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| CMS-PAS-EXO-17-014 | ||
| Search for dark matter in association with a $\mathrm{t\overline{t}}$ pair at $\sqrt{s}= $ 13 TeV in the dilepton channel with 2016 data | ||
| CMS Collaboration | ||
| March 2018 | ||
| Abstract: A search is performed for dark matter produced in association with $\mathrm{t\overline{t}}$ pairs in data from proton-proton collisions at a center-of-mass energy of 13 TeV at the LHC. The data corresponds to 35.9 fb$^{-1}$ collected with the CMS detector in 2016. The analysis looks for an excess of events with large imbalance in transverse momentum and a top quark pair decaying in the dileptonic mode. The results are interpreted in the context of simplified models of dark matter production. Assuming unitary coupling values to standard model (SM) particles $g_{\text{q}}$, and dark matter (DM) particles $g_{\chi}$, and DM mass $m_{\chi} = $ 1 GeV, the observed (expected) 95% CL exclusions for a scalar mediator are $m_{\phi} < $ 74 (99) GeV. The 95% CL exclusion expected for a pseudoscalar mediator is $m_{a} < $ 50 GeV, while none is observed. | ||
| Links: CDS record (PDF) ; inSPIRE record ; CADI line (restricted) ; | ||
| Figures | |
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Figure 1:
Leading order Feynman diagram describing the production of DM particles ($\chi $) in association with a top (bottom) quark pair through a spin-0 mediator ($\phi /a$). |
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Figure 2:
The $M_{\text {T2}}^{\ell \ell}$ distributions for events passing selection requirements for the (a) $\mathrm{e}\mu$ and (b) $ \mathrm{ee} + \mu\mu$ channels. The $M_{\text{T2}}^{\ell\ell}$ distribution of two example signals (scalar and pseudoscalar mediator, $m_{\phi/a} = $ 100 GeV) with $m_{\chi}= $ 1 GeV is scaled up by a factor of 200. The last bin includes overflow. Uncertainties are statistical only. |
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Figure 2-a:
The $M_{\text {T2}}^{\ell \ell}$ distributions for events passing selection requirements for the $\mathrm{e}\mu$ channel. The $M_{\text{T2}}^{\ell\ell}$ distribution of two example signals (scalar and pseudoscalar mediator, $m_{\phi/a} = $ 100 GeV) with $m_{\chi}= $ 1 GeV is scaled up by a factor of 200. The last bin includes overflow. Uncertainties are statistical only. |
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Figure 2-b:
The $M_{\text {T2}}^{\ell \ell}$ distributions for events passing selection requirements for the $ \mathrm{ee} + \mu\mu$ channel. The $M_{\text{T2}}^{\ell\ell}$ distribution of two example signals (scalar and pseudoscalar mediator, $m_{\phi/a} = $ 100 GeV) with $m_{\chi}= $ 1 GeV is scaled up by a factor of 200. The last bin includes overflow. Uncertainties are statistical only. |
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Figure 3:
The pre-fit $ {p_{\text {T}}^{\text {miss}}}$ distributions in the four signal extraction regions. The $ {p_{\text {T}}^{\text {miss}}}$ distribution of two example signals (scalar and pseudoscalar mediator, $m_{\phi /a} = $ 100 GeV) with $m_{\chi}= $ 1 GeV is scaled up by a factor of 200. The last bin includes overflow. Uncertainties are statistical only. |
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Figure 3-a:
The pre-fit $ {p_{\text {T}}^{\text {miss}}}$ distributions in the high-purity $\mathrm{e}\mu$ extraction region. The $ {p_{\text {T}}^{\text {miss}}}$ distribution of two example signals (scalar and pseudoscalar mediator, $m_{\phi /a} = $ 100 GeV) with $m_{\chi}= $ 1 GeV is scaled up by a factor of 200. The last bin includes overflow. Uncertainties are statistical only. |
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Figure 3-b:
The pre-fit $ {p_{\text {T}}^{\text {miss}}}$ distributions in the high-purity $ \mathrm{ee} + \mu\mu$ extraction region. The $ {p_{\text {T}}^{\text {miss}}}$ distribution of two example signals (scalar and pseudoscalar mediator, $m_{\phi /a} = $ 100 GeV) with $m_{\chi}= $ 1 GeV is scaled up by a factor of 200. The last bin includes overflow. Uncertainties are statistical only. |
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Figure 3-c:
The pre-fit $ {p_{\text {T}}^{\text {miss}}}$ distributions in the low-purity $\mathrm{e}\mu$ extraction region. The $ {p_{\text {T}}^{\text {miss}}}$ distribution of two example signals (scalar and pseudoscalar mediator, $m_{\phi /a} = $ 100 GeV) with $m_{\chi}= $ 1 GeV is scaled up by a factor of 200. The last bin includes overflow. Uncertainties are statistical only. |
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Figure 3-d:
The pre-fit $ {p_{\text {T}}^{\text {miss}}}$ distributions in the low-purity $ \mathrm{ee} + \mu\mu$ extraction region. The $ {p_{\text {T}}^{\text {miss}}}$ distribution of two example signals (scalar and pseudoscalar mediator, $m_{\phi /a} = $ 100 GeV) with $m_{\chi}= $ 1 GeV is scaled up by a factor of 200. The last bin includes overflow. Uncertainties are statistical only. |
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Figure 4:
The (a) significance and (b) $M_{\mathrm{T}}^{\ell\mathrm{b},\ell\mathrm{b}} $ distributions for events passing selection requirements. The distributions of two example signals (scalar and pseudoscalar mediator, $m_{\phi/a} = $ 100 GeV) with $m_{\chi}= $ 1 GeV are scaled up by a factor of 200. The $\mathrm{ee}$, $\mathrm{e}\mu$ and $\mu\mu$ channels have been combined. The last bin contains overflow. Uncertainties are statistical only. |
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Figure 4-a:
The significance distribution for events passing selection requirements. The distributions of two example signals (scalar and pseudoscalar mediator, $m_{\phi/a} = $ 100 GeV) with $m_{\chi}= $ 1 GeV are scaled up by a factor of 200. The $\mathrm{ee}$, $\mathrm{e}\mu$ and $\mu\mu$ channels have been combined. The last bin contains overflow. Uncertainties are statistical only. |
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Figure 4-b:
The $M_{\mathrm{T}}^{\ell\mathrm{b},\ell\mathrm{b}} $ distribution for events passing selection requirements. The distributions of two example signals (scalar and pseudoscalar mediator, $m_{\phi/a} = $ 100 GeV) with $m_{\chi}= $ 1 GeV are scaled up by a factor of 200. The $\mathrm{ee}$, $\mathrm{e}\mu$ and $\mu\mu$ channels have been combined. The last bin contains overflow. Uncertainties are statistical only. |
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Figure 5:
The (a) $ \Delta \phi ( p_{\mathrm{T}}, \ell\ell ) $ and the (b) log of the kinematic reconstruction weight ($\ln w$) distributions for events passing selection requirements. The distributions of two example signals (scalar and pseudoscalar mediator, $m_{\phi/a} = $ 100 GeV) with $m_{\chi}= $ 1 GeV are scaled up by a factor of 200. The $\mathrm{ee}$, $\mathrm{e}\mu$ and $\mu\mu$ channels have been combined. Uncertainties are statistical only. |
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Figure 5-a:
The $ \Delta \phi ( p_{\mathrm{T}}, \ell\ell ) $ distribution for events passing selection requirements. The distributions of two example signals (scalar and pseudoscalar mediator, $m_{\phi/a} = $ 100 GeV) with $m_{\chi}= $ 1 GeV are scaled up by a factor of 200. The $\mathrm{ee}$, $\mathrm{e}\mu$ and $\mu\mu$ channels have been combined. Uncertainties are statistical only. |
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Figure 5-b:
The log of the kinematic reconstruction weight ($\ln w$) distribution for events passing selection requirements. The distributions of two example signals (scalar and pseudoscalar mediator, $m_{\phi/a} = $ 100 GeV) with $m_{\chi}= $ 1 GeV are scaled up by a factor of 200. The $\mathrm{ee}$, $\mathrm{e}\mu$ and $\mu\mu$ channels have been combined. Uncertainties are statistical only. |
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Figure 6:
The (a) $\Delta \eta_{\ell^{+},\ell^{-}}$ and (b) $\cos{\Phi_{\ell^{+}\ell^{-}}}$ distributions for events passing selection requirements. The distributions of two example signals (scalar and pseudoscalar mediator, $m_{\phi/a} = $ 100 GeV) with $m_{\chi}= $ 1 GeV are scaled up by a factor of 200. The $\mathrm{ee}$, $\mathrm{e}\mu$ and $\mu\mu$ channels have been combined. The last bin contains overflow. Uncertainties are statistical only. |
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Figure 6-a:
The $\Delta \eta_{\ell^{+},\ell^{-}}$ distribution for events passing selection requirements. The distributions of two example signals (scalar and pseudoscalar mediator, $m_{\phi/a} = $ 100 GeV) with $m_{\chi}= $ 1 GeV are scaled up by a factor of 200. The $\mathrm{ee}$, $\mathrm{e}\mu$ and $\mu\mu$ channels have been combined. The last bin contains overflow. Uncertainties are statistical only. |
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Figure 6-b:
The $\cos{\Phi_{\ell^{+}\ell^{-}}}$ distribution for events passing selection requirements. The distributions of two example signals (scalar and pseudoscalar mediator, $m_{\phi/a} = $ 100 GeV) with $m_{\chi}= $ 1 GeV are scaled up by a factor of 200. The $\mathrm{ee}$, $\mathrm{e}\mu$ and $\mu\mu$ channels have been combined. The last bin contains overflow. Uncertainties are statistical only. |
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Figure 7:
The pre-fit BDT discriminant distribution trained on the working point 1 GeV dark matter mass and 500 GeV mediator mass for a (a) scalar mediator and a (b) pseudoscalar mediator. The distributions of two example signals (scalar and pseudoscalar mediator, $m_{\phi/a} = $ 100 GeV) with $m_{\chi}= $ 1 GeV are scaled up by a factor of 200. The $\mathrm{ee}$, $\mathrm{e}\mu$ and $\mu\mu$ channels have been combined. Uncertainties are statistical only. |
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Figure 7-a:
The pre-fit BDT discriminant distribution trained on the working point 1 GeV dark matter mass and 500 GeV mediator mass for a scalar mediator. The distributions of two example signals (scalar and pseudoscalar mediator, $m_{\phi/a} = $ 100 GeV) with $m_{\chi}= $ 1 GeV are scaled up by a factor of 200. The $\mathrm{ee}$, $\mathrm{e}\mu$ and $\mu\mu$ channels have been combined. Uncertainties are statistical only. |
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Figure 7-b:
The pre-fit BDT discriminant distribution trained on the working point 1 GeV dark matter mass and 500 GeV mediator mass for a pseudoscalar mediator. The distributions of two example signals (scalar and pseudoscalar mediator, $m_{\phi/a} = $ 100 GeV) with $m_{\chi}= $ 1 GeV are scaled up by a factor of 200. The $\mathrm{ee}$, $\mathrm{e}\mu$ and $\mu\mu$ channels have been combined. Uncertainties are statistical only. |
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Figure 8:
Distribution of the $p_{\text {T}}^{\text {Dark}} $input variable used to train the ANN for a scalar signal model hypothesis with $m_{\chi}= $ 1 GeV, and $m_{\phi}= $ 100 GeV. The signal distributions are scaled up by a factor of 200. The last bin contains overflow. Uncertainties are statistical only. |
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Figure 9:
Distribution of the ANN discriminant for a signal model with $m_{\chi}=$ 1 GeV and $m_{\phi}= $ 100 GeV for a (a) scalar hypothesis and a (b) pseudoscalar hypothesis. The signal distributions are scaled up by a factor of 200. Uncertainties are statistical only. |
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Figure 9-a:
Distribution of the ANN discriminant for a signal model with $m_{\chi}=$ 1 GeV and $m_{\phi}= $ 100 GeV for a scalar hypothesis. The signal distributions are scaled up by a factor of 200. Uncertainties are statistical only. |
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Figure 9-b:
Distribution of the ANN discriminant for a signal model with $m_{\chi}=$ 1 GeV and $m_{\phi}= $ 100 GeV for a pseudoscalar hypothesis. The signal distributions are scaled up by a factor of 200. Uncertainties are statistical only. |
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Figure 10:
The post-fit $ {p_{\text {T}}^{\text {miss}}}$ distributions in the four signal extraction regions of the $ {p_{\text {T}}^{\text {miss}}}$ shape analysis. The pre-fit $ {p_{\text {T}}^{\text {miss}}}$ distributions for two example signals (scalar and pseudoscalar mediator, $m_{\phi /a}= $ 100 GeV) with $m_{\chi}= $ 1 GeV are scaled up by a factor of 200. The last bin includes overflow. Statistical and systematic uncertainties are shown. |
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Figure 10-a:
The post-fit $ {p_{\text {T}}^{\text {miss}}}$ distribution in the high-purity $\mathrm{e}\mu$ extraction region of the $ {p_{\text {T}}^{\text {miss}}}$ shape analysis. The pre-fit $ {p_{\text {T}}^{\text {miss}}}$ distributions for two example signals (scalar and pseudoscalar mediator, $m_{\phi /a}= $ 100 GeV) with $m_{\chi}= $ 1 GeV are scaled up by a factor of 200. The last bin includes overflow. Statistical and systematic uncertainties are shown. |
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Figure 10-b:
The post-fit $ {p_{\text {T}}^{\text {miss}}}$ distribution in the high-purity $ \mathrm{ee} + \mu\mu$ extraction region of the $ {p_{\text {T}}^{\text {miss}}}$ shape analysis. The pre-fit $ {p_{\text {T}}^{\text {miss}}}$ distributions for two example signals (scalar and pseudoscalar mediator, $m_{\phi /a}= $ 100 GeV) with $m_{\chi}= $ 1 GeV are scaled up by a factor of 200. The last bin includes overflow. Statistical and systematic uncertainties are shown. |
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Figure 10-c:
The post-fit $ {p_{\text {T}}^{\text {miss}}}$ distribution in the low-purity $\mathrm{e}\mu$ extraction region of the $ {p_{\text {T}}^{\text {miss}}}$ shape analysis. The pre-fit $ {p_{\text {T}}^{\text {miss}}}$ distributions for two example signals (scalar and pseudoscalar mediator, $m_{\phi /a}= $ 100 GeV) with $m_{\chi}= $ 1 GeV are scaled up by a factor of 200. The last bin includes overflow. Statistical and systematic uncertainties are shown. |
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Figure 10-d:
The post-fit $ {p_{\text {T}}^{\text {miss}}}$ distribution in the low-purity $ \mathrm{ee} + \mu\mu$ extraction region of the $ {p_{\text {T}}^{\text {miss}}}$ shape analysis. The pre-fit $ {p_{\text {T}}^{\text {miss}}}$ distributions for two example signals (scalar and pseudoscalar mediator, $m_{\phi /a}= $ 100 GeV) with $m_{\chi}= $ 1 GeV are scaled up by a factor of 200. The last bin includes overflow. Statistical and systematic uncertainties are shown. |
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Figure 11:
The post-fit BDT discriminant distribution in the signal region trained on the working point for $m_\phi= $ 500 GeV, $m_\chi= $ 1 GeV for a (a) scalar mediator and a (b) pseudoscalar mediator. The pre-fit BDT discriminant distributions for two example signals (scalar and pseudoscalar mediator, $m_{\phi/a} = $ 100 GeV) with $m_{\chi} = $ 1 GeV are scaled up by a factor of 200. Statistical and systematic uncertainties are shown. |
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Figure 11-a:
The post-fit BDT discriminant distribution in the signal region trained on the working point for $m_\phi= $ 500 GeV, $m_\chi= $ 1 GeV for a scalar mediator. The pre-fit BDT discriminant distributions for two example signals (scalar and pseudoscalar mediator, $m_{\phi/a} = $ 100 GeV) with $m_{\chi} = $ 1 GeV are scaled up by a factor of 200. Statistical and systematic uncertainties are shown. |
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Figure 11-b:
The post-fit BDT discriminant distribution in the signal region trained on the working point for $m_\phi= $ 500 GeV, $m_\chi= $ 1 GeV for a pseudoscalar mediator. The pre-fit BDT discriminant distributions for two example signals (scalar and pseudoscalar mediator, $m_{\phi/a} = $ 100 GeV) with $m_{\chi} = $ 1 GeV are scaled up by a factor of 200. Statistical and systematic uncertainties are shown. |
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Figure 12:
The post-fit ANN discriminant distribution in the signal region trained on the working point for $m_{\phi/a}= $ 100 GeV, $m_\chi= $ 1 GeV for a (a) scalar mediator and a (b) pseudoscalar mediator. The pre-fit ANN discriminant distributions for two example signals (scalar and pseudoscalar mediator, $m_{\phi/a} = $ 100 GeV) with $m_{\chi} = $ 1 GeV are scaled up by a factor of 200. Statistical and systematic uncertainties are shown. The apparent bin width has been fixed to the same size to improve the clarity. The axis labels indicate the actual sizes of the different bins. |
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Figure 12-a:
The post-fit ANN discriminant distribution in the signal region trained on the working point for $m_{\phi/a}= $ 100 GeV, $m_\chi= $ 1 GeV for a scalar mediator. The pre-fit ANN discriminant distributions for two example signals (scalar and pseudoscalar mediator, $m_{\phi/a} = $ 100 GeV) with $m_{\chi} = $ 1 GeV are scaled up by a factor of 200. Statistical and systematic uncertainties are shown. The apparent bin width has been fixed to the same size to improve the clarity. The axis labels indicate the actual sizes of the different bins. |
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Figure 12-b:
The post-fit ANN discriminant distribution in the signal region trained on the working point for $m_{\phi/a}= $ 100 GeV, $m_\chi= $ 1 GeV for a pseudoscalar mediator. The pre-fit ANN discriminant distributions for two example signals (scalar and pseudoscalar mediator, $m_{\phi/a} = $ 100 GeV) with $m_{\chi} = $ 1 GeV are scaled up by a factor of 200. Statistical and systematic uncertainties are shown. The apparent bin width has been fixed to the same size to improve the clarity. The axis labels indicate the actual sizes of the different bins. |
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Figure 13:
The expected and observed limits for the three different strategies for scalar (left) and pseudoscalar models (right) with $m_{\chi}= $ 1 GeV and $g_{\textrm {q}} = g_{\chi}=$ 1. |
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Figure 13-a:
Expected and observed limits for the $p_{\mathrm{T}}^{\text{miss}}$ shape strategy, for the scalar model with $m_{\chi}= $ 1 GeV and $g_{\textrm {q}} = g_{\chi}=$ 1. |
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Figure 13-b:
Expected and observed limits for the $p_{\mathrm{T}}^{\text{miss}}$ shape strategy, for the pseudoscalar model with $m_{\chi}= $ 1 GeV and $g_{\textrm {q}} = g_{\chi}=$ 1. |
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Figure 13-c:
Expected and observed limits for the BDT discriminant strategy, for the scalar model with $m_{\chi}= $ 1 GeV and $g_{\textrm {q}} = g_{\chi}=$ 1. |
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Figure 13-d:
Expected and observed limits for the BDT discriminant strategy, for the pseudoscalar model with $m_{\chi}= $ 1 GeV and $g_{\textrm {q}} = g_{\chi}=$ 1. |
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Figure 13-e:
Expected and observed limits for the ANN discriminant strategy, for the scalar model with $m_{\chi}= $ 1 GeV and $g_{\textrm {q}} = g_{\chi}=$ 1. |
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Figure 13-f:
Expected and observed limits for the ANN discriminant strategy, for the pseudoscalar model with $m_{\chi}= $ 1 GeV and $g_{\textrm {q}} = g_{\chi}=$ 1. |
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Figure 14:
The expected limits at 95% CL for the $ {p_{\text {T}}^{\text {miss}}}$ shape (magenta), BDT shape (green), and ANN shape (orange) strategies for (a) scalar and (b) pseudoscalar models with $m_{\chi}= $ 1 GeV and $g_{\textrm{q}} = g_{\chi}= $ 1. |
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Figure 14-a:
The expected limits at 95% CL for the $ {p_{\text {T}}^{\text {miss}}}$ shape (magenta), BDT shape (green), and ANN shape (orange) strategies for scalar model with $m_{\chi}= $ 1 GeV and $g_{\textrm{q}} = g_{\chi}= $ 1. |
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Figure 14-b:
The expected limits at 95% CL for the $ {p_{\text {T}}^{\text {miss}}}$ shape (magenta), BDT shape (green), and ANN shape (orange) strategies for pseudoscalar model with $m_{\chi}= $ 1 GeV and $g_{\textrm{q}} = g_{\chi}= $ 1. |
| Tables | |
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Table 1:
Summary of the signal MC samples and their corresponding NLO cross sections as used in this analysis. $m_\chi =$ 1 GeV is assumed in all samples. Left column: mass of the mediators; central: scalar mediator case; right: pseudoscalar mediator case. |
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Table 2:
Expected and observed limits on the signal strength including the one sigma uncertainties for 35.9 fb$^{-1}$ of data for the scalar hypothesis with $m_{\chi} = $ 1 GeV in the respective strategies. |
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Table 3:
Expected and observed limits on the signal strength including the one sigma uncertainties for 35.9 fb$^{-1}$ of data for the pseudoscalar hypothesis with $m_{\chi} = $ 1 GeV in the respective strategies. |
| Summary |
| A search for an excess of events with large $ {p_{\mathrm{T}}^{\text{miss}}} $ produced in association with a top quark pair decaying to the dilepton final state has been presented. The integrated luminosity of the proton-proton collision data sample used corresponds to 35.9 fb$^{-1}$ , and was collected by the CMS detector at $\sqrt{s}=$ 13 TeV at the LHC during 2016. Observations are consistent with no significant deviation from the SM background expectation in the $ {p_{\mathrm{T}}^{\text{miss}}} $ spectrum, the BDT discriminant distribution, and the ANN discriminant distribution in same and opposite lepton flavor channels. Results are interpreted in terms of simplified dark matter (DM) models with scalar and pseudoscalar mediators using the NLO cross sections as shown in Table xxxxx. Assuming coupling values of $g_{\textrm{q}} = g_{\chi} = $ 1 and DM mass $m_{\chi} = $ 1 GeV, the observed (expected) 95% CL exclusions for a scalar mediator are $m_{\phi} < $ 74 (99) GeV, and the expected exclusion for a pseudoscalar mediator is $m_{a} < $ 50 GeV, while no pseudoscalar mediator exclusion is observed using the $ {p_{\mathrm{T}}^{\text{miss}}} $ shape strategy. This result improves upon the previous search for dark matter production in association with a top quark pair [50] performed using a data sample with integrated luminosity of 2.2 fb$^{-1}$ collected by the CMS detector at $\sqrt{s}=$ 13 TeV at the LHC during 2015. The previous search fell short of observing and expecting to observe an exclusion at 95% CL for simplified DM models with scalar and pseudoscalar mediators. |
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