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| CMS-PAS-SUS-24-012 | ||
| Search for compressed electroweakinos with low-momentum isolated tracks | ||
| CMS Collaboration | ||
| 9 April 2025 | ||
| Abstract: A search is presented for higgsino dark matter (DM) in final states with a low momentum (soft), isolated track and large missing transverse momentum. In the minimal supersymmetric standard model (MSSM), charginos are most often produced in association with a nearly mass-degenerate neutralino or another chargino, and predominantly decay into the lightest neutralino (DM candidate) and a soft pion. For a mass difference $ \Delta m^{\pm} $ less than 1 GeV, a discernible displacement of the pion's track with respect to the primary vertex can arise, reaching up to about 1 cm for the smallest allowed $ \Delta m^{\pm} $. A parameterized multivariate classifier is employed to distinguish the signal track from background tracks, optimally targeting a range of $ \Delta m^{\pm} $ by exploiting the track transverse momentum, impact parameter, and event topology to varying degrees depending on the assumed $ \Delta m^{\pm} $. The analyzed data correspond to an integrated luminosity of 138 fb$^{-1} $ collected by the CMS experiment in proton-proton collisions at $ \sqrt{s}=$ 13 TeV. No evidence of new physics is observed, and limits are set at the 95% confidence level in the mass plane of the model. Assuming MSSM cross sections, values of $ \Delta m^{\pm} $ between 0.28 and 1.15 GeV are excluded for a 100 GeV mass chargino, and chargino masses up to 185 GeV are excluded for $ \Delta m^{\pm} $ of 0.55 GeV. | ||
| Links: CDS record (PDF) ; CADI line (restricted) ; | ||
| Figures | |
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Figure 1:
Feynman diagrams for electroweakino pair production. |
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Figure 1-a:
Feynman diagrams for electroweakino pair production. |
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Figure 1-b:
Feynman diagrams for electroweakino pair production. |
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Figure 1-c:
Feynman diagrams for electroweakino pair production. |
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Figure 2:
Distributions of kinematic track observables for background tracks and signal tracks from four example signal model points. The filled histograms show distributions for background tracks in SM background events, normalized to the integral of the total background. Shown as lines are distributions of chargino-matched tracks in signal events for selected benchmark models. |
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Figure 2-a:
Distributions of kinematic track observables for background tracks and signal tracks from four example signal model points. The filled histograms show distributions for background tracks in SM background events, normalized to the integral of the total background. Shown as lines are distributions of chargino-matched tracks in signal events for selected benchmark models. |
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Figure 2-b:
Distributions of kinematic track observables for background tracks and signal tracks from four example signal model points. The filled histograms show distributions for background tracks in SM background events, normalized to the integral of the total background. Shown as lines are distributions of chargino-matched tracks in signal events for selected benchmark models. |
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Figure 2-c:
Distributions of kinematic track observables for background tracks and signal tracks from four example signal model points. The filled histograms show distributions for background tracks in SM background events, normalized to the integral of the total background. Shown as lines are distributions of chargino-matched tracks in signal events for selected benchmark models. |
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Figure 2-d:
Distributions of kinematic track observables for background tracks and signal tracks from four example signal model points. The filled histograms show distributions for background tracks in SM background events, normalized to the integral of the total background. Shown as lines are distributions of chargino-matched tracks in signal events for selected benchmark models. |
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Figure 3:
Left: distributions of the three track variables subject to refinement. The distributions for data are shown alongside those for (unrefined) MC. Right: distributions of $ \mathrm{ \tilde{P} }_{\text{max}}(\text{Signal} | \Delta m = 0.3\ \text{GeV}) $, $ \mathrm{ \tilde{P} }_{\text{max}}(\text{Signal} | \Delta m = 0.6\ \text{GeV}) $, and $ \mathrm{ \tilde{P} }_{\text{max}}(\text{Signal} | \Delta m = 1.0\ \text{GeV}) $ for events in data as well as in MC with and without refinement applied to the input variables. |
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Figure 3-a:
Left: distributions of the three track variables subject to refinement. The distributions for data are shown alongside those for (unrefined) MC. Right: distributions of $ \mathrm{ \tilde{P} }_{\text{max}}(\text{Signal} | \Delta m = 0.3\ \text{GeV}) $, $ \mathrm{ \tilde{P} }_{\text{max}}(\text{Signal} | \Delta m = 0.6\ \text{GeV}) $, and $ \mathrm{ \tilde{P} }_{\text{max}}(\text{Signal} | \Delta m = 1.0\ \text{GeV}) $ for events in data as well as in MC with and without refinement applied to the input variables. |
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Figure 3-b:
Left: distributions of the three track variables subject to refinement. The distributions for data are shown alongside those for (unrefined) MC. Right: distributions of $ \mathrm{ \tilde{P} }_{\text{max}}(\text{Signal} | \Delta m = 0.3\ \text{GeV}) $, $ \mathrm{ \tilde{P} }_{\text{max}}(\text{Signal} | \Delta m = 0.6\ \text{GeV}) $, and $ \mathrm{ \tilde{P} }_{\text{max}}(\text{Signal} | \Delta m = 1.0\ \text{GeV}) $ for events in data as well as in MC with and without refinement applied to the input variables. |
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Figure 3-c:
Left: distributions of the three track variables subject to refinement. The distributions for data are shown alongside those for (unrefined) MC. Right: distributions of $ \mathrm{ \tilde{P} }_{\text{max}}(\text{Signal} | \Delta m = 0.3\ \text{GeV}) $, $ \mathrm{ \tilde{P} }_{\text{max}}(\text{Signal} | \Delta m = 0.6\ \text{GeV}) $, and $ \mathrm{ \tilde{P} }_{\text{max}}(\text{Signal} | \Delta m = 1.0\ \text{GeV}) $ for events in data as well as in MC with and without refinement applied to the input variables. |
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Figure 3-d:
Left: distributions of the three track variables subject to refinement. The distributions for data are shown alongside those for (unrefined) MC. Right: distributions of $ \mathrm{ \tilde{P} }_{\text{max}}(\text{Signal} | \Delta m = 0.3\ \text{GeV}) $, $ \mathrm{ \tilde{P} }_{\text{max}}(\text{Signal} | \Delta m = 0.6\ \text{GeV}) $, and $ \mathrm{ \tilde{P} }_{\text{max}}(\text{Signal} | \Delta m = 1.0\ \text{GeV}) $ for events in data as well as in MC with and without refinement applied to the input variables. |
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Figure 3-e:
Left: distributions of the three track variables subject to refinement. The distributions for data are shown alongside those for (unrefined) MC. Right: distributions of $ \mathrm{ \tilde{P} }_{\text{max}}(\text{Signal} | \Delta m = 0.3\ \text{GeV}) $, $ \mathrm{ \tilde{P} }_{\text{max}}(\text{Signal} | \Delta m = 0.6\ \text{GeV}) $, and $ \mathrm{ \tilde{P} }_{\text{max}}(\text{Signal} | \Delta m = 1.0\ \text{GeV}) $ for events in data as well as in MC with and without refinement applied to the input variables. |
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Figure 3-f:
Left: distributions of the three track variables subject to refinement. The distributions for data are shown alongside those for (unrefined) MC. Right: distributions of $ \mathrm{ \tilde{P} }_{\text{max}}(\text{Signal} | \Delta m = 0.3\ \text{GeV}) $, $ \mathrm{ \tilde{P} }_{\text{max}}(\text{Signal} | \Delta m = 0.6\ \text{GeV}) $, and $ \mathrm{ \tilde{P} }_{\text{max}}(\text{Signal} | \Delta m = 1.0\ \text{GeV}) $ for events in data as well as in MC with and without refinement applied to the input variables. |
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Figure 4:
The first three plots show the expected and observed data in the signal-sensitive observables $ \mathrm{ \tilde{P} }_{\text{max}}(\text{Signal} | \Delta m = 0.3,\,0.6,\,1.0\ \text{GeV}) $. In the lower ratio panel, the gray shaded region shows the relative statistical uncertainty in the background estimate, while the black vertical error bars indicate the total uncertainty in each bin. The fourth plot shows the output of the $ \tau $ node of tracks with a loose selection of $ \mathrm{ \tilde{P} }_{\text{max}}(\text{Signal} | \Delta m = 0.3\ \text{GeV}) > - $2, indicating the $ \tau $-based signal proxy region. |
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Figure 4-a:
The first three plots show the expected and observed data in the signal-sensitive observables $ \mathrm{ \tilde{P} }_{\text{max}}(\text{Signal} | \Delta m = 0.3,\,0.6,\,1.0\ \text{GeV}) $. In the lower ratio panel, the gray shaded region shows the relative statistical uncertainty in the background estimate, while the black vertical error bars indicate the total uncertainty in each bin. The fourth plot shows the output of the $ \tau $ node of tracks with a loose selection of $ \mathrm{ \tilde{P} }_{\text{max}}(\text{Signal} | \Delta m = 0.3\ \text{GeV}) > - $2, indicating the $ \tau $-based signal proxy region. |
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Figure 4-b:
The first three plots show the expected and observed data in the signal-sensitive observables $ \mathrm{ \tilde{P} }_{\text{max}}(\text{Signal} | \Delta m = 0.3,\,0.6,\,1.0\ \text{GeV}) $. In the lower ratio panel, the gray shaded region shows the relative statistical uncertainty in the background estimate, while the black vertical error bars indicate the total uncertainty in each bin. The fourth plot shows the output of the $ \tau $ node of tracks with a loose selection of $ \mathrm{ \tilde{P} }_{\text{max}}(\text{Signal} | \Delta m = 0.3\ \text{GeV}) > - $2, indicating the $ \tau $-based signal proxy region. |
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Figure 4-c:
The first three plots show the expected and observed data in the signal-sensitive observables $ \mathrm{ \tilde{P} }_{\text{max}}(\text{Signal} | \Delta m = 0.3,\,0.6,\,1.0\ \text{GeV}) $. In the lower ratio panel, the gray shaded region shows the relative statistical uncertainty in the background estimate, while the black vertical error bars indicate the total uncertainty in each bin. The fourth plot shows the output of the $ \tau $ node of tracks with a loose selection of $ \mathrm{ \tilde{P} }_{\text{max}}(\text{Signal} | \Delta m = 0.3\ \text{GeV}) > - $2, indicating the $ \tau $-based signal proxy region. |
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Figure 4-d:
The first three plots show the expected and observed data in the signal-sensitive observables $ \mathrm{ \tilde{P} }_{\text{max}}(\text{Signal} | \Delta m = 0.3,\,0.6,\,1.0\ \text{GeV}) $. In the lower ratio panel, the gray shaded region shows the relative statistical uncertainty in the background estimate, while the black vertical error bars indicate the total uncertainty in each bin. The fourth plot shows the output of the $ \tau $ node of tracks with a loose selection of $ \mathrm{ \tilde{P} }_{\text{max}}(\text{Signal} | \Delta m = 0.3\ \text{GeV}) > - $2, indicating the $ \tau $-based signal proxy region. |
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Figure 5:
Left: Expected and observed counts in the 12 SRs. The black error bars and gray band in the lower panel show the relative statistical uncertainties in the data and prediction. Four signal benchmark models are shown, corresponding to the inclusive production of all possible pairs of higgsino-like electroweakinos. Right: The heat map shows the 95% CL upper limit on the cross section. The expected (red) and observed (black) bounds indicate the region to the left of which the model space is excluded, assuming theoretical cross sections calculated at NLO-NLL [56,57]. Red dashed lines show the expected limits varied by the experimental uncertainties while black dashed lines are the observed limits with theoretical cross sections varied by their uncertainty. Branching fractions of the chargino are taken from [58]. |
png pdf |
Figure 5-a:
Left: Expected and observed counts in the 12 SRs. The black error bars and gray band in the lower panel show the relative statistical uncertainties in the data and prediction. Four signal benchmark models are shown, corresponding to the inclusive production of all possible pairs of higgsino-like electroweakinos. Right: The heat map shows the 95% CL upper limit on the cross section. The expected (red) and observed (black) bounds indicate the region to the left of which the model space is excluded, assuming theoretical cross sections calculated at NLO-NLL [56,57]. Red dashed lines show the expected limits varied by the experimental uncertainties while black dashed lines are the observed limits with theoretical cross sections varied by their uncertainty. Branching fractions of the chargino are taken from [58]. |
png pdf |
Figure 5-b:
Left: Expected and observed counts in the 12 SRs. The black error bars and gray band in the lower panel show the relative statistical uncertainties in the data and prediction. Four signal benchmark models are shown, corresponding to the inclusive production of all possible pairs of higgsino-like electroweakinos. Right: The heat map shows the 95% CL upper limit on the cross section. The expected (red) and observed (black) bounds indicate the region to the left of which the model space is excluded, assuming theoretical cross sections calculated at NLO-NLL [56,57]. Red dashed lines show the expected limits varied by the experimental uncertainties while black dashed lines are the observed limits with theoretical cross sections varied by their uncertainty. Branching fractions of the chargino are taken from [58]. |
| Tables | |
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Table 1:
The analysis baseline selection criteria. |
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Table 2:
Expected and observed yields in the 12 SRs with a breakdown of the background components and three benchmark signal models, as well as the definitions of the SRs based on the bin boundaries in the range of $ \mathrm{ \tilde{P} }_{\text{max}}(\text{Signal} | \Delta m = 0.3,\,0.6,\,1.0\ \text{GeV}) $. Uncertainties are statistical only. |
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Table 3:
Variables used by the NN soft track classifier. |
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Table 4:
Custom track observables defined using the track helix extrapolation. All quantities are defined with respect to (a) the leading PV, (b) the closest PV from pileup interactions, (c) the PV associated to the track during reconstruction, and (d) the closest PV excluding the associated vertex. |
| Summary |
| A search for compressed higgsino and wino dark matter has been performed using events containing a soft, slightly displaced track and large $ p_{\mathrm{T}}^\text{miss} $. A parameterized NN was employed to optimize sensitivity across a wide range of models not previously probed, and no significant excess above the standard model prediction is observed. The resulting 95% confidence level limits exclude chargino masses up to 185 GeV for a mass splitting of 0.55 GeV, as well as mass splittings of 0.33-1.2 GeV for a 100 GeV higgsino -- currently the most stringent constraints in this model space -- thereby exerting additional pressure on natural SUSY dark matter scenarios. |
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