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| CMS-PAS-EXO-20-004 | ||
| Search for new particles in events with energetic jets and large missing transverse momentum in proton-proton collisions at $\sqrt{s}= $ 13 TeV | ||
| CMS Collaboration | ||
| June 2021 | ||
| Abstract: A search is presented for new particles produced in proton-proton collisions at $\sqrt{s}= $ 13 TeV at the LHC, using events with energetic jets and large missing transverse momentum. The analysis is based on a data sample corresponding to an integrated luminosity of 101 fb$^{-1}$, collected in 2017-2018 with the CMS detector. Separate categories are defined for events with narrow jets from initial-state radiation and with large-radius jets consistent with a hadronic decay of a W or a Z boson. Novel machine learning techniques are used to identify hadronic W and Z boson decays. The analysis is combined with an earlier search based on a data sample corresponding to an integrated luminosity of 36 fb$^{-1}$, collected in 2016. No significant excess of events is observed with respect to the standard model background expectation, as determined from control samples in data. The results are interpreted in terms of limits on the branching fraction of an invisible decay of the Higgs boson, as well as constraints on simplified models of dark matter, on first-generation scalar leptoquarks decaying to quarks and neutrinos, and on gravitons in models with large extra dimensions. Several of the new limits are the most restrictive to date. | ||
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Links:
CDS record (PDF) ;
inSPIRE record ;
HepData record ;
CADI line (restricted) ;
These preliminary results are superseded in this paper, Submitted to JHEP. The superseded preliminary plots can be found here. |
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| Figures | Summary | Additional Figures | References | CMS Publications |
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MadAnalysis implementation:
https://doi.org/10.14428/DVN/IRF7ZL. |
| Figures | |
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Figure 1:
Ratio of the dilepton and single-lepton control region yields predicted using simulation (red solid line), and observed in data (black points). The gray band represents the total uncertainty in the ratio. In the lower panels, the ratio of data over prediction is shown. From upper to lower, the rows show the monojet, low-purity, and high-purity mono-V categories, while the left (right) column represents the 2017 (2018) data set. |
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Figure 1-a:
Ratio of the dilepton and single-lepton control region yields predicted using simulation (red solid line), and observed in data (black points). The gray band represents the total uncertainty in the ratio. In the lower panels, the ratio of data over prediction is shown. From upper to lower, the rows show the monojet, low-purity, and high-purity mono-V categories, while the left (right) column represents the 2017 (2018) data set. |
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Figure 1-b:
Ratio of the dilepton and single-lepton control region yields predicted using simulation (red solid line), and observed in data (black points). The gray band represents the total uncertainty in the ratio. In the lower panels, the ratio of data over prediction is shown. From upper to lower, the rows show the monojet, low-purity, and high-purity mono-V categories, while the left (right) column represents the 2017 (2018) data set. |
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Figure 1-c:
Ratio of the dilepton and single-lepton control region yields predicted using simulation (red solid line), and observed in data (black points). The gray band represents the total uncertainty in the ratio. In the lower panels, the ratio of data over prediction is shown. From upper to lower, the rows show the monojet, low-purity, and high-purity mono-V categories, while the left (right) column represents the 2017 (2018) data set. |
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Figure 1-d:
Ratio of the dilepton and single-lepton control region yields predicted using simulation (red solid line), and observed in data (black points). The gray band represents the total uncertainty in the ratio. In the lower panels, the ratio of data over prediction is shown. From upper to lower, the rows show the monojet, low-purity, and high-purity mono-V categories, while the left (right) column represents the 2017 (2018) data set. |
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Figure 1-e:
Ratio of the dilepton and single-lepton control region yields predicted using simulation (red solid line), and observed in data (black points). The gray band represents the total uncertainty in the ratio. In the lower panels, the ratio of data over prediction is shown. From upper to lower, the rows show the monojet, low-purity, and high-purity mono-V categories, while the left (right) column represents the 2017 (2018) data set. |
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Figure 1-f:
Ratio of the dilepton and single-lepton control region yields predicted using simulation (red solid line), and observed in data (black points). The gray band represents the total uncertainty in the ratio. In the lower panels, the ratio of data over prediction is shown. From upper to lower, the rows show the monojet, low-purity, and high-purity mono-V categories, while the left (right) column represents the 2017 (2018) data set. |
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Figure 2:
Same as Fig. 1, but for the ratio of the dilepton and photon control regions. |
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Figure 2-a:
Same as Fig. 1, but for the ratio of the dilepton and photon control regions. |
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Figure 2-b:
Same as Fig. 1, but for the ratio of the dilepton and photon control regions. |
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Figure 2-c:
Same as Fig. 1, but for the ratio of the dilepton and photon control regions. |
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Figure 2-d:
Same as Fig. 1, but for the ratio of the dilepton and photon control regions. |
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Figure 2-e:
Same as Fig. 1, but for the ratio of the dilepton and photon control regions. |
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Figure 2-f:
Same as Fig. 1, but for the ratio of the dilepton and photon control regions. |
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Figure 3:
Comparison between data and the background prediction in the monojet signal regions before and after the simultaneous fit. The fit includes all control samples and the signal region in all categories and both data taking years, and the background-only fit model is used. The resulting distributions are shown separately for 2017 (left) and 2018 (right column). Templates for two signal hypothesis are shown overlaid as dark blue and dark red solid lines. The last bin includes the overflow. In the middle panels, ratios of data to the pre-fit background prediction (red open points) and post-fit background prediction (blue solid points) are shown. The gray band in the lower panels indicates the post-fit uncertainty after combining all the systematic uncertainties. Finally, the distribution of the pulls, defined as the difference between data and the post-fit background prediction divided by the quadratic sum of the post-fit uncertainty in the prediction and statistical uncertainty in data, is shown in the lower panels. |
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Figure 3-a:
Comparison between data and the background prediction in the monojet signal regions before and after the simultaneous fit. The fit includes all control samples and the signal region in all categories and both data taking years, and the background-only fit model is used. The resulting distributions are shown separately for 2017 (left) and 2018 (right column). Templates for two signal hypothesis are shown overlaid as dark blue and dark red solid lines. The last bin includes the overflow. In the middle panels, ratios of data to the pre-fit background prediction (red open points) and post-fit background prediction (blue solid points) are shown. The gray band in the lower panels indicates the post-fit uncertainty after combining all the systematic uncertainties. Finally, the distribution of the pulls, defined as the difference between data and the post-fit background prediction divided by the quadratic sum of the post-fit uncertainty in the prediction and statistical uncertainty in data, is shown in the lower panels. |
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Figure 3-b:
Comparison between data and the background prediction in the monojet signal regions before and after the simultaneous fit. The fit includes all control samples and the signal region in all categories and both data taking years, and the background-only fit model is used. The resulting distributions are shown separately for 2017 (left) and 2018 (right column). Templates for two signal hypothesis are shown overlaid as dark blue and dark red solid lines. The last bin includes the overflow. In the middle panels, ratios of data to the pre-fit background prediction (red open points) and post-fit background prediction (blue solid points) are shown. The gray band in the lower panels indicates the post-fit uncertainty after combining all the systematic uncertainties. Finally, the distribution of the pulls, defined as the difference between data and the post-fit background prediction divided by the quadratic sum of the post-fit uncertainty in the prediction and statistical uncertainty in data, is shown in the lower panels. |
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Figure 4:
Comparison between data and the background prediction in the mono-V signal regions before and after the simultaneous fit. The fit includes all control samples and the signal region in all categories and both data taking years, and the background-only fit model is used. The resulting distributions are shown separately for 2017 (left column) and 2018 (right column), as well as for the low- and high-purity categories (upper and lower rows, respectively). |
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Figure 4-a:
Comparison between data and the background prediction in the mono-V signal regions before and after the simultaneous fit. The fit includes all control samples and the signal region in all categories and both data taking years, and the background-only fit model is used. The resulting distributions are shown separately for 2017 (left column) and 2018 (right column), as well as for the low- and high-purity categories (upper and lower rows, respectively). |
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Figure 4-b:
Comparison between data and the background prediction in the mono-V signal regions before and after the simultaneous fit. The fit includes all control samples and the signal region in all categories and both data taking years, and the background-only fit model is used. The resulting distributions are shown separately for 2017 (left column) and 2018 (right column), as well as for the low- and high-purity categories (upper and lower rows, respectively). |
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Figure 4-c:
Comparison between data and the background prediction in the mono-V signal regions before and after the simultaneous fit. The fit includes all control samples and the signal region in all categories and both data taking years, and the background-only fit model is used. The resulting distributions are shown separately for 2017 (left column) and 2018 (right column), as well as for the low- and high-purity categories (upper and lower rows, respectively). |
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Figure 4-d:
Comparison between data and the background prediction in the mono-V signal regions before and after the simultaneous fit. The fit includes all control samples and the signal region in all categories and both data taking years, and the background-only fit model is used. The resulting distributions are shown separately for 2017 (left column) and 2018 (right column), as well as for the low- and high-purity categories (upper and lower rows, respectively). |
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Figure 5:
Upper limits at 95% CL on the branching fraction of the Higgs boson to invisible final states. The results are shown separately for the monojet and mono-V categories, as well as for their combination. The final combined limit is 27.8% (25.3% expected). |
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Figure 6:
Exclusion limits at 95% CL on the signal strength $\mu =\sigma /\sigma _\text {theo}$ in the ${m_\text {med}} - {m_\text {DM}}$ plane for the coupling values of $ {g_\mathrm{q}} =$ 0.25, $ {g_\chi} =$ 1.0 for an axial-vector (upper) or vector (lower) mediator. The blue solid line indicates the observed exclusion boundary $\mu =$ 1. The blue dashed and dotted lines represent the expected exclusion and the the 68% confidence level interval around the expected boundary, respectively. Parameter combinations with larger values of $\mu $ (indicated by a darker shade in the color scale) are excluded. The observed exclusion reaches up to $ {m_\text {med}} = $ 2.0 TeV for low values of $ {m_\text {DM}} = $ 1 GeV (2.2 TeV expected). Yellow solid and dashed lines represent the observed and expected exclusion boundaries from Ref. [20]. The gray dashed line indicates the diagonal $ {m_\text {med}} =2 {m_\text {DM}} $, above which only off-shell mediator production contributes to the jet+$ {{p_{\mathrm {T}}} ^\text {miss}}$ final state. The steep increase of the signal strength limit above the diagonal leads to fluctuations of the exclusion contour, which are due to finite precision in the interpolation method in this region. The gray solid lines represent parameter combinations for which the simplified model reproduces the observed DM relic density in the universe under the assumption of a thermal freeze-out mechanism [57,76]. |
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Figure 6-a:
Exclusion limits at 95% CL on the signal strength $\mu =\sigma /\sigma _\text {theo}$ in the ${m_\text {med}} - {m_\text {DM}}$ plane for the coupling values of $ {g_\mathrm{q}} =$ 0.25, $ {g_\chi} =$ 1.0 for an axial-vector (upper) or vector (lower) mediator. The blue solid line indicates the observed exclusion boundary $\mu =$ 1. The blue dashed and dotted lines represent the expected exclusion and the the 68% confidence level interval around the expected boundary, respectively. Parameter combinations with larger values of $\mu $ (indicated by a darker shade in the color scale) are excluded. The observed exclusion reaches up to $ {m_\text {med}} = $ 2.0 TeV for low values of $ {m_\text {DM}} = $ 1 GeV (2.2 TeV expected). Yellow solid and dashed lines represent the observed and expected exclusion boundaries from Ref. [20]. The gray dashed line indicates the diagonal $ {m_\text {med}} =2 {m_\text {DM}} $, above which only off-shell mediator production contributes to the jet+$ {{p_{\mathrm {T}}} ^\text {miss}}$ final state. The steep increase of the signal strength limit above the diagonal leads to fluctuations of the exclusion contour, which are due to finite precision in the interpolation method in this region. The gray solid lines represent parameter combinations for which the simplified model reproduces the observed DM relic density in the universe under the assumption of a thermal freeze-out mechanism [57,76]. |
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Figure 6-b:
Exclusion limits at 95% CL on the signal strength $\mu =\sigma /\sigma _\text {theo}$ in the ${m_\text {med}} - {m_\text {DM}}$ plane for the coupling values of $ {g_\mathrm{q}} =$ 0.25, $ {g_\chi} =$ 1.0 for an axial-vector (upper) or vector (lower) mediator. The blue solid line indicates the observed exclusion boundary $\mu =$ 1. The blue dashed and dotted lines represent the expected exclusion and the the 68% confidence level interval around the expected boundary, respectively. Parameter combinations with larger values of $\mu $ (indicated by a darker shade in the color scale) are excluded. The observed exclusion reaches up to $ {m_\text {med}} = $ 2.0 TeV for low values of $ {m_\text {DM}} = $ 1 GeV (2.2 TeV expected). Yellow solid and dashed lines represent the observed and expected exclusion boundaries from Ref. [20]. The gray dashed line indicates the diagonal $ {m_\text {med}} =2 {m_\text {DM}} $, above which only off-shell mediator production contributes to the jet+$ {{p_{\mathrm {T}}} ^\text {miss}}$ final state. The steep increase of the signal strength limit above the diagonal leads to fluctuations of the exclusion contour, which are due to finite precision in the interpolation method in this region. The gray solid lines represent parameter combinations for which the simplified model reproduces the observed DM relic density in the universe under the assumption of a thermal freeze-out mechanism [57,76]. |
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Figure 7:
Exclusion limits at 95% CL on the couplings ${g_\chi}$ (left) and ${g_\mathrm{q}}$ (right) for an axial-vector mediator. In each panel, the result is shown as a function of the mediator mass $ {m_\text {med}} $, with the mass of the DM candidate fixed to $ {m_\text {DM}} = {m_\text {med}} /3$. In either case, only one coupling is varied, while the other coupling is fixed at its default value ($ {g_\mathrm{q}} =$ 0.25 or $ {g_\chi} =$ 1.0). |
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Figure 7-a:
Exclusion limits at 95% CL on the couplings ${g_\chi}$ (left) and ${g_\mathrm{q}}$ (right) for an axial-vector mediator. In each panel, the result is shown as a function of the mediator mass $ {m_\text {med}} $, with the mass of the DM candidate fixed to $ {m_\text {DM}} = {m_\text {med}} /3$. In either case, only one coupling is varied, while the other coupling is fixed at its default value ($ {g_\mathrm{q}} =$ 0.25 or $ {g_\chi} =$ 1.0). |
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Figure 7-b:
Exclusion limits at 95% CL on the couplings ${g_\chi}$ (left) and ${g_\mathrm{q}}$ (right) for an axial-vector mediator. In each panel, the result is shown as a function of the mediator mass $ {m_\text {med}} $, with the mass of the DM candidate fixed to $ {m_\text {DM}} = {m_\text {med}} /3$. In either case, only one coupling is varied, while the other coupling is fixed at its default value ($ {g_\mathrm{q}} =$ 0.25 or $ {g_\chi} =$ 1.0). |
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Figure 8:
Upper limits at 95% CL on the signal strength $\mu =\sigma /\sigma _\text {theo}$ as a function of ${m_\text {med}}$ for scenarios with scalar (left) and pseudoscalar (right) mediators and coupling values of $ {g_\mathrm{q}} =$ 1.0, $ {g_\chi} =$ 1.0, for a constant value of $ {m_\text {DM}} = $ 1 GeV. The red solid line indicates the exclusion boundary $\mu =1$. In the case of a pseudoscalar mediator, ${m_\text {med}}$ values up to 480 GeV are excluded (440 GeV expected). |
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Figure 8-a:
Upper limits at 95% CL on the signal strength $\mu =\sigma /\sigma _\text {theo}$ as a function of ${m_\text {med}}$ for scenarios with scalar (left) and pseudoscalar (right) mediators and coupling values of $ {g_\mathrm{q}} =$ 1.0, $ {g_\chi} =$ 1.0, for a constant value of $ {m_\text {DM}} = $ 1 GeV. The red solid line indicates the exclusion boundary $\mu =1$. In the case of a pseudoscalar mediator, ${m_\text {med}}$ values up to 480 GeV are excluded (440 GeV expected). |
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Figure 8-b:
Upper limits at 95% CL on the signal strength $\mu =\sigma /\sigma _\text {theo}$ as a function of ${m_\text {med}}$ for scenarios with scalar (left) and pseudoscalar (right) mediators and coupling values of $ {g_\mathrm{q}} =$ 1.0, $ {g_\chi} =$ 1.0, for a constant value of $ {m_\text {DM}} = $ 1 GeV. The red solid line indicates the exclusion boundary $\mu =1$. In the case of a pseudoscalar mediator, ${m_\text {med}}$ values up to 480 GeV are excluded (440 GeV expected). |
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Figure 9:
Exclusion limits at 95% CL in in the plane of the mediator mass $m_\phi $ and the DM candidate mass $m_\chi $. The black dashed and dotted lines represent the median expected exclusion and the 68% confidence level interval around it, respectively. The black solid line represents the observed exclusion. The yellow solid line shows the observed exclusion from Ref. [20] for comparison. |
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Figure 10:
Exclusion limits at 95% CL on $M_{D}$ in the ADD scenario for different values of the number of extra dimensions d. The blue markers show the result from Ref. [20] for comparison. |
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Figure 11:
Upper limits at 95% CL on the leptoquark coupling $\lambda $ as a function of the leptoquark mass. The branching fraction for the decay of the leptoquark into an electron neutrino and up quark is assumed to be 100% ($\beta =$ 0). The dashed line indicates the median expected exclusion contour. For a leptoquark mass value of 1 TeV, coupling values as low as approximately 0.5 are excluded (0.4 expected). The upper limit increases with the leptoquark mass increase, reaching $\lambda =$ 0.9 at a mass of 1.5 TeV (0.7 expected) and $\lambda =$ 1.8 at 2 TeV (1.25 expected). |
| Summary |
| A search for physics beyond the standard model in events with energetic jets and large missing transverse momentum is presented. A data set of proton-proton collisions at a center-of-mass energy of 13 TeV, corresponding to and integrated luminosity of 101 fb$^{-1}$ is analyzed, and the analysis results are combined with those of an earlier search using an independent data set collected at the same center-of-mass energy, corresponding to an integrated luminosity of 36 fb$^{-1}$ [20]. Separate analysis categories are defined for events with a large-radius jet consistent with a decay of a W or a Z boson, and for events without such a jet. A joint maximum-likelihood fit over a combination of signal and control regions is used to constrain standard model (SM) background processes and to extract possible signal. The data are found to be in a good agreement with the fit results, with no evidence for a significant signal contribution found. The result is interpreted in terms of exclusion limits on the parameters of a number of models of beyond-the-SM physics. In simplified models of the production of dark matter (DM) candidates via a spin-1 $s$-channel mediator, values of the mediator mass of up to 1.95 TeV are excluded, assuming the couplings of ${g_\mathrm{q}} =$ 0.25 between the mediator and quarks, and ${g_\chi} =$ 1.0 between the mediator and the Dirac fermion DM particles. Assuming a fixed ratio ${m_\text{DM}} = m_{\text{med}}$/3, coupling values as low as ${g_\mathrm{q}} =$ 0.018 and ${g_\chi} =$ 0.070 can be excluded for a mediator mass value of $ m_{\text{med}} = $ 100 GeV. In a similar model with a pseudoscalar spin-0 mediator, mediator masses of up to 470 GeV are excluded. We further constrain the branching fraction of the Higgs boson decay to invisible particles to be below 27.8%. In a model of large extra dimensions, values of the fundamental Planck scale below from 10.7 to 5.2 TeV can be excluded, depending on the number of extra dimensions between 2 and 7. Finally, the production of leptoquarks decaying into the up quark and the electron neutrino is excluded for coupling values between the leptoquarks and the SM fermions larger than between 10$^{-5}$ and 1.8 for leptoquark masses between 0.5 and 1.8 TeV. |
| Additional Figures | |
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Additional Figure 1:
Hadronic recoil distributions in the photon (a, b), dimuon (c, d) and dielectron control regions (e, f) in the monojet category. The 2017 (a, c, e) and 2018 (b, d, f) data-taking periods are shown separately. The other backgrounds include QCD multijet production (photon), top quark, diboson and W+jets processes (dimuon and dielectron). The fit result is based on the background-only model and includes the full combination of data-taking periods and analysis regions. |
png pdf |
Additional Figure 1-a:
Hadronic recoil distributions in the photon (a, b), dimuon (c, d) and dielectron control regions (e, f) in the monojet category. The 2017 (a, c, e) and 2018 (b, d, f) data-taking periods are shown separately. The other backgrounds include QCD multijet production (photon), top quark, diboson and W+jets processes (dimuon and dielectron). The fit result is based on the background-only model and includes the full combination of data-taking periods and analysis regions. |
png pdf |
Additional Figure 1-b:
Hadronic recoil distributions in the photon (a, b), dimuon (c, d) and dielectron control regions (e, f) in the monojet category. The 2017 (a, c, e) and 2018 (b, d, f) data-taking periods are shown separately. The other backgrounds include QCD multijet production (photon), top quark, diboson and W+jets processes (dimuon and dielectron). The fit result is based on the background-only model and includes the full combination of data-taking periods and analysis regions. |
png pdf |
Additional Figure 1-c:
Hadronic recoil distributions in the photon (a, b), dimuon (c, d) and dielectron control regions (e, f) in the monojet category. The 2017 (a, c, e) and 2018 (b, d, f) data-taking periods are shown separately. The other backgrounds include QCD multijet production (photon), top quark, diboson and W+jets processes (dimuon and dielectron). The fit result is based on the background-only model and includes the full combination of data-taking periods and analysis regions. |
png pdf |
Additional Figure 1-d:
Hadronic recoil distributions in the photon (a, b), dimuon (c, d) and dielectron control regions (e, f) in the monojet category. The 2017 (a, c, e) and 2018 (b, d, f) data-taking periods are shown separately. The other backgrounds include QCD multijet production (photon), top quark, diboson and W+jets processes (dimuon and dielectron). The fit result is based on the background-only model and includes the full combination of data-taking periods and analysis regions. |
png pdf |
Additional Figure 1-e:
Hadronic recoil distributions in the photon (a, b), dimuon (c, d) and dielectron control regions (e, f) in the monojet category. The 2017 (a, c, e) and 2018 (b, d, f) data-taking periods are shown separately. The other backgrounds include QCD multijet production (photon), top quark, diboson and W+jets processes (dimuon and dielectron). The fit result is based on the background-only model and includes the full combination of data-taking periods and analysis regions. |
png pdf |
Additional Figure 1-f:
Hadronic recoil distributions in the photon (a, b), dimuon (c, d) and dielectron control regions (e, f) in the monojet category. The 2017 (a, c, e) and 2018 (b, d, f) data-taking periods are shown separately. The other backgrounds include QCD multijet production (photon), top quark, diboson and W+jets processes (dimuon and dielectron). The fit result is based on the background-only model and includes the full combination of data-taking periods and analysis regions. |
png pdf |
Additional Figure 2:
Hadronic recoil distributions in the single muon (a, b) and single electron regions (c, d) in the monojet category. The 2017 (a, c) and 2018 (b, d) data-taking periods are shown separately. The fit result is based on the background-only model and includes the full combination of data-taking periods and analysis regions. The other backgrounds include top quark, diboson, and QCD multijet processes. |
png pdf |
Additional Figure 2-a:
Hadronic recoil distributions in the single muon (a, b) and single electron regions (c, d) in the monojet category. The 2017 (a, c) and 2018 (b, d) data-taking periods are shown separately. The fit result is based on the background-only model and includes the full combination of data-taking periods and analysis regions. The other backgrounds include top quark, diboson, and QCD multijet processes. |
png pdf |
Additional Figure 2-b:
Hadronic recoil distributions in the single muon (a, b) and single electron regions (c, d) in the monojet category. The 2017 (a, c) and 2018 (b, d) data-taking periods are shown separately. The fit result is based on the background-only model and includes the full combination of data-taking periods and analysis regions. The other backgrounds include top quark, diboson, and QCD multijet processes. |
png pdf |
Additional Figure 2-c:
Hadronic recoil distributions in the single muon (a, b) and single electron regions (c, d) in the monojet category. The 2017 (a, c) and 2018 (b, d) data-taking periods are shown separately. The fit result is based on the background-only model and includes the full combination of data-taking periods and analysis regions. The other backgrounds include top quark, diboson, and QCD multijet processes. |
png pdf |
Additional Figure 2-d:
Hadronic recoil distributions in the single muon (a, b) and single electron regions (c, d) in the monojet category. The 2017 (a, c) and 2018 (b, d) data-taking periods are shown separately. The fit result is based on the background-only model and includes the full combination of data-taking periods and analysis regions. The other backgrounds include top quark, diboson, and QCD multijet processes. |
png pdf |
Additional Figure 3:
Hadronic recoil distributions in the photon (a, b), dimuon (c, d) and dielectron control regions (e, f) in the low-purity mono-V category. The 2017 (a, c, e) and 2018 (b, d, f) data-taking periods are shown separately. The other backgrounds include QCD multijet production (photon), top quark, diboson and W+jets processes (dimuon and dielectron). The fit result is based on the background-only model and includes the full combination of data-taking periods and analysis regions. |
png pdf |
Additional Figure 3-a:
Hadronic recoil distributions in the photon (a, b), dimuon (c, d) and dielectron control regions (e, f) in the low-purity mono-V category. The 2017 (a, c, e) and 2018 (b, d, f) data-taking periods are shown separately. The other backgrounds include QCD multijet production (photon), top quark, diboson and W+jets processes (dimuon and dielectron). The fit result is based on the background-only model and includes the full combination of data-taking periods and analysis regions. |
png pdf |
Additional Figure 3-b:
Hadronic recoil distributions in the photon (a, b), dimuon (c, d) and dielectron control regions (e, f) in the low-purity mono-V category. The 2017 (a, c, e) and 2018 (b, d, f) data-taking periods are shown separately. The other backgrounds include QCD multijet production (photon), top quark, diboson and W+jets processes (dimuon and dielectron). The fit result is based on the background-only model and includes the full combination of data-taking periods and analysis regions. |
png pdf |
Additional Figure 3-c:
Hadronic recoil distributions in the photon (a, b), dimuon (c, d) and dielectron control regions (e, f) in the low-purity mono-V category. The 2017 (a, c, e) and 2018 (b, d, f) data-taking periods are shown separately. The other backgrounds include QCD multijet production (photon), top quark, diboson and W+jets processes (dimuon and dielectron). The fit result is based on the background-only model and includes the full combination of data-taking periods and analysis regions. |
png pdf |
Additional Figure 3-d:
Hadronic recoil distributions in the photon (a, b), dimuon (c, d) and dielectron control regions (e, f) in the low-purity mono-V category. The 2017 (a, c, e) and 2018 (b, d, f) data-taking periods are shown separately. The other backgrounds include QCD multijet production (photon), top quark, diboson and W+jets processes (dimuon and dielectron). The fit result is based on the background-only model and includes the full combination of data-taking periods and analysis regions. |
png pdf |
Additional Figure 3-e:
Hadronic recoil distributions in the photon (a, b), dimuon (c, d) and dielectron control regions (e, f) in the low-purity mono-V category. The 2017 (a, c, e) and 2018 (b, d, f) data-taking periods are shown separately. The other backgrounds include QCD multijet production (photon), top quark, diboson and W+jets processes (dimuon and dielectron). The fit result is based on the background-only model and includes the full combination of data-taking periods and analysis regions. |
png pdf |
Additional Figure 3-f:
Hadronic recoil distributions in the photon (a, b), dimuon (c, d) and dielectron control regions (e, f) in the low-purity mono-V category. The 2017 (a, c, e) and 2018 (b, d, f) data-taking periods are shown separately. The other backgrounds include QCD multijet production (photon), top quark, diboson and W+jets processes (dimuon and dielectron). The fit result is based on the background-only model and includes the full combination of data-taking periods and analysis regions. |
png pdf |
Additional Figure 4:
Hadronic recoil distributions in the single muon (a, b) and single electron regions (c, d) in the low-purity mono-V category. The 2017 (a, c) and 2018 (b, df) data-taking periods are shown separately. The fit result is based on the background-only model and includes the full combination of data-taking periods and analysis regions. The other backgrounds include top quark, diboson, and QCD multijet processes. |
png pdf |
Additional Figure 4-a:
Hadronic recoil distributions in the single muon (a, b) and single electron regions (c, d) in the low-purity mono-V category. The 2017 (a, c) and 2018 (b, df) data-taking periods are shown separately. The fit result is based on the background-only model and includes the full combination of data-taking periods and analysis regions. The other backgrounds include top quark, diboson, and QCD multijet processes. |
png pdf |
Additional Figure 4-b:
Hadronic recoil distributions in the single muon (a, b) and single electron regions (c, d) in the low-purity mono-V category. The 2017 (a, c) and 2018 (b, df) data-taking periods are shown separately. The fit result is based on the background-only model and includes the full combination of data-taking periods and analysis regions. The other backgrounds include top quark, diboson, and QCD multijet processes. |
png pdf |
Additional Figure 4-c:
Hadronic recoil distributions in the single muon (a, b) and single electron regions (c, d) in the low-purity mono-V category. The 2017 (a, c) and 2018 (b, df) data-taking periods are shown separately. The fit result is based on the background-only model and includes the full combination of data-taking periods and analysis regions. The other backgrounds include top quark, diboson, and QCD multijet processes. |
png pdf |
Additional Figure 4-d:
Hadronic recoil distributions in the single muon (a, b) and single electron regions (c, d) in the low-purity mono-V category. The 2017 (a, c) and 2018 (b, df) data-taking periods are shown separately. The fit result is based on the background-only model and includes the full combination of data-taking periods and analysis regions. The other backgrounds include top quark, diboson, and QCD multijet processes. |
png pdf |
Additional Figure 5:
Hadronic recoil distributions in the photon (a, b), dimuon (c, d) and dielectron control regions (e, f) in the high-purity mono-V category. The 2017 (a, c, e) and 2018 (b, d, f) data-taking periods are shown separately. The other backgrounds include QCD multijet production (photon), top quark, diboson and W+jets processes (dimuon and dielectron). The fit result is based on the background-only model and includes the full combination of data-taking periods and analysis regions. |
png pdf |
Additional Figure 5-a:
Hadronic recoil distributions in the photon (a, b), dimuon (c, d) and dielectron control regions (e, f) in the high-purity mono-V category. The 2017 (a, c, e) and 2018 (b, d, f) data-taking periods are shown separately. The other backgrounds include QCD multijet production (photon), top quark, diboson and W+jets processes (dimuon and dielectron). The fit result is based on the background-only model and includes the full combination of data-taking periods and analysis regions. |
png pdf |
Additional Figure 5-b:
Hadronic recoil distributions in the photon (a, b), dimuon (c, d) and dielectron control regions (e, f) in the high-purity mono-V category. The 2017 (a, c, e) and 2018 (b, d, f) data-taking periods are shown separately. The other backgrounds include QCD multijet production (photon), top quark, diboson and W+jets processes (dimuon and dielectron). The fit result is based on the background-only model and includes the full combination of data-taking periods and analysis regions. |
png pdf |
Additional Figure 5-c:
Hadronic recoil distributions in the photon (a, b), dimuon (c, d) and dielectron control regions (e, f) in the high-purity mono-V category. The 2017 (a, c, e) and 2018 (b, d, f) data-taking periods are shown separately. The other backgrounds include QCD multijet production (photon), top quark, diboson and W+jets processes (dimuon and dielectron). The fit result is based on the background-only model and includes the full combination of data-taking periods and analysis regions. |
png pdf |
Additional Figure 5-d:
Hadronic recoil distributions in the photon (a, b), dimuon (c, d) and dielectron control regions (e, f) in the high-purity mono-V category. The 2017 (a, c, e) and 2018 (b, d, f) data-taking periods are shown separately. The other backgrounds include QCD multijet production (photon), top quark, diboson and W+jets processes (dimuon and dielectron). The fit result is based on the background-only model and includes the full combination of data-taking periods and analysis regions. |
png pdf |
Additional Figure 5-e:
Hadronic recoil distributions in the photon (a, b), dimuon (c, d) and dielectron control regions (e, f) in the high-purity mono-V category. The 2017 (a, c, e) and 2018 (b, d, f) data-taking periods are shown separately. The other backgrounds include QCD multijet production (photon), top quark, diboson and W+jets processes (dimuon and dielectron). The fit result is based on the background-only model and includes the full combination of data-taking periods and analysis regions. |
png pdf |
Additional Figure 5-f:
Hadronic recoil distributions in the photon (a, b), dimuon (c, d) and dielectron control regions (e, f) in the high-purity mono-V category. The 2017 (a, c, e) and 2018 (b, d, f) data-taking periods are shown separately. The other backgrounds include QCD multijet production (photon), top quark, diboson and W+jets processes (dimuon and dielectron). The fit result is based on the background-only model and includes the full combination of data-taking periods and analysis regions. |
png pdf |
Additional Figure 6:
Hadronic recoil distributions in the single muon (a, b) and single electron regions (c, d) in the high-purity mono-V category. The 2017 (a, c) and 2018 (b, d) data-taking periods are shown separately. The fit result is based on the background-only model and includes the full combination of data-taking periods and analysis regions. The other backgrounds include top quark, diboson, and QCD multijet processes. |
png pdf |
Additional Figure 6-a:
Hadronic recoil distributions in the single muon (a, b) and single electron regions (c, d) in the high-purity mono-V category. The 2017 (a, c) and 2018 (b, d) data-taking periods are shown separately. The fit result is based on the background-only model and includes the full combination of data-taking periods and analysis regions. The other backgrounds include top quark, diboson, and QCD multijet processes. |
png pdf |
Additional Figure 6-b:
Hadronic recoil distributions in the single muon (a, b) and single electron regions (c, d) in the high-purity mono-V category. The 2017 (a, c) and 2018 (b, d) data-taking periods are shown separately. The fit result is based on the background-only model and includes the full combination of data-taking periods and analysis regions. The other backgrounds include top quark, diboson, and QCD multijet processes. |
png pdf |
Additional Figure 6-c:
Hadronic recoil distributions in the single muon (a, b) and single electron regions (c, d) in the high-purity mono-V category. The 2017 (a, c) and 2018 (b, d) data-taking periods are shown separately. The fit result is based on the background-only model and includes the full combination of data-taking periods and analysis regions. The other backgrounds include top quark, diboson, and QCD multijet processes. |
png pdf |
Additional Figure 6-d:
Hadronic recoil distributions in the single muon (a, b) and single electron regions (c, d) in the high-purity mono-V category. The 2017 (a, c) and 2018 (b, d) data-taking periods are shown separately. The fit result is based on the background-only model and includes the full combination of data-taking periods and analysis regions. The other backgrounds include top quark, diboson, and QCD multijet processes. |
png pdf |
Additional Figure 7:
Comparison of the simplified model constraints from this search (red line) to results from direction-detection experiments (blue lines). The comparison is shown separately for the vector (a) and axial-vector (b) mediators, which translate into spin-independent and spin-dependent DM-nucleon couplings, respectively [78]. In the case of spin-independent couplings, results from CRESST-II [79], CDMSlite [80], LUX [81], DarkSide-50 [82], XENON1T [83], and Panda-X II [84] are shown for comparison. For spin-dependent couplings, PICO-2L [85], PICASSO [86], and PICO-60 [87] limits are displayed. |
png pdf |
Additional Figure 7-a:
Comparison of the simplified model constraints from this search (red line) to results from direction-detection experiments (blue lines). The comparison is shown separately for the vector (a) and axial-vector (b) mediators, which translate into spin-independent and spin-dependent DM-nucleon couplings, respectively [78]. In the case of spin-independent couplings, results from CRESST-II [79], CDMSlite [80], LUX [81], DarkSide-50 [82], XENON1T [83], and Panda-X II [84] are shown for comparison. For spin-dependent couplings, PICO-2L [85], PICASSO [86], and PICO-60 [87] limits are displayed. |
png pdf |
Additional Figure 7-b:
Comparison of the simplified model constraints from this search (red line) to results from direction-detection experiments (blue lines). The comparison is shown separately for the vector (a) and axial-vector (b) mediators, which translate into spin-independent and spin-dependent DM-nucleon couplings, respectively [78]. In the case of spin-independent couplings, results from CRESST-II [79], CDMSlite [80], LUX [81], DarkSide-50 [82], XENON1T [83], and Panda-X II [84] are shown for comparison. For spin-dependent couplings, PICO-2L [85], PICASSO [86], and PICO-60 [87] limits are displayed. |
png pdf |
Additional Figure 8:
Distributions of the variables used for the identification of ${\to {\mathrm {q}} {\mathrm {q}}}$ candidate jets. Panels (a) and (b) show the softdrop corrected jet mass and the DeepAK8 classifier value, respectively. In each panel, the distributions are shown for the ${{\mathrm {Z}} \to {\nu} {\overline {\nu}}}$ background, which contains jets from QCD radiation, as well as the WH(inv) and ZH(inv) signals, which contain genuine hadronic decays of W or Z bosons. The distributions are shown after applying the mono-V signal region selection, with the exception of the requirements on the two variables shown here. Vertical dashed lines indicate the acceptance boundaries of different regions. |
png pdf |
Additional Figure 8-a:
Distributions of the variables used for the identification of ${\to {\mathrm {q}} {\mathrm {q}}}$ candidate jets. Panels (a) and (b) show the softdrop corrected jet mass and the DeepAK8 classifier value, respectively. In each panel, the distributions are shown for the ${{\mathrm {Z}} \to {\nu} {\overline {\nu}}}$ background, which contains jets from QCD radiation, as well as the WH(inv) and ZH(inv) signals, which contain genuine hadronic decays of W or Z bosons. The distributions are shown after applying the mono-V signal region selection, with the exception of the requirements on the two variables shown here. Vertical dashed lines indicate the acceptance boundaries of different regions. |
png pdf |
Additional Figure 8-b:
Distributions of the variables used for the identification of ${\to {\mathrm {q}} {\mathrm {q}}}$ candidate jets. Panels (a) and (b) show the softdrop corrected jet mass and the DeepAK8 classifier value, respectively. In each panel, the distributions are shown for the ${{\mathrm {Z}} \to {\nu} {\overline {\nu}}}$ background, which contains jets from QCD radiation, as well as the WH(inv) and ZH(inv) signals, which contain genuine hadronic decays of W or Z bosons. The distributions are shown after applying the mono-V signal region selection, with the exception of the requirements on the two variables shown here. Vertical dashed lines indicate the acceptance boundaries of different regions. |
png pdf |
Additional Figure 9:
Wide-jet tagging efficiencies for use in reinterpretation. The efficiencies are shown separately for the low- and high-purity selections in the top and bottom panels, respectively, and for the 2017 (a, c) and 2018 data-taking periods (b, d). The efficiencies include the effect of the DeepAK8 tagger, as well as the softdrop mass requirement. In each panel, individual curves represent the efficiency for different types of jets, based on whether or not the jets are matched to a generator-level W boson, Z boson, or neither ("QCD jet''). Simulation-to-data corrections are included. Efficiencies are provided for the low- and high-purity version of the tagging requirement. Note that for the low-purity tagger, overlap removal with the high-purity category is already taken into account. The efficiency is calculated separately for AK8 jets matching a generator-level Z boson, W boson, or not matching either ("QCD jet''). A jet is considered to be matching a boson if their angular separation $\Delta R = \sqrt {\Delta \phi ^2 + \Delta \eta ^2} < $ 0.8. In order to apply the efficiencies to simulated events, one should first apply all other selection criteria. No selection based on jet mass or other substructure variables should be applied. Then, depending on the matching status of the jet, one should apply the respective efficiency, evaluated at the ${p_{\mathrm {T}}}$ of the jet, as an event weight. |
png pdf |
Additional Figure 9-a:
Wide-jet tagging efficiencies for use in reinterpretation. The efficiencies are shown separately for the low- and high-purity selections in the top and bottom panels, respectively, and for the 2017 (a, c) and 2018 data-taking periods (b, d). The efficiencies include the effect of the DeepAK8 tagger, as well as the softdrop mass requirement. In each panel, individual curves represent the efficiency for different types of jets, based on whether or not the jets are matched to a generator-level W boson, Z boson, or neither ("QCD jet''). Simulation-to-data corrections are included. Efficiencies are provided for the low- and high-purity version of the tagging requirement. Note that for the low-purity tagger, overlap removal with the high-purity category is already taken into account. The efficiency is calculated separately for AK8 jets matching a generator-level Z boson, W boson, or not matching either ("QCD jet''). A jet is considered to be matching a boson if their angular separation $\Delta R = \sqrt {\Delta \phi ^2 + \Delta \eta ^2} < $ 0.8. In order to apply the efficiencies to simulated events, one should first apply all other selection criteria. No selection based on jet mass or other substructure variables should be applied. Then, depending on the matching status of the jet, one should apply the respective efficiency, evaluated at the ${p_{\mathrm {T}}}$ of the jet, as an event weight. |
png pdf |
Additional Figure 9-b:
Wide-jet tagging efficiencies for use in reinterpretation. The efficiencies are shown separately for the low- and high-purity selections in the top and bottom panels, respectively, and for the 2017 (a, c) and 2018 data-taking periods (b, d). The efficiencies include the effect of the DeepAK8 tagger, as well as the softdrop mass requirement. In each panel, individual curves represent the efficiency for different types of jets, based on whether or not the jets are matched to a generator-level W boson, Z boson, or neither ("QCD jet''). Simulation-to-data corrections are included. Efficiencies are provided for the low- and high-purity version of the tagging requirement. Note that for the low-purity tagger, overlap removal with the high-purity category is already taken into account. The efficiency is calculated separately for AK8 jets matching a generator-level Z boson, W boson, or not matching either ("QCD jet''). A jet is considered to be matching a boson if their angular separation $\Delta R = \sqrt {\Delta \phi ^2 + \Delta \eta ^2} < $ 0.8. In order to apply the efficiencies to simulated events, one should first apply all other selection criteria. No selection based on jet mass or other substructure variables should be applied. Then, depending on the matching status of the jet, one should apply the respective efficiency, evaluated at the ${p_{\mathrm {T}}}$ of the jet, as an event weight. |
png pdf |
Additional Figure 9-c:
Wide-jet tagging efficiencies for use in reinterpretation. The efficiencies are shown separately for the low- and high-purity selections in the top and bottom panels, respectively, and for the 2017 (a, c) and 2018 data-taking periods (b, d). The efficiencies include the effect of the DeepAK8 tagger, as well as the softdrop mass requirement. In each panel, individual curves represent the efficiency for different types of jets, based on whether or not the jets are matched to a generator-level W boson, Z boson, or neither ("QCD jet''). Simulation-to-data corrections are included. Efficiencies are provided for the low- and high-purity version of the tagging requirement. Note that for the low-purity tagger, overlap removal with the high-purity category is already taken into account. The efficiency is calculated separately for AK8 jets matching a generator-level Z boson, W boson, or not matching either ("QCD jet''). A jet is considered to be matching a boson if their angular separation $\Delta R = \sqrt {\Delta \phi ^2 + \Delta \eta ^2} < $ 0.8. In order to apply the efficiencies to simulated events, one should first apply all other selection criteria. No selection based on jet mass or other substructure variables should be applied. Then, depending on the matching status of the jet, one should apply the respective efficiency, evaluated at the ${p_{\mathrm {T}}}$ of the jet, as an event weight. |
png pdf |
Additional Figure 9-d:
Wide-jet tagging efficiencies for use in reinterpretation. The efficiencies are shown separately for the low- and high-purity selections in the top and bottom panels, respectively, and for the 2017 (a, c) and 2018 data-taking periods (b, d). The efficiencies include the effect of the DeepAK8 tagger, as well as the softdrop mass requirement. In each panel, individual curves represent the efficiency for different types of jets, based on whether or not the jets are matched to a generator-level W boson, Z boson, or neither ("QCD jet''). Simulation-to-data corrections are included. Efficiencies are provided for the low- and high-purity version of the tagging requirement. Note that for the low-purity tagger, overlap removal with the high-purity category is already taken into account. The efficiency is calculated separately for AK8 jets matching a generator-level Z boson, W boson, or not matching either ("QCD jet''). A jet is considered to be matching a boson if their angular separation $\Delta R = \sqrt {\Delta \phi ^2 + \Delta \eta ^2} < $ 0.8. In order to apply the efficiencies to simulated events, one should first apply all other selection criteria. No selection based on jet mass or other substructure variables should be applied. Then, depending on the matching status of the jet, one should apply the respective efficiency, evaluated at the ${p_{\mathrm {T}}}$ of the jet, as an event weight. |
png pdf |
Additional Figure 10:
Exclusion limits at 95% CL on the signal strength couplings ${g_\chi}$ (a) and ${g_ {\mathrm {q}}}$ (b) for a vector mediator. In each panel, the result is shown as a function of the mediator mass $ {m_\text {med}} $, and the mass of the DM candidate is fixed to $ {m_\text {DM}} = {m_\text {med}} /3$. In either case, only one coupling is varied, and the respective other coupling is fixed at its default value ($ {g_ {\mathrm {q}}} =$ 0.25, $ {g_\chi} =$ 1.0). |
png pdf |
Additional Figure 10-a:
Exclusion limits at 95% CL on the signal strength couplings ${g_\chi}$ (a) and ${g_ {\mathrm {q}}}$ (b) for a vector mediator. In each panel, the result is shown as a function of the mediator mass $ {m_\text {med}} $, and the mass of the DM candidate is fixed to $ {m_\text {DM}} = {m_\text {med}} /3$. In either case, only one coupling is varied, and the respective other coupling is fixed at its default value ($ {g_ {\mathrm {q}}} =$ 0.25, $ {g_\chi} =$ 1.0). |
png pdf |
Additional Figure 10-b:
Exclusion limits at 95% CL on the signal strength couplings ${g_\chi}$ (a) and ${g_ {\mathrm {q}}}$ (b) for a vector mediator. In each panel, the result is shown as a function of the mediator mass $ {m_\text {med}} $, and the mass of the DM candidate is fixed to $ {m_\text {DM}} = {m_\text {med}} /3$. In either case, only one coupling is varied, and the respective other coupling is fixed at its default value ($ {g_ {\mathrm {q}}} =$ 0.25, $ {g_\chi} =$ 1.0). |
png pdf |
Additional Figure 11:
Exclusion limits at 95% CL on the branching fraction of the Higgs boson to invisible final states. The result is shown separately for the monojet and mono-V categories in each data-taking year, as well as their combination. The final combined limit is 27.8% (25.3% expected). |
png pdf |
Additional Figure 12:
Distribution of ${{p_{\mathrm {T}}} ^\text {miss}}$ in the monojet category. The distribution is shown including the contributions from all data-taking years. It does not directly represent the input to the statistical method, which instead relies on distributions separated by data-taking year. The background estimate is obtained from the background-only fit to all years, regions and categories, including mono-V. The total uncertainty of the background estimate, shown as a gray band in the middle panel, takes into account all relevant correlations. The signal templates from the Higgs portal and axial-vector mediator hypothesis are overlaid (solid lines). In both cases, contributions from all production modes are taken into account. |
png pdf |
Additional Figure 13:
Exclusion limits at 95% CL on the signal strength $\mu =\sigma /\sigma _\text {theo}$ in the ${m_\text {med}} - {m_\text {DM}}$ plane for coupling values of $ {g_ {\mathrm {q}}} = {g_\chi} =$ 1.0 and a pseudoscalar mediator. The blue solid line indicates the observed exclusion boundary $\mu =$ 1. The blue dashed and dotted lines represent the median expected exclusion and the 1 s.d. interval of the expected boundary, respectively. Parameter combinations with larger values of $\mu $ (indicated by a darker shade in the color scale) are excluded. The gray dashed line indicates the diagonal $ {m_\text {med}} =2 {m_\text {DM}} $, above which only off-shell mediator production contributes to the jet+$ {{p_{\mathrm {T}}} ^\text {miss}}$ final state. The gray solid lines represent parameter combinations for which the simplified model reproduces the observed DM relic density in the universe under the assumption of a thermal freeze-out mechanism [57,76]. |
png pdf |
Additional Figure 14:
In order to promote this analysis for reinterpretation, we implement the selection for the monojet category of this analysis in the MadAnalysis reinterpretation framework [88]. MadAnalysis is a framework for the reinterpretation of existing analyses in terms of arbitrary new physics models. The framework provides infrastructure for the implementation of event selections that can be run over simulated signal events. Detector simulation can be handled by the independent Delphes software framework [89], or internally to MadAnalysis [90]. Once an implementation is available, it is indexed in a public data base ("PAD'') that allows users to automatically download and execute it [91]. After detector simulation and event selection on signal events, approximate statistical inference is made possible through usage of the simplified likelihood scheme [92], the input information for which is also provided as additional material with this note. The implementation made here will be made available in the PAD shortly for public use. Users who prefer other reinterpretation frameworks (a recent overview is given in Ref. [93]), might still profit from this implementation by reading the source code or even copying it into other frameworks. A total of 66 analysis regions are defined, with each of the regions representing one recoil bin in one data-taking year. The selections applied for the 2016 and 2017 data sets are identical, and additional criteria are applied for the 2018 data set, where additional mitigation requirements are applied due to a localized failure of the hadronic calorimeter. In order to validate the implementation, generator-level information from the CMS-internal signal samples is fed into the Delphes framework, which performs fast parameterized event simulation, and is available to the general public. The MadAnalysis implementation is then run based on the Delphes output, and the final yields per signal region bin are compared to the signal prediction obtained from the CMS-internal analysis framework, which includes more elaborate detector simulation based on {Geant4} [38]. The comparison is made using signal samples for the ADD interpretation, which are generated using PYTHIA, and are therefore relatively easy to reproduce. The resulting comparison of the final signal templates is (a), (b) and (c) for the individual data-taking years for a representative choice of parameter points. It is found that the Delphes/MadAnalysis-based result agrees with the CMS result to better than 20% in every bin. In most bins, the agreement is significantly better still, with an average agreement of 10% or better. While only a few parameter points are shown here, it has been verified that the agreement is similar for the full range of parameters. The level of agreement observed here is sufficiently good to enable reliable reinterpretation. |
png pdf |
Additional Figure 14-a:
In order to promote this analysis for reinterpretation, we implement the selection for the monojet category of this analysis in the MadAnalysis reinterpretation framework [88]. MadAnalysis is a framework for the reinterpretation of existing analyses in terms of arbitrary new physics models. The framework provides infrastructure for the implementation of event selections that can be run over simulated signal events. Detector simulation can be handled by the independent Delphes software framework [89], or internally to MadAnalysis [90]. Once an implementation is available, it is indexed in a public data base ("PAD'') that allows users to automatically download and execute it [91]. After detector simulation and event selection on signal events, approximate statistical inference is made possible through usage of the simplified likelihood scheme [92], the input information for which is also provided as additional material with this note. The implementation made here will be made available in the PAD shortly for public use. Users who prefer other reinterpretation frameworks (a recent overview is given in Ref. [93]), might still profit from this implementation by reading the source code or even copying it into other frameworks. A total of 66 analysis regions are defined, with each of the regions representing one recoil bin in one data-taking year. The selections applied for the 2016 and 2017 data sets are identical, and additional criteria are applied for the 2018 data set, where additional mitigation requirements are applied due to a localized failure of the hadronic calorimeter. In order to validate the implementation, generator-level information from the CMS-internal signal samples is fed into the Delphes framework, which performs fast parameterized event simulation, and is available to the general public. The MadAnalysis implementation is then run based on the Delphes output, and the final yields per signal region bin are compared to the signal prediction obtained from the CMS-internal analysis framework, which includes more elaborate detector simulation based on {Geant4} [38]. The comparison is made using signal samples for the ADD interpretation, which are generated using PYTHIA, and are therefore relatively easy to reproduce. The resulting comparison of the final signal templates is (a), (b) and (c) for the individual data-taking years for a representative choice of parameter points. It is found that the Delphes/MadAnalysis-based result agrees with the CMS result to better than 20% in every bin. In most bins, the agreement is significantly better still, with an average agreement of 10% or better. While only a few parameter points are shown here, it has been verified that the agreement is similar for the full range of parameters. The level of agreement observed here is sufficiently good to enable reliable reinterpretation. |
png pdf |
Additional Figure 14-b:
In order to promote this analysis for reinterpretation, we implement the selection for the monojet category of this analysis in the MadAnalysis reinterpretation framework [88]. MadAnalysis is a framework for the reinterpretation of existing analyses in terms of arbitrary new physics models. The framework provides infrastructure for the implementation of event selections that can be run over simulated signal events. Detector simulation can be handled by the independent Delphes software framework [89], or internally to MadAnalysis [90]. Once an implementation is available, it is indexed in a public data base ("PAD'') that allows users to automatically download and execute it [91]. After detector simulation and event selection on signal events, approximate statistical inference is made possible through usage of the simplified likelihood scheme [92], the input information for which is also provided as additional material with this note. The implementation made here will be made available in the PAD shortly for public use. Users who prefer other reinterpretation frameworks (a recent overview is given in Ref. [93]), might still profit from this implementation by reading the source code or even copying it into other frameworks. A total of 66 analysis regions are defined, with each of the regions representing one recoil bin in one data-taking year. The selections applied for the 2016 and 2017 data sets are identical, and additional criteria are applied for the 2018 data set, where additional mitigation requirements are applied due to a localized failure of the hadronic calorimeter. In order to validate the implementation, generator-level information from the CMS-internal signal samples is fed into the Delphes framework, which performs fast parameterized event simulation, and is available to the general public. The MadAnalysis implementation is then run based on the Delphes output, and the final yields per signal region bin are compared to the signal prediction obtained from the CMS-internal analysis framework, which includes more elaborate detector simulation based on {Geant4} [38]. The comparison is made using signal samples for the ADD interpretation, which are generated using PYTHIA, and are therefore relatively easy to reproduce. The resulting comparison of the final signal templates is (a), (b) and (c) for the individual data-taking years for a representative choice of parameter points. It is found that the Delphes/MadAnalysis-based result agrees with the CMS result to better than 20% in every bin. In most bins, the agreement is significantly better still, with an average agreement of 10% or better. While only a few parameter points are shown here, it has been verified that the agreement is similar for the full range of parameters. The level of agreement observed here is sufficiently good to enable reliable reinterpretation. |
png pdf |
Additional Figure 14-c:
In order to promote this analysis for reinterpretation, we implement the selection for the monojet category of this analysis in the MadAnalysis reinterpretation framework [88]. MadAnalysis is a framework for the reinterpretation of existing analyses in terms of arbitrary new physics models. The framework provides infrastructure for the implementation of event selections that can be run over simulated signal events. Detector simulation can be handled by the independent Delphes software framework [89], or internally to MadAnalysis [90]. Once an implementation is available, it is indexed in a public data base ("PAD'') that allows users to automatically download and execute it [91]. After detector simulation and event selection on signal events, approximate statistical inference is made possible through usage of the simplified likelihood scheme [92], the input information for which is also provided as additional material with this note. The implementation made here will be made available in the PAD shortly for public use. Users who prefer other reinterpretation frameworks (a recent overview is given in Ref. [93]), might still profit from this implementation by reading the source code or even copying it into other frameworks. A total of 66 analysis regions are defined, with each of the regions representing one recoil bin in one data-taking year. The selections applied for the 2016 and 2017 data sets are identical, and additional criteria are applied for the 2018 data set, where additional mitigation requirements are applied due to a localized failure of the hadronic calorimeter. In order to validate the implementation, generator-level information from the CMS-internal signal samples is fed into the Delphes framework, which performs fast parameterized event simulation, and is available to the general public. The MadAnalysis implementation is then run based on the Delphes output, and the final yields per signal region bin are compared to the signal prediction obtained from the CMS-internal analysis framework, which includes more elaborate detector simulation based on {Geant4} [38]. The comparison is made using signal samples for the ADD interpretation, which are generated using PYTHIA, and are therefore relatively easy to reproduce. The resulting comparison of the final signal templates is (a), (b) and (c) for the individual data-taking years for a representative choice of parameter points. It is found that the Delphes/MadAnalysis-based result agrees with the CMS result to better than 20% in every bin. In most bins, the agreement is significantly better still, with an average agreement of 10% or better. While only a few parameter points are shown here, it has been verified that the agreement is similar for the full range of parameters. The level of agreement observed here is sufficiently good to enable reliable reinterpretation. |
png pdf |
Additional Figure 15:
Graphical representation of a representative high-$ {{p_{\mathrm {T}}} ^\text {miss}} $ event from the monojet category in the 2018 data set. In this event, a single high-$ {p_{\mathrm {T}}}$ jet (calorimeter deposits indicated by red and blue towers) recoils against large ${{p_{\mathrm {T}}} ^\text {miss}}$ (indicated by the red arrow). |
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