLAS.
Introduction The energetic proton-proton ( ) collisions produced by the Large Hadron Collider (LHC) yield final states that are predominantly characterized by jets, or collimated sprays of charged and neutral hadrons. Jets constitute an essential piece of the physics programme carried out using the ATLAS detector due to their presence in the signal processes being measured and searched for, the various background processes that hide those signals, and the additional activity due to simultaneous   collisions. Measurements of the energy scale and resolution of these complex objects, as well as their associated systematic uncertainties, are therefore essential both for precision measurements of the Standard Model (SM) and for sensitive searches for new physics beyond it. This paper presents the strategy used for the determination of the jet energy scale (JES) and resolution (JER) by the ATLAS experiment and its implementation as it pertains to the analysis of data from Run 2 of the LHC.
Results for the JES and JER are presented using data collected during 2015-2017, corresponding to integrated luminosities in the range 36-81 fb -1 , depending on the analysis method and its goals. This publication focuses on calibrating jets reconstructed with the anti-   [1]  algorithm with radius parameter  = 0. 4. The ATLAS Collaboration has published previous calibrations and uncertainties of the energy scale and resolution for this jet definition with data taken in 2010  [2] [3] [4] , 2011  [5] , 2012  [6] , and 2015  [7]. Additionally, some ATLAS publications have targeted different jet definitions. In particular, the Run 1 papers include dedicated calibrations 1 of jets reconstructed with the anti-  algorithm with  = 0. 6 and  = 1. 0, and a dedicated in situ calibration of large-radius jets has also been completed in Run 2 data  [9].
This publication extends and improves on previous calibrations of anti-   = 0. 4 jets, taking full advantage of the larger dataset recorded over the period of 2015-2017. The significant increase in the number of proton collisions per bunch crossing in 2016 and 2017 data-taking leads to a correspondingly more difficult environment for jet reconstruction, and this result presents new jet energy scale and resolution measurements in these unique high pile-up conditions. Section 2 describes the ATLAS detector, and Section 3 describes the recorded data and the Monte Carlo (MC) simulation samples used in this paper. Section 4 presents the inputs and algorithms used to reconstruct the jets. Section 5 and Section 6 present the methods used and the result of both the calibration and the resulting systematic uncertainties of the JES and the JER, respectively.
Data and Monte Carlo simulated samples The data used for the measurements presented here were collected in   collisions at the LHC with a centre-of-mass energy of 13 TeV and a 25 ns proton bunch crossing interval during 2015-2017. The integrated luminosities of the datasets used are in the range 36-81 fb -1 after requiring that all detector subsystems were operational during data recording. Additional   collisions in the same and nearby bunch crossings are referred to as pile-up. The number of reconstructed primary vertices ( PV ) and the mean number of interactions per bunch crossing () are optimal observables to quantify the level of pile-up activity. The average value of  is 13. 7, 24. 9, and 37. 8 in the 2015, 2016, and 2017 datasets, respectively  [16]. As described below, these conditions are accounted for in the production and reconstruction of simulated data.
Event topology uncertainties are estimated by varying the event selections used and observing the impact on the final MC simulation to data ratio. A rebinning procedure is applied to each systematic uncertainty to ensure that the features represented in the final result are statistically significant and not the result of fluctuations in small numbers of simulated or data events. This is only performed where the response does not vary sharply with  T , ensuring it does not obscure real physics effects. The rebinning procedure follows a bootstrapping method: pseudo-experiment datasets are created by sampling from a Poisson distribution with a mean of one for each event in the data or MC simulation  [43]. The pseudo-experiments are therefore statistically correlated yet unique, and the root mean square of the response distribution across the pseudo-experiments provides a measure of the statistical uncertainty of the analysis.
The measured result for each systematic uncertainty is then rebinned as appropriate for each analysis to ensure that the final shapes are statistically significant. The /+jet and MJB calibrations and uncertainties are derived from the full 2015-2017 combined datasets with a total luminosity of 80 fb -1. The  intercalibration analysis uses a dataset of total size 81 fb -1 , but since this analysis is more sensitive than the others to year-by-year fluctuations, the dataset is split into two blocks and a time-dependent result is computed instead. One  intercalibration is derived from and applies to the 2015 + 2016 dataset while a second independent calibration is derived from the 2017 dataset and applies to 2017 + 2018 data. These two data periods are treated separately due to a change in LAr calorimeter read-out that occurred between 2016 and 2017 data taking and affected jet reconstruction in the endcap regions.
With no changes of similar scale made between 2017 and 2018 data taking, the 2017 calibration can be reasonably applied to 2018 as well. The post-calibration jet performance is consistent between these two different data periods and therefore a single set of uncertainties based on the 2015 + 2016 dataset is used for the  intercalibration in all years, with only a small localized additional uncertainty added for 2018 as described in Section 5. 2. 1. Certain common selection criteria are applied to all three in situ analyses. Each event must have a reconstructed vertex with at least two associated tracks of  T > 500 MeV. All jets must satisfy quality criteria to reject non-collision background, calorimeter noise, and cosmic rays  [44]. Furthermore, each jet with 20 GeV <  T < 60 GeV and | det | < 2.
Additional uncertainties in the physics and topology modelling are assessed by varying the  third T , Δ, and pile-up suppression cuts and using a bootstrapping method to ensure observed shapes are statistically significant as discussed in Section 5. 2. Similarly, the JVT uncertainty is determined by comparison with tighter and looser working points. These uncertainties can take positive or negative values. The statistical uncertainty is strictly positive and is taken from the data and MC simulation sample sizes. Finally, a non-closure uncertainty is assessed by comparing the response in data with that in P +P 8 after applying the derived  intercalibration. This uncertainty is largest for | det | ∼ 2. 1-2. 6, where detector transitions make modelling of the LAr pulse shape particularly difficult  [34] , and for jets near the kinematic limit, where they have the maximum possible  T for a given  det subject to the constraint of a 13 TeV centre-of-mass energy.
The non-closure uncertainty is treated as three independent nuisance parameters, two covering the regions around ±2. 4 in  det and one at the kinematic limit, since these two non-closure uncertainties are uncorrelated. After being corrected each with their dedicated calibration, the 2015+2016 and 2017 datasets are in good agreement, and therefore a single set of uncertainties is sufficient to cover both cases. The uncertainties calculated with the 2015+2016 dataset are selected for this role. The only dataset-dependent uncertainty is an additional small non-closure uncertainty used for 2018 data only. It covers the region around  = ±1. 5 to account for the difference in Tile calorimeter calibration during this year of data-taking and has a maximum size of 2%. The method uncertainties are shown in Figure  10.
Other independently derived uncertainties correspond to pile-up and punch-through effects and are propagated through the MJB analysis via the recoil system. The +jet, +jet, and  intercalibration uncertainties are propagated from the previous stages of in situ analysis. Event selection uncertainties are determined by varying each of the analysis cuts and determining the effects ; its difference from the P result defines the 'MC generator' uncertainty in (b). This uncertainty is defined in a single-sided way by the measured response difference and therefore it is not symmetrised for display in (b) but instead its full one-sided value is shown. Other uncertainties come from the event selection and MC simulation/data statistics or are propagated from the +jet, +jet, flavour, pile-up,  intercalibration, and punch-through studies.
Pile-up and the in situ analyses One of the primary changes in LHC run conditions over the course of Run 2 was an increase in pile-up. The average number of interactions per crossing () during 2015+2016 data taking was 23. 7, which increased to 37. 8 in 2017. The data taken during 2018 and to which the calibrations in this paper are also applied has an average of 36. 1 interactions per crossing  [16]. The consistency of the calibrations for events with different pile-up conditions is therefore an important feature of the methods. Figure  17  shows individual bins in the response ratios of the +jet and +jet analyses separated out as a function of number of primary vertices in the event. The +jet results are shown for 25 GeV <  ref T < 30 GeV and the +jet results for 45 GeV <  ref T < 65 GeV, in the regions where each has appropriate statistical significance.
The increase in JES uncertainty around 800 GeV is a result of the single-particle uncertainty (see Section 5. 3): the jet energy scale calibration used for the dĳet energy resolution measurement is necessarily based on a smaller dataset than the one presented in this paper, allowing the two measurements to converge simultaneously, and as a result the statistics were lower and the single-particle uncertainty became dominant at a lower  jet T value than in Figure  22(a). Additional systematic uncertainties are estimated by varying the analysis cuts and the JVT selection and by comparing the result with one obtained from an alternative MC generator (S 2. 1. 1). for PFlow jets in the central region of the detector, measured using the dĳet balance method. The resolution in data is shown in black points with error bars indicating statistical uncertainties; the resolution in detector-level simulated events is shown by the blue curve with total systematic uncertainty given by the blue band.
Conclusions The calibration of the jet energy scale and resolution for jets reconstructed with the anti-  algorithm with radius parameter  = 0. 4 is presented. Jets are built from either the energy deposits that form topological clusters of calorimeter cells or a combination of charged-particle tracks and topological clusters. The measurements discussed here use 36-81 fb -1 of data recorded with the ATLAS detector during 2015-2017 in   collisions at a centre-of-mass energy of 13 TeV at the Large Hadron Collider. It is the first full calibration of PFlow jets performed by the ATLAS collaboration, the first jet energy scale measurement in the high pile-up conditions of late Run 2 data-taking, and the first jet energy resolution measurement in 13 TeV data. A sequence of simulation-based corrections removes the contribution to the jet energy from additional proton-proton interactions in the same or nearby bunch crossings, corrects the jet so that it agrees in energy and direction with particle-level jets and, improves the jet energy resolution.
Acknowledgements We thank  CERN  for the very successful operation of the LHC, 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, Australia ;  BMWFW  and  FWF , Austria; ANAS, Azerbaĳan; SSTC, Belarus;  CNPq  and  FAPESP, Brazil ;  NSERC ,  NRC  and  CFI ,  Canada ; CERN;  ANID, Chile ;  CAS ,  MOST  and  NSFC, China ;  COLCIENCIAS, Colombia; MSMT CR, MPO CR  and  VSC CR, Czech Republic ;  DNRF  and  DNSRC, Denmark ;  IN2P3-CNRS  and  CEA-DRF/IRFU , France; SRNSFG, Georgia; BMBF,  HGF  and  MPG , Germany; GSRT, Greece; RGC and  Hong Kong SAR, China ;  ISF and Benoziyo Center, Israel ;  INFN, Italy ;  MEXT  and  JSPS, Japan ; CNRST, Morocco; NWO, Netherlands; RCN, Norway;  MNiSW  and  NCN , Poland; FCT, Portugal; MNE/IFA, Romania; JINR;  MES of Russia  and  NRC KI , Russian Federation; MESTD, Serbia; MSSR, Slovakia; ARRS and MIZŠ, Slovenia; DST/NRF, South Africa; MICINN, 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 ,  CANARIE, Compute Canada ,  CRC  and  IVADO ,  Canada ;  Beĳing Municipal Science & Technology Commission, China ;  COST ,  ERC ,  ERDF ,  Horizon 2020  and  Marie Skłodowska-Curie Actions ,  European Union ;  Investissements d'Avenir Labex, Investissements d'Avenir Idex  and  ANR, France ;  DFG and AvH Foundation ,  Germany; Herakleitos ,  Thales  and  Aristeia  programmes co-financed by  EU-ESF  and the  Greek NSRF, Greece; BSF-NSF  and GIF, Israel;  La Caixa Banking Foundation ,  CERCA Programme Generalitat de Catalunya and PROMETEO and GenT Programmes Generalitat Valenciana, Spain; Göran Gustafssons Stiftelse, Sweden ;  The Royal Society and Leverhulme Trust ,  United Kingdom. The crucial computing support from all WLCG partners is acknowledged gratefully, in particular from  CERN , the ATLAS Tier-1 facilities 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) , the Tier-2 facilities worldwide and large non-WLCG resource providers.