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| CMS-EXO-21-014 ; CERN-EP-2022-276 | ||
| Search for long-lived particles using out-of-time trackless jets in proton-proton collisions at $ \sqrt{s} = $ 13 TeV | ||
| CMS Collaboration | ||
| 13 December 2022 | ||
| JHEP 07 (2023) 210 | ||
| Abstract: A search for long-lived particles decaying in the outer regions of the CMS silicon tracker or in the calorimeters is presented. The search is based on a data sample of proton-proton collisions at $ \sqrt{s} = $ 13 TeV recorded with the CMS detector at the LHC in 2016--2018, corresponding to an integrated luminosity of 138 fb$ ^{-1} $. A novel technique, using nearly trackless and out-of-time jet information combined in a deep neural network discriminator, is employed to identify decays of long-lived particles. The results are interpreted in a simplified model of chargino-neutralino production, where the neutralino is the next-to-lightest supersymmetric particle, is long-lived, and decays to a gravitino and either a Higgs or Z boson. This search is most sensitive to neutralino proper decay lengths of approximately 0.5 m, for which masses up to 1.18 TeV are excluded at 95% confidence level. The current search is the best result to date in the mass range from the kinematic limit imposed by the Higgs boson mass up to 1.8 TeV. | ||
| Links: e-print arXiv:2212.06695 [hep-ex] (PDF) ; CDS record ; inSPIRE record ; HepData record ; CADI line (restricted) ; | ||
| Figures | |
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Figure 1:
Feynman diagrams of the effective neutralino pair production in the GMSB simplified model in which the two neutralinos decay into two gravitinos ($ \tilde{\mathrm{G}} $) and two Z bosons (left), a Z and a Higgs boson (H) (center), or two Higgs bosons (right). |
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Figure 1-a:
Feynman diagram of the effective neutralino pair production in the GMSB simplified model in which the two neutralinos decay into two gravitinos ($ \tilde{\mathrm{G}} $) and two Z bosons. |
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Figure 1-b:
Feynman diagram of the effective neutralino pair production in the GMSB simplified model in which the two neutralinos decay into two gravitinos ($ \tilde{\mathrm{G}} $) and a Z and a Higgs boson. |
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Figure 1-c:
Feynman diagram of the effective neutralino pair production in the GMSB simplified model in which the two neutralinos decay into two gravitinos ($ \tilde{\mathrm{G}} $) and two Higgs bosons. |
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Figure 2:
The distributions of the most impactful input variables to the TD jet tagger for signal (red, lighter) and collision background (blue, darker). They include the charged (upper left) and neutral (upper right) hadron energy fractions, the number of track constituents in the jet (middle left), the $ \Delta R $ between the jet axis and the closest track associated with the PV (middle right), and the jet time (lower). |
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Figure 2-a:
Distribution of the charged hadron energy fraction for signal (red, lighter) and collision background (blue, darker). |
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Figure 2-b:
Distribution of the neutral hadron energy fraction for signal (red, lighter) and collision background (blue, darker). |
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Figure 2-c:
Distribution of the number of track constituents in the jet for signal (red, lighter) and collision background (blue, darker). |
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Figure 2-d:
Distribution of the $ \Delta R $ between the jet axis and the closest track associated with the PV for signal (red, lighter) and collision background (blue, darker). |
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Figure 2-e:
Distribution of the jet time for signal (red, lighter) and collision background (blue, darker). |
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Figure 3:
TD jet tagger score distributions (left) for signal (red, lighter) and collision background (blue, darker). Identification probability for the signal versus the misidentification probability for the background (right) with the tagger working point (w. p.) used in the analysis shown as a blue marker. Both are evaluated using an independent sample of testing events. |
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Figure 3-a:
TD jet tagger score distributions for signal (red, lighter) and collision background (blue, darker). |
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Figure 3-b:
Identification probability for the signal versus the misidentification probability for the background with the tagger working point (w. p.) used in the analysis shown as a blue marker. Both are evaluated using an independent sample of testing events. |
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Figure 4:
The efficiency of the TD jet tagger working point used in the analysis is shown as a function of the lab frame transverse decay length for simulated signals with $ \tilde{\chi}_{1}^{0} $ mass of 400 GeV. The uncertainties shown account for lifetime dependence and statistical uncertainty. |
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Figure 5:
The TD jet tagger score distributions for simulation (shaded histogram) and data (black markers) when using electrons from $ \mathrm{W}\to\mathrm{e}\nu_{\!\mathrm{e}} $ events as proxy objects for signal jets. The histograms and data points have been normalized to unit area. The last bin contains jets with tagger scores greater than 0.996, the threshold used to tag signal jets. Similar levels of agreement are observed for photon proxy objects from the $ \mathrm{Z}\to\ell^{+}\ell^{-}\gamma $ sample. |
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Figure 6:
The TD jet tagger misidentification probability measured using the nominal W+jets MR (black round markers) is shown along with the systematic uncertainty (gray band), quantifying the degree of process dependence measured from alternative MRs. The measurements in the alternative MRs are displayed as well (Z+jets MR as green round markers, $ \mathrm{t} \overline{\mathrm{t}} $ MR as red squared markers, QCD MR as blue triangular markers) along with their respective statistical uncertainty. On the left, this probability is shown for the first 19.9 fb$ ^{-1} $ of data collected in 2016, while on the right it is shown for the last 16.4 fb$ ^{-1} $ of data collected in 2016 combined with data collected in 2017--2018. |
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Figure 6-a:
The TD jet tagger misidentification probability measured using the nominal W+jets MR (black round markers) is shown along with the systematic uncertainty (gray band), quantifying the degree of process dependence measured from alternative MRs. The measurements in the alternative MRs are displayed as well (Z+jets MR as green round markers, $ \mathrm{t} \overline{\mathrm{t}} $ MR as red squared markers, QCD MR as blue triangular markers) along with their respective statistical uncertainty. This probability is shown for the first 19.9 fb$ ^{-1} $ of data collected in 2016. |
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Figure 6-b:
The TD jet tagger misidentification probability measured using the nominal W+jets MR (black round markers) is shown along with the systematic uncertainty (gray band), quantifying the degree of process dependence measured from alternative MRs. The measurements in the alternative MRs are displayed as well (Z+jets MR as green round markers, $ \mathrm{t} \overline{\mathrm{t}} $ MR as red squared markers, QCD MR as blue triangular markers) along with their respective statistical uncertainty. This probability is shown for the last 16.4 fb$ ^{-1} $ of data collected in 2016 combined with data collected in 2017--2018. |
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Figure 7:
Distribution of the number of TD-tagged jets for the $ m_{\tilde{\chi}_{1}^{0}} = $ 400 GeV simulated signal samples with $ c\tau_{\tilde{\chi}_{1}^{0}}= $ 0.5 m (solid red line) and $ c\tau_{\tilde{\chi}_{1}^{0}}= $ 3.0 m (dotted green line), estimated background (blue square markers), and data (black round markers). The signal distributions are normalized to the expected cross section limit. The blue shaded region indicates the systematic uncertainty in the background prediction. No background prediction is shown for the bin with zero TD-tagged jets as it is the main control region used to predict the background for the other two bins. There are zero observed events in the bin with two or more TD-tagged jets. |
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Figure 8:
Expected and observed 95% CL upper limits on $ \sigma_{\tilde{\chi}_{1}^{0}\tilde{\chi}_{1}^{0}} $ as functions of $ m_{\tilde{\chi}_{1}^{0}} $ in a scenario with $ \mathcal{B}(\tilde{\chi}_{1}^{0}\to\mathrm{H}\tilde{\mathrm{G}}) = $ 0.5 and $ c\tau = $ 0.5 m (left) or 3 m (right). |
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Figure 8-a:
Expected and observed 95% CL upper limits on $ \sigma_{\tilde{\chi}_{1}^{0}\tilde{\chi}_{1}^{0}} $ as functions of $ m_{\tilde{\chi}_{1}^{0}} $ in a scenario with $ \mathcal{B}(\tilde{\chi}_{1}^{0}\to\mathrm{H}\tilde{\mathrm{G}}) = $ 0.5 and $ c\tau = $ 0.5 m. |
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Figure 8-b:
Expected and observed 95% CL upper limits on $ \sigma_{\tilde{\chi}_{1}^{0}\tilde{\chi}_{1}^{0}} $ as functions of $ m_{\tilde{\chi}_{1}^{0}} $ in a scenario with $ \mathcal{B}(\tilde{\chi}_{1}^{0}\to\mathrm{H}\tilde{\mathrm{G}}) = $ 0.5 and $ c\tau = $ 3 m. |
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Figure 9:
Expected and observed 95% CL upper limits on $ \sigma_{\tilde{\chi}_{1}^{0}\tilde{\chi}_{1}^{0}} $ as functions of $ c\tau_{\tilde{\chi}_{1}^{0}} $ in a scenario with $ \mathcal{B}(\tilde{\chi}_{1}^{0}\to\mathrm{H}\tilde{\mathrm{G}}) = $ 0.5 and $ m_{\tilde{\chi}_{1}^{0}}= $ 400 GeV (left) or 1000 GeV (right). |
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Figure 9-a:
Expected and observed 95% CL upper limits on $ \sigma_{\tilde{\chi}_{1}^{0}\tilde{\chi}_{1}^{0}} $ as functions of $ c\tau_{\tilde{\chi}_{1}^{0}} $ in a scenario with $ \mathcal{B}(\tilde{\chi}_{1}^{0}\to\mathrm{H}\tilde{\mathrm{G}}) = $ 0.5 and $ m_{\tilde{\chi}_{1}^{0}}= $ 400 GeV. |
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Figure 9-b:
Expected and observed 95% CL upper limits on $ \sigma_{\tilde{\chi}_{1}^{0}\tilde{\chi}_{1}^{0}} $ as functions of $ c\tau_{\tilde{\chi}_{1}^{0}} $ in a scenario with $ \mathcal{B}(\tilde{\chi}_{1}^{0}\to\mathrm{H}\tilde{\mathrm{G}}) = $ 0.5 and $ m_{\tilde{\chi}_{1}^{0}}= $ 1000 GeV. |
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Figure 10:
The observed 95% CL upper limit on $ \sigma_{\tilde{\chi}_{1}^{0}\tilde{\chi}_{1}^{0}} $ as a function of $ m_{\tilde{\chi}_{1}^{0}} $ and $ c\tau_{\tilde{\chi}_{1}^{0}} $ in a scenario with $ \mathcal{B}(\tilde{\chi}_{1}^{0}\to\mathrm{H}\tilde{\mathrm{G}}) = $ 0.5. The area enclosed by the dotted black line corresponds to the observed excluded region. |
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Figure 11:
The distribution of the jet charged hadron energy fraction, a variable used as input to the TD jet tagger score, for simulation (shaded histogram) and data (black markers) when using electrons from $ \mathrm{W}\to\mathrm{e}\nu_{\!\mathrm{e}} $ events as proxy objects for signal jets. The histograms and data points have been normalized to unit area. Similar levels of agreement are observed for photon proxy objects from the $ \mathrm{Z}\to\ell^{+}\ell^{-}\gamma $ sample. |
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Figure 12:
The distribution of the jet neutral hadron energy fraction, a variable used as input to the TD jet tagger score, for simulation (shaded histogram) and data (black markers) when using electrons from $ \mathrm{W}\to\mathrm{e}\nu_{\!\mathrm{e}} $ events as proxy objects for signal jets. The histograms and data points have been normalized to unit area. Similar levels of agreement are observed for photon proxy objects from the $ \mathrm{Z}\to\ell^{+}\ell^{-}\gamma $ sample. |
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Figure 13:
The distribution of the number of track constituents in the jet, a variable used as input to the TD jet tagger score, for simulation (shaded histogram) and data (black markers) when using electrons from $ \mathrm{W}\to\mathrm{e}\nu_{\!\mathrm{e}} $ events as proxy objects for signal jets. The histograms and data points have been normalized to unit area. Similar levels of agreement are observed for photon proxy objects from the $ \mathrm{Z}\to\ell^{+}\ell^{-}\gamma $ sample. |
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Figure 14:
The $ \eta $ distribution of TD-tagged jets in a background-enriched control region that comprises events with exactly one TD-tagged jet. Observed data (black round markers) and the corresponding prediction based on control samples in data (empty squared markers), measured using the nominal W+jets MR, are compared. The prediction uncertainty (gray band) includes the systematic uncertainty quantifying the degree of process dependence measured from alternative MRs. The predictions for the shape and the normalization of the $ \eta $ distribution are consistent with the data. |
| Tables | |
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Table 1:
Definitions of the measurement regions used to quantify the process dependence of the TD jet misidentification probability. |
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Table 2:
Summary of combined statistical and systematic uncertainties, the size of their effect, and whether it applies to the signal or background yield predictions. Ranges for signal systematic uncertainties reflect their impact on different signal parameter space points. |
| Summary |
| A search for long-lived particles has been carried out using proton-proton collision data at $ \sqrt{s} = $ 13 TeV, corresponding to an integrated luminosity of 138 fb$ ^{-1} $, using missing transverse momentum and a novel and highly discriminating deep neural network tagger for trackless and delayed (TD) jets. Each additional TD-tagged jet required suppresses standard model background processes by more than three orders of magnitude while maintaining the signal efficiency above 80%. A background estimation method based on control samples in data uses the tagger's measured misidentification probability to extrapolate from event samples with one or fewer tagged jets to the signal region comprising events with two or more tagged jets. The results are interpreted in the context of a simplified model of electroweak production of chargino-neutralino pairs. For a neutralino ($ \tilde{\chi}_{1}^{0} $) proper decay length of $ c\tau_{\tilde{\chi}_{1}^{0}}= $ 0.5 m, we exclude cross sections of 160, 2.6, and 0.8 fb for $ \tilde{\chi}_{1}^{0} $ masses ($ m_{\tilde{\chi}_{1}^{0}} $) of 200, 400, and 600 GeV, respectively, at 95% confidence level. Compared to previous searches for promptly decaying $ \tilde{\chi}_{1}^{0} $ in the same simplified model, the sensitivity of the current search expressed in terms of cross section limit is about 20 (10) times better at $ m_{\tilde{\chi}_{1}^{0}} = $ 400 (600) GeV. In the case of a long-lived $ \tilde{\chi}_{1}^{0} $ with $ c\tau_{\tilde{\chi}_{1}^{0}}= $ 0.5 m, $ \tilde{\chi}_{1}^{0} $ masses up to 1.18 TeV are excluded at 95% confidence level. The current search is the best result to date in the mass range from the kinematic limit imposed by the Higgs boson mass up to 1.8 TeV. |
| Instructions for Reinterpretation |
We provide the efficiency of identifying a LLP decay as a TD-tagged jet in bins of the LLP transverse and longitudinal decay position. The samples used to compute the efficiency contain events with pair production of χ10 with a lifetime of 0.5 and 3 m, and considering the combinations of the χ10 decay modes considered in this search (HG → bb̅ G or ZG → qq̅ G).
The efficiency is calculated on top of three acceptance definitions.
efficiency_maps.py in attachment. The two dimensional histograms are also provided as ROOT files.
|
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