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| CMS-EXO-22-015 ; CERN-EP-2024-049 | ||
| Search for dark QCD with emerging jets in proton-proton collisions at $ \sqrt{s} = $ 13 TeV | ||
| CMS Collaboration | ||
| 3 March 2024 | ||
| JHEP 07 (2024) 142 | ||
| Abstract: A search for ``emerging jets'' produced in proton-proton collisions at a center-of-mass energy of 13 TeV is performed using data collected by the CMS experiment corresponding to an integrated luminosity of 138 fb$ ^{-1} $. This search examines a hypothetical dark quantum chromodynamics (QCD) sector that couples to the standard model (SM) through a scalar mediator. The scalar mediator decays into an SM quark and a dark sector quark. As the dark sector quark showers and hadronizes, it produces long-lived dark mesons that subsequently decay into SM particles, resulting in a jet, known as an emerging jet, with multiple displaced vertices. This search looks for pair production of the scalar mediator at the LHC, which yields events with two SM jets and two emerging jets at leading order. The results are interpreted using two dark sector models with different flavor structures, and exclude mediator masses up to 1950 (1950) GeV for an unflavored (flavor-aligned) dark QCD model. The unflavored results surpass a previous search for emerging jets by setting the most stringent mediator mass exclusion limits to date, while the flavor-aligned results provide the first direct mediator mass exclusion limits to date. | ||
| Links: e-print arXiv:2403.01556 [hep-ex] (PDF) ; CDS record ; inSPIRE record ; CADI line (restricted) ; | ||
| Figures | |
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Figure 1:
Feynman diagrams for pair production of dark mediator particles via gluon-gluon fusion (left) and quark-antiquark annihilation (right), with each mediator decaying to an SM quark and a dark quark. |
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Figure 1-a:
Feynman diagrams for pair production of dark mediator particles via gluon-gluon fusion (left) and quark-antiquark annihilation (right), with each mediator decaying to an SM quark and a dark quark. |
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Figure 1-b:
Feynman diagrams for pair production of dark mediator particles via gluon-gluon fusion (left) and quark-antiquark annihilation (right), with each mediator decaying to an SM quark and a dark quark. |
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Figure 2:
Distributions of the jet variables $ \langle {d_{xy}} \rangle $ (left) and $ \alpha_\text{3D} $ with $ D_{N}^\text{max}= $ 4 (right) used for the model-agnostic EJ tagging that targets the unflavored dark sector models are shown for data (points), SM multijet simulation (gray line), and signal jets in simulation (colored lines). The sums of the entries are normalized to unity. |
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Figure 2-a:
Distributions of the jet variables $ \langle {d_{xy}} \rangle $ (left) and $ \alpha_\text{3D} $ with $ D_{N}^\text{max}= $ 4 (right) used for the model-agnostic EJ tagging that targets the unflavored dark sector models are shown for data (points), SM multijet simulation (gray line), and signal jets in simulation (colored lines). The sums of the entries are normalized to unity. |
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Figure 2-b:
Distributions of the jet variables $ \langle {d_{xy}} \rangle $ (left) and $ \alpha_\text{3D} $ with $ D_{N}^\text{max}= $ 4 (right) used for the model-agnostic EJ tagging that targets the unflavored dark sector models are shown for data (points), SM multijet simulation (gray line), and signal jets in simulation (colored lines). The sums of the entries are normalized to unity. |
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Figure 3:
Distributions of the jet variables used for the model-agnostic EJ tagging targeting flavor-aligned dark sector models for jets obtained in data (points), SM multijet simulation (gray line), and simulated signal jets (colored lines). The distribution of the number of tracks with $ d_{xy} > $ 10$^{-2.2}$ cm (jet girth) is shown on the left (right). The sums of the entries are normalized to unity. |
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Figure 3-a:
Distributions of the jet variables used for the model-agnostic EJ tagging targeting flavor-aligned dark sector models for jets obtained in data (points), SM multijet simulation (gray line), and simulated signal jets (colored lines). The distribution of the number of tracks with $ d_{xy} > $ 10$^{-2.2}$ cm (jet girth) is shown on the left (right). The sums of the entries are normalized to unity. |
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Figure 3-b:
Distributions of the jet variables used for the model-agnostic EJ tagging targeting flavor-aligned dark sector models for jets obtained in data (points), SM multijet simulation (gray line), and simulated signal jets (colored lines). The distribution of the number of tracks with $ d_{xy} > $ 10$^{-2.2}$ cm (jet girth) is shown on the left (right). The sums of the entries are normalized to unity. |
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Figure 4:
Distributions of the output score of the uGNN (left) and aGNN (right) for the data (points with error bars), SM multijet simulation (dark gray line), and signal simulation (colored lines). The signal distributions in the left (right) plot are generated from the unflavored (flavor-aligned) model. Bins are chosen to correspond to the jet selection criteria defined in Table 5. The uncertainties in the SM multijet simulation are too small to be visible. The systematic uncertainties in the simulated signal distributions are small and have been omitted for reasons of clarity. The sums of the entries are normalized to unity. |
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Figure 4-a:
Distributions of the output score of the uGNN (left) and aGNN (right) for the data (points with error bars), SM multijet simulation (dark gray line), and signal simulation (colored lines). The signal distributions in the left (right) plot are generated from the unflavored (flavor-aligned) model. Bins are chosen to correspond to the jet selection criteria defined in Table 5. The uncertainties in the SM multijet simulation are too small to be visible. The systematic uncertainties in the simulated signal distributions are small and have been omitted for reasons of clarity. The sums of the entries are normalized to unity. |
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Figure 4-b:
Distributions of the output score of the uGNN (left) and aGNN (right) for the data (points with error bars), SM multijet simulation (dark gray line), and signal simulation (colored lines). The signal distributions in the left (right) plot are generated from the unflavored (flavor-aligned) model. Bins are chosen to correspond to the jet selection criteria defined in Table 5. The uncertainties in the SM multijet simulation are too small to be visible. The systematic uncertainties in the simulated signal distributions are small and have been omitted for reasons of clarity. The sums of the entries are normalized to unity. |
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Figure 5:
Template fit of the DEEPJET discriminator used to determine the b jet fraction of the non-EJ tagged jets for data events that pass the ``u-set validation'' (uGNN validation) selection criteria shown on the left (right), except with the requirement on the number of EJ-tagged jets changed from 2 to 1. The lower panels show the ratio of the number of jets in the data compared to the sum of the fitted template distributions. |
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Figure 6:
The EJ tagger misidentification probability for b quark jets (red, orange) and light jets (light blue, dark blue) as a function of jet $ p_{\mathrm{T}} $ for the model-agnostic tagger ``u-tag 1'' (left) and the ML-based tagger ``uGNN tag 1'' (right), as defined in Tables 3 and 5, evaluated using data (red, dark blue) and generator-level flavor information from simulated samples (orange, light blue) in events containing a high-$ p_{\mathrm{T}} $ photon. The lower panel shows the pull, defined as the difference between the mistag rate calculated in simulation and mistag rate measured in data, scaled down by the uncertainty measured in data. The error bars indicate the uncertainties in the mistag rates measured in simulation scaled by the uncertainties measured in data. |
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Figure 6-a:
The EJ tagger misidentification probability for b quark jets (red, orange) and light jets (light blue, dark blue) as a function of jet $ p_{\mathrm{T}} $ for the model-agnostic tagger ``u-tag 1'' (left) and the ML-based tagger ``uGNN tag 1'' (right), as defined in Tables 3 and 5, evaluated using data (red, dark blue) and generator-level flavor information from simulated samples (orange, light blue) in events containing a high-$ p_{\mathrm{T}} $ photon. The lower panel shows the pull, defined as the difference between the mistag rate calculated in simulation and mistag rate measured in data, scaled down by the uncertainty measured in data. The error bars indicate the uncertainties in the mistag rates measured in simulation scaled by the uncertainties measured in data. |
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Figure 6-b:
The EJ tagger misidentification probability for b quark jets (red, orange) and light jets (light blue, dark blue) as a function of jet $ p_{\mathrm{T}} $ for the model-agnostic tagger ``u-tag 1'' (left) and the ML-based tagger ``uGNN tag 1'' (right), as defined in Tables 3 and 5, evaluated using data (red, dark blue) and generator-level flavor information from simulated samples (orange, light blue) in events containing a high-$ p_{\mathrm{T}} $ photon. The lower panel shows the pull, defined as the difference between the mistag rate calculated in simulation and mistag rate measured in data, scaled down by the uncertainty measured in data. The error bars indicate the uncertainties in the mistag rates measured in simulation scaled by the uncertainties measured in data. |
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Figure 7:
The 95% CL upper limits on the production cross section for various signal models in the unflavored scenario (upper plots) and the flavor-aligned scenario (lower plots) with $ m_{\pi_\text{dark}}= $ 10 GeV using the model-agnostic (GNN) EJ tagging method, on the left (right). The red curve is the expected exclusion limit, with the band representing its 68% CL variation. The black curve is the observed limit. The dark blue dotted curves in the upper plots are the expected and observed limits previously obtained by CMS [21]. |
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Figure 7-a:
The 95% CL upper limits on the production cross section for various signal models in the unflavored scenario (upper plots) and the flavor-aligned scenario (lower plots) with $ m_{\pi_\text{dark}}= $ 10 GeV using the model-agnostic (GNN) EJ tagging method, on the left (right). The red curve is the expected exclusion limit, with the band representing its 68% CL variation. The black curve is the observed limit. The dark blue dotted curves in the upper plots are the expected and observed limits previously obtained by CMS [21]. |
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Figure 7-b:
The 95% CL upper limits on the production cross section for various signal models in the unflavored scenario (upper plots) and the flavor-aligned scenario (lower plots) with $ m_{\pi_\text{dark}}= $ 10 GeV using the model-agnostic (GNN) EJ tagging method, on the left (right). The red curve is the expected exclusion limit, with the band representing its 68% CL variation. The black curve is the observed limit. The dark blue dotted curves in the upper plots are the expected and observed limits previously obtained by CMS [21]. |
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Figure 7-c:
The 95% CL upper limits on the production cross section for various signal models in the unflavored scenario (upper plots) and the flavor-aligned scenario (lower plots) with $ m_{\pi_\text{dark}}= $ 10 GeV using the model-agnostic (GNN) EJ tagging method, on the left (right). The red curve is the expected exclusion limit, with the band representing its 68% CL variation. The black curve is the observed limit. The dark blue dotted curves in the upper plots are the expected and observed limits previously obtained by CMS [21]. |
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Figure 7-d:
The 95% CL upper limits on the production cross section for various signal models in the unflavored scenario (upper plots) and the flavor-aligned scenario (lower plots) with $ m_{\pi_\text{dark}}= $ 10 GeV using the model-agnostic (GNN) EJ tagging method, on the left (right). The red curve is the expected exclusion limit, with the band representing its 68% CL variation. The black curve is the observed limit. The dark blue dotted curves in the upper plots are the expected and observed limits previously obtained by CMS [21]. |
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Figure 8:
The 95% CL upper limits on the production cross section for various signal models in the unflavored scenario (upper plots) and the flavor-aligned scenario (lower plots) with $ m_{\pi_\text{dark}}= $ 20 GeV using the model-agnostic (GNN) EJ tagging method, on the left (right). The red curve is the expected exclusion limit, with the band representing its 68% CL variation. The black curve is the observed limit. |
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Figure 8-a:
The 95% CL upper limits on the production cross section for various signal models in the unflavored scenario (upper plots) and the flavor-aligned scenario (lower plots) with $ m_{\pi_\text{dark}}= $ 20 GeV using the model-agnostic (GNN) EJ tagging method, on the left (right). The red curve is the expected exclusion limit, with the band representing its 68% CL variation. The black curve is the observed limit. |
png pdf |
Figure 8-b:
The 95% CL upper limits on the production cross section for various signal models in the unflavored scenario (upper plots) and the flavor-aligned scenario (lower plots) with $ m_{\pi_\text{dark}}= $ 20 GeV using the model-agnostic (GNN) EJ tagging method, on the left (right). The red curve is the expected exclusion limit, with the band representing its 68% CL variation. The black curve is the observed limit. |
png pdf |
Figure 8-c:
The 95% CL upper limits on the production cross section for various signal models in the unflavored scenario (upper plots) and the flavor-aligned scenario (lower plots) with $ m_{\pi_\text{dark}}= $ 20 GeV using the model-agnostic (GNN) EJ tagging method, on the left (right). The red curve is the expected exclusion limit, with the band representing its 68% CL variation. The black curve is the observed limit. |
png pdf |
Figure 8-d:
The 95% CL upper limits on the production cross section for various signal models in the unflavored scenario (upper plots) and the flavor-aligned scenario (lower plots) with $ m_{\pi_\text{dark}}= $ 20 GeV using the model-agnostic (GNN) EJ tagging method, on the left (right). The red curve is the expected exclusion limit, with the band representing its 68% CL variation. The black curve is the observed limit. |
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Figure 9:
The 95% CL upper limits on the production cross section for various signal models in the flavor-aligned scenario with $ m_{\pi_\text{dark}}= $ 6 GeV using the model-agnostic (GNN) EJ tagging method, on the left (right). The red curve is the expected exclusion limit, with the band representing its 68% CL variation. The black curve is the observed limit. |
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Figure 9-a:
The 95% CL upper limits on the production cross section for various signal models in the flavor-aligned scenario with $ m_{\pi_\text{dark}}= $ 6 GeV using the model-agnostic (GNN) EJ tagging method, on the left (right). The red curve is the expected exclusion limit, with the band representing its 68% CL variation. The black curve is the observed limit. |
png pdf |
Figure 9-b:
The 95% CL upper limits on the production cross section for various signal models in the flavor-aligned scenario with $ m_{\pi_\text{dark}}= $ 6 GeV using the model-agnostic (GNN) EJ tagging method, on the left (right). The red curve is the expected exclusion limit, with the band representing its 68% CL variation. The black curve is the observed limit. |
| Tables | |
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Table 1:
Model parameters for the unflavored model. |
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Table 2:
Parameters used for the flavor-aligned model. In order to probe a range of lifetimes, the values of $ \kappa_0 $ listed in columns 3-7 are tuned to give the desired $ c\tau_{\pi_\text{dark}}^{\text{max}} $ values of 5, 25, 45, 100, and 500 mm. In addition, samples were made with fixed $ \kappa_0= $ 1, with a resultant value of $ c\tau_{\pi_\text{dark}}^{\text{max}} $ that depends on the other model parameters. |
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Table 3:
Emerging jet selection criteria for the model-agnostic analysis designed for the unflavored scenario. The validation regions are discussed in Section 6. The symbols in parentheses indicate a minimum ($> $) or maximum ($< $) requirement. |
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Table 4:
Emerging jet selection criteria for the model-agnostic analysis designed for the flavor-aligned scenario. The validation tag is described in Section 6. The symbols in parentheses indicate a minimum ($> $) or maximum ($< $) requirement. |
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Table 5:
The GNN score range used to identify a jet as an EJ. The uGNN (aGNN) tag indicates that the tagger uses the output score of the GNN trained on the unflavored (flavor-aligned) simulated signal samples. The validation tags are described in Section 6. |
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Table 6:
Event selection criteria used for the analysis. The validation selection criteria are described in Section 6. |
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Table 7:
The observed yield of events in data satisfying the validation selection criteria with at least two jets passing the corresponding validation tag, and the estimation based on the misidentification rate calculated using validation events with exactly one jet passing the validation tagger scaled by the factor given in Eq. \eqrefeq:scalefactor_sum. The statistical and systematic uncertainties are reported for the estimated yields. |
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Table 8:
Mean and standard deviation (std.) of the relative uncertainty calculated on the background estimations, by source, in percent. |
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Table 9:
Mean and standard deviation (std.) of the relative uncertainty calculated on the unflavored and flavor-aligned samples, by source, in percent. |
| Summary |
| A search for emerging jet signatures arising from a strongly interacting dark sector produced in proton-proton collisions has been presented, using data corresponding to an integrated luminosity of 138 fb$ ^{-1} $ at $ \sqrt{s}= $ 13 TeV. The signal model contains a family of dark quarks that couple to the standard model (SM) quarks via a scalar mediator $ \text{X}_\text{dark} $. Dark pions ($ \pi_\text{dark} $) with a significant lifetime ($ c\tau_{\pi_\text{dark}} $) are produced by the hadronization of the dark quarks; these then decay to SM particles at vertices displaced from the proton-proton interaction point. As the scalar mediator is assumed to be produced in pairs, and each decays to an SM quark and a dark quark, the signature of this process is two SM jets plus two jets of particles with constituents emerging from displaced vertices. Both unflavored and flavor-aligned couplings between the SM quarks and the dark quarks are examined in the search. Events are selected using either a traditional cut-based approach or a graph neural network to identify emerging jets, in combination with other event-level selection criteria. The overall selection requirements are optimized for each coupling scenario and for different combinations of the mediator particle mass, dark pion mass, and dark pion lifetime. No excess of events beyond the SM expectations is found, and the observed 95% confidence level exclusion limits agree with the expected limits. For the unflavored model, dark mediator masses $ m_{\text{X}_\text{dark}} < $ 1950 GeV are excluded for $ c\tau_{\pi_\text{dark}}\approx $ 100 mm and $ m_{\pi_\text{dark}}= $ 10 GeV, while the flavor-aligned model result excludes $ m_{\text{X}_\text{dark}} < $ 1850 GeV at $ c\tau_{\pi_\text{dark}}^{\text{max}}\approx $ 500 mm for $ m_{\pi_\text{dark}}= $ 10 GeV. This result surpasses the previous search for emerging jets in the unflavored scenario, increasing the experimental limit of the dark mediator particle by $ {\approx} $ 500 GeV to set the most stringent limits to date, and provides the first direct exclusion of the flavor-aligned scenario. |
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