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| CMS-PAS-EXO-24-029 | ||
| Search for $ s $-channel production of lepton-enriched semivisible jets in proton-proton collisions at 13 TeV | ||
| CMS Collaboration | ||
| 2025-08-23 | ||
| Abstract: A search for resonant production of strongly-coupled dark matter manifesting as lepton-enriched semivisible jets in proton-proton collisions at the LHC is presented. The search is performed using data collected by the CMS experiment during 2016--2018 at a center-of-mass energy of 13 TeV corresponding to a total integrated luminosity of 138 fb$ ^{-1} $. Final states with missing transverse momentum aligned to jets containing enhanced proportions of leptons are considered, as the signal jets are made of visible particles from the standard model sector and stable bound states from the dark sector. The analysis employs a machine learning-based strategy, utilizing both a graph neural network jet identification algorithm to discriminate between signal and background and a deep neural network-based approach to estimate the background in the signal region. Assuming the resonantly-produced mediator, a Z' boson, has a universal coupling of 0.25 to the standard model quarks, the search excludes mediator masses from 1.5 TeV up to 4.7 TeV at 95% confidence level, depending on the other signal model parameters. These results exclude a wide range of strongly-coupled dark matter models with lepton-enriched semivisible jet signatures for the first time. | ||
| Links: CDS record (PDF) ; CADI line (restricted) ; | ||
| Figures & Tables | Summary | Additional Figures | References | CMS Publications |
|---|
| Figures | |
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Figure 1:
A diagram of $ s $-channel production of the SVJ$ \ell $ signature. |
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Figure 2:
Illustration of the Lund tree before and after pruning, and its conversion to an IRC-safe graph. The edge colors indicate different Lund planes, with dashed edges indicating further Lund planes that are not fully shown. |
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Figure 3:
Left: LundNet jet tagger score for the two highest $ p_{\mathrm{T}} $ jets in the multilepton $ \Delta\eta $-extended region for different signal models, simulated backgrounds, and data. Right: ROC curves for different signal models. The AUC is computed as the area under the ROC curve for the given signal model against the total background. |
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Figure 3-a:
Left: LundNet jet tagger score for the two highest $ p_{\mathrm{T}} $ jets in the multilepton $ \Delta\eta $-extended region for different signal models, simulated backgrounds, and data. Right: ROC curves for different signal models. The AUC is computed as the area under the ROC curve for the given signal model against the total background. |
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Figure 3-b:
Left: LundNet jet tagger score for the two highest $ p_{\mathrm{T}} $ jets in the multilepton $ \Delta\eta $-extended region for different signal models, simulated backgrounds, and data. Right: ROC curves for different signal models. The AUC is computed as the area under the ROC curve for the given signal model against the total background. |
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Figure 4:
Sketch of the MD-ABCDisCoTEC background estimation method. On the left, the ABCD plane is shown, defined by the scores of the MD-ABCDisCoTEC neural network. On the right, the effect of mass decorrelation during the network training is illustrated: it results in similar $ m_{\mathrm{T}} $ distribution shapes across the different regions of the ABCD plane, enabling robust background estimation. |
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Figure 5:
Left: evolution of the different components of the loss function in the training of the MD-ABCDisCoTEC model. Right: density distribution of simulated background and signal events, represented as Gaussian kernel density estimators (KDEs), in the ABCD plane defined by the two network scores in the multilepton $ \Delta\eta $-extended region. The dashed blue lines represent the ABCD boundaries chosen via the optimization procedure. |
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Figure 5-a:
Left: evolution of the different components of the loss function in the training of the MD-ABCDisCoTEC model. Right: density distribution of simulated background and signal events, represented as Gaussian kernel density estimators (KDEs), in the ABCD plane defined by the two network scores in the multilepton $ \Delta\eta $-extended region. The dashed blue lines represent the ABCD boundaries chosen via the optimization procedure. |
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Figure 5-b:
Left: evolution of the different components of the loss function in the training of the MD-ABCDisCoTEC model. Right: density distribution of simulated background and signal events, represented as Gaussian kernel density estimators (KDEs), in the ABCD plane defined by the two network scores in the multilepton $ \Delta\eta $-extended region. The dashed blue lines represent the ABCD boundaries chosen via the optimization procedure. |
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Figure 6:
Comparison of estimated background and observed data in the multilepton low-$ \Delta\eta $ region for the SVJ$ \ell $ search. The distributions from several signal model examples are superimposed. The last bin of the distribution includes all events with $ m_{\mathrm{T}} > $ 3 TeV. |
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Figure 7:
The 95% CL upper limits on $ \sigma_{{\mathrm{Z}}^{\prime}}\mathcal{B}_{\text{dark}} $ for the SVJ$ \ell $ model as a function of $ m_{{\mathrm{Z}}^{\prime}} $, for $ r_{\text{inv}}= $ 0.3, 0.5, 0.7 and $ m_{\text{dark}}= $ 16 (left) and 32 GeV (right). The red solid line labeled ``Theory'' represents the nominal Z' cross section. |
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Figure 7-a:
The 95% CL upper limits on $ \sigma_{{\mathrm{Z}}^{\prime}}\mathcal{B}_{\text{dark}} $ for the SVJ$ \ell $ model as a function of $ m_{{\mathrm{Z}}^{\prime}} $, for $ r_{\text{inv}}= $ 0.3, 0.5, 0.7 and $ m_{\text{dark}}= $ 16 (left) and 32 GeV (right). The red solid line labeled ``Theory'' represents the nominal Z' cross section. |
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Figure 7-b:
The 95% CL upper limits on $ \sigma_{{\mathrm{Z}}^{\prime}}\mathcal{B}_{\text{dark}} $ for the SVJ$ \ell $ model as a function of $ m_{{\mathrm{Z}}^{\prime}} $, for $ r_{\text{inv}}= $ 0.3, 0.5, 0.7 and $ m_{\text{dark}}= $ 16 (left) and 32 GeV (right). The red solid line labeled ``Theory'' represents the nominal Z' cross section. |
| Tables | |
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Table showing the absolute efficiency (in percentage) of each selection applied in the analysis for different signal models. The data quality filters refer to: the $ p_{\mathrm{T}}^\text{miss} $ filters, $ \Delta R(\mathrm{j}_{1,2},c_{\text{nonfunctional}}) > 0.1\ $, and $ \text{veto} f_{\gamma}(\mathrm{j}_{1}) > $ 0.7 & $ p_{\mathrm{T}}(\mathrm{j}_{1}) > $ 1.0 TeV. :
|
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Table 2:
The range of effects on the signal yield for each signal-related systematic uncertainty. The variation in the yield effects arises from the different years of data taking and the range of signal models considered. Values less than 0.01% are rounded to 0.0%. |
| Summary |
| We present the first collider search for resonant production of lepton-enriched semivisible jets (the SVJ$ \ell $ signature). The search uses proton-proton collision data collected with the CMS detector in 2016--2018, corresponding to an integrated luminosity of 138 fb$ ^{-1} $ at a center-of-mass energy of 13 TeV. The signal model introduces a dark sector with multiple flavors of dark quarks that are charged under a dark confining force, giving rise to sprays of collimated stable and unstable dark hadrons. The stable dark hadrons constitute dark matter candidates, while the unstable dark hadrons decay promptly and democratically to standard model (SM) quarks and leptons, producing lepton-enriched semivisible jets. The hidden sector communicates with the SM via multiple portals: a Z' boson and a dark photon A'. The Z' mediator has a TeV-scale mass and can decay to dark quarks, while the A' mediator mainly governs the branching fractions for the dark hadron decays to leptons and quarks. We consider variations in several parameters of the hidden sector signal model: the Z' mass, $ m_{{\mathrm{Z}}^{\prime}} $; the dark hadron mass, $ m_{\text{dark}} $; and the fraction of stable dark hadrons, $ r_{\text{inv}} $. We adopt a machine learning-based approach, employing an extension of the LundNet algorithm to distinguish lepton-enriched semivisible jets from SM jets. Additionally, we utilize a deep neural network that takes the LundNet discriminators and other event-level and lepton-related variables as input to improve the discrimination of signal from background and to estimate the background in the signal region. The first 95% confidence level exclusion limits on the SVJ$ \ell $ signature are set for $ m_{{\mathrm{Z}}^{\prime}} $ masses from 1.5 TeV up to 4.7 TeV, depending on $ m_{\text{dark}} $ and $ r_{\text{inv}} $. These results target, for the first time, semivisible jet signatures with leptonic final states, complementing existing searches for dijet resonances, dark matter events with missing transverse momentum and initial-state radiation, and the fully hadronic semivisible jet search [40]. Compared to the existing fully hadronic semivisible jet results, the search presented here explores a new phase space. It leverages the presence of additional leptons expected in the signal events to reduce the extensive SM multijet background that dominates for the fully hadronic signature. |
| Additional Figures | |
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Additional Figure 1:
The distributions of the $ \Delta\eta $ variable in the $ \Delta\eta $-extended region after applying the selection requirements, for the simulated background processes and various signal models. |
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Additional Figure 2:
The distributions of the $ \Delta\phi_{\text{min}} $ variable in the $ \Delta\eta $-extended region after applying the selection requirements, for the simulated background processes and various signal models. |
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Additional Figure 3:
The distribution of the $ m_{\mathrm{T}} $ variable in the $ \Delta\eta $-extended region after applying the selection requirements, for the simulated background processes and various signal models. |
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Additional Figure 4:
The distributions of the $ R_{\mathrm{T}} $ variable in the $ \Delta\eta $-extended region after applying the selection requirements, for the simulated background processes and various signal models. |
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Additional Figure 5:
The distributions of the muon $ p_{\mathrm{T}} $ in the $ \Delta\eta $-extended region after applying the selection requirements, except the mini-isolation requirement, for the simulated background processes and various signal models. |
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Additional Figure 6:
The distributions of the muon mini-isolation $ I_{\text{mini}}(\mu) $ in the $ \Delta\eta $-extended region after applying the selection requirements, except the mini-isolation requirement, for the simulated background processes and various signal models. |
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Additional Figure 7:
Pie chart showing the expected proportions of each background process, estimated from simulation, in the $ \Delta\eta $-extended region after applying the selection requirements, except the mini-isolation requirement. |
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Additional Figure 8:
Pie chart showing the expected proportions of each background process, estimated from simulation, in the $ \Delta\eta $-extended region after applying the selection requirements, including the mini-isolation requirement. |
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Additional Figure 9:
Left: EdgeConv block and its application to a node of the IRC-safe Lund graph. Right: architecture of the LundNet model. |
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Additional Figure 10:
Example of Lund tree for a SVJ$ \ell $ signal jet. The color code refers to the energy fraction of muons in each pseudojet defined at each declustering step. |
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Additional Figure 11:
Example of Lund tree for a QCD background jet. The color code refers to the energy fraction of muons in each pseudojet defined at each declustering step. |
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Additional Figure 12:
Example of Lund tree for a $ \mathrm{t} \overline{\mathrm{t}} $ background jet. The color code refers to the energy fraction of muons in each pseudojet defined at each declustering step. |
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Additional Figure 13:
Example of Lund tree for a W\text+jets} background jet. The color code refers to the energy fraction of muons in each pseudojet defined at each declustering step. |
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Additional Figure 14:
ROC curves for different signal models for a LundNet trained without energy fractions assigned as node features and without applying $ k_{\mathrm{T}} $-pruning. The AUC is computed as the area under the ROC curve for the given signal model against the total background. |
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Additional Figure 15:
ROC curves for different signal models for a LundNet trained with energy fractions assigned as node features and without applying $ k_{\mathrm{T}} $-pruning. The AUC is computed as the area under the ROC curve for the given signal model against the total background. |
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Additional Figure 16:
The LundNet jet tagger score distribution for the two highest $ p_{\mathrm{T}} $ jets in the multilepton $ \Delta\eta $-extended region for different signal models, simulated backgrounds, and data. The LundNet is trained with energy fractions assigned as node features and without applying $ k_{\mathrm{T}} $-pruning. The ratio plot in the lower panel refers to data against total background MC. |
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Additional Figure 17:
The LundNet jet tagger score distribution for the two highest $ p_{\mathrm{T}} $ jets in the multilepton $ \Delta\eta $-extended region for different signal models, simulated backgrounds, and data. The LundNet is trained with energy fractions assigned as node features and applying $ k_{\mathrm{T}} $-pruning. The ratio plot in the lower panel refers to data against total background MC. |
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Additional Figure 18:
Comparison of estimated background and observed data in the multilepton high-$ \Delta\eta $ region for the SVJ$ \ell $ search. The last bin of the distribution includes all events with $ m_{\mathrm{T}} > $ 3 TeV. |
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Additional Figure 19:
The 95% CL upper limits on $ \sigma_{{\mathrm{Z}}^{\prime}}\mathcal{B}_{\text{dark}} $, where $ \sigma_{{\mathrm{Z}}^{\prime}} $ is the production cross section for $ q\bar{q}\to{\mathrm{Z}}^{\prime} $, and $ \mathcal{B}_{\text{dark}} $ is branching ratio to dark quarks for the SVJ$ \ell $ model as a function of $ m_{{\mathrm{Z}}^{\prime}} $, for $ r_{\text{inv}}= $ 0.3, 0.5, 0.7 and $ m_{\text{dark}}= $ 8 GeV. The red solid line labeled ``Theory'' represents the nominal $ {\mathrm{Z}}^{\prime} $ cross section. |
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Additional Figure 20:
The 95% CL upper limits on $ \sigma_{{\mathrm{Z}}^{\prime}}\mathcal{B}_{\text{dark}} $, where $ \sigma_{{\mathrm{Z}}^{\prime}} $ is the production cross section for $ q\bar{q}\to{\mathrm{Z}}^{\prime} $, and $ \mathcal{B}_{\text{dark}} $ is branching ratio to dark quarks for the SVJ$ \ell $ model as a function of $ m_{{\mathrm{Z}}^{\prime}} $, for $ r_{\text{inv}}= $ 0.3 and $ m_{\text{dark}}= $ 8 GeV. The red solid line labeled ``Theory'' represents the nominal $ {\mathrm{Z}}^{\prime} $ cross section. |
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Additional Figure 21:
The 95% CL upper limits on $ \sigma_{{\mathrm{Z}}^{\prime}}\mathcal{B}_{\text{dark}} $, where $ \sigma_{{\mathrm{Z}}^{\prime}} $ is the production cross section for $ q\bar{q}\to{\mathrm{Z}}^{\prime} $, and $ \mathcal{B}_{\text{dark}} $ is branching ratio to dark quarks for the SVJ$ \ell $ model as a function of $ m_{{\mathrm{Z}}^{\prime}} $, for $ r_{\text{inv}}= $ 0.5 and $ m_{\text{dark}}= $ 8 GeV. The red solid line labeled ``Theory'' represents the nominal $ {\mathrm{Z}}^{\prime} $ cross section. |
png pdf |
Additional Figure 22:
The 95% CL upper limits on $ \sigma_{{\mathrm{Z}}^{\prime}}\mathcal{B}_{\text{dark}} $, where $ \sigma_{{\mathrm{Z}}^{\prime}} $ is the production cross section for $ q\bar{q}\to{\mathrm{Z}}^{\prime} $, and $ \mathcal{B}_{\text{dark}} $ is branching ratio to dark quarks for the SVJ$ \ell $ model as a function of $ m_{{\mathrm{Z}}^{\prime}} $, for $ r_{\text{inv}}= $ 0.7 and $ m_{\text{dark}}= $ 8 GeV. The red solid line labeled ``Theory'' represents the nominal $ {\mathrm{Z}}^{\prime} $ cross section. |
png pdf |
Additional Figure 23:
The 95% CL upper limits on $ \sigma_{{\mathrm{Z}}^{\prime}}\mathcal{B}_{\text{dark}} $, where $ \sigma_{{\mathrm{Z}}^{\prime}} $ is the production cross section for $ q\bar{q}\to{\mathrm{Z}}^{\prime} $, and $ \mathcal{B}_{\text{dark}} $ is branching ratio to dark quarks for the SVJ$ \ell $ model as a function of $ m_{{\mathrm{Z}}^{\prime}} $, for $ r_{\text{inv}}= $ 0.3 and $ m_{\text{dark}}= $ 16 GeV. The red solid line labeled ``Theory'' represents the nominal $ {\mathrm{Z}}^{\prime} $ cross section. |
png pdf |
Additional Figure 24:
The 95% CL upper limits on $ \sigma_{{\mathrm{Z}}^{\prime}}\mathcal{B}_{\text{dark}} $, where $ \sigma_{{\mathrm{Z}}^{\prime}} $ is the production cross section for $ q\bar{q}\to{\mathrm{Z}}^{\prime} $, and $ \mathcal{B}_{\text{dark}} $ is branching ratio to dark quarks for the SVJ$ \ell $ model as a function of $ m_{{\mathrm{Z}}^{\prime}} $, for $ r_{\text{inv}}= $ 0.5 and $ m_{\text{dark}}= $ 16 GeV. The red solid line labeled ``Theory'' represents the nominal $ {\mathrm{Z}}^{\prime} $ cross section. |
png pdf |
Additional Figure 25:
The 95% CL upper limits on $ \sigma_{{\mathrm{Z}}^{\prime}}\mathcal{B}_{\text{dark}} $, where $ \sigma_{{\mathrm{Z}}^{\prime}} $ is the production cross section for $ q\bar{q}\to{\mathrm{Z}}^{\prime} $, and $ \mathcal{B}_{\text{dark}} $ is branching ratio to dark quarks for the SVJ$ \ell $ model as a function of $ m_{{\mathrm{Z}}^{\prime}} $, for $ r_{\text{inv}}= $ 0.7 and $ m_{\text{dark}}= $ 16 GeV. The red solid line labeled ``Theory'' represents the nominal $ {\mathrm{Z}}^{\prime} $ cross section. |
png pdf |
Additional Figure 26:
The 95% CL upper limits on $ \sigma_{{\mathrm{Z}}^{\prime}}\mathcal{B}_{\text{dark}} $, where $ \sigma_{{\mathrm{Z}}^{\prime}} $ is the production cross section for $ q\bar{q}\to{\mathrm{Z}}^{\prime} $, and $ \mathcal{B}_{\text{dark}} $ is branching ratio to dark quarks for the SVJ$ \ell $ model as a function of $ m_{{\mathrm{Z}}^{\prime}} $, for $ r_{\text{inv}}= $ 0.3 and $ m_{\text{dark}}= $ 32 GeV. The red solid line labeled ``Theory'' represents the nominal $ {\mathrm{Z}}^{\prime} $ cross section. |
png pdf |
Additional Figure 27:
The 95% CL upper limits on $ \sigma_{{\mathrm{Z}}^{\prime}}\mathcal{B}_{\text{dark}} $, where $ \sigma_{{\mathrm{Z}}^{\prime}} $ is the production cross section for $ q\bar{q}\to{\mathrm{Z}}^{\prime} $, and $ \mathcal{B}_{\text{dark}} $ is branching ratio to dark quarks for the SVJ$ \ell $ model as a function of $ m_{{\mathrm{Z}}^{\prime}} $, for $ r_{\text{inv}}= $ 0.5 and $ m_{\text{dark}}= $ 32 GeV. The red solid line labeled ``Theory'' represents the nominal $ {\mathrm{Z}}^{\prime} $ cross section. |
png pdf |
Additional Figure 28:
The 95% CL upper limits on $ \sigma_{{\mathrm{Z}}^{\prime}}\mathcal{B}_{\text{dark}} $, where $ \sigma_{{\mathrm{Z}}^{\prime}} $ is the production cross section for $ q\bar{q}\to{\mathrm{Z}}^{\prime} $, and $ \mathcal{B}_{\text{dark}} $ is branching ratio to dark quarks for the SVJ$ \ell $ model as a function of $ m_{{\mathrm{Z}}^{\prime}} $, for $ r_{\text{inv}}= $ 0.7 and $ m_{\text{dark}}= $ 32 GeV. The red solid line labeled ``Theory'' represents the nominal $ {\mathrm{Z}}^{\prime} $ cross section. |
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