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| CMS-PAS-JME-16-003 | ||
| Jet algorithms performance in 13 TeV data | ||
| CMS Collaboration | ||
| March 2017 | ||
| Abstract: The performance of jet algorithms with data collected by the CMS detector at the LHC in 2015 with a center-of-mass energy of 13 TeV, corresponding to 2.3 fb$^{-1}$ of integrated luminosity, is reported. The criteria used to reject jets originating from detector noise are discussed and the efficiency and noise jet rejection rate are measured. A likelihood discriminant designed to differentiate jets initiated by light-quark partons from jets initiated from gluons is studied. A multivariate discriminator is built to distinguish jets initiated by a single high $p_{\mathrm{T}}$ quark or gluon from jets originating from the overlap of multiple low $p_{\mathrm{T}}$ particles from non-primary vertices (pileup jets). Algorithms used to identify large radius jets reconstructed from the decay products of highly Lorentz boosted W bosons and top quarks are discussed, and the efficiency and background rejection rates of these algorithms are measured. | ||
| Links: CDS record (PDF) ; inSPIRE record ; CADI line (restricted) ; | ||
| Figures & Tables | Summary | Additional Figures | References | CMS Publications |
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| Figures | |
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Figure 1:
The distributions of $ {E_{\mathrm {T}}^{\text {miss}}} $ over $\Sigma p_{\mathrm{T}}$ for signal enriched back-to-back dijet events (black) and for noise enriched events from a minimum bias selection (red) before applying the PF jet ID. |
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Figure 2:
Distributions of PF jet variables for central jets ($|\eta | < $ 0.5) as measured in signal enriched back-to-back dijet events (black) and for noise enriched events from a minimum bias selection (red) before applying the PF jet ID. The quantities plotted are: (a) charged hadron energy fraction, (b) neutral hadron energy fraction, (c) charged electromagnetic energy fraction, (d) neutral electromagnetic energy fraction, (e) muon energy fraction, (f) photon multiplicity, (g) charged multiplicity, (h) neutral multiplicity. |
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Figure 2-a:
Distribution of the charged hadron energy fraction for central jets ($|\eta | < $ 0.5) as measured in signal enriched back-to-back dijet events (black) and for noise enriched events from a minimum bias selection (red) before applying the PF jet ID. |
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Figure 2-b:
Distribution of the neutral hadron energy fraction for central jets ($|\eta | < $ 0.5) as measured in signal enriched back-to-back dijet events (black) and for noise enriched events from a minimum bias selection (red) before applying the PF jet ID. |
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Figure 2-c:
Distribution of the charged electromagnetic energy fraction for central jets ($|\eta | < $ 0.5) as measured in signal enriched back-to-back dijet events (black) and for noise enriched events from a minimum bias selection (red) before applying the PF jet ID. |
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Figure 2-d:
Distribution of the neutral electromagnetic energy fraction for central jets ($|\eta | < $ 0.5) as measured in signal enriched back-to-back dijet events (black) and for noise enriched events from a minimum bias selection (red) before applying the PF jet ID. |
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Figure 2-e:
Distribution of the muon energy fraction for central jets ($|\eta | < $ 0.5) as measured in signal enriched back-to-back dijet events (black) and for noise enriched events from a minimum bias selection (red) before applying the PF jet ID. |
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Figure 2-f:
Distribution of the photon multiplicity for central jets ($|\eta | < $ 0.5) as measured in signal enriched back-to-back dijet events (black) and for noise enriched events from a minimum bias selection (red) before applying the PF jet ID. |
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Figure 2-g:
Distribution of the charged multiplicity for central jets ($|\eta | < $ 0.5) as measured in signal enriched back-to-back dijet events (black) and for noise enriched events from a minimum bias selection (red) before applying the PF jet ID. |
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Figure 2-h:
Distribution of the neutral multiplicity for central jets ($|\eta | < $ 0.5) as measured in signal enriched back-to-back dijet events (black) and for noise enriched events from a minimum bias selection (red) before applying the PF jet ID. |
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Figure 3:
The tight PF jet ID efficiency (a) as a function of $ {p_{\mathrm {T}}} $ for central jets ($|\eta | < $ 0.5) and (b) as a function of $|\eta |$ for 30 $ < {p_{\mathrm {T}}} < $ 100 GeV. |
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Figure 3-a:
The tight PF jet ID efficiency as a function of $ {p_{\mathrm {T}}} $ for central jets ($|\eta | < $ 0.5). |
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Figure 3-b:
The tight PF jet ID efficiency as a function of $|\eta |$ for 30 $ < {p_{\mathrm {T}}} < $ 100 GeV. |
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Figure 4:
The noise jet background rejection rate for the tight PF jet ID criteria (a) as a function of $ {p_{\mathrm {T}}} $ for central jets ($|\eta | < $ 0.5) and (b) as a function of $\eta $ for 30 $ < {p_{\mathrm {T}}} < $ 100 GeV. |
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Figure 4-a:
The noise jet background rejection rate for the tight PF jet ID criteria as a function of $ {p_{\mathrm {T}}} $ for central jets ($|\eta | < $ 0.5). |
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Figure 4-b:
The noise jet background rejection rate for the tight PF jet ID criteria as a function of $\eta $ for 30 $ < {p_{\mathrm {T}}} < $ 100 GeV. |
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Figure 5:
Quark-gluon discrimination variables from simulation: (a) $ {p_{\mathrm {T}}} {D}$ (b) multiplicity (c) $\sigma _{2}$ (d) the quark-gluon likelihood. |
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Figure 5-a:
Quark-gluon discrimination variable from simulation: $ {p_{\mathrm {T}}} {D}$. |
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Figure 5-b:
Quark-gluon discrimination variable from simulation: multiplicity. |
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Figure 5-c:
Quark-gluon discrimination variable from simulation: $\sigma _{2}$. |
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Figure 5-d:
Quark-gluon discrimination variable from simulation: the quark-gluon likelihood. |
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Figure 6:
Quark jet tagging efficiency as a function of the gluon jet rejection rate: (a) individual variable discrimination rate compared to the full likelihood (b) likelihood performance in different kinematic regions. |
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Figure 6-a:
Quark jet tagging efficiency as a function of the gluon jet rejection rate: individual variable discrimination rate compared to the full likelihood. |
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Figure 6-b:
Quark jet tagging efficiency as a function of the gluon jet rejection rate: likelihood performance in different kinematic regions. |
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Figure 7:
Data-MC comparisons, for jets with 80 $ < p_{\mathrm {T}} < $ 100 GeV and $|\eta |< $ 2 in Z+jets events, of the three input variables used in the discriminator: multiplicity (right), $ {p_{\mathrm {T}}} D$ (center) and $\sigma _2$ (right). The data (black markers) are compared to the MadGraph /PYTHIA simulation, on which the different components are shown: quarks (blue), gluon (red) and unmatched/pileup (grey). |
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Figure 7-a:
Data-MC comparisons, for jets with 80 $ < p_{\mathrm {T}} < $ 100 GeV and $|\eta |< $ 2 in Z+jets events, of one of the three input variables used in the discriminator: multiplicity. The data (black markers) are compared to the MadGraph /PYTHIA simulation, on which the different components are shown: quarks (blue), gluon (red) and unmatched/pileup (grey). |
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Figure 7-b:
Data-MC comparisons, for jets with 80 $ < p_{\mathrm {T}} < $ 100 GeV and $|\eta |< $ 2 in Z+jets events, of one of the three input variables used in the discriminator: $ {p_{\mathrm {T}}} D$. The data (black markers) are compared to the MadGraph /PYTHIA simulation, on which the different components are shown: quarks (blue), gluon (red) and unmatched/pileup (grey). |
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Figure 7-c:
Data-MC comparisons, for jets with 80 $ < p_{\mathrm {T}} < $ 100 GeV and $|\eta |< $ 2 in Z+jets events, of one of the three input variables used in the discriminator: $\sigma _2$. The data (black markers) are compared to the MadGraph /PYTHIA simulation, on which the different components are shown: quarks (blue), gluon (red) and unmatched/pileup (grey). |
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Figure 8:
Data-MC comparison for the quark-gluon discriminant in Z+jets (left) and dijet (right) events for jets in the central region with 80 $ < p_{\mathrm {T}} < $ 100 GeV. The data(black markers) are compared to the MadGraph /PYTHIA simulation, on which the different components are shown: quarks (blue), gluon (red) and unmatched/pileup (grey). |
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Figure 8-a:
Data-MC comparison for the quark-gluon discriminant in Z+jets events for jets in the central region with 80 $ < p_{\mathrm {T}} < $ 100 GeV. The data (black markers) are compared to the MadGraph /PYTHIA simulation, on which the different components are shown: quarks (blue), gluon (red) and unmatched/pileup (grey). |
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Figure 8-b:
Data-MC comparison for the quark-gluon discriminant in dijet events for jets in the central region with 80 $ < p_{\mathrm {T}} < $ 100 GeV. The data (black markers) are compared to the MadGraph /PYTHIA simulation, on which the different components are shown: quarks (blue), gluon (red) and unmatched/pileup (grey). |
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Figure 9:
Data-MC comparison for the quark-gluon discriminant in Z+jets (left) and dijet (right) events for jets in the central region with 80 $ < p_{\mathrm {T}} < $ 100 GeV, after the data-driven systematics reshaping procedure. The data (black markers) are compared to the reshaped MadGraph /PYTHIA simulation, on which the different components are shown: quarks (blue), gluon (red) and unmatched/pileup (grey). |
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Figure 9-a:
Data-MC comparison for the quark-gluon discriminant in Z+jets events for jets in the central region with 80 $ < p_{\mathrm {T}} < $ 100 GeV, after the data-driven systematics reshaping procedure. The data (black markers) are compared to the reshaped MadGraph /PYTHIA simulation, on which the different components are shown: quarks (blue), gluon (red) and unmatched/pileup (grey). |
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Figure 9-b:
Data-MC comparison for the quark-gluon discriminant in dijet events for jets in the central region with 80 $ < p_{\mathrm {T}} < $ 100 GeV, after the data-driven systematics reshaping procedure. The data (black markers) are compared to the reshaped MadGraph /PYTHIA simulation, on which the different components are shown: quarks (blue), gluon (red) and unmatched/pileup (grey). |
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Figure 10:
Gluon- and quark-jet selection efficiencies by applying a fixed cut on the likelihood output LD $>$ 0.5 (left), 0.7 (center) and 0.9 (right). Efficiencies are evaluated in dijet events, as a function of the jet $ {p_{\mathrm {T}}} $, before and after the reshaping of the outputs. |
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Figure 10-a:
Gluon- and quark-jet selection efficiencies by applying a fixed cut on the likelihood output LD $>$ 0.5. Efficiencies are evaluated in dijet events, as a function of the jet $ {p_{\mathrm {T}}} $, before and after the reshaping of the outputs. |
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Figure 10-b:
Gluon- and quark-jet selection efficiencies by applying a fixed cut on the likelihood output LD $>$ 0.7. Efficiencies are evaluated in dijet events, as a function of the jet $ {p_{\mathrm {T}}} $, before and after the reshaping of the outputs. |
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Figure 10-c:
Gluon- and quark-jet selection efficiencies by applying a fixed cut on the likelihood output LD $>$ 0.9. Efficiencies are evaluated in dijet events, as a function of the jet $ {p_{\mathrm {T}}} $, before and after the reshaping of the outputs. |
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Figure 11:
HERWIG++ (v2.7.0 with CUETHS1 tune) and PYTHIA-8 (v8.205 with CUETP8M1 tune) gluon- and quark-jet selection efficiencies by applying a fixed cut on the likelihood output LD $>$ 0.5. Efficiencies are evaluated in dijet events (left) or Z+jet events (right), as a function of the jet $ {p_{\mathrm {T}}} $ with or without the data-driven reshaping of the outputs. |
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Figure 11-a:
HERWIG++ (v2.7.0 with CUETHS1 tune) and PYTHIA-8 (v8.205 with CUETP8M1 tune) gluon- and quark-jet selection efficiencies by applying a fixed cut on the likelihood output LD $>$ 0.5. Efficiencies are evaluated in dijet events, as a function of the jet $ {p_{\mathrm {T}}} $ with or without the data-driven reshaping of the outputs. |
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Figure 11-b:
HERWIG++ (v2.7.0 with CUETHS1 tune) and PYTHIA-8 (v8.205 with CUETP8M1 tune) gluon- and quark-jet selection efficiencies by applying a fixed cut on the likelihood output LD $>$ 0.5. Efficiencies are evaluated in Z+jet events, as a function of the jet $ {p_{\mathrm {T}}} $ with or without the data-driven reshaping of the outputs. |
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Figure 12:
Fraction of rejected pileup jets as a function of the fraction of true quark and gluon jets which are correctly tagged for jets. Curves are shown for both quark initiated and gluon initiated jets in bins of 20 $ < {p_{\mathrm {T}}} < $ 30 GeV and 30 $ < {p_{\mathrm {T}}} < $ 50 GeV in four different $|\eta |$ regions: (a) $|\eta |< $ 2.5, (b) 2.5 $ < |\eta | < $ 2.75, (c) 2.75 $ < |\eta | < $ 3, (d) 3 $ < |\eta | < $ 5. |
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Figure 12-a:
Fraction of rejected pileup jets as a function of the fraction of true quark and gluon jets which are correctly tagged for jets. Curves are shown for both quark initiated and gluon initiated jets in bins of 20 $ < {p_{\mathrm {T}}} < $ 30 GeV and 30 $ < {p_{\mathrm {T}}} < $ 50 GeV for $|\eta |< $ 2.5. |
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Figure 12-b:
Fraction of rejected pileup jets as a function of the fraction of true quark and gluon jets which are correctly tagged for jets. Curves are shown for both quark initiated and gluon initiated jets in bins of 20 $ < {p_{\mathrm {T}}} < $ 30 GeV and 30 $ < {p_{\mathrm {T}}} < $ 50 GeV for 2.5 $ < |\eta | < $ 2.75. |
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Figure 12-c:
Fraction of rejected pileup jets as a function of the fraction of true quark and gluon jets which are correctly tagged for jets. Curves are shown for both quark initiated and gluon initiated jets in bins of 20 $ < {p_{\mathrm {T}}} < $ 30 GeV and 30 $ < {p_{\mathrm {T}}} < $ 50 GeV for 2.75 $ < |\eta | < $ 3. |
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Figure 12-d:
Fraction of rejected pileup jets as a function of the fraction of true quark and gluon jets which are correctly tagged for jets. Curves are shown for both quark initiated and gluon initiated jets in bins of 20 $ < {p_{\mathrm {T}}} < $ 30 GeV and 30 $ < {p_{\mathrm {T}}} < $ 50 GeV for 3 $ < |\eta | < $ 5. |
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Figure 13:
Pileup jet MVA discriminant (left) $\beta $ measured in central region (right) $ < \Delta R^2 > $ measured in forward region. |
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Figure 13-a:
Pileup jet MVA discriminant $\beta $ measured in central region. |
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Figure 13-b:
Pileup jet MVA discriminant $ < \Delta R^2 > $ measured in forward region. |
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Figure 14:
Pileup jet MVA discriminant for forward jets. |
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Figure 14-a:
Pileup jet MVA discriminant for central jets. |
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Figure 14-b:
Pileup jet MVA discriminant for forward jets. |
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Figure 15:
Data-MC comparison of the MVA (loose working point) pileup jet identification efficiency on the Z$(\to \mu \mu )+$jets sample for PF jets with $ {p_{\mathrm {T}}} > $ 20 GeV: the efficiency is shown as a function of the jet pseudorapidity (top) and as a function of $ {p_{\mathrm {T}}} $ for jets with $|\eta |< $ 2.5 (bottom-left) and 3 $ < |\eta | < $ 5 (bottom-right). |
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Figure 15-a:
Data-MC comparison of the MVA (loose working point) pileup jet identification efficiency on the Z$(\to \mu \mu )+$jets sample for PF jets with $ {p_{\mathrm {T}}} > $ 20 GeV: the efficiency is shown as a function of the jet pseudorapidity. |
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Figure 15-b:
Data-MC comparison of the MVA (loose working point) pileup jet identification efficiency on the Z$(\to \mu \mu )+$jets sample for PF jets with $ {p_{\mathrm {T}}} > $ 20 GeV: the efficiency is shown as a function of $ {p_{\mathrm {T}}} $ for jets with $|\eta |< $ 2.5. |
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Figure 15-c:
Data-MC comparison of the MVA (loose working point) pileup jet identification efficiency on the Z$(\to \mu \mu )+$jets sample for PF jets with $ {p_{\mathrm {T}}} > $ 20 GeV: the efficiency is shown as a function of $ {p_{\mathrm {T}}} $ for jets with 3 $ < |\eta | < $ 5. |
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Figure 16:
Performance of several discriminants in the background-signal efficiency plane. The baseline selection for W tagging requiring a PF+CHS pruned or PF+PUPPI softdrop jet mass of 65 $ < m_{\mathrm {jet}} < $ 105 GeV, and N-subjettiness ratio (PF+CHS inputs) of $\tau _2/\tau _1 < $ 0.45 or N-subjettiness ratio (PF+PUPPI inputs) of $\tau _2/\tau _1 < $ 0.4 or $\tau _{21}^\text {DDT} < $ 0.52 are indicated with symbols. |
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Figure 17:
Efficiency of the PF+CHS pruned jet mass and PF+PUPPI softdrop jet mass selection and the combined (PUPPI) $\tau _2/\tau _1$ (DDT) and $m_{\mathrm {jet}}$ selection on WW signal samples as a function of (a) $ {p_{\mathrm {T}}} $ and (b) the number of reconstructed vertices. Reconstructed jets enter (the denominator and numerator of) the efficiency only if at generator level both quarks from the W decay are within $\Delta R< $ 0.8 of the jet axis. (c) Mistag rate of the PF+CHS pruned jet mass and PF+PUPPI softdrop jet mass selection and the combined (PUPPI) $\tau _2/\tau _1$ (DDT) and $m_{\mathrm {jet}}$ selection on WW signal samples as a function of (c) $ {p_{\mathrm {T}}} $ and (d) the number of reconstructed vertices. The error bars represent the statistical uncertainty in the MC simulation and the horizontal ones the binning. |
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Figure 17-a:
Efficiency of the PF+CHS pruned jet mass and PF+PUPPI softdrop jet mass selection and the combined (PUPPI) $\tau _2/\tau _1$ (DDT) and $m_{\mathrm {jet}}$ selection on WW signal samples as a function of $ {p_{\mathrm {T}}} $. Reconstructed jets enter (the denominator and numerator of) the efficiency only if at generator level both quarks from the W decay are within $\Delta R< $ 0.8 of the jet axis. |
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Figure 17-b:
Efficiency of the PF+CHS pruned jet mass and PF+PUPPI softdrop jet mass selection and the combined (PUPPI) $\tau _2/\tau _1$ (DDT) and $m_{\mathrm {jet}}$ selection on WW signal samples as a function of the number of reconstructed vertices. Reconstructed jets enter (the denominator and numerator of) the efficiency only if at generator level both quarks from the W decay are within $\Delta R< $ 0.8 of the jet axis. |
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Figure 17-c:
Mistag rate of the PF+CHS pruned jet mass and PF+PUPPI softdrop jet mass selection and the combined (PUPPI) $\tau _2/\tau _1$ (DDT) and $m_{\mathrm {jet}}$ selection on WW signal samples as a function of $ {p_{\mathrm {T}}} $. The error bars represent the statistical uncertainty in the MC simulation and the horizontal ones the binning. |
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Figure 17-d:
Mistag rate of the PF+CHS pruned jet mass and PF+PUPPI softdrop jet mass selection and the combined (PUPPI) $\tau _2/\tau _1$ (DDT) and $m_{\mathrm {jet}}$ selection on WW signal samples as a function of the number of reconstructed vertices. The error bars represent the statistical uncertainty in the MC simulation and the horizontal ones the binning. |
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Figure 18:
PF+CHS pruned jet mass distribution that (a) pass and (b) fail the PF+CHS $\tau _2 / \tau _1 < $ 0.45 selection in the $\mathrm {t\overline {t}}$ control sample. PF+PUPPI softdrop jet mass distribution that (c) pass and (d) fail the PF+PUPPI $\tau _2 / \tau _1 < $ 0.40 selection. The result of the fit to data and simulation are shown, respectively, by the solid and long-dashed line and the background components of the fit are shown as dashed-dotted and short-dashed line. |
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Figure 18-a:
PF+CHS pruned jet mass distribution that pass the PF+CHS $\tau _2 / \tau _1 < $ 0.45 selection in the $\mathrm {t\overline {t}}$ control sample. The result of the fit to data and simulation are shown, respectively, by the solid and long-dashed line and the background components of the fit are shown as dashed-dotted and short-dashed line. |
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Figure 18-b:
PF+CHS pruned jet mass distribution that fail the PF+CHS $\tau _2 / \tau _1 < $ 0.45 selection in the $\mathrm {t\overline {t}}$ control sample. The result of the fit to data and simulation are shown, respectively, by the solid and long-dashed line and the background components of the fit are shown as dashed-dotted and short-dashed line. |
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Figure 18-c:
PF+PUPPI softdrop jet mass distribution that pass the PF+PUPPI $\tau _2 / \tau _1 < $ 0.40 selection. The result of the fit to data and simulation are shown, respectively, by the solid and long-dashed line and the background components of the fit are shown as dashed-dotted and short-dashed line. |
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Figure 18-d:
PF+PUPPI softdrop jet mass distribution that fail the PF+PUPPI $\tau _2 / \tau _1 < $ 0.40 selection. The result of the fit to data and simulation are shown, respectively, by the solid and long-dashed line and the background components of the fit are shown as dashed-dotted and short-dashed line. |
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Figure 19:
Fraction of jets passing the $m_{\mathrm {jet}}$ and $\tau _2/\tau _1$ selections in a dijet data sample and in simulation as a function of $ {p_{\mathrm {T}}} $ , comparing (a) HERWIG++, (b) PYTHIA-8 and (c) PYTHIA-8 with MadGraph as matrix-element generator. The data over simulation ratio is shown for the combination of the $m_{\mathrm {jet}}$ and $\tau _2/\tau _1$ selections. |
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Figure 19-a:
Fraction of jets passing the $m_{\mathrm {jet}}$ and $\tau _2/\tau _1$ selections in a dijet data sample and in simulation as a function of $ {p_{\mathrm {T}}} $ , comparing HERWIG++. The data over simulation ratio is shown for the combination of the $m_{\mathrm {jet}}$ and $\tau _2/\tau _1$ selections. |
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Figure 19-b:
Fraction of jets passing the $m_{\mathrm {jet}}$ and $\tau _2/\tau _1$ selections in a dijet data sample and in simulation as a function of $ {p_{\mathrm {T}}} $ , comparing PYTHIA-8. The data over simulation ratio is shown for the combination of the $m_{\mathrm {jet}}$ and $\tau _2/\tau _1$ selections. |
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Figure 19-c:
Fraction of jets passing the $m_{\mathrm {jet}}$ and $\tau _2/\tau _1$ selections in a dijet data sample and in simulation as a function of $ {p_{\mathrm {T}}} $ , comparing PYTHIA-8 with MadGraph as matrix-element generator. The data over simulation ratio is shown for the combination of the $m_{\mathrm {jet}}$ and $\tau _2/\tau _1$ selections. |
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Figure 20:
Kinematic distributions for the AK8 jet associated to the boosted top hadronic decay in selected semi-leptonic ${\mathrm{ t } {}\mathrm{ \bar{t} } } $ events. The ${\mathrm{ t } {}\mathrm{ \bar{t} } } $ MC and the selected backgrounds are stacked. The distributions are: (a) Corrected softdrop mass, (b) Transverse momentum. A hashed band indicates the sum in quadrature of the statistical and systematic uncertainties of the simulation. The ratio of data to simulation is displayed below the distribution. A dark shaded and a light shaded band indicate the statistical uncertainty of the simulation and the systematic uncertainty of the modeling of top $ {p_{\mathrm {T}}} $ spectrum, respectively. |
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Figure 20-a:
Distribution of the corrected softdrop mass for the AK8 jet associated to the boosted top hadronic decay in selected semi-leptonic ${\mathrm{ t } {}\mathrm{ \bar{t} } } $ events. The ${\mathrm{ t } {}\mathrm{ \bar{t} } } $ MC and the selected backgrounds are stacked. A hashed band indicates the sum in quadrature of the statistical and systematic uncertainties of the simulation. The ratio of data to simulation is displayed below the distribution. A dark shaded and a light shaded band indicate the statistical uncertainty of the simulation and the systematic uncertainty of the modeling of top $ {p_{\mathrm {T}}} $ spectrum, respectively. |
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Figure 20-b:
Distribution of the transverse momentum for the AK8 jet associated to the boosted top hadronic decay in selected semi-leptonic ${\mathrm{ t } {}\mathrm{ \bar{t} } } $ events. The ${\mathrm{ t } {}\mathrm{ \bar{t} } } $ MC and the selected backgrounds are stacked. A hashed band indicates the sum in quadrature of the statistical and systematic uncertainties of the simulation. The ratio of data to simulation is displayed below the distribution. A dark shaded and a light shaded band indicate the statistical uncertainty of the simulation and the systematic uncertainty of the modeling of top $ {p_{\mathrm {T}}} $ spectrum, respectively. |
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Figure 21:
Top tagging variable distributions for the AK8 jet associated to the boosted top hadronic decay in selected semi-leptonic ${\mathrm{ t } {}\mathrm{ \bar{t} } } $ events for the loose (3.0% nominal background mistag rate) high $ p_{\mathrm{T}} $ softdrop/PUPPI working point. The ${\mathrm{ t } {}\mathrm{ \bar{t} } } $ MC and the selected backgrounds are stacked. (a) Corrected softdrop mass (b) ungroomed N-subjettiness. A hashed band indicates the sum in quadrature of the statistical and systematic uncertainties of the simulation. The ratio of data to simulation is displayed below the distribution. A dark shaded and a light shaded band indicate the statistical uncertainty of the simulation and the systematic uncertainty of the modeling of top $ p_{\mathrm{T}} $ spectrum, respectively. |
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Figure 21-a:
Corrected softdrop mass distribution for the AK8 jet associated to the boosted top hadronic decay in selected semi-leptonic ${\mathrm{ t } {}\mathrm{ \bar{t} } } $ events for the loose (3.0% nominal background mistag rate) high $ p_{\mathrm{T}} $ softdrop/PUPPI working point. The ${\mathrm{ t } {}\mathrm{ \bar{t} } } $ MC and the selected backgrounds are stacked. A hashed band indicates the sum in quadrature of the statistical and systematic uncertainties of the simulation. The ratio of data to simulation is displayed below the distribution. A dark shaded and a light shaded band indicate the statistical uncertainty of the simulation and the systematic uncertainty of the modeling of top $ p_{\mathrm{T}} $ spectrum, respectively. |
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Figure 21-b:
Ungroomed N-subjettiness distribution for the AK8 jet associated to the boosted top hadronic decay in selected semi-leptonic ${\mathrm{ t } {}\mathrm{ \bar{t} } } $ events for the loose (3.0% nominal background mistag rate) high $ p_{\mathrm{T}} $ softdrop/PUPPI working point. The ${\mathrm{ t } {}\mathrm{ \bar{t} } } $ MC and the selected backgrounds are stacked. A hashed band indicates the sum in quadrature of the statistical and systematic uncertainties of the simulation. The ratio of data to simulation is displayed below the distribution. A dark shaded and a light shaded band indicate the statistical uncertainty of the simulation and the systematic uncertainty of the modeling of top $ p_{\mathrm{T}} $ spectrum, respectively. |
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Figure 22:
Top tagging variable distributions for the CA15 jet associated to the boosted top hadronic decay in selected semi-leptonic ${\mathrm{ t } {}\mathrm{ \bar{t} } } $ events for the loose (1.0 % nominal background mis-tag rate) low $ {p_{\mathrm {T}}} $ HTT V2/CHS working point. The ${\mathrm{ t } {}\mathrm{ \bar{t} } } $ MC and the selected backgrounds are stacked. The distributions are: (a) HTT V2 mass. (b) $f_{Rec}$. (c) softdrop groomed N-subjettiness A hashed band indicates the sum in quadrature of the statistical and systematic uncertainties of the simulation. The ratio of data to simulation is displayed below the distribution. A dark shaded and a light shaded band indicate the statistical uncertainty of the simulation and the systematic uncertainty of the modeling of top $ {p_{\mathrm {T}}} $ spectrum, respectively. |
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Figure 22-a:
HTT V2 mass distribution for the CA15 jet associated to the boosted top hadronic decay in selected semi-leptonic ${\mathrm{ t } {}\mathrm{ \bar{t} } } $ events for the loose (1.0 % nominal background mis-tag rate) low $ {p_{\mathrm {T}}} $ HTT V2/CHS working point. The ${\mathrm{ t } {}\mathrm{ \bar{t} } } $ MC and the selected backgrounds are stacked. |
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Figure 22-b:
$f_{Rec}$ distribution for the CA15 jet associated to the boosted top hadronic decay in selected semi-leptonic ${\mathrm{ t } {}\mathrm{ \bar{t} } } $ events for the loose (1.0 % nominal background mis-tag rate) low $ {p_{\mathrm {T}}} $ HTT V2/CHS working point. The ${\mathrm{ t } {}\mathrm{ \bar{t} } } $ MC and the selected backgrounds are stacked. A hashed band indicates the sum in quadrature of the statistical and systematic uncertainties of the simulation. The ratio of data to simulation is displayed below the distribution. A dark shaded and a light shaded band indicate the statistical uncertainty of the simulation and the systematic uncertainty of the modeling of top $ {p_{\mathrm {T}}} $ spectrum, respectively. |
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Figure 22-c:
Softdrop groomed N-subjettiness distribution for the CA15 jet associated to the boosted top hadronic decay in selected semi-leptonic ${\mathrm{ t } {}\mathrm{ \bar{t} } } $ events for the loose (1.0 % nominal background mis-tag rate) low $ {p_{\mathrm {T}}} $ HTT V2/CHS working point. The ${\mathrm{ t } {}\mathrm{ \bar{t} } } $ MC and the selected backgrounds are stacked. |
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Figure 23:
Top tagging efficiency as measured in ${\mathrm{ t } {}\mathrm{ \bar{t} } } $ simulation. |
| Tables | |
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Table 1:
The PF jet ID criteria for the whole $\eta $ region from $-5$ up to $5$. |
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Table 2:
Data-to-simulation scale factors for the W-tagging procedure, as extracted from a top enriched data sample and from simulation, for both categories (high purity and low purity) for two different working points. The systematic uncertainties on the scale factor due to the simulation of the $ {\mathrm{ t } {}\mathrm{ \bar{t} } } $ topology and the choice of the signal and background fit model are listed as well. |
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Table 4:
Result scale factors for the for softdrop high $ {p_{\mathrm {T}}} $ working points and different $ {p_{\mathrm {T}}} $ ranges. The reported uncertainties are statistical only. The scale factors are labelled by expected background mistag rate $\epsilon (B)$ and expected signal efficiency $\epsilon (S)$ on MC simulated events for the selection including the b-tag requirement. For comparison the inclusive scale factor is also presented without this cut. |
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Table 5:
Result scale factors for the for HEPTo$ p_{\mathrm{T}} $aggerV2/CHS low $ {p_{\mathrm {T}}} $ working points and different $ {p_{\mathrm {T}}} $ ranges. The reported uncertainties are statistical only. The scale factors are labelled by expected background mistag rate $\epsilon (B)$ and expected signal efficiency $\epsilon (S)$ on MC simulated events for the selection including the b-tag requirement. For comparison the inclusive scale factor is also presented without this cut. |
| Summary |
| The performance of jet and jet substructure algorithms has been studied in data collected by the CMS experiment at the LHC with a center-of-mass energy of 13 TeV. The rejection rate of jet identification criteria against noise has been measured using a noise enriched minimum bias event selection, while the efficiency for identifying real physical jets has been meaured in data using a tag-and-probe procedure. The background rejection rejection rate has been measured to be greater than 99.999% in the barrel region and greater than 92% in the forward detector region. A multivariate BDT which uses vertex and jet shape information to discriminate pileup jets has been discussed, and its performance has been measured in data and in simulation. For central jets with $|\eta| < $ 2.5 and 30 $ < p_{\mathrm{T}} < $ 50 GeV, the pileup jet identification BDT rejects 89% of pileup jets while maintaining 96% of gluon jets. A likelihood-based tagger which relies on the internal structure of jets to discriminate jets initiated by light-quark partons from those initiated by gluons has been studied. A recipe to evaluate the systematic uncertainties associated to the use of the quark/gluon discriminator has been given, based on the observed data versus MC differences in the validation samples. The efficiency and mistag rate of W tagging and top tagging algorithms has been discussed, and scale factors have been measured. A new W tagger based on DDT corrected N-subjettiness has been studied and found to yield a mistag rate that is independent of $p_{\mathrm{T}}$. W tagging and top tagging techniques relying on PUPPI pileup suppression have been validated in data for the first time and were found to maintain W and top tagging performance up to at least 40 simultaneous interactions. |
| Additional Figures | |
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Additional Figure 1:
$< \Delta R^2 > $ (left) central (right) forward. |
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Additional Figure 1-a:
$< \Delta R^2 > $ central. |
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Additional Figure 1-b:
$< \Delta R^2 > $ forward. |
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Additional Figure 2:
Major axis (left) central (right) forward. |
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Additional Figure 2-a:
Major axis central. |
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Additional Figure 2-b:
Major axis forward. |
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Additional Figure 3:
Minor axis (left) central (right) forward. |
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Additional Figure 3-a:
Minor axis central. |
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Additional Figure 3-b:
Minor axis forward. |
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Additional Figure 4:
$\beta $ central. |
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Additional Figure 5:
$f_{ring0}$ (left) central (right) forward. |
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Additional Figure 5-a:
$f_{ring0}$ central. |
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Additional Figure 5-b:
$f_{ring0}$ forward. |
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Additional Figure 6:
$f_{ring1}$ (left) central (right) forward. |
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Additional Figure 6-a:
$f_{ring1}$ central. |
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Additional Figure 6-b:
$f_{ring1}$ forward. |
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Additional Figure 7:
$f_{ring2}$ (left) central (right) forward. |
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Additional Figure 7-a:
$f_{ring2}$ central. |
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Additional Figure 7-b:
$f_{ring2}$ forward. |
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Additional Figure 8:
$f_{ring3}$ (left) central (right) forward. |
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Additional Figure 8-a:
$f_{ring3}$ central. |
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Additional Figure 8-b:
$f_{ring3}$ forward. |
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Additional Figure 9:
Leading jet constituent $ p_{\mathrm{T}} $ fraction (left) central (right) forward. |
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Additional Figure 9-a:
Leading jet constituent $ p_{\mathrm{T}} $ fraction central. |
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Additional Figure 9-b:
Leading jet constituent $ p_{\mathrm{T}} $ fraction forward. |
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Additional Figure 10:
Leading jet charged constituent $ p_{\mathrm{T}} $ fraction central. |
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Additional Figure 11:
Charged multiplicity central. |
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Additional Figure 12:
Multiplicity (left) central (right) forward. |
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Additional Figure 12-a:
Multiplicity central. |
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Additional Figure 12-b:
Multiplicity forward. |
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Additional Figure 13:
Nvtx (left) central (right) forward. |
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Additional Figure 13-a:
Nvtx central. |
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Additional Figure 13-b:
Nvtx forward. |
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Additional Figure 14:
$ {p_{\mathrm {T}}} {D}$ (left) central (right) forward. |
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Additional Figure 14-a:
$ {p_{\mathrm {T}}} {D}$ central. |
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Additional Figure 14-b:
$ {p_{\mathrm {T}}} {D}$ forward. |
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Additional Figure 15:
pull (left) central (right) forward. |
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Additional Figure 15-a:
pull central. |
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Additional Figure 15-b:
pull forward. |
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Additional Figure 16:
The (a) Gaussian peak position of the fitted generator level W-jet softdrop mass distribution as a function of jet $ {p_{\mathrm {T}}} $ and (b) difference in reconstructed PUPPI softdrop mass and generated softdrop mass normalized by reconstructed PUPPI softdrop mass as a function of jet $ {p_{\mathrm {T}}} $ in two $\eta $ bins (right). |
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Additional Figure 16-a:
The Gaussian peak position of the fitted generator level W-jet softdrop mass distribution as a function of jet $ {p_{\mathrm {T}}} $ as a function of jet $ {p_{\mathrm {T}}} $ in two $\eta $ bins. |
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Additional Figure 16-b:
The difference in reconstructed PUPPI softdrop mass and generated softdrop mass normalized by reconstructed PUPPI softdrop mass as a function of jet $ {p_{\mathrm {T}}} $ in two $\eta $ bins. |
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Additional Figure 17:
The W/Z/H-jet PUPPI softdrop mass after jet mass corrections have been applied for jets from different signal samples with masses of 1 and 4 TeV. |
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Additional Figure 18:
PUPPI softdrop jet mass distribution that (a) pass and (b) fail the PUPPI $\tau _2 / \tau _1$ DDT $ < $ 0.52 selection. The result of the fit to data and simulation are shown, respectively, by the solid and long-dashed line and the background components of the fit are shown as dashed-dotted and short-dashed line. |
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Additional Figure 18-a:
PUPPI softdrop jet mass distribution that passes the PUPPI $\tau _2 / \tau _1$ DDT $ < $ 0.52 selection. The result of the fit to data and simulation are shown, respectively, by the solid and long-dashed line and the background components of the fit are shown as dashed-dotted and short-dashed line. |
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Additional Figure 18-b:
PUPPI softdrop jet mass distribution that fails the PUPPI $\tau _2 / \tau _1$ DDT $ < $ 0.52 selection. The result of the fit to data and simulation are shown, respectively, by the solid and long-dashed line and the background components of the fit are shown as dashed-dotted and short-dashed line. |
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