CMS logoCMS event Hgg
Compact Muon Solenoid
LHC, CERN

CMS-TOP-21-012 ; CERN-EP-2022-222
Measurement of the differential $ \mathrm{t} \overline{\mathrm{t}} $ production cross section as a function of the jet mass and extraction of the top quark mass in hadronic decays of boosted top quarks
Eur. Phys. J. C 83 (2023) 560
Abstract: A measurement of the jet mass distribution in hadronic decays of Lorentz-boosted top quarks is presented. The measurement is performed in the lepton+jets channel of top quark pair production ( $ \mathrm{t} \overline{\mathrm{t}} $) events, where the lepton is an electron or muon. The products of the hadronic top quark decay are reconstructed using a single large-radius jet with transverse momentum greater than 400 GeV. The data were collected with the CMS detector at the LHC in proton-proton collisions and correspond to an integrated luminosity of 138 fb$ ^{-1} $. The differential $ \mathrm{t} \overline{\mathrm{t}} $ production cross section as a function of the jet mass is unfolded to the particle level and is used to extract the top quark mass. The jet mass scale is calibrated using the hadronic W boson decay within the large-radius jet. The uncertainties in the modelling of the final state radiation are reduced by studying angular correlations in the jet substructure. These developments lead to a significant increase in precision, and a top quark mass of 173.06 $ \pm $ 0.84 GeV.
Figures & Tables Summary References CMS Publications
Figures

png pdf
Figure 1:
Distribution in $ m_\text{jet} $ at the particle level after the selection of the fiducial region in the lepton+jets channel of $ \mathrm{t} \overline{\mathrm{t}} $, simulated with POWHEG. The contributions from fully merged events (blue solid) and not merged events (red dashed) are displayed, as well as the sum of the two (black solid).

png pdf
Figure 2:
Distributions in the reconstructed XCone jet $ p_{\mathrm{T}} $ (left) and $ m_\text{jet} $ (right), after the full event selection. The vertical bars on the markers show the statistical uncertainty. The hatched regions show the total uncertainty in the simulation, including the statistical and experimental systematic uncertainties. The lower panels show the ratio of the data to the simulation. The uncertainty bands include the experimental systematic uncertainties and statistical uncertainties in the simulation. In the ratios, the statistical (light grey) and total (dark grey) uncertainties are shown separately.

png pdf
Figure 2-a:
Distribution in the reconstructed XCone jet $ p_{\mathrm{T}} $, after the full event selection. The vertical bars on the markers show the statistical uncertainty. The hatched region shows the total uncertainty in the simulation, including the statistical and experimental systematic uncertainties. The lower panel shows the ratio of the data to the simulation. The uncertainty band includes the experimental systematic uncertainties and statistical uncertainties in the simulation. In the ratio, the statistical (light grey) and total (dark grey) uncertainties are shown separately.

png pdf
Figure 2-b:
Distribution in the reconstructed XCone jet $ m_\text{jet} $, after the full event selection. The vertical bars on the markers show the statistical uncertainty. The hatched region shows the total uncertainty in the simulation, including the statistical and experimental systematic uncertainties. The lower panel shows the ratio of the data to the simulation. The uncertainty band includes the experimental systematic uncertainties and statistical uncertainties in the simulation. In the ratio, the statistical (light grey) and total (dark grey) uncertainties are shown separately.

png pdf
Figure 3:
Distributions in reconstructed $ p_{\mathrm{T}} $ of the $ p_{\mathrm{T}} $-leading XCone subjet (upper left), second XCone subjet (upper right) and third XCone subjet (lower). The vertical bars on the markers show the statistical uncertainty. The hatched regions show the total uncertainty in the simulation, including the statistical and experimental systematic uncertainties. The lower panels show the ratio of the data to the simulation. The uncertainty bands include the experimental systematic uncertainties and statistical uncertainties in the simulation. In the ratios, the statistical (light grey) and total (dark grey) uncertainties are shown separately.

png pdf
Figure 3-a:
Distribution in reconstructed $ p_{\mathrm{T}} $ of the $ p_{\mathrm{T}} $-leading XCone subjet. The vertical bars on the markers show the statistical uncertainty. The hatched region shows the total uncertainty in the simulation, including the statistical and experimental systematic uncertainties. The lower panel shows the ratio of the data to the simulation. The uncertainty band include the experimental systematic uncertainties and statistical uncertainties in the simulation. In the ratio, the statistical (light grey) and total (dark grey) uncertainties are shown separately.

png pdf
Figure 3-b:
Distribution in reconstructed $ p_{\mathrm{T}} $ of the $ p_{\mathrm{T}} $-second XCone subjet. The vertical bars on the markers show the statistical uncertainty. The hatched region shows the total uncertainty in the simulation, including the statistical and experimental systematic uncertainties. The lower panel shows the ratio of the data to the simulation. The uncertainty band include the experimental systematic uncertainties and statistical uncertainties in the simulation. In the ratio, the statistical (light grey) and total (dark grey) uncertainties are shown separately.

png pdf
Figure 3-c:
Distribution in reconstructed $ p_{\mathrm{T}} $ of the $ p_{\mathrm{T}} $-third XCone subjet. The vertical bars on the markers show the statistical uncertainty. The hatched region shows the total uncertainty in the simulation, including the statistical and experimental systematic uncertainties. The lower panel shows the ratio of the data to the simulation. The uncertainty band include the experimental systematic uncertainties and statistical uncertainties in the simulation. In the ratio, the statistical (light grey) and total (dark grey) uncertainties are shown separately.

png pdf
Figure 4:
Peak region of the reconstructed W boson mass in the four regions $ p_{\mathrm{T}}^\mathrm{W} < $ 300 GeV and $ r_{p_{\mathrm{T}}} < $ 0.7 (upper left), $ p_{\mathrm{T}}^\mathrm{W} < $ 300 GeV and $ r_{p_{\mathrm{T}}} > $ 0.7 (upper right), $ p_{\mathrm{T}}^\mathrm{W} > $ 300 GeV and $ r_{p_{\mathrm{T}}} < $ 0.7 (lower left), and $ p_{\mathrm{T}}^\mathrm{W} > $ 300 GeV and $ r_{p_{\mathrm{T}}} > $ 0.7 (lower right). The background-subtracted data and the $ \mathrm{t} \overline{\mathrm{t}} $ simulation are normalised to unit area. For illustration, the $ \mathrm{t} \overline{\mathrm{t}} $ simulation is also shown with the JEC and XCone correction factors varied by one standard deviation. The lower panels show the ratios to the nominal $ \mathrm{t} \overline{\mathrm{t}} $ simulation.

png pdf
Figure 4-a:
Peak region of the reconstructed W boson mass in the $ p_{\mathrm{T}}^\mathrm{W} < $ 300 GeV and $ r_{p_{\mathrm{T}}} < $ 0.7 region. The background-subtracted data and the $ \mathrm{t} \overline{\mathrm{t}} $ simulation are normalised to unit area. For illustration, the $ \mathrm{t} \overline{\mathrm{t}} $ simulation is also shown with the JEC and XCone correction factors varied by one standard deviation. The lower panel shows the ratios to the nominal $ \mathrm{t} \overline{\mathrm{t}} $ simulation.

png pdf
Figure 4-b:
Peak region of the reconstructed W boson mass in the $ p_{\mathrm{T}}^\mathrm{W} < $ 300 GeV and $ r_{p_{\mathrm{T}}} > $ 0.7 region. The background-subtracted data and the $ \mathrm{t} \overline{\mathrm{t}} $ simulation are normalised to unit area. For illustration, the $ \mathrm{t} \overline{\mathrm{t}} $ simulation is also shown with the JEC and XCone correction factors varied by one standard deviation. The lower panel shows the ratios to the nominal $ \mathrm{t} \overline{\mathrm{t}} $ simulation.

png pdf
Figure 4-c:
Peak region of the reconstructed W boson mass in the $ p_{\mathrm{T}}^\mathrm{W} > $ 300 GeV and $ r_{p_{\mathrm{T}}} < $ 0.7 region. The background-subtracted data and the $ \mathrm{t} \overline{\mathrm{t}} $ simulation are normalised to unit area. For illustration, the $ \mathrm{t} \overline{\mathrm{t}} $ simulation is also shown with the JEC and XCone correction factors varied by one standard deviation. The lower panel shows the ratios to the nominal $ \mathrm{t} \overline{\mathrm{t}} $ simulation.

png pdf
Figure 4-d:
Peak region of the reconstructed W boson mass in the $ p_{\mathrm{T}}^\mathrm{W} > $ 300 GeV and $ r_{p_{\mathrm{T}}} > $ 0.7 region. The background-subtracted data and the $ \mathrm{t} \overline{\mathrm{t}} $ simulation are normalised to unit area. For illustration, the $ \mathrm{t} \overline{\mathrm{t}} $ simulation is also shown with the JEC and XCone correction factors varied by one standard deviation. The lower panel shows the ratios to the nominal $ \mathrm{t} \overline{\mathrm{t}} $ simulation.

png pdf
Figure 5:
The two-dimensional $ \chi^2 $ as a function of $ f^\text{JEC} $ and $ f^\text{XCone} $, obtained from a comparison of background-subtracted data with the predictions from $ \mathrm{t} \overline{\mathrm{t}} $ production in the reconstructed $ m_{\mathrm{W}} $ distributions. The minimum is indicated by a black cross, and the borders of the 68 and 95% CL intervals are shown by the light and dark red ellipses, respectively.

png pdf
Figure 6:
Jet mass distribution of hadronic decays of the W boson, reconstructed from two XCone subjets. The vertical bars on the markers show the statistical uncertainty. The hatched regions show the total uncertainty in the simulation, including the statistical and experimental systematic uncertainties. The lower panel shows the ratio of the data to the simulation. The uncertainty bands include the experimental systematic uncertainties and statistical uncertainties in the simulation. The statistical (light grey) and total (dark grey) uncertainties are shown separately in the ratio.

png pdf
Figure 7:
Mean values of the $ m_\text{jet} $ distribution for t and W boson decays, as a function of the number of primary vertices $ N_{\text{PV}} $ (left). Data (markers) are compared with $ \mathrm{t} \overline{\mathrm{t}} $ simulation (filled areas). The vertical bars and size of the filled areas show the statistical uncertainties in the calculation of the mean values. Jet mass resolution in simulation as a function of particle-level XCone-jet $ p_{\mathrm{T}} $, given for different intervals in the number of primary vertices (right). The vertical bars indicate the statistical uncertainties and the horizontal bars indicate the bin width.

png pdf
Figure 7-a:
Mean values of the $ m_\text{jet} $ distribution for t and W boson decays, as a function of the number of primary vertices $ N_{\text{PV}} $. Data (markers) are compared with $ \mathrm{t} \overline{\mathrm{t}} $ simulation (filled areas). The vertical bars and size of the filled areas show the statistical uncertainties in the calculation of the mean values.

png pdf
Figure 7-b:
Jet mass resolution in simulation as a function of particle-level XCone-jet $ p_{\mathrm{T}} $, given for different intervals in the number of primary vertices. The vertical bars indicate the statistical uncertainties and the horizontal bars indicate the bin width.

png pdf
Figure 8:
The normalised distributions in $ \tau_{32} $ for AK8 jets with $ m_\text{jet} > $ 140 GeV from the hadronic decay of boosted top quarks. Shown are distributions for 2016 (left) and the combination of 2017 and 2018 (right). The background-subtracted data are compared to $ \mathrm{t} \overline{\mathrm{t}} $ simulations with the UE tunes CUETP8M2T4 for 2016 and CP5 for the combination of 2017 and 2018, and different values of $ f_\text{FSR} $ are shown as well. The lower panels show the ratio to the $ \mathrm{t} \overline{\mathrm{t}} $ simulation with $ f_\text{FSR}= $ 1.

png pdf
Figure 8-a:
The normalised distribution in $ \tau_{32} $ for AK8 jets with $ m_\text{jet} > $ 140 GeV from the hadronic decay of boosted top quarks for 2016. The background-subtracted data are compared to $ \mathrm{t} \overline{\mathrm{t}} $ simulations with the UE tune CUETP8M2T4 and different values of $ f_\text{FSR} $ are shown as well. The lower panel shows the ratio to the $ \mathrm{t} \overline{\mathrm{t}} $ simulation with $ f_\text{FSR}= $ 1.

png pdf
Figure 8-b:
The normalised distribution in $ \tau_{32} $ for AK8 jets with $ m_\text{jet} > $ 140 GeV from the hadronic decay of boosted top quarks for the combination of 2017 and 2018. The background-subtracted data are compared to $ \mathrm{t} \overline{\mathrm{t}} $ simulations with the UE tune CP5 and different values of $ f_\text{FSR} $ are shown as well. The lower panel shows the ratio to the $ \mathrm{t} \overline{\mathrm{t}} $ simulation with $ f_\text{FSR}= $ 1.

png pdf
Figure 9:
Relative experimental (left) and model (right) uncertainties in the measurement of $ m_\text{jet} $. Various sources are displayed as coloured lines and compared to the total experimental or model uncertainty, respectively. The uncertainty sources are calculated as the square root of the diagonal entries from the respective covariance matrix, and do not include bin-to-bin correlations.

png pdf
Figure 9-a:
Relative experimental uncertainties in the measurement of $ m_\text{jet} $. Various sources are displayed as coloured lines and compared to the total experimental or model uncertainty, respectively. The uncertainty sources are calculated as the square root of the diagonal entries from the respective covariance matrix, and do not include bin-to-bin correlations.

png pdf
Figure 9-b:
Relative model uncertainties in the measurement of $ m_\text{jet} $. Various sources are displayed as coloured lines and compared to the total experimental or model uncertainty, respectively. The uncertainty sources are calculated as the square root of the diagonal entries from the respective covariance matrix, and do not include bin-to-bin correlations.

png pdf
Figure 10:
Relative experimental (left) and model (right) uncertainties after normalising the measurement to the total cross section. Various sources are displayed as coloured lines and compared to the total experimental or model uncertainty, respectively. The uncertainty sources are calculated as the square root of the diagonal entries from the respective covariance matrix, and do not include bin-to-bin correlations.

png pdf
Figure 10-a:
Relative experimental uncertainties after normalising the measurement to the total cross section. Various sources are displayed as coloured lines and compared to the total experimental or model uncertainty, respectively. The uncertainty sources are calculated as the square root of the diagonal entries from the respective covariance matrix, and do not include bin-to-bin correlations.

png pdf
Figure 10-b:
Relative model uncertainties after normalising the measurement to the total cross section. Various sources are displayed as coloured lines and compared to the total experimental or model uncertainty, respectively. The uncertainty sources are calculated as the square root of the diagonal entries from the respective covariance matrix, and do not include bin-to-bin correlations.

png pdf
Figure 11:
Differential $ \mathrm{t} \overline{\mathrm{t}} $ production cross section as a function of $ m_\text{jet} $ compared to predictions obtained with POWHEG: absolute (left) and normalised (right). For the normalised measurement, the data are compared to predictions with different $ m_{\mathrm{t}} $. The vertical bars represent the total uncertainties, and the statistical uncertainties are shown by short horizontal bars. The long horizontal bars reflect the bin widths. Theoretical uncertainties in the prediction are indicated by the bands. The lower panels show the ratio of the theoretical prediction to data.

png pdf
Figure 11-a:
Absolute differential $ \mathrm{t} \overline{\mathrm{t}} $ production cross section as a function of $ m_\text{jet} $ compared to predictions obtained with POWHEG. The vertical bars represent the total uncertainties, and the statistical uncertainties are shown by short horizontal bars. The long horizontal bars reflect the bin widths. Theoretical uncertainties in the prediction are indicated by the bands. The lower panel shows the ratio of the theoretical prediction to data.

png pdf
Figure 11-b:
Normalised differential $ \mathrm{t} \overline{\mathrm{t}} $ production cross section as a function of $ m_\text{jet} $ compared to predictions obtained with POWHEG. The data are compared to predictions with different $ m_{\mathrm{t}} $. The vertical bars represent the total uncertainties, and the statistical uncertainties are shown by short horizontal bars. The long horizontal bars reflect the bin widths. Theoretical uncertainties in the prediction are indicated by the bands. The lower panel shows the ratio of the theoretical prediction to data.

png pdf
Figure 12:
Correlations between the bins in the unfolding before (left) and after (right) normalising the distribution to the total cross section. Boxes with crosses indicate negative values of the correlation coefficient.

png pdf
Figure 12-a:
Correlations between the bins in the unfolding before normalising the distribution to the total cross section. Boxes with crosses indicate negative values of the correlation coefficient.

png pdf
Figure 12-b:
Correlations between the bins in the unfolding after normalising the distribution to the total cross section. Boxes with crosses indicate negative values of the correlation coefficient.

png pdf
Figure 13:
Extracted top quark mass from simulation compared to the true value. The vertical error bars show the total uncertainty in the extraction of $ m_{\mathrm{t}} $.
Tables

png pdf
Table 1:
Total and individual uncertainties in the extraction of $ m_{\mathrm{t}} $ from the normalised differential cross section. The uncertainties are grouped into experimental, model, theory, and statistical uncertainties. Uncertainties from the choice of the PDF, b tagging, the luminosity measurement, and the lepton triggers, identification and reconstruction are smaller than 0.01 GeV and are not listed.
Summary
A measurement of the differential top quark pair ($ \mathrm{t} \overline{\mathrm{t}} $) production cross section as a function of the jet mass $ m_\text{jet} $ in hadronic decays of boosted top quarks has been presented. The normalised distribution in $ m_\text{jet} $ is sensitive to the top quark mass $ m_{\mathrm{t}} $, which is measured to be 173.06 $ \pm $ 0.84 GeV. This value is compatible with earlier precision measurements in fully resolved final states. With respect to an earlier CMS analysis, the precision is improved by a factor of more than three. This has been achieved by a dedicated calibration of the jet mass scale, a study of the effects of final state radiation inside large-radius jets, and about 4 times more data. With these improvements, the uncertainty in the extraction of $ m_{\mathrm{t}} $ at high top quark boosts becomes comparable to direct measurements close to the $ \mathrm{t} \overline{\mathrm{t}} $ production threshold. The sources of the leading systematic uncertainties are very different, highlighting the complementarity of this measurement. In addition, the study of boosted top quarks offers the possibility to directly compare the distribution in $ m_\text{jet} $ to analytic calculations. When these calculations become available, the unfolded $ m_\text{jet} $ distribution can be used to measure the top quark pole mass directly. The precisely measured differential cross section as a function of $ m_\text{jet} $ represents an important step towards understanding and resolving the ambiguities between the top quark mass extracted from a direct reconstruction of $ m_{\mathrm{t}} $, and the top quark pole mass.
References
1 CDF Collaboration Observation of top quark production in $ \bar{p}p $ collisions PRL 74 (1995) 2626 hep-ex/9503002
2 D0 Collaboration Observation of the top quark PRL 74 (1995) 2632 hep-ex/9503003
3 ALEPH, CDF, D0, DELPHI, L3, OPAL, and SLD Collaborations, the LEP Electroweak Working Group, the Tevatron Electroweak Working Group, and the SLD Electroweak and Heavy Flavour Groups Precision electroweak measurements and constraints on the standard model 1012.2367
4 J. Haller et al. Update of the global electroweak fit and constraints on two-Higgs-doublet models EPJC 78 (2018) 675 1803.01853
5 Particle Data Group , P. A. Zyla et al. Review of particle physics Prog. Theor. Exp. Phys. 2020 (2020) 083C01
6 G. Degrassi et al. Higgs mass and vacuum stability in the standard model at NNLO JHEP 08 (2012) 098 1205.6497
7 F. Bezrukov, M. Y. Kalmykov, B. A. Kniehl, and M. Shaposhnikov Higgs boson mass and new physics JHEP 10 (2012) 140 1205.2893
8 A. V. Bednyakov, B. A. Kniehl, A. F. Pikelner, and O. L. Veretin Stability of the electroweak vacuum: Gauge independence and advanced precision PRL 115 (2015) 201802 1507.08833
9 ATLAS Collaboration Measurement of the top quark mass in the $ {\mathrm{t}\overline{\mathrm{t}}} \rightarrow $ dilepton channel from $ \sqrt{s}= $ 8 TeV ATLAS data PLB 761 (2016) 350 1606.02179
10 ATLAS Collaboration Top-quark mass measurement in the all-hadronic $ {\mathrm{t}\overline{\mathrm{t}}} $ decay channel at $ \sqrt{s}= $ 8 TeV with the ATLAS detector JHEP 09 (2017) 118 1702.07546
11 ATLAS Collaboration Measurement of the top quark mass in the $ {\mathrm{t}\overline{\mathrm{t}}} \rightarrow $ lepton+jets channel from $ \sqrt{s}= $ 8 TeV ATLAS data and combination with previous results EPJC 79 (2019) 290 1810.01772
12 CMS Collaboration Measurement of the top quark mass using proton-proton data at $ {\sqrt{s}} = $ 7 and 8 TeV PRD 93 (2016) 072004 CMS-TOP-14-022
1509.04044
13 CMS Collaboration Measurement of the top quark mass in the dileptonic $ \mathrm{t} \overline{\mathrm{t}} $ decay channel using the mass observables $ {M}_{\mathrm{b}\ell} $, $ {M}_{ {\text T}2 } $, and $ {M}_{\mathrm{b}\ell\nu} $ in pp collisions at $ \sqrt{s}= $ 8 TeV PRD 96 (2017) 032002 CMS-TOP-15-008
1704.06142
14 CMS Collaboration Measurement of the top quark mass with lepton+jets final states using pp collisions at $ \sqrt{s}= $ 13 TeV EPJC 78 (2018) 891 CMS-TOP-17-007
1805.01428
15 CMS Collaboration Measurement of the top quark mass in the all-jets final state at $ \sqrt{s} = $ 13 TeV and combination with the lepton+jets channel EPJC 79 (2019) 313 CMS-TOP-17-008
1812.10534
16 A. H. Hoang et al. The MSR mass and the $ \mathcal{O}({\Lambda}_{\mathrm{qcd}}) $ renormalon sum rule JHEP 04 (2018) 003 1704.01580
17 A. H. Hoang What is the top quark mass? Ann. Rev. Nucl. Part. Sci. 70 (2020) 225 2004.12915
18 D0 Collaboration Determination of the pole and $ \overline{MS} $ masses of the top quark from the $ \mathrm{t} \overline{\mathrm{t}} $ cross section PLB 703 (2011) 422 1104.2887
19 D0 Collaboration Measurement of the inclusive $ \mathrm{t} \overline{\mathrm{t}} $ production cross section in $ \rm{p\bar{p}} $ collisions at $ \sqrt{s}= $ 1.96 TeV and determination of the top quark pole mass PRD 94 (2016) 092004 1605.06168
20 ATLAS Collaboration Measurement of the $ \mathrm{t} \overline{\mathrm{t}} $ production cross-section using $ e\mu $ events with b-tagged jets in pp collisions at $ \sqrt{s} = $ 7 and 8 TeV with the ATLAS detector EPJC 74 (2014) 3109 1406.5375
21 ATLAS Collaboration Measurement of lepton differential distributions and the top quark mass in $ \mathrm{t} \overline{\mathrm{t}} $ production in pp collisions at $ \sqrt{s}= $ 8 TeV with the ATLAS detector EPJC 77 (2017) 804 1709.09407
22 ATLAS Collaboration Measurement of the $ \mathrm{t} \overline{\mathrm{t}} $ production cross-section and lepton differential distributions in $ e\mu $ dilepton events from pp collisions at $ \sqrt{s}= $ 13 TeV with the ATLAS detector EPJC 80 (2020) 528 1910.08819
23 CMS Collaboration Determination of the top-quark pole mass and strong coupling constant from the $ \mathrm{t} \overline{\mathrm{t}} $ production cross section in pp collisions at $ \sqrt{s} $ = 7 TeV PLB 728 (2014) 496 CMS-TOP-12-022
1307.1907
24 CMS Collaboration Measurement of the $ \mathrm{t} \overline{\mathrm{t}} $ production cross section in the e$ \mu $ channel in proton-proton collisions at $ \sqrt{s} = $ 7 and 8 TeV JHEP 08 (2016) 029 CMS-TOP-13-004
1603.02303
25 CMS Collaboration Measurement of the $ \mathrm{t} \overline{\mathrm{t}} $ production cross section, the top quark mass, and the strong coupling constant using dilepton events in pp collisions at $ \sqrt{s} = $ 13 TeV EPJC 79 (2019) 368 CMS-TOP-17-001
1812.10505
26 CMS Collaboration Measurement of $ \mathrm{t} \overline{\mathrm{t}} $ normalised multi-differential cross sections in pp collisions at $ \sqrt{s}= $ 13 TeV, and simultaneous determination of the strong coupling strength, top quark pole mass, and parton distribution functions EPJC 80 (2020) 658 CMS-TOP-18-004
1904.05237
27 A. J. Larkoski, I. Moult, and B. Nachman Jet substructure at the Large Hadron Collider: A review of recent advances in theory and machine learning Phys. Rep. 841 (2020) 1 1709.04464
28 R. Kogler et al. Jet substructure at the large hadron collider Rev. Mod. Phys. 91 (2019) 045003 1803.06991
29 R. Kogler Advances in jet substructure at the LHC: Algorithms, measurements and searches for new physical phenomena volume 284 of Springer Tracts Mod. Phys. Springer, 2021
link
30 A. H. Hoang, S. Mantry, A. Pathak, and I. W. Stewart Extracting a short distance top mass with light grooming PRD 100 (2019) 074021 1708.02586
31 CMS Collaboration Measurement of the top quark pole mass using $ \mathrm{t} \overline{\mathrm{t}} $ +jet events in the dilepton final state in proton-proton collisions at $ \sqrt{s} $ = 13 TeV Submitted to JHEP, 2022 CMS-TOP-21-008
2207.02270
32 CMS Collaboration Measurement of the jet mass in highly boosted $ \mathrm{t} \overline{\mathrm{t}} $ events from pp collisions at $ \sqrt{s}= $ 8 TeV EPJC 77 (2017) 467 CMS-TOP-15-015
1703.06330
33 CMS Collaboration Measurement of the jet mass distribution and top quark mass in hadronic decays of boosted top quarks in pp collisions at $ \sqrt{s} = $ 13 TeV PRL 124 (2020) 202001 CMS-TOP-19-005
1911.03800
34 CMS Collaboration Determination of jet energy calibration and transverse momentum resolution in CMS JINST 6 (2011) P11002 CMS-JME-10-011
1107.4277
35 CMS Collaboration Jet energy scale and resolution in the CMS experiment in pp collisions at 8 TeV JINST 12 (2017) P02014 CMS-JME-13-004
1607.03663
36 J. Thaler and K. Van Tilburg Identifying boosted objects with $ {N} $-subjettiness JHEP 03 (2011) 015 1011.2268
37 J. Thaler and K. Van Tilburg Maximizing boosted top identification by minimizing $ {N} $-subjettiness JHEP 02 (2012) 093 1108.2701
38 P. Skands, S. Carrazza, and J. Rojo Tuning PYTHIA 8.1: The Monash 2013 Tune EPJC 74 (2014) 3024 1404.5630
39 CMS Collaboration Measurement of jet substructure observables in $ \mathrm{t} \overline{\mathrm{t}} $ events from proton-proton collisions at $ \sqrt{s}= $ 13 TeV PRD 98 (2018) 092014 CMS-TOP-17-013
1808.07340
40 CMS Collaboration HEPData record for this analysis link
41 CMS Collaboration The CMS experiment at the CERN LHC JINST 3 (2008) S08004
42 Tracker Group of the CMS Collaboration The CMS Phase-1 pixel detector upgrade JINST 16 (2021) P02027 2012.14304
43 CMS Collaboration Performance of the CMS Level-1 trigger in proton-proton collisions at $ \sqrt{s} = $ 13 TeV JINST 15 (2020) P10017 CMS-TRG-17-001
2006.10165
44 CMS Collaboration The CMS trigger system JINST 12 (2017) P01020 CMS-TRG-12-001
1609.02366
45 CMS Collaboration Precision luminosity measurement in proton-proton collisions at $ \sqrt{s} = $ 13 TeV in 2015 and 2016 at CMS EPJC 81 (2021) 800 CMS-LUM-17-003
2104.01927
46 CMS Collaboration CMS luminosity measurement for the 2017 data-taking period at $ \sqrt{s} = $ 13 TeV CMS Physics Analysis Summary, 2018
CMS-PAS-LUM-17-004
CMS-PAS-LUM-17-004
47 CMS Collaboration CMS luminosity measurement for the 2018 data-taking period at $ \sqrt{s} = $ 13 TeV CMS Physics Analysis Summary, 2019
CMS-PAS-LUM-18-002
CMS-PAS-LUM-18-002
48 P. Nason A new method for combining NLO QCD with shower Monte Carlo algorithms JHEP 11 (2004) 040 hep-ph/0409146
49 S. Frixione, P. Nason, and C. Oleari Matching NLO QCD computations with parton shower simulations: the POWHEG method JHEP 11 (2007) 070 0709.2092
50 S. Alioli, P. Nason, C. Oleari, and E. Re A general framework for implementing NLO calculations in shower Monte Carlo programs: the POWHEG BOX JHEP 06 (2010) 043 1002.2581
51 S. Frixione, P. Nason, and G. Ridolfi A positive-weight next-to-leading-order Monte Carlo for heavy flavour hadroproduction JHEP 09 (2007) 126 0707.3088
52 S. Alioli, P. Nason, C. Oleari, and E. Re NLO single-top production matched with shower in POWHEG: $ s $- and $ t $-channel contributions JHEP 09 (2009) 111 0907.4076
53 E. Re Single-top Wt-channel production matched with parton showers using the POWHEG method EPJC 71 (2011) 1547 1009.2450
54 M. Czakon and A. Mitov Top++: A program for the calculation of the top-pair cross-section at hadron colliders Comput. Phys. Commun. 185 (2014) 2930 1112.5675
55 J. Alwall et al. The automated computation of tree-level and next-to-leading order differential cross sections, and their matching to parton shower simulations JHEP 07 (2014) 079 1405.0301
56 S. Frixione and B. R. Webber Matching NLO QCD computations and parton shower simulations JHEP 06 (2002) 029 hep-ph/0204244
57 N. Kidonakis Two-loop soft anomalous dimensions for single top quark associated production with a $ {\mathrm{W^-}} $ or $ {\mathrm{H}}^- $ PRD 82 (2010) 054018 1005.4451
58 N. Kidonakis Top quark production in Helmholtz International Summer School on Physics of Heavy Quarks and Hadrons, 2013
link
1311.0283
59 M. Aliev et al. HATHOR --- HAdronic Top and Heavy quarks crOss section calculatoR Comput. Phys. Commun. 182 (2011) 1034 1007.1327
60 Y. Li and F. Petriello Combining QCD and electroweak corrections to dilepton production in FEWZ PRD 86 (2012) 094034 1208.5967
61 T. Sjöstrand et al. An introduction to PYTHIA 8.2 Comput. Phys. Commun. 191 (2015) 159 1410.3012
62 NNPDF Collaboration Parton distributions for the LHC Run II JHEP 04 (2015) 040 1410.8849
63 NNPDF Collaboration Parton distributions from high-precision collider data EPJC 77 (2017) 663 1706.00428
64 R. Frederix and S. Frixione Merging meets matching in MC@NLO JHEP 12 (2012) 061 1209.6215
65 J. Alwall et al. Comparative study of various algorithms for the merging of parton showers and matrix elements in hadronic collisions EPJC 53 (2008) 473 0706.2569
66 CMS Collaboration Extraction and validation of a new set of CMS PYTHIA8 tunes from underlying-event measurements EPJC 80 (2020) 4 CMS-GEN-17-001
1903.12179
67 CMS Collaboration Event generator tunes obtained from underlying event and multiparton scattering measurements EPJC 76 (2016) 155 CMS-GEN-14-001
1512.00815
68 GEANT4 Collaboration GEANT 4 --- A simulation toolkit NIM A 506 (2003) 250
69 J. Allison et al. GEANT 4 developments and applications IEEE Trans. Nucl. Sci. 53 (2006) 270
70 CMS Collaboration Measurement of the inelastic proton-proton cross section at $ \sqrt{s}= $ 13 TeV JHEP 07 (2018) 161 CMS-FSQ-15-005
1802.02613
71 CMS Collaboration Particle-flow reconstruction and global event description with the CMS detector JINST 12 (2017) P10003 CMS-PRF-14-001
1706.04965
72 M. Cacciari, G. P. Salam, and G. Soyez The anti-$ k_{\mathrm{T}} $ jet clustering algorithm JHEP 04 (2008) 063 0802.1189
73 M. Cacciari, G. P. Salam, and G. Soyez Fastjet user manual EPJC 72 (2012) 1896 1111.6097
74 CMS Collaboration Technical proposal for the Phase-II upgrade of the Compact Muon Solenoid CMS Technical Proposal CERN-LHCC-2015-010, CMS-TDR-15-02, 2015
CDS
75 CMS Collaboration Performance of the CMS muon detector and muon reconstruction with proton-proton collisions at $ \sqrt{s}= $ 13 TeV JINST 13 (2018) P06015 CMS-MUO-16-001
1804.04528
76 CMS Collaboration Electron and photon reconstruction and identification with the CMS experiment at the CERN LHC JINST 16 (2021) P05014 CMS-EGM-17-001
2012.06888
77 I. W. Stewart et al. XCone: $ {N} $-jettiness as an exclusive cone jet algorithm JHEP 11 (2015) 072 1508.01516
78 J. Thaler and T. F. Wilkason Resolving boosted jets with XCone JHEP 12 (2015) 051 1508.01518
79 D. Krohn, J. Thaler, and L.-T. Wang Jet trimming JHEP 02 (2010) 084 0912.1342
80 Y. L. Dokshitzer, G. D. Leder, S. Moretti, and B. R. Webber Better jet clustering algorithms JHEP 08 (1997) 001 hep-ph/9707323
81 M. Wobisch and T. Wengler Hadronization corrections to jet cross sections in deep-inelastic scattering in Workshop on Monte Carlo generators for HERA physics. DESY, Hamburg, Germany, 1998
link
hep-ph/9907280
82 CMS Collaboration Search for resonant $ \mathrm{t} \overline{\mathrm{t}} $ production in proton-proton collisions at $ \sqrt{s}= $ 13 TeV JHEP 04 (2019) 031 1810.05905
83 CMS Collaboration Search for a heavy resonance decaying to a top quark and a vector-like top quark in the lepton+jets final state in pp collisions at $ \sqrt{s} = $ 13 TeV EPJC 79 (2019) 208 1812.06489
84 CMS Collaboration Performance of b tagging algorithms in proton-proton collisions at 13 TeV with Phase 1 CMS detector CMS Detector Performance Note CMS-DP-2018-033, 2018
CDS
85 E. Bols et al. Jet flavour classification using DeepJet JINST 15 (2020) P12012 2008.10519
86 CMS Collaboration Performance of missing transverse momentum reconstruction in proton-proton collisions at $ \sqrt{s} = $ 13 TeV using the CMS detector JINST 14 (2019) P07004 CMS-JME-17-001
1903.06078
87 CMS Collaboration Measurement of differential $ {\mathrm{t}\overline{\mathrm{t}}} $ production cross sections in the full kinematic range using lepton+jets events from proton-proton collisions at $ \sqrt {s} $ = 13 TeV PRD 104 (2021) 092013 CMS-TOP-20-001
2108.02803
88 ATLAS Collaboration Measurements of differential cross-sections in top-quark pair events with a high transverse momentum top quark and limits on beyond the standard model contributions to top-quark pair production with the ATLAS detector at $ \sqrt{s} = $ 13 TeV JHEP 06 (2022) 063 2202.12134
89 ATLAS Collaboration Differential $ {\mathrm{t}\overline{\mathrm{t}}} $ cross-section measurements using boosted top quarks in the all-hadronic final state with 139 fb$ ^{-1} $ of ATLAS data 2205.02817
90 F. Herren and M. Steinhauser Version 3 of RunDec and CRunDec Comput. Phys. Commun. 224 (2018) 333 1703.03751
91 S. Schmitt TUnfold: An algorithm for correcting migration effects in high energy physics JINST 7 (2012) T10003 1205.6201
92 S. Schmitt Data unfolding methods in high energy physics in 12th Conference on Quark Confinement and the Hadron Spectrum. Confinement XII, Thessaloniki, Greece, 2016
link
1611.01927
93 CMS Collaboration Identification of heavy-flavour jets with the CMS detector in pp collisions at 13 TeV JINST 13 (2018) P05011 CMS-BTV-16-002
1712.07158
94 CMS Collaboration Measurement of the $ \mathrm{t} \overline{\mathrm{t}} $ production cross section using events in the $ \mathrm{e} \mu $ final state in pp collisions at $ \sqrt{s} = $ 13 TeV EPJC 77 (2017) 172 CMS-TOP-16-005
1611.04040
95 CMS Collaboration Measurement of inclusive W and Z boson production cross sections in pp collisions at $ \sqrt{s} = $ 8 TeV PRL 112 (2014) 191802 CMS-SMP-12-011
1402.0923
96 CMS Collaboration Cross section measurement of $ t $-channel single top quark production in pp collisions at $ \sqrt{s} = $ 13 TeV PLB 772 (2017) 752 CMS-TOP-16-003
1610.00678
97 N. Kidonakis NNLL threshold resummation for top-pair and single-top production Phys. Part. Nucl. 45 (2014) 714 1210.7813
98 T. Gehrmann et al. W$^{+}$W$^{-}$ production at hadron colliders in next-to-next-to-leading order QCD PRL 113 (2014) 212001 1408.5243
99 CMS Collaboration Measurement of the WZ production cross section in pp collisions at $ \sqrt{s} = $ 13 TeV PLB 766 (2017) 268 CMS-SMP-16-002
1607.06943
100 T. Sjöstrand and M. van Zijl A multiple interaction model for the event structure in hadron collisions PRD 36 (1987) 2019
101 S. Argyropoulos and T. Sjöstrand Effects of color reconnection on $ {\mathrm{t}\overline{\mathrm{t}}} $ final states at the LHC JHEP 11 (2014) 043 1407.6653
102 J. R. Christiansen and P. Z. Skands String formation beyond leading colour JHEP 08 (2015) 003 1505.01681
103 J. Butterworth et al. PDF4LHC recommendations for LHC Run II JPG 43 (2016) 023001 1510.03865
104 D. Britzger The linear template fit EPJC 82 (2022) 731 2112.01548
105 CMS Collaboration Identification of heavy, energetic, hadronically decaying particles using machine-learning techniques JINST 15 (2020) P06005 CMS-JME-18-002
2004.08262
Compact Muon Solenoid
LHC, CERN