Compact Muon Solenoid
LHC, CERN
Visit us: CMS Public Website, CMS Physics ;
Contact us: CMS Publications Committee
| CMS-EXO-24-029 ; CERN-EP-2026-198 | ||
| Search for resonant production of lepton-enriched semivisible jets in proton-proton collisions at $ \sqrt{s} = $ 13 TeV | ||
| CMS Collaboration | ||
| 5 August 2026 | ||
| Submitted to the European Physical Journal C | ||
| Abstract: This search targets the resonant production of lepton-enriched semivisible jets (SVJs) from a strongly coupled dark sector, using 138 fb$ ^{-1} $ of proton-proton collision data collected with the CMS detector at the CERN LHC at $ \sqrt{s}= $ 13 TeV. Two scenarios are investigated: jets enriched in all lepton flavors (SVJ$ \ell$ signature) and jets predominantly enriched in tau leptons (SVJ$ \tau $ signature). The analysis focuses on final states in which the missing transverse momentum is aligned with jets containing nonisolated leptons. A dual machine-learning strategy is employed, using a graph neural network for jet identification and a fully connected neural network that combines jet- and event-level information to enhance signal sensitivity and background estimation. The signal models assume a heavy $ {\mathrm{Z}}^{\prime} $ mediator with a benchmark coupling of 0.25 to standard model quarks, together with prompt decays of unstable dark hadrons. In the SVJ$ \ell$ scenario, mediator masses up to 4.7 TeV are excluded at 95% confidence level, while masses between 1.8 and 3.5 TeV are excluded in the SVJ$ \tau $ scenario. These results provide the first experimental constraints on lepton-enriched semivisible jets. | ||
| Links: e-print arXiv:2608.05323 [hep-ex] (PDF) ; CDS record ; inSPIRE record ; HepData record ; CADI line (restricted) ; | ||
| Figures | |
png pdf |
Figure 1:
Left: diagram of $ s $-channel production of SVJs. Right: dark hadrons decay modes in the SVJ$ \ell$ and SVJ$ \tau $ scenarios. |
png pdf |
Figure 2:
Distribution of $ I_{\text{mini}}(\mu) $ in the $ \Delta\eta $-extended region after applying all selection requirements (except for $ I_{\text{mini}} $ itself) for simulated background processes and various models of SVJ$ \ell$ with $ m_{\text{dark}}= $ 16 GeV (left) and SVJ$ \tau $ with $ m_{\text{dark}}= $ 8 GeV (right). The dashed vertical lines indicate the selection requirement for isolated leptons. The sum of the background contributions as well as each individual signal process are normalized to unity. |
png pdf |
Figure 2-a:
Distribution of $ I_{\text{mini}}(\mu) $ in the $ \Delta\eta $-extended region after applying all selection requirements (except for $ I_{\text{mini}} $ itself) for simulated background processes and various models of SVJ$ \ell$ with $ m_{\text{dark}}= $ 16 GeV (left) and SVJ$ \tau $ with $ m_{\text{dark}}= $ 8 GeV (right). The dashed vertical lines indicate the selection requirement for isolated leptons. The sum of the background contributions as well as each individual signal process are normalized to unity. |
png pdf |
Figure 2-b:
Distribution of $ I_{\text{mini}}(\mu) $ in the $ \Delta\eta $-extended region after applying all selection requirements (except for $ I_{\text{mini}} $ itself) for simulated background processes and various models of SVJ$ \ell$ with $ m_{\text{dark}}= $ 16 GeV (left) and SVJ$ \tau $ with $ m_{\text{dark}}= $ 8 GeV (right). The dashed vertical lines indicate the selection requirement for isolated leptons. The sum of the background contributions as well as each individual signal process are normalized to unity. |
png pdf |
Figure 3:
Illustration of the Lund tree before and after pruning, and its conversion into a pruned graph fed to LUNDNET. The edge colors indicate different Lund planes, with dashed edges indicating further Lund planes that are not fully shown. Each node of the graph has a set of features $ \mathcal{T}^{(i)} $ associated, representing the kinematic information about the splittings encoded in the Lund tree. |
png pdf |
Figure 4:
Left: LUNDNET jet tagger score for the two highest $ p_{\mathrm{T}} $ jets in the multilepton category ($ \Delta\eta $-extended region) for different SVJ$ \ell$ signal models (with $ m_{\text{dark}}= $ 16 GeV), simulated backgrounds, and data. The sum of the background contributions, data as well as each individual signal process are normalized to unity. Statistical uncertainties in the data are shown with vertical bars on the marker. Right: the ROC curves presenting the possible working points of LUNDNET in the background efficiency versus signal efficiency plane from simulations for different SVJ$ \ell$ signal models. The AUC is computed as the area under the ROC curve for a given signal model against the total background. |
png pdf |
Figure 4-a:
Left: LUNDNET jet tagger score for the two highest $ p_{\mathrm{T}} $ jets in the multilepton category ($ \Delta\eta $-extended region) for different SVJ$ \ell$ signal models (with $ m_{\text{dark}}= $ 16 GeV), simulated backgrounds, and data. The sum of the background contributions, data as well as each individual signal process are normalized to unity. Statistical uncertainties in the data are shown with vertical bars on the marker. Right: the ROC curves presenting the possible working points of LUNDNET in the background efficiency versus signal efficiency plane from simulations for different SVJ$ \ell$ signal models. The AUC is computed as the area under the ROC curve for a given signal model against the total background. |
png pdf |
Figure 4-b:
Left: LUNDNET jet tagger score for the two highest $ p_{\mathrm{T}} $ jets in the multilepton category ($ \Delta\eta $-extended region) for different SVJ$ \ell$ signal models (with $ m_{\text{dark}}= $ 16 GeV), simulated backgrounds, and data. The sum of the background contributions, data as well as each individual signal process are normalized to unity. Statistical uncertainties in the data are shown with vertical bars on the marker. Right: the ROC curves presenting the possible working points of LUNDNET in the background efficiency versus signal efficiency plane from simulations for different SVJ$ \ell$ signal models. The AUC is computed as the area under the ROC curve for a given signal model against the total background. |
png pdf |
Figure 5:
Left: the ROC curves presenting the possible working points of LUNDNET in the background efficiency versus signal efficiency plane from simulations for different SVJ$ \tau $ signal models (with $ m_{\text{dark}}= $ 8 GeV) in the 0-lepton category ($ \Delta\eta $-extended region). Right: the ROC curves presenting the possible working points of LUNDNET in the background efficiency versus signal efficiency plane from simulations for different SVJ$ \tau $ signal models (with $ m_{\text{dark}}= $ 8 GeV) in the multilepton category ($ \Delta\eta $-extended region). The AUC is computed as the area under the ROC curve for a given signal model against the total background. |
png pdf |
Figure 5-a:
Left: the ROC curves presenting the possible working points of LUNDNET in the background efficiency versus signal efficiency plane from simulations for different SVJ$ \tau $ signal models (with $ m_{\text{dark}}= $ 8 GeV) in the 0-lepton category ($ \Delta\eta $-extended region). Right: the ROC curves presenting the possible working points of LUNDNET in the background efficiency versus signal efficiency plane from simulations for different SVJ$ \tau $ signal models (with $ m_{\text{dark}}= $ 8 GeV) in the multilepton category ($ \Delta\eta $-extended region). The AUC is computed as the area under the ROC curve for a given signal model against the total background. |
png pdf |
Figure 5-b:
Left: the ROC curves presenting the possible working points of LUNDNET in the background efficiency versus signal efficiency plane from simulations for different SVJ$ \tau $ signal models (with $ m_{\text{dark}}= $ 8 GeV) in the 0-lepton category ($ \Delta\eta $-extended region). Right: the ROC curves presenting the possible working points of LUNDNET in the background efficiency versus signal efficiency plane from simulations for different SVJ$ \tau $ signal models (with $ m_{\text{dark}}= $ 8 GeV) in the multilepton category ($ \Delta\eta $-extended region). The AUC is computed as the area under the ROC curve for a given signal model against the total background. |
png pdf |
Figure 6:
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}} $ background distribution shapes across the different regions of the ABCD plane, enabling robust background estimation. The shift between the distributions in the sketch is due to the total normalization difference in the four regions of the ABCD plane. |
png pdf |
Figure 7:
Left: evolution of the different components of the loss function in the training of the MD-ABCDISCOTEC model for the SVJ$ \ell$ signal in the multilepton category ($ \Delta\eta $-extended region). Right: density distribution of simulated background and SVJ$ \ell$ signal events, represented as Gaussian kernel density estimators (KDEs), in the ABCD plane defined by the two network scores in the multilepton category ($ \Delta\eta $-extended region). Contour lines for the signal at 0.25, 0.5, and 0.75 are overlaid with dashed red lines. The dashed blue lines represent the ABCD boundaries chosen via the optimization procedure. In the legend the values of the DisCo for signal and background are reported. |
png pdf |
Figure 7-a:
Left: evolution of the different components of the loss function in the training of the MD-ABCDISCOTEC model for the SVJ$ \ell$ signal in the multilepton category ($ \Delta\eta $-extended region). Right: density distribution of simulated background and SVJ$ \ell$ signal events, represented as Gaussian kernel density estimators (KDEs), in the ABCD plane defined by the two network scores in the multilepton category ($ \Delta\eta $-extended region). Contour lines for the signal at 0.25, 0.5, and 0.75 are overlaid with dashed red lines. The dashed blue lines represent the ABCD boundaries chosen via the optimization procedure. In the legend the values of the DisCo for signal and background are reported. |
png pdf |
Figure 7-b:
Left: evolution of the different components of the loss function in the training of the MD-ABCDISCOTEC model for the SVJ$ \ell$ signal in the multilepton category ($ \Delta\eta $-extended region). Right: density distribution of simulated background and SVJ$ \ell$ signal events, represented as Gaussian kernel density estimators (KDEs), in the ABCD plane defined by the two network scores in the multilepton category ($ \Delta\eta $-extended region). Contour lines for the signal at 0.25, 0.5, and 0.75 are overlaid with dashed red lines. The dashed blue lines represent the ABCD boundaries chosen via the optimization procedure. In the legend the values of the DisCo for signal and background are reported. |
png pdf |
Figure 8:
Left: density distribution of simulated background and SVJ$ \tau $ signal events, represented as Gaussian kernel density estimators (KDEs), in the ABCD plane defined by the two network scores in the 0-lepton category ($ \Delta\eta $-extended region). Right: density distribution of simulated background and SVJ$ \tau $ signal events, represented as Gaussian kernel density estimators (KDEs), in the ABCD plane defined by the two network scores in the multilepton category ($ \Delta\eta $-extended region). Contour lines for the signal at 0.25, 0.5, and 0.75 are overlaid with dashed red lines. The dashed blue lines represent the ABCD boundaries chosen via the optimization procedure. In the legends the values of the DisCo for signal and background are reported. |
png pdf |
Figure 8-a:
Left: density distribution of simulated background and SVJ$ \tau $ signal events, represented as Gaussian kernel density estimators (KDEs), in the ABCD plane defined by the two network scores in the 0-lepton category ($ \Delta\eta $-extended region). Right: density distribution of simulated background and SVJ$ \tau $ signal events, represented as Gaussian kernel density estimators (KDEs), in the ABCD plane defined by the two network scores in the multilepton category ($ \Delta\eta $-extended region). Contour lines for the signal at 0.25, 0.5, and 0.75 are overlaid with dashed red lines. The dashed blue lines represent the ABCD boundaries chosen via the optimization procedure. In the legends the values of the DisCo for signal and background are reported. |
png pdf |
Figure 8-b:
Left: density distribution of simulated background and SVJ$ \tau $ signal events, represented as Gaussian kernel density estimators (KDEs), in the ABCD plane defined by the two network scores in the 0-lepton category ($ \Delta\eta $-extended region). Right: density distribution of simulated background and SVJ$ \tau $ signal events, represented as Gaussian kernel density estimators (KDEs), in the ABCD plane defined by the two network scores in the multilepton category ($ \Delta\eta $-extended region). Contour lines for the signal at 0.25, 0.5, and 0.75 are overlaid with dashed red lines. The dashed blue lines represent the ABCD boundaries chosen via the optimization procedure. In the legends the values of the DisCo for signal and background are reported. |
png pdf |
Figure 9:
Comparison of estimated background and observed data in the multilepton low-$ \Delta\eta $ region for the SVJ$ \ell$ search. The distributions from several signal model examples (with $ m_{\text{dark}}= $ 16 GeV) are superimposed. The last bin of the distribution includes all events with $ m_{\mathrm{T}} > $ 3 TeV. In the upper panel, the uncertainty in the background prediction is represented by the gray bands, while statistical uncertainties on data are shown with vertical bars on the marker. In the lower panel, the difference between the data and the background prediction divided by the total uncertainty is shown. |
png pdf |
Figure 10:
Comparison of estimated background and observed data in the 0-lepton low-$ \Delta\eta $ region for the SVJ$ \tau $ search. The distributions from several signal model examples (with $ m_{\text{dark}}= $ 8 GeV) are superimposed. The last bin of the distribution includes all events with $ m_{\mathrm{T}} > $ 3 TeV. In the upper panel, the uncertainty in the background prediction is represented by the gray bands, while statistical uncertainties on data are shown with vertical bars on the marker. In the lower panel, the difference between the data and the background prediction divided by the total uncertainty is shown. |
png pdf |
Figure 11:
Comparison of estimated background and observed data in the multilepton low-$ \Delta\eta $ region for the SVJ$ \tau $ search. The distributions from several signal model examples (with $ m_{\text{dark}}= $ 8 GeV) are superimposed. The last bin of the distribution includes all events with $ m_{\mathrm{T}} > $ 3 TeV. In the upper panel, the uncertainty on the background prediction is represented by the gray bands, while statistical uncertainties on data are shown with vertical bars on the marker. In the lower panel, the difference between the data and the background prediction divided by the total uncertainty is shown. |
png pdf |
Figure 12:
The 95% CL upper limits on $ \sigma_{{\mathrm{Z}}^{\prime}}\mathcal{B}_{\text{dark}} $ for the SVJ$ \ell$ model as functions of $ m_{{\mathrm{Z}}^{\prime}} $, for $ r_{\text{inv}} $ values of 0.3 (upper), 0.5 (middle), and 0.7 (lower), and $ m_{\text{dark}}= $ 16 (left) and 32 GeV (right). The red solid line labeled ``Theory'' represents the product of the nominal $ {\mathrm{Z}}^{\prime} $ cross section and $ \mathcal{B}_{\text{dark}} $. |
png pdf |
Figure 12-a:
The 95% CL upper limits on $ \sigma_{{\mathrm{Z}}^{\prime}}\mathcal{B}_{\text{dark}} $ for the SVJ$ \ell$ model as functions of $ m_{{\mathrm{Z}}^{\prime}} $, for $ r_{\text{inv}} $ values of 0.3 (upper), 0.5 (middle), and 0.7 (lower), and $ m_{\text{dark}}= $ 16 (left) and 32 GeV (right). The red solid line labeled ``Theory'' represents the product of the nominal $ {\mathrm{Z}}^{\prime} $ cross section and $ \mathcal{B}_{\text{dark}} $. |
png pdf |
Figure 12-b:
The 95% CL upper limits on $ \sigma_{{\mathrm{Z}}^{\prime}}\mathcal{B}_{\text{dark}} $ for the SVJ$ \ell$ model as functions of $ m_{{\mathrm{Z}}^{\prime}} $, for $ r_{\text{inv}} $ values of 0.3 (upper), 0.5 (middle), and 0.7 (lower), and $ m_{\text{dark}}= $ 16 (left) and 32 GeV (right). The red solid line labeled ``Theory'' represents the product of the nominal $ {\mathrm{Z}}^{\prime} $ cross section and $ \mathcal{B}_{\text{dark}} $. |
png pdf |
Figure 13:
The 95% $ \text{CL}_\text{s} $ upper limits on $ \sigma_{{\mathrm{Z}}^{\prime}}\mathcal{B}_{\text{dark}} $ for the SVJ$ \tau $ model as functions of $ m_{{\mathrm{Z}}^{\prime}} $, for $ r_{\text{inv}}= $ 0.3 ($ \mathcal{B}_{\tau}= $ 0.3) (upper); $ r_{\text{inv}}= $ 0.5 ($ \mathcal{B}_{\tau}= $ 0.3) (upper middle); $ r_{\text{inv}}= $ 0.7 ($ \mathcal{B}_{\tau}= $ 0.3) (lower middle); $ r_{\text{inv}}= $ 0.3 ($ \mathcal{B}_{\tau}= $ 0.7) (lower); and $ m_{\text{dark}}= $ 8 (left) and 12 GeV (right). The red solid line labeled ``Theory'' represents the product of the nominal $ {\mathrm{Z}}^{\prime} $ cross section and $ \mathcal{B}_{\text{dark}} $. |
png pdf |
Figure 13-a:
The 95% $ \text{CL}_\text{s} $ upper limits on $ \sigma_{{\mathrm{Z}}^{\prime}}\mathcal{B}_{\text{dark}} $ for the SVJ$ \tau $ model as functions of $ m_{{\mathrm{Z}}^{\prime}} $, for $ r_{\text{inv}}= $ 0.3 ($ \mathcal{B}_{\tau}= $ 0.3) (upper); $ r_{\text{inv}}= $ 0.5 ($ \mathcal{B}_{\tau}= $ 0.3) (upper middle); $ r_{\text{inv}}= $ 0.7 ($ \mathcal{B}_{\tau}= $ 0.3) (lower middle); $ r_{\text{inv}}= $ 0.3 ($ \mathcal{B}_{\tau}= $ 0.7) (lower); and $ m_{\text{dark}}= $ 8 (left) and 12 GeV (right). The red solid line labeled ``Theory'' represents the product of the nominal $ {\mathrm{Z}}^{\prime} $ cross section and $ \mathcal{B}_{\text{dark}} $. |
png pdf |
Figure 13-b:
The 95% $ \text{CL}_\text{s} $ upper limits on $ \sigma_{{\mathrm{Z}}^{\prime}}\mathcal{B}_{\text{dark}} $ for the SVJ$ \tau $ model as functions of $ m_{{\mathrm{Z}}^{\prime}} $, for $ r_{\text{inv}}= $ 0.3 ($ \mathcal{B}_{\tau}= $ 0.3) (upper); $ r_{\text{inv}}= $ 0.5 ($ \mathcal{B}_{\tau}= $ 0.3) (upper middle); $ r_{\text{inv}}= $ 0.7 ($ \mathcal{B}_{\tau}= $ 0.3) (lower middle); $ r_{\text{inv}}= $ 0.3 ($ \mathcal{B}_{\tau}= $ 0.7) (lower); and $ m_{\text{dark}}= $ 8 (left) and 12 GeV (right). The red solid line labeled ``Theory'' represents the product of the nominal $ {\mathrm{Z}}^{\prime} $ cross section and $ \mathcal{B}_{\text{dark}} $. |
| Tables | |
png pdf |
Table 1:
Parameter ranges considered for the SVJ$ \ell$ and SVJ$ \tau $ models. The $ \rho_{\text{dark}} $ mass values obtained from lattice QCD fits are rounded to the nearest integers. |
png pdf |
Table 2:
Summary of the inclusive selection and categorization. |
png pdf |
Table 3:
The range of effects on the signal yield for each signal-related systematic uncertainty in each analysis category. The variation in the yield effects arises from the different years of data taking and the range of signal models considered. Values less than 0.05% are rounded to 0%. |
| Summary |
| \tolerance=1200 The first search for resonant production of lepton-enriched semivisible jets has been presented. Two scenarios are considered: the first leading to semivisible jets enriched in all lepton flavors (SVJ$ \ell$ signature), and the second leading to semivisible jets enriched in tau leptons (SVJ$ \tau $ 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 models considered arise from 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, whereas the unstable dark hadrons decay promptly to standard model quarks and leptons, producing lepton-enriched semivisible jets.\par In the SVJ$ \ell$ scenario, the hidden sector communicates with the standard model via multiple portals: a $ {\mathrm{Z}}^{\prime} $ boson and a dark photon $ {\mathrm{A}}^{\prime} $. The $ {\mathrm{Z}}^{\prime} $ mediator has a TeVns-scale mass and can decay to dark quarks, whereas the $ {\mathrm{A}}^{\prime} $ mediator mainly governs the branching fractions for the dark hadron decays to leptons and quarks of all generations. In the SVJ$ \tau $ scenario, the $ {\mathrm{Z}}^{\prime} $ boson couples to tau leptons, quarks, and dark quarks. The dark hadrons decay predominantly into the heaviest up-type quark kinematically accessible and into tau leptons. We adopt a machine-learning approach, employing an extension of the LUNDNET algorithm to distinguish lepton-enriched semivisible jets from standard model 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 the SVJ$ \ell$ and SVJ$ \tau $ signals from background and to estimate the background in the signal region. The data are found to agree with the standard model within uncertainties, and exclusion limits at 95% confidence level on the SVJ$ \ell$ and SVJ$ \tau $ models are established by scanning several hypotheses of the signal model parameters. For the SVJ$ \ell$ (SVJ$ \tau $) signature, $ m_{{\mathrm{Z}}^{\prime}} $ masses are excluded in the range 1.5--4.7 (1.8--3.5) TeV, depending on $ m_{\text{dark}} $ and $ r_{\text{inv}} $ ($ m_{\text{dark}} $, $ r_{\text{inv}} $, and $ \mathcal{B}_{\tau} $). This analysis targets, for the first time, the SVJ$ \ell$ and SVJ$ \tau $ final states, complementing existing searches for dijet resonances, dark matter in events with missing transverse momentum and initial-state radiation, and semivisible jets in fully hadronic final states. Compared to the existing fully hadronic semivisible jet results, the searches presented explore a new parameter space. |
| References | ||||
| 1 | G. Bertone, D. Hooper, and J. Silk | Particle dark matter: Evidence, candidates and constraints | Phys. Rept. 405 (2005) 279 | hep-ph/0404175 |
| 2 | Planck Collaboration | Planck 2018 results. VI. Cosmological parameters | [Erratum: Astron. Astrophys. 652 () C4], 2020 Astron. Astrophys. 641 (2020) A6 |
1807.06209 |
| 3 | B. W. Lee and S. Weinberg | Cosmological lower bound on heavy neutrino masses | PRL 39 (1977) 165 | |
| 4 | G. Jungman, M. Kamionkowski, and K. Griest | Supersymmetric dark matter | Phys. Rept. 267 (1996) 195 | hep-ph/9506380 |
| 5 | CMS Collaboration | Search for new physics in final states with a single photon and missing transverse momentum in proton-proton collisions at $ \sqrt{s} = $ 13 TeV | JHEP 02 (2019) 074 | CMS-EXO-16-053 1810.00196 |
| 6 | CMS Collaboration | Search for dark matter produced in association with a leptonically decaying Z boson in proton-proton collisions at $ \sqrt{s} = $ 13 TeV | EPJC 81 (2021) 333 | CMS-EXO-19-003 2008.04735 |
| 7 | CMS Collaboration | Search for new particles in events with energetic jets and large missing transverse momentum in proton-proton collisions at $ \sqrt{s} = $ 13 TeV | JHEP 11 (2021) 153 | CMS-EXO-20-004 2107.13021 |
| 8 | CMS Collaboration | Search for invisible decays of a Higgs boson produced through vector boson fusion in proton-proton collisions at $ \sqrt{s} = $ 13 TeV | PLB 793 (2019) 520 | CMS-HIG-17-023 1809.05937 |
| 9 | CMS Collaboration | Search for dark matter particles in W$ ^{+} $W$ ^{-} $ events with transverse momentum imbalance in proton-proton collisions at $ \sqrt{s} = $ 13 TeV | JHEP 03 (2024) 134 | CMS-EXO-21-012 2310.12229 |
| 10 | ATLAS Collaboration | Search for new particles in events with a hadronically decaying W or Z boson and large missing transverse momentum at $ \sqrt{s} = $ 13 TeV using the ATLAS detector | JHEP 11 (2024) 126 | 2406.01272 |
| 11 | ATLAS Collaboration | Search for invisible Higgs boson decays in vector boson fusion at $ \sqrt{s} = $ 13 TeV with the ATLAS detector | PLB 793 (2019) 499 | 1809.06682 |
| 12 | ATLAS Collaboration | Constraints on mediator-based dark matter and scalar dark energy models using $ \sqrt s = $ 13 TeV $ pp $ collision data collected by the ATLAS detector | JHEP 05 (2019) 142 | 1903.01400 |
| 13 | ATLAS Collaboration | Search for new phenomena in events with two opposite-charge leptons, jets and missing transverse momentum in pp collisions at $ \sqrt{\mathrm{s}} = $ 13 TeV with the ATLAS detector | JHEP 04 (2021) 165 | 2102.01444 |
| 14 | ATLAS Collaboration | Search for new phenomena with top quark pairs in final states with one lepton, jets, and missing transverse momentum in $ pp $ collisions at $ \sqrt{s} = $ 13 TeV with the ATLAS detector | JHEP 04 (2021) 174 | 2012.03799 |
| 15 | ATLAS Collaboration | Search for new phenomena in events with an energetic jet and missing transverse momentum in $ pp $ collisions at $ \sqrt {s} = $ 13 TeV with the ATLAS detector | PRD 103 (2021) 112006 | 2102.10874 |
| 16 | ATLAS Collaboration | Constraints on simplified dark matter models involving an s-channel mediator with the ATLAS detector in pp collisions at $ \sqrt{s} = $ 13 TeV | EPJC 84 (2024) 1102 | 2404.15930 |
| 17 | CMS Collaboration | Search for dark matter produced in association with a single top quark or a top quark pair in proton-proton collisions at $ \sqrt{s}= $ 13 TeV | JHEP 03 (2019) 141 | CMS-EXO-18-010 1901.01553 |
| 18 | ATLAS Collaboration | Search for dark matter produced in association with a single top quark and an energetic $ W $ boson in $ \sqrt{s}= $ 13 TeV $ pp $ collisions with the ATLAS detector | EPJC 83 (2023) 603 | 2211.13138 |
| 19 | CMS Collaboration | Search for dark matter produced in association with one or two top quarks in proton-proton collisions at $ \sqrt{\text{s}} = $ 13 TeV | JHEP 08 (2025) 085 | CMS-EXO-22-014 2505.05300 |
| 20 | CMS Collaboration | Search for dark matter produced in association with a Higgs boson decaying to a pair of bottom quarks in proton--proton collisions at $ \sqrt{s}= $ 13 TeV | EPJC 79 (2019) 280 | CMS-EXO-16-050 1811.06562 |
| 21 | CMS Collaboration | Search for dark matter particles produced in association with a Higgs boson in proton-proton collisions at $ \sqrt{\mathrm{s}} = $ 13 TeV | JHEP 03 (2020) 025 | CMS-EXO-18-011 1908.01713 |
| 22 | ATLAS Collaboration | Search for dark matter produced in association with a Standard Model Higgs boson decaying into b-quarks using the full Run 2 dataset from the ATLAS detector | JHEP 11 (2021) 209 | 2108.13391 |
| 23 | M. J. Strassler and K. M. Zurek | Echoes of a hidden valley at hadron colliders | PLB 651 (2007) 374 | hep-ph/0604261 |
| 24 | H. Beauchesne, E. Bertuzzo, and G. Grilli Di Cortona | Dark matter in hidden valley models with stable and unstable light dark mesons | JHEP 04 (2019) 118 | 1809.10152 |
| 25 | E. Bernreuther, F. Kahlhoefer, M. Kr ä mer, and P. Tunney | Strongly interacting dark sectors in the early universe and at the LHC through a simplified portal | JHEP 01 (2020) 162 | 1907.04346 |
| 26 | G. D. Kribs and E. T. Neil | Review of strongly-coupled composite dark matter models and lattice simulations | Int. J. Mod. Phys. A 31 (2016) 1643004 | 1604.04627 |
| 27 | N. Craig, A. Katz, M. Strassler, and R. Sundrum | Naturalness in the dark at the LHC | JHEP 07 (2015) 105 | 1501.05310 |
| 28 | Y. Bai and A. Rajaraman | Dark matter jets at the LHC | 1109.6009 | |
| 29 | P. Schwaller, D. Stolarski, and A. Weiler | Emerging jets | JHEP 05 (2015) 059 | 1502.05409 |
| 30 | N. Daci et al. | Simplified SIMPs and the LHC | JHEP 11 (2015) 108 | 1503.05505 |
| 31 | M. Park and M. Zhang | Tagging a jet from a dark sector with jet-substructures at colliders | PRD 100 (2019) 115009 | 1712.09279 |
| 32 | T. Cohen, J. Doss, and M. Freytsis | Jet substructure from dark sector showers | JHEP 09 (2020) 118 | 2004.00631 |
| 33 | T. Cohen, M. Lisanti, and H. K. Lou | Semivisible jets: Dark matter undercover at the LHC | PRL 115 (2015) 171804 | 1503.00009 |
| 34 | T. Cohen, M. Lisanti, H. K. Lou, and S. Mishra-Sharma | LHC searches for dark sector showers | JHEP 11 (2017) 196 | 1707.05326 |
| 35 | CMS Collaboration | Search for new particles decaying to a jet and an emerging jet | JHEP 02 (2019) 179 | CMS-EXO-18-001 1810.10069 |
| 36 | CMS Collaboration | Search for resonant production of strongly coupled dark matter in proton-proton collisions at 13 TeV | JHEP 06 (2022) 156 | CMS-EXO-19-020 2112.11125 |
| 37 | ATLAS Collaboration | Search for non-resonant production of semi-visible jets using run 2 data in ATLAS | PLB 848 (2024) 138324 | 2305.18037 |
| 38 | ATLAS Collaboration | Search for resonant production of dark quarks in the dijet final state with the ATLAS detector | JHEP 02 (2024) 128 | 2311.03944 |
| 39 | CMS Collaboration | Search for dark QCD with emerging jets in proton-proton collisions at $ \sqrt{s} = $ 13 TeV | JHEP 07 (2024) 142 | CMS-EXO-22-015 2403.01556 |
| 40 | CMS Collaboration | Search for long-lived particles decaying in the CMS muon detectors in proton-proton collisions at $ \sqrt{s}= $ 13 TeV | PRD 110 (2024) 032007 | CMS-EXO-21-008 2402.01898 |
| 41 | ATLAS Collaboration | Search for new physics in final states with semivisible jets or anomalous signatures using the ATLAS detector | PRD 112 (2025) 012021 | 2505.01634 |
| 42 | ATLAS Collaboration | Search for emerging jets in pp collisions at $ \sqrt{s} = $ 13.6 TeV with the ATLAS experiment | no. 9, 097801, 2025 Rept. Prog. Phys. 88 (2025) |
2505.02429 |
| 43 | CMS Collaboration | Search for low-mass hidden-valley dark showers with non-prompt muon pairs in proton-proton collisions at $ \sqrt{s}= $ 13 TeV | JHEP 03 (2026) 189 | CMS-EXO-24-008 2511.11888 |
| 44 | C. Cazzaniga and A. de Cosa | Leptons lurking in semi-visible jets at the LHC | EPJC 82 (2022) 793 | 2206.03909 |
| 45 | H. Beauchesne et al. | Uncovering tau leptons-enriched semi-visible jets at the LHC | EPJC 83 (2023) 599 | 2212.11523 |
| 46 | 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 |
| 47 | F. A. Dreyer and H. Qu | Jet tagging in the Lund plane with graph networks | JHEP 03 (2021) 052 | 2012.08526 |
| 48 | CMS Collaboration | Machine learning method for enforcing variable independence in background estimation with LHC data: ABCDisCoTEC | 6,. Accepted by \emphMach. Learn. Sci. Tech., 2025 | CMS-MLG-23-003 2506.08826 |
| 49 | CMSnoop | none | \hrefHEPData record for this analysis, 2026 link |
|
| 50 | D. Curtin, R. Essig, S. Gori, and J. Shelton | Illuminating dark photons with high-energy colliders | JHEP 02 (2015) 157 | 1412.0018 |
| 51 | ATLAS Collaboration | Search for high-mass dilepton resonances using 139 fb$ ^{-1} $ of $ pp $ collision data collected at $ \sqrt{s}= $13 TeV with the ATLAS detector | PLB 796 (2019) 68 | 1903.06248 |
| 52 | LHCb Collaboration | Search for $ a'\to\mu^+\mu^- $ decays | PRL 124 (2020) 041801 | 1910.06926 |
| 53 | CMS Collaboration | Search for a narrow resonance lighter than 200 GeV decaying to a pair of muons in proton-proton collisions at $ \sqrt{s} = $ 13 TeV | PRL 124 (2020) 131802 | CMS-EXO-19-018 1912.04776 |
| 54 | CMS Collaboration | Search for direct production of GeV-scale resonances decaying to a pair of muons in proton-proton collisions at $ \sqrt{s} = $ 13 TeV | JHEP 12 (2023) 070 | CMS-EXO-21-005 2309.16003 |
| 55 | CMS Collaboration | The CMS experiment at the CERN LHC | JINST 3 (2008) S08004 | |
| 56 | CMS Collaboration | Development of the CMS detector for the CERN LHC Run 3 | JINST 19 (2024) P05064 | CMS-PRF-21-001 2309.05466 |
| 57 | 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 |
| 58 | CMS Collaboration | The CMS trigger system | JINST 12 (2017) P01020 | CMS-TRG-12-001 1609.02366 |
| 59 | CMS Collaboration | Performance of the CMS high-level trigger during LHC Run 2 | JINST 19 (2024) P11021 | CMS-TRG-19-001 2410.17038 |
| 60 | 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 |
| 61 | 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 |
| 62 | CMS Collaboration | Description and performance of track and primary-vertex reconstruction with the CMS tracker | JINST 9 (2014) P10009 | CMS-TRK-11-001 1405.6569 |
| 63 | T. Sjöstrand et al. | An introduction to PYTHIA 8.2 | Comput. Phys. Commun. 191 (2015) 159 | 1410.3012 |
| 64 | CMS Collaboration | CMS PYTHIA 8 colour reconnection tunes based on underlying-event data | EPJC 83 (2023) 587 | CMS-GEN-17-002 2205.02905 |
| 65 | NNPDF Collaboration | Parton distributions from high-precision collider data | EPJC 77 (2017) 663 | 1706.00428 |
| 66 | GEANT4 Collaboration | GEANT 4---a simulation toolkit | NIM A 506 (2003) 250 | |
| 67 | L. Carloni and T. Sjöstrand | Visible effects of invisible hidden valley radiation | JHEP 09 (2010) 105 | 1006.2911 |
| 68 | L. Carloni, J. Rathsman, and T. Sjöstrand | Discerning secluded sector gauge structures | JHEP 04 (2011) 091 | 1102.3795 |
| 69 | G. Albouy et al. | Theory, phenomenology, and experimental avenues for dark showers: a Snowmass 2021 report | EPJC 82 (2022) 1132 | 2203.09503 |
| 70 | A. Albert et al. | Recommendations of the LHC dark matter working group: Comparing LHC searches for dark matter mediators in visible and invisible decay channels and calculations of the thermal relic density | Phys. Dark Univ. 26 (2019) 100377 | 1703.05703 |
| 71 | A. Boveia et al. | Recommendations on presenting LHC searches for missing transverse energy signals using simplified $ s $-channel models of dark matter | Phys. Dark Univ. 27 (2020) 100365 | 1603.04156 |
| 72 | S. Knapen, J. Shelton, and D. Xu | Perturbative benchmark models for a dark shower search program | PRD 103 (2021) 115013 | 2103.01238 |
| 73 | 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 |
| 74 | 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 |
| 75 | M. Beneke, P. Falgari, S. Klein, and C. Schwinn | Hadronic top-quark pair production with NNLL threshold resummation | NPB 855 (2012) 695 | 1109.1536 |
| 76 | M. Cacciari et al. | Top-pair production at hadron colliders with next-to-next-to-leading logarithmic soft-gluon resummation | PLB 710 (2012) 612 | 1111.5869 |
| 77 | P. B ä rnreuther, M. Czakon, and A. Mitov | Percent level precision physics at the Tevatron: First genuine NNLO QCD corrections to $ \mathrm{q}\overline{\mathrm{q}}\to{\mathrm{t}\overline{\mathrm{t}}} +X $ | PRL 109 (2012) 132001 | 1204.5201 |
| 78 | M. Czakon and A. Mitov | NNLO corrections to top-pair production at hadron colliders: the all-fermionic scattering channels | JHEP 12 (2012) 054 | 1207.0236 |
| 79 | M. Czakon and A. Mitov | NNLO corrections to top pair production at hadron colliders: the quark-gluon reaction | JHEP 01 (2013) 080 | 1210.6832 |
| 80 | M. Czakon, P. Fiedler, and A. Mitov | Total top-quark pair-production cross section at hadron colliders through $ O(\alpha_\mathrm{S}^4) $ | PRL 110 (2013) 252004 | 1303.6254 |
| 81 | Y. Li and F. Petriello | Combining QCD and electroweak corrections to dilepton production in FEWZ | PRD 86 (2012) 094034 | 1208.5967 |
| 82 | CMS Collaboration | Particle-flow reconstruction and global event description with the CMS detector | JINST 12 (2017) P10003 | CMS-PRF-14-001 1706.04965 |
| 83 | M. Cacciari, G. P. Salam, and G. Soyez | The anti-$ k_{\mathrm{T}} $ jet clustering algorithm | JHEP 04 (2008) 063 | 0802.1189 |
| 84 | M. Cacciari, G. P. Salam, and G. Soyez | Fastjet user manual | EPJC 72 (2012) 1896 | 1111.6097 |
| 85 | 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 |
| 86 | M. Cacciari and G. P. Salam | Pileup subtraction using jet areas | PLB 659 (2008) 119 | 0707.1378 |
| 87 | CMS Collaboration | Pileup mitigation at CMS in 13 TeV data | JINST 15 (2020) P09018 | CMS-JME-18-001 2003.00503 |
| 88 | D. Bertolini, P. Harris, M. Low, and N. Tran | Pileup per particle identification | JHEP 10 (2014) 059 | 1407.6013 |
| 89 | 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 |
| 90 | K. Rehermann and B. Tweedie | Efficient identification of boosted semileptonic top quarks at the LHC | JHEP 03 (2011) 059 | 1007.2221 |
| 91 | F. A. Dreyer, G. P. Salam, and G. Soyez | The Lund jet plane | JHEP 12 (2018) 064 | 1807.04758 |
| 92 | T. Cohen, J. Roloff, and C. Scherb | Dark sector showers in the Lund jet plane | PRD 108 (2023) L031501 | 2301.07732 |
| 93 | S. Bentvelsen and I. Meyer | The Cambridge jet algorithm: Features and applications | EPJC 4 (1998) 623 | hep-ph/9803322 |
| 94 | Y. Wang et al. | Dynamic graph CNN for learning on point clouds | ACM Trans. Graph. 38 (2019) | 1801.07829 |
| 95 | A. Paszke et al. | PyTorch: an imperative style, high-performance deep learning library | in 3rd International Conference on Neural Information Processing Systems, arXiv:.01703, Curran Associates Inc, 1912 Proceedings of the 3 (1912) 721 |
|
| 96 | D. P. Kingma and J. Ba | Adam: A method for stochastic optimization | 1412.6980 | |
| 97 | G. J. Sz é kely, M. L. Rizzo, and N. K. Bakirov | Measuring and testing dependence by correlation of distances | Ann. Stat. 35 (2007) 2769 | 0803.4101 |
| 98 | K. Pearson | Note on regression and inheritance in the case of two parents | Proceedings of the Royal Society of London 5 (1900) 8 | |
| 99 | J. Platt and A. Barr | Constrained differential optimization | in Proceedings of the 1st International Conference on Neural Information Processing Systems, p. 612. MIT Press, Cambridge, MA, USA, 1987 link |
|
| 100 | G. Kasieczka, B. Nachman, M. D. Schwartz, and D. Shih | Automating the ABCD method with machine learning | PRD 103 (2021) 035021 | 2007.14400 |
| 101 | R. D. Cousins | Generalization of chisquare goodness-of-fit test for binned data using saturated models, with application to histograms | link | |
| 102 | CMS Collaboration | Precision luminosity measurement in proton-proton collisions at a center-of-mass energy of 13 TeV with the CMS detector at the Large Hadron Collider | Submitted to Phys. Rev. X, 2026 | CMS-LUM-20-001 2606.26832 |
| 103 | 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 |
| 104 | NNPDF Collaboration | Parton distributions with QED corrections | NPB 877 (2013) 290 | 1308.0598 |
| 105 | A. Kalogeropoulos and J. Alwall | The SysCalc code: A tool to derive theoretical systematic uncertainties | 1801.08401 | |
| 106 | S. Catani, D. de Florian, M. Grazzini, and P. Nason | Soft gluon resummation for Higgs boson production at hadron colliders | JHEP 07 (2003) 028 | hep-ph/0306211 |
| 107 | M. Cacciari et al. | The $ \mathrm{t} \overline{\mathrm{t}} $ cross-section at 1.8 TeV and 1.96 TeV: a study of the systematics due to parton densities and scale dependence | JHEP 04 (2004) 068 | hep-ph/0303085 |
| 108 | S. Mrenna and P. Skands | Automated parton-shower variations in Pythia 8 | PRD 94 (2016) 074005 | 1605.08352 |
| 109 | G. Cowan, K. Cranmer, E. Gross, and O. Vitells | Asymptotic formulae for likelihood-based tests of new physics | EPJC 73 (2013) 2501 | 1007.1727 |
| 110 | CMS Collaboration | Precise determination of the mass of the Higgs boson and tests of compatibility of its couplings with the standard model predictions using proton collisions at 7 and 8 TeV | EPJC 75 (2015) 212 | CMS-HIG-14-009 1412.8662 |
| 111 | T. Junk | Confidence level computation for combining searches with small statistics | NIM A 434 (1999) 435 | hep-ex/9902006 |
| 112 | A. L. Read | Presentation of search results: The CL(s) technique | JPG 28 (2002) 2693 | |
| 113 | CMS Collaboration | The CMS statistical analysis and combination tool: Combine | Comput. Softw. Big Sci. 8 (2024) 19 | CMS-CAT-23-001 2404.06614 |
| 114 | E. Gross and O. Vitells | Trial factors for the look elsewhere effect in high energy physics | EPJC 70 (2010) 525 | 1005.1891 |
|
|
Compact Muon Solenoid LHC, CERN |
|
|
|
|
|
|