1$\tau$ MediumMass SR eff.

2$\tau$ Compressed SR eff.

2$\tau$ Compressed SR eff.

2$\tau$ HighMass SR eff.

2$\tau$ HighMass SR eff.

2$\tau$ multibin SR eff.

2$\tau$ multibin SR eff.

2$\tau$ GMSB SR eff.

2$\tau$ GMSB SR eff.

1$\tau$ Compressed SR eff.

1$\tau$ Compressed SR eff.

1$\tau$ MediumMass SR eff.

1$\tau$ MediumMass SR eff.

2$\tau$ Compressed SR eff.

2$\tau$ Compressed SR eff.

2$\tau$ HighMass SR eff.

2$\tau$ HighMass SR eff.

2$\tau$ multibin SR eff.

2$\tau$ multibin SR eff.

2$\tau$ GMSB SR eff.

2$\tau$ GMSB SR eff.

1$\tau$ Compressed SR acceptance.

1$\tau$ Compressed SR acceptance.

1$\tau$ MediumMass SR acceptance.

1$\tau$ MediumMass SR acceptance.

2$\tau$ Compressed SR acceptance.

2$\tau$ Compressed SR acceptance.

2$\tau$ HighMass SR acceptance.

2$\tau$ HighMass SR acceptance.

2$\tau$ multibin SR acceptance.

2$\tau$ multibin SR acceptance.

2$\tau$ GMSB SR acceptance.

2$\tau$ GMSB SR acceptance.

1$\tau$ Compressed SR acceptance.

1$\tau$ Compressed SR acceptance.

1$\tau$ MediumMass SR acceptance.

1$\tau$ MediumMass SR acceptance.

2$\tau$ Compressed SR acceptance.

2$\tau$ Compressed SR acceptance.

2$\tau$ HighMass SR acceptance.

2$\tau$ HighMass SR acceptance.

2$\tau$ multibin SR acceptance.

2$\tau$ multibin SR acceptance.

2$\tau$ GMSB SR acceptance.

2$\tau$ GMSB SR acceptance.

Cutflow table of the $1\tau$ compressed SR for the four signal benchmark scenarios of low, medium, and high mass-splitting in the simplified model as well as the GMSB model.

Cutflow table of the $1\tau$ compressed SR for the four signal benchmark scenarios of low, medium, and high mass-splitting in the simplified model as well as the GMSB model.

Cutflow table of the $1\tau$ medium-mass SR for the four signal benchmark scenarios of low, medium, and high mass-splitting in the simplified model as well as the GMSB model.

Cutflow table of the $1\tau$ medium-mass SR for the four signal benchmark scenarios of low, medium, and high mass-splitting in the simplified model as well as the GMSB model.

Cutflow table of the $2\tau$ compressed SR for the four signal benchmark scenarios of low, medium, and high mass-splitting in the simplified model as well as the GMSB model.

Cutflow table of the $2\tau$ compressed SR for the four signal benchmark scenarios of low, medium, and high mass-splitting in the simplified model as well as the GMSB model.

Cutflow table of the $2\tau$ high-mass SR for the four signal benchmark scenarios of low, medium, and high mass-splitting in the simplified model as well as the GMSB model.

Cutflow table of the $2\tau$ high-mass SR for the four signal benchmark scenarios of low, medium, and high mass-splitting in the simplified model as well as the GMSB model.

Cutflow table of the $2\tau$ multibin SR for the four signal benchmark scenarios of low, medium, and high mass-splitting in the simplified model as well as the GMSB model.

Cutflow table of the $2\tau$ multibin SR for the four signal benchmark scenarios of low, medium, and high mass-splitting in the simplified model as well as the GMSB model.

Cutflow table of the $2\tau$ GMSB SR for the four signal benchmark scenarios of low, medium, and high mass-splitting in the simplified model as well as the GMSB model.

Cutflow table of the $2\tau$ GMSB SR for the four signal benchmark scenarios of low, medium, and high mass-splitting in the simplified model as well as the GMSB model.

Best performing fit setups entering the final combination as a function of the LSP mass and the gluino mass. 'S' marks the simultaneous fit of the four simplified model single-bin SRs, 'M' denotes the simultaneous fit of the two $1\tau$ SRs and the $2\tau$ multibin SR.

Best performing fit setups entering the final combination as a function of the LSP mass and the gluino mass. 'S' marks the simultaneous fit of the four simplified model single-bin SRs, 'M' denotes the simultaneous fit of the two $1\tau$ SRs and the $2\tau$ multibin SR.

Observed exclusion contour at 95% CL as a function of tanBeta and the SUSY-breaking mass scale Lambda.

Observed exclusion contour at 95% CL as a function of tanBeta and the SUSY-breaking mass scale Lambda.

Expected exclusion contour at 95% CL as a function of tanBeta and the SUSY-breaking mass scale Lambda.

Expected exclusion contour at 95% CL as a function of tanBeta and the SUSY-breaking mass scale Lambda.

Observed exclusion contour at 95% CL as a function of the LSP mass and the gluino mass.

Observed exclusion contour at 95% CL as a function of the LSP mass and the gluino mass.

Expected exclusion contour at 95% CL as a function of the LSP mass and the gluino mass.

Expected exclusion contour at 95% CL as a function of the LSP mass and the gluino mass.

Observed upper limits on the production cross section at 95% CL in pb as a function of tanBeta and SUSY breaking mass scale Lambda.

Observed upper limits on the production cross section at 95% CL in pb as a function of tanBeta and SUSY breaking mass scale Lambda.

Observed upper limits on the production cross section at 95% CL in pb as a function of the LSP mass and the gluino mass.

Observed upper limits on the production cross section at 95% CL in pb as a function of the LSP mass and the gluino mass.

Yields of the expected background from the SM in the bins of the multibin SR of the $2\tau$ channel with all bins being simultaneously used to constrain the background prediction. Expectation is given with the scalings computed in the combined fit applied. Uncertainties are statistial plus systematrics. Only the subsamples contributing the respective region are considered.

Yields of the expected background from the SM in the bins of the multibin SR of the $2\tau$ channel with all bins being simultaneously used to constrain the background prediction. Expectation is given with the scalings computed in the combined fit applied. Uncertainties are statistial plus systematrics. Only the subsamples contributing the respective region are considered.

$m_{\mathrm{T}}^{\tau}$ in the compressed $m_{\mathrm{T}}^{\tau}$ VR of the $1\tau$ channel, illustrating the background modeling after the fit. The last bin includes overflow events.

$m_{\mathrm{T}}^{\tau}$ in the compressed $m_{\mathrm{T}}^{\tau}$ VR of the $1\tau$ channel, illustrating the background modeling after the fit. The last bin includes overflow events.

$E_{\mathrm{T}}^{\mathrm{miss}}$ in the compressed $E_{\mathrm{T}}^{\mathrm{miss}}$ VR of the $1\tau$ channel, illustrating the background modeling after the fit. The last bin includes overflow events.

$E_{\mathrm{T}}^{\mathrm{miss}}$ in the compressed $E_{\mathrm{T}}^{\mathrm{miss}}$ VR of the $1\tau$ channel, illustrating the background modeling after the fit. The last bin includes overflow events.

$m_{\mathrm{T}}^{\tau}$ in the medium-mass $m_{\mathrm{T}}^{\tau}$ VR of the $1\tau$ channel, illustrating the background modeling after the fit. The last bin includes overflow events.

$m_{\mathrm{T}}^{\tau}$ in the medium-mass $m_{\mathrm{T}}^{\tau}$ VR of the $1\tau$ channel, illustrating the background modeling after the fit. The last bin includes overflow events.

$E_{\mathrm{T}}^{\mathrm{miss}}$ in the medium-mass $E_{\mathrm{T}}^{\mathrm{miss}}$ VR of the $1\tau$ channel, illustrating the background modeling after the fit. The last bin includes overflow events.

$E_{\mathrm{T}}^{\mathrm{miss}}$ in the medium-mass $E_{\mathrm{T}}^{\mathrm{miss}}$ VR of the $1\tau$ channel, illustrating the background modeling after the fit. The last bin includes overflow events.

$H_{\mathrm{T}}$ in the medium-mass $H_{\mathrm{T}}$ VR of the $1\tau$ channel, illustrating the background modeling after the fit. The last bin includes overflow events.

$H_{\mathrm{T}}$ in the medium-mass $H_{\mathrm{T}}$ VR of the $1\tau$ channel, illustrating the background modeling after the fit. The last bin includes overflow events.

$m_{\mathrm{T}}^{\tau_1}$ + $m_{\mathrm{T}}^{\tau_2}$ in the top VR of the $2\tau$ channel, illustrating the background modeling after the fit. The last bin includes overflow events.

$m_{\mathrm{T}}^{\tau_1}$ + $m_{\mathrm{T}}^{\tau_2}$ in the top VR of the $2\tau$ channel, illustrating the background modeling after the fit. The last bin includes overflow events.

$H_{\mathrm{T}}$ in the $W$ VR of the $2\tau$ channel, illustrating the background modeling after the fit. The last bin includes overflow events.

$H_{\mathrm{T}}$ in the $W$ VR of the $2\tau$ channel, illustrating the background modeling after the fit. The last bin includes overflow events.

$m_{\mathrm{T}}^{\tau_1}$ + $m_{\mathrm{T}}^{\tau_2}$ in the $Z$ VR of the $2\tau$ channel, illustrating the background modeling after the fit. The last bin includes overflow events.

$m_{\mathrm{T}}^{\tau_1}$ + $m_{\mathrm{T}}^{\tau_2}$ in the $Z$ VR of the $2\tau$ channel, illustrating the background modeling after the fit. The last bin includes overflow events.

$m_{\mathrm{T}}^{\tau}$ in the compressed SR of the $1\tau$ channel before application of the $m_{\mathrm{T}}^{\tau}$ > 80 GeV requirement. The last bin includes overflow events. Signal predictions corresponding to the simplified model scenarios of low (LM), medium (MM), and high mass-splitting (HM) as well as for the GMSB benchmark are given.

$m_{\mathrm{T}}^{\tau}$ in the compressed SR of the $1\tau$ channel before application of the $m_{\mathrm{T}}^{\tau}$ > 80 GeV requirement. The last bin includes overflow events. Signal predictions corresponding to the simplified model scenarios of low (LM), medium (MM), and high mass-splitting (HM) as well as for the GMSB benchmark are given.

$H_{\mathrm{T}}$ in the medium-mass SR of the $1\tau$ channel before application of the $H_{\mathrm{T}}$ > 1000 GeV requirement. The last bin includes overflow events. Signal predictions corresponding to the simplified model scenarios of low (LM), medium (MM), and high mass-splitting (HM) as well as for the GMSB benchmark are given.

$H_{\mathrm{T}}$ in the medium-mass SR of the $1\tau$ channel before application of the $H_{\mathrm{T}}$ > 1000 GeV requirement. The last bin includes overflow events. Signal predictions corresponding to the simplified model scenarios of low (LM), medium (MM), and high mass-splitting (HM) as well as for the GMSB benchmark are given.

$m_{\mathrm{T}}^{\mathrm{sum}}$ in the compressed SR of the $2\tau$ channel before application of the $m_{\mathrm{T}}^{\mathrm{sum}}$ > 1600 GeV requirement. The last bin includes overflow events. Signal predictions corresponding to the simplified model scenarios of low (LM), medium (MM), and high mass-splitting (HM) as well as for the GMSB benchmark are given.

$m_{\mathrm{T}}^{\mathrm{sum}}$ in the compressed SR of the $2\tau$ channel before application of the $m_{\mathrm{T}}^{\mathrm{sum}}$ > 1600 GeV requirement. The last bin includes overflow events. Signal predictions corresponding to the simplified model scenarios of low (LM), medium (MM), and high mass-splitting (HM) as well as for the GMSB benchmark are given.

$H_{\mathrm{T}}$ in the high-mass SR of the $2\tau$ channel before application of the $H_{\mathrm{T}}$ > 1100 GeV requirement. The last bin includes overflow events. Signal predictions corresponding to the simplified model scenarios of low (LM), medium (MM), and high mass-splitting (HM) as well as for the GMSB benchmark are given.

$H_{\mathrm{T}}$ in the high-mass SR of the $2\tau$ channel before application of the $H_{\mathrm{T}}$ > 1100 GeV requirement. The last bin includes overflow events. Signal predictions corresponding to the simplified model scenarios of low (LM), medium (MM), and high mass-splitting (HM) as well as for the GMSB benchmark are given.

mT(tau_1) + mT(tau_2) in the multibin SR of the 2T channel. The last bin includes overflow events. Signal predictions corresponding to the simplified model scenarios of low (LM), medium (MM), and high mass-splitting (HM) as well as for the GMSB benchmark are given.

mT(tau_1) + mT(tau_2) in the multibin SR of the 2T channel. The last bin includes overflow events. Signal predictions corresponding to the simplified model scenarios of low (LM), medium (MM), and high mass-splitting (HM) as well as for the GMSB benchmark are given.

$H_{\mathrm{T}}$ in the GMSB SR of the $2\tau$ channel before application of the $H_{\mathrm{T}}$ > 1900 GeV requirement. The last bin includes overflow events. Signal predictions corresponding to the simplified model scenarios of low (LM), medium (MM), and high mass-splitting (HM) as well as for the GMSB benchmark are given.

$H_{\mathrm{T}}$ in the GMSB SR of the $2\tau$ channel before application of the $H_{\mathrm{T}}$ > 1900 GeV requirement. The last bin includes overflow events. Signal predictions corresponding to the simplified model scenarios of low (LM), medium (MM), and high mass-splitting (HM) as well as for the GMSB benchmark are given.

The cross section times BR(A->ZH) times BR(H->bb) limits for an A boson in the type-I, tanbeta 1, 2HDM. For each signal point, characterised by the mass pair (mA, mH), two limits are provided, the observed and the expected. The correct width as predicted by this particular parameter choice of the 2HDM is used. For type-I only gluon-gluon fusion is used and the nb=2 category only.

The cross section times BR(A->ZH) times BR(H->bb) limits for an A boson in the type-I, tanbeta 5, 2HDM. For each signal point, characterised by the mass pair (mA, mH), two limits are provided, the observed and the expected. The correct width as predicted by this particular parameter choice of the 2HDM is used. For type-I only gluon-gluon fusion is used and the nb=2 category only.

The cross section times BR(A->ZH) times BR(H->bb) limits for an A boson in the type-I, tanbeta 10, 2HDM. For each signal point, characterised by the mass pair (mA, mH), two limits are provided, the observed and the expected. The correct width as predicted by this particular parameter choice of the 2HDM is used. For type-I only gluon-gluon fusion is used and the nb=2 category only.

The cross section times BR(A->ZH) times BR(H->bb) limits for an A boson in the type-I, tanbeta 20, 2HDM. For each signal point, characterised by the mass pair (mA, mH), two limits are provided, the observed and the expected. The correct width as predicted by this particular parameter choice of the 2HDM is used. For type-I only gluon-gluon fusion is used and the nb=2 category only.

The cross section times BR(A->ZH) times BR(H->bb) limits for an A boson in the type-II, tanbeta 1, 2HDM. For each signal point, characterised by the mass pair (mA, mH), two limits are provided, the observed and the expected. The correct width as predicted by this particular parameter choice of the 2HDM is used. For type-II both gluon-gluon fusion and b-associated production are used and the nb=2 and nb>=3 categories are combined.

The cross section times BR(A->ZH) times BR(H->bb) limits for an A boson in the type-II, tanbeta 5, 2HDM. For each signal point, characterised by the mass pair (mA, mH), two limits are provided, the observed and the expected. The correct width as predicted by this particular parameter choice of the 2HDM is used. For type-II both gluon-gluon fusion and b-associated production are used and the nb=2 and nb>=3 categories are combined.

The cross section times BR(A->ZH) times BR(H->bb) limits for an A boson in the type-II, tanbeta 10, 2HDM. For each signal point, characterised by the mass pair (mA, mH), two limits are provided, the observed and the expected. The correct width as predicted by this particular parameter choice of the 2HDM is used. For type-II both gluon-gluon fusion and b-associated production are used and the nb=2 and nb>=3 categories are combined.

The cross section times BR(A->ZH) times BR(H->bb) limits for an A boson in the type-II, tanbeta 20, 2HDM. For each signal point, characterised by the mass pair (mA, mH), two limits are provided, the observed and the expected. The correct width as predicted by this particular parameter choice of the 2HDM is used. For type-II both gluon-gluon fusion and b-associated production are used and the nb=2 and nb>=3 categories are combined.

The cross section times BR(A->ZH) times BR(H->bb) limits for an A boson in the lepton specific, tanbeta 1, 2HDM. For each signal point, characterised by the mass pair (mA, mH), two limits are provided, the observed and the expected. The correct width as predicted by this particular parameter choice of the 2HDM is used. For lepton specific only gluon-gluon fusion is used and the nb=2 category only.

The cross section times BR(A->ZH) times BR(H->bb) limits for an A boson in the lepton specific, tanbeta 2, 2HDM. For each signal point, characterised by the mass pair (mA, mH), two limits are provided, the observed and the expected. The correct width as predicted by this particular parameter choice of the 2HDM is used. For lepton specific only gluon-gluon fusion is used and the nb=2 category only.

The cross section times BR(A->ZH) times BR(H->bb) limits for an A boson in the lepton specific, tanbeta 3, 2HDM. For each signal point, characterised by the mass pair (mA, mH), two limits are provided, the observed and the expected. The correct width as predicted by this particular parameter choice of the 2HDM is used. For lepton specific only gluon-gluon fusion is used and the nb=2 category only.

The cross section times BR(A->ZH) times BR(H->bb) limits for an A boson in the flipped, tanbeta 1, 2HDM. For each signal point, characterised by the mass pair (mA, mH), two limits are provided, the observed and the expected. The correct width as predicted by this particular parameter choice of the 2HDM is used. For flipped both gluon-gluon fusion and b-associated production are used and the nb=2 and nb>=3 categories are combined.

The cross section times BR(A->ZH) times BR(H->bb) limits for an A boson in the flipped, tanbeta 5, 2HDM. For each signal point, characterised by the mass pair (mA, mH), two limits are provided, the observed and the expected. The correct width as predicted by this particular parameter choice of the 2HDM is used. For flipped both gluon-gluon fusion and b-associated production are used and the nb=2 and nb>=3 categories are combined.

The cross section times BR(A->ZH) times BR(H->bb) limits for an A boson in the flipped, tanbeta 10, 2HDM. For each signal point, characterised by the mass pair (mA, mH), two limits are provided, the observed and the expected. The correct width as predicted by this particular parameter choice of the 2HDM is used. For flipped both gluon-gluon fusion and b-associated production are used and the nb=2 and nb>=3 categories are combined.

The cross section times BR(A->ZH) times BR(H->bb) limits for an A boson in the flipped, tanbeta 20, 2HDM. For each signal point, characterised by the mass pair (mA, mH), two limits are provided, the observed and the expected. The correct width as predicted by this particular parameter choice of the 2HDM is used. For flipped both gluon-gluon fusion and b-associated production are used and the nb=2 and nb>=3 categories are combined.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 130 GeV for the nb = 2 (2 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 130 GeV for the nb >= 3 (3 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 140 GeV for the nb = 2 (2 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 140 GeV for the nb >= 3 (3 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 150 GeV for the nb = 2 (2 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 150 GeV for the nb >= 3 (3 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 160 GeV for the nb = 2 (2 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 160 GeV for the nb >= 3 (3 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 170 GeV for the nb = 2 (2 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 170 GeV for the nb >= 3 (3 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 180 GeV for the nb = 2 (2 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 180 GeV for the nb >= 3 (3 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 190 GeV for the nb = 2 (2 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 190 GeV for the nb >= 3 (3 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 210 GeV for the nb = 2 (2 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 210 GeV for the nb >= 3 (3 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 220 GeV for the nb = 2 (2 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 220 GeV for the nb >= 3 (3 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 230 GeV for the nb = 2 (2 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 230 GeV for the nb >= 3 (3 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 240 GeV for the nb = 2 (2 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 240 GeV for the nb >= 3 (3 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 250 GeV for the nb = 2 (2 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 250 GeV for the nb >= 3 (3 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 260 GeV for the nb = 2 (2 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 260 GeV for the nb >= 3 (3 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 270 GeV for the nb = 2 (2 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 270 GeV for the nb >= 3 (3 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 280 GeV for the nb = 2 (2 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 280 GeV for the nb >= 3 (3 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 290 GeV for the nb = 2 (2 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 290 GeV for the nb >= 3 (3 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 310 GeV for the nb = 2 (2 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 310 GeV for the nb >= 3 (3 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 320 GeV for the nb = 2 (2 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 320 GeV for the nb >= 3 (3 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 330 GeV for the nb = 2 (2 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 330 GeV for the nb >= 3 (3 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 340 GeV for the nb = 2 (2 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 340 GeV for the nb >= 3 (3 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 350 GeV for the nb = 2 (2 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 350 GeV for the nb >= 3 (3 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 360 GeV for the nb = 2 (2 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 360 GeV for the nb >= 3 (3 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 370 GeV for the nb = 2 (2 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 370 GeV for the nb >= 3 (3 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 380 GeV for the nb = 2 (2 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 380 GeV for the nb >= 3 (3 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 390 GeV for the nb = 2 (2 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 390 GeV for the nb >= 3 (3 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 400 GeV for the nb = 2 (2 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 400 GeV for the nb >= 3 (3 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 410 GeV for the nb = 2 (2 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 410 GeV for the nb >= 3 (3 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 420 GeV for the nb = 2 (2 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 420 GeV for the nb >= 3 (3 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 430 GeV for the nb = 2 (2 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 430 GeV for the nb >= 3 (3 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 440 GeV for the nb = 2 (2 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 440 GeV for the nb >= 3 (3 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 450 GeV for the nb = 2 (2 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 450 GeV for the nb >= 3 (3 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 460 GeV for the nb = 2 (2 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 460 GeV for the nb >= 3 (3 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 470 GeV for the nb = 2 (2 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 470 GeV for the nb >= 3 (3 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 480 GeV for the nb = 2 (2 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 480 GeV for the nb >= 3 (3 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 490 GeV for the nb = 2 (2 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 490 GeV for the nb >= 3 (3 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 510 GeV for the nb = 2 (2 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 510 GeV for the nb >= 3 (3 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 520 GeV for the nb = 2 (2 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 520 GeV for the nb >= 3 (3 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 530 GeV for the nb = 2 (2 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 530 GeV for the nb >= 3 (3 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 540 GeV for the nb = 2 (2 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 540 GeV for the nb >= 3 (3 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 550 GeV for the nb = 2 (2 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 550 GeV for the nb >= 3 (3 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 560 GeV for the nb = 2 (2 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 560 GeV for the nb >= 3 (3 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 570 GeV for the nb = 2 (2 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 570 GeV for the nb >= 3 (3 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 580 GeV for the nb = 2 (2 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 580 GeV for the nb >= 3 (3 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 590 GeV for the nb = 2 (2 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 590 GeV for the nb >= 3 (3 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 600 GeV for the nb = 2 (2 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 600 GeV for the nb >= 3 (3 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 610 GeV for the nb = 2 (2 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 610 GeV for the nb >= 3 (3 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 620 GeV for the nb = 2 (2 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 620 GeV for the nb >= 3 (3 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 630 GeV for the nb = 2 (2 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 630 GeV for the nb >= 3 (3 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 640 GeV for the nb = 2 (2 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 640 GeV for the nb >= 3 (3 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 650 GeV for the nb = 2 (2 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 650 GeV for the nb >= 3 (3 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 660 GeV for the nb = 2 (2 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 660 GeV for the nb >= 3 (3 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 670 GeV for the nb = 2 (2 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 670 GeV for the nb >= 3 (3 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 680 GeV for the nb = 2 (2 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 680 GeV for the nb >= 3 (3 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 690 GeV for the nb = 2 (2 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 690 GeV for the nb >= 3 (3 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 700 GeV for the nb = 2 (2 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 700 GeV for the nb >= 3 (3 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 200 GeV for the nb = 2 (2 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 200 GeV for the nb >= 3 (3 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 300 GeV for the nb = 2 (2 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 300 GeV for the nb >= 3 (3 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 500 GeV for the nb = 2 (2 tag) category.

The mass distribution of the llbb system used in the fit to derive the limits for the mbb window centered at 500 GeV for the nb >= 3 (3 tag) category.

Dijet mass spectra after the background only fit with the background prediction in the high-mass region with two b-tags.

Dijet mass spectra after the background only fit with the background prediction in the low-mass region with two b-tags.

The online b-tagging efficiency with respect to the offline b-tagging efficiency as a function of pT. The b-tagging online and offline working points correspond to an efficiency of 60% and 70%, respectively.

Observed and expected 95% credibility-level upper limits on the cross-section for the b* model in the high-mass region with inclusive b-jet selection.

Observed and expected 95% credibility-level upper limits on the cross-section times branching ratio for the SSM and leptophobic Z' models in the low- and high-mass region with two b-tags selection.

Observed and expected 95% credibility-level upper limits on the cross-section for DM Z' models in the low-mass region with two b-tags selection. The Z' is expected to decay to all five quark flavors other than the top quark and the mediator to SM quark coupling (gSM) equal to 0.1 is assumed.

Observed and expected 95% credibility-level upper limits on the cross-section times branching ratio for DM Z'->bb models in the high-mass region with two b-tags selection. The Z' is expected to decay to bb only and the mediator to SM quark coupling (gSM) equal to 0.25 is assumed.

Observed and expected 95% credibility-level upper limits on cross section times acceptance times branching ratio of X --> bb, including kinematic acceptance and b-tagging efficiencies, for resonances with intrinsic width smaller than the detector resolution. The width of the Gaussian reconstructed shape is dominated by the dijet mass resolution. The table shows the limits obtained from the high-mass inclusive one b-tag selection.

Observed and expected 95% credibility-level upper limits on cross section times acceptance times branching ratio of X --> bb, including kinematic acceptance and b-tagging efficiencies, for resonances with intrinsic width smaller than the detector resolution. The width of the Gaussian reconstructed shape is dominated by the dijet mass resolution. The table shows the limits obtained from the combined low- and high-mass two b-tags selection.

The mass distributions for the inclusive one b-tagged selection and two b-tagged selection using an integrated luminosity of 36.1 $fb^{-1}$. The inclusive one b-tagged Pythia8 MC distribution is normalized to the inclusive one b-tagged data. The two b-tagged Pythia8 MC distribution is normalized to the two b-tagged data. The systematic uncertainty band is dominated by the b-tagging scale factor and the b-jet energy scale.

Signal acceptance times efficiency for inclusive 1 b-tag and 2 b-tag categories as a function of the simulated b* and the Z' masses.

Signal acceptance times efficiency for 2 b-tag categories as a function of the simulated Z' masses.

The flavor composition of the simulated dijet background as a function of dijet mass before tagging. The fraction of the six combinations of the b-jet , c-jet and light-flavor jet are shown. All offline selections are applied.

The flavor composition of the simulated dijet background as a function of dijet mass with inclusive one b-tag. The fraction of the six combinations of the b-jet , c-jet and light-flavor jet are shown. All offline selections are applied.

The flavor composition of the simulated dijet background as a function of dijet mass with two b-tags. The fraction of the six combinations of the b-jet , c-jet and light-flavor jet are shown. All offline selections are applied.

Observed and expected 95% credibility-level upper limits on cross section times acceptance times branching ratio of X --> bb, including kinematic acceptance and b-tagging efficiencies, for resonances exhibiting a generic Gaussian shape at particle level. The table shows the limits obtained from the inclusive b-jet selection. The limits corresponding to Gaussian-shaped resonances with width of Γ(X)/m(X) = 3%.

Observed and expected 95% credibility-level upper limits on cross section times acceptance times branching ratio of X --> bb, including kinematic acceptance and b-tagging efficiencies, for resonances exhibiting a generic Gaussian shape at particle level. The table shows the limits obtained from the inclusive b-jet selection. The limits corresponding to Gaussian-shaped resonances with width of Γ(X)/m(X) = 7%.

Observed and expected 95% credibility-level upper limits on cross section times acceptance times branching ratio of X --> bb, including kinematic acceptance and b-tagging efficiencies, for resonances exhibiting a generic Gaussian shape at particle level. The table shows the limits obtained from the inclusive b-jet selection. The limits corresponding to Gaussian-shaped resonances with width of Γ(X)/m(X) = 10%.

Observed and expected 95% credibility-level upper limits on cross section times acceptance times branching ratio of X --> bb, including kinematic acceptance and b-tagging efficiencies, for resonances exhibiting a generic Gaussian shape at particle level. The table shows the limits obtained from the inclusive b-jet selection. The limits corresponding to Gaussian-shaped resonances with width of Γ(X)/m(X) = 15%.

Observed and expected 95% credibility-level upper limits on cross section times acceptance times branching ratio of X --> bb, including kinematic acceptance and b-tagging efficiencies, for resonances exhibiting a generic Gaussian shape at particle level. The table shows the limits obtained from the combined low- and high-mass two b-tags selection. The limits corresponding to Gaussian-shaped resonances with width of Γ(X)/m(X) = 3%.

Observed and expected 95% credibility-level upper limits on cross section times acceptance times branching ratio of X --> bb, including kinematic acceptance and b-tagging efficiencies, for resonances exhibiting a generic Gaussian shape at particle level. The table shows the limits obtained from the combined low- and high-mass two b-tags selection. The limits corresponding to Gaussian-shaped resonances with width of Γ(X)/m(X) = 7%.

Observed and expected 95% credibility-level upper limits on cross section times acceptance times branching ratio of X --> bb, including kinematic acceptance and b-tagging efficiencies, for resonances exhibiting a generic Gaussian shape at particle level. The table shows the limits obtained from the combined low- and high-mass two b-tags selection. The limits corresponding to Gaussian-shaped resonances with width of Γ(X)/m(X) = 10%.

Observed and expected 95% credibility-level upper limits on cross section times acceptance times branching ratio of X --> bb, including kinematic acceptance and b-tagging efficiencies, for resonances exhibiting a generic Gaussian shape at particle level. The table shows the limits obtained from the combined low- and high-mass two b-tags selection. The limits corresponding to Gaussian-shaped resonances with width of Γ(X)/m(X) = 15%.

Lower mass requirement for signal regions.

Lower mass requirement for signal regions.

Lower mass requirement for signal regions.

Expected and observed events in the 16 discovery regions along with the according control regions.

Expected and observed events in the 16 discovery regions along with the according control regions.

Expected signal yield and acceptance x efficiency, estimated background and observed number of events in data for the full range of simulated masses in the MS-agnostic R-hadron search.

Expected signal yield and acceptance x efficiency, estimated background and observed number of events in data for the full range of simulated masses in the MS-agnostic R-hadron search.

Expected signal yield and acceptance x efficiency, estimated background and observed number of events in data for the full range of simulated masses in the full-detector R-hadron search.

Expected signal yield and acceptance x efficiency, estimated background and observed number of events in data for the full range of simulated masses in the full-detector R-hadron search.

p0-values and model-independent upper limits on cross-section x acceptance x efficiency for the 16 discovery regions.

p0-values and model-independent upper limits on cross-section x acceptance x efficiency for the 16 discovery regions.

Expected signal yield and acceptance x efficiency, estimated background and observed number of events in data for the full range of simulated masses in the MS-agnostic search for metastable gluino R-hadrons.

Expected signal yield and acceptance x efficiency, estimated background and observed number of events in data for the full range of simulated masses in the MS-agnostic search for metastable gluino R-hadrons.

Expected signal yield and acceptance x efficiency, estimated background and observed number of events in data for the full range of simulated masses in the full-detector direct-stau search.

Expected signal yield and acceptance x efficiency, estimated background and observed number of events in data for the full range of simulated masses in the full-detector direct-stau search.

Expected signal yield and acceptance x efficiency, estimated background and observed number of events in data for the full range of simulated masses in the full-detector chargino search.

Expected signal yield and acceptance x efficiency, estimated background and observed number of events in data for the full range of simulated masses in the full-detector chargino search.

Upper cross-section limit in gluino R-hadron search.

Upper cross-section limit in gluino R-hadron search.

Upper cross-section limit in sbottom R-hadron search.

Upper cross-section limit in sbottom R-hadron search.

Upper cross-section limit in stop R-hadron search.

Upper cross-section limit in stop R-hadron search.

Upper cross-section limit in stau search.

Upper cross-section limit in stau search.

Upper cross-section limit in chargino search.

Upper cross-section limit in chargino search.

Lower mass limit as function of gluino lifetime.

Lower mass limit as function of gluino lifetime.

Acceptance x efficiency, acceptance and efficiency for the full range of simulated masses in the MS-agnostic R-hadron search.

Acceptance x efficiency, acceptance and efficiency for the full range of simulated masses in the MS-agnostic R-hadron search.

Upper cross-section limit in meta-stable gluino R-hadron search.

Upper cross-section limit in meta-stable gluino R-hadron search.

Flavor composition of 800 GeV stop R-hadrons simulated using the generic model as a function of radial distance from the interaction point.

Flavor composition of 800 GeV stop R-hadrons simulated using the generic model as a function of radial distance from the interaction point.

Flavor composition of 800 GeV anti-stop R-hadrons simulated using the generic model as a function of radial distance from the interaction point.

Flavor composition of 800 GeV anti-stop R-hadrons simulated using the generic model as a function of radial distance from the interaction point.

Flavor composition of 800 GeV stop R-hadrons simulated using the Regge model as a function of radial distance from the interaction point.

Flavor composition of 800 GeV stop R-hadrons simulated using the Regge model as a function of radial distance from the interaction point.

Flavor composition of 800 GeV anti-stop R-hadrons simulated using the Regge model as a function of radial distance from the interaction point.

Flavor composition of 800 GeV anti-stop R-hadrons simulated using the Regge model as a function of radial distance from the interaction point.

ETmiss trigger efficiency as function of true ETmiss (EtmissTurnOn).

ETmiss trigger efficiency as function of true ETmiss (EtmissTurnOn).

Single-muon trigger efficiency as function of $|\eta|$ and $\beta$ (SingleMuTurnOn).

Single-muon trigger efficiency as function of $|\eta|$ and $\beta$ (SingleMuTurnOn).

Candidate reconstruction efficiency for ID+Calo selection (IDCaloEff).

Candidate reconstruction efficiency for ID+Calo selection (IDCaloEff).

Candidate reconstruction efficiency for loose selection (LooseEff).

Candidate reconstruction efficiency for loose selection (LooseEff).

Efficiency for a loose candidate to be promoted to a tight candidate (TightPromotionEff).

Efficiency for a loose candidate to be promoted to a tight candidate (TightPromotionEff).

Resolution and average of reconstructed dE/dx mass for a given simulated mass for ID+calo candidates.

Resolution and average of reconstructed dE/dx mass for a given simulated mass for ID+calo candidates.

Resolution and average of reconstructed ToF mass for a given simulated mass for ID+calo candidates.

Resolution and average of reconstructed ToF mass for a given simulated mass for ID+calo candidates.

Resolution and average of reconstructed ToF mass for a given simulated mass for FullDet candidates.

Resolution and average of reconstructed ToF mass for a given simulated mass for FullDet candidates.

Vertex selection efficiency for the $\tilde{t}$ $R$-hadron benchmark model as a function of the transverse decay distance $r_{DV}$.

Track multiplicity $n_{Tracks}$ for preselected DVs in MET-triggered events with at least one muon passing the full selection. Along with the data shown with black markers, the stacked filled histograms represent the background estimates, and predictions for signal scenarios are overlaid with dashed lines. The errors include statistical and systematic uncertainties and are indicated by hatched bands. The DV full selection requirements, $n_{Tracks} \geq 3$ and $m_{DV} > 20$ GeV are visualized with a black arrow.

Track multiplicity $n_{Tracks}$ for preselected DVs in MET-triggered events with at least one muon passing the full selection. Along with the data shown with black markers, the stacked filled histograms represent the background estimates, and predictions for signal scenarios are overlaid with dashed lines. The errors include statistical and systematic uncertainties and are indicated by hatched bands. The DV full selection requirements, $n_{Tracks} \geq 3$ and $m_{DV} > 20$ GeV are visualized with a black arrow.

Track multiplicity $n_{Tracks}$ for preselected DVs in muon-triggered events with at least one muon passing the full selection. Along with the data shown with black markers, the stacked filled histograms represent the background estimates, and predictions for signal scenarios are overlaid with dashed lines. The errors include statistical and systematic uncertainties and are indicated by hatched bands. The DV full selection requirements, $n_{Tracks} \geq 3$ and $m_{DV} > 20$ GeV are visualized with a black arrow.

Track multiplicity $n_{Tracks}$ for preselected DVs in muon-triggered events with at least one muon passing the full selection. Along with the data shown with black markers, the stacked filled histograms represent the background estimates, and predictions for signal scenarios are overlaid with dashed lines. The errors include statistical and systematic uncertainties and are indicated by hatched bands. The DV full selection requirements, $n_{Tracks} \geq 3$ and $m_{DV} > 20$ GeV are visualized with a black arrow.

Invariant mass $m_{DV}$ for the highest-mass preselected DV with at least three associated tracks in MET-triggered events with at least one muon passing the full selection. Along with the data shown with black markers, the stacked filled histograms represent the background estimates, and predictions for signal scenarios are overlaid with dashed lines. The errors include statistical and systematic uncertainties and are indicated by hatched bands. The DV full selection requirements, $n_{Tracks} \geq 3$ and $m_{DV} > 20$ GeV are visualized with a black arrow.

Invariant mass $m_{DV}$ for the highest-mass preselected DV with at least three associated tracks in MET-triggered events with at least one muon passing the full selection. Along with the data shown with black markers, the stacked filled histograms represent the background estimates, and predictions for signal scenarios are overlaid with dashed lines. The errors include statistical and systematic uncertainties and are indicated by hatched bands. The DV full selection requirements, $n_{Tracks} \geq 3$ and $m_{DV} > 20$ GeV are visualized with a black arrow.

Invariant mass $m_{DV}$ for the highest-mass preselected DV with at least three associated tracks in muon-triggered events with at least one muon passing the full selection. Along with the data shown with black markers, the stacked filled histograms represent the background estimates, and predictions for signal scenarios are overlaid with dashed lines. The errors include statistical and systematic uncertainties and are indicated by hatched bands. The DV full selection requirements, $n_{Tracks} \geq 3$ and $m_{DV} > 20$ GeV are visualized with a black arrow.

Invariant mass $m_{DV}$ for the highest-mass preselected DV with at least three associated tracks in muon-triggered events with at least one muon passing the full selection. Along with the data shown with black markers, the stacked filled histograms represent the background estimates, and predictions for signal scenarios are overlaid with dashed lines. The errors include statistical and systematic uncertainties and are indicated by hatched bands. The DV full selection requirements, $n_{Tracks} \geq 3$ and $m_{DV} > 20$ GeV are visualized with a black arrow.

The observed event yields in the control, validation and signal regions are shown for the MET Trigger selections, along with the predicted background yields. The bottom panel shows the ratio of observed events to the total background yields. The errors represent the total uncertainty of the backgrounds prediction, including the statistical and systematic uncertainties added in quadrature.

The observed event yields in the control, validation and signal regions are shown for the MET Trigger selections, along with the predicted background yields. The bottom panel shows the ratio of observed events to the total background yields. The errors represent the total uncertainty of the backgrounds prediction, including the statistical and systematic uncertainties added in quadrature.

The observed event yields in the control, validation and signal regions are shown for the Muon Trigger selections, along with the predicted background yields. The bottom panel shows the ratio of observed events to the total background yields. The errors represent the total uncertainty of the backgrounds prediction, including the statistical and systematic uncertainties added in quadrature.

The observed event yields in the control, validation and signal regions are shown for the Muon Trigger selections, along with the predicted background yields. The bottom panel shows the ratio of observed events to the total background yields. The errors represent the total uncertainty of the backgrounds prediction, including the statistical and systematic uncertainties added in quadrature.

Expected exclusion limits at 95% CL on m($\tilde{t}$) as a function of $\tau(\tilde{t})$.

Expected exclusion limits at 95% CL on m($\tilde{t}$) as a function of $\tau(\tilde{t})$.

Expected (1 sigma band) exclusion limits at 95% CL on m($\tilde{t}$) as a function of $\tau(\tilde{t})$.

Expected (1 sigma band) exclusion limits at 95% CL on m($\tilde{t}$) as a function of $\tau(\tilde{t})$.

Expected (2 sigma band) exclusion limits at 95% CL on m($\tilde{t}$) as a function of $\tau(\tilde{t})$.

Expected (2 sigma band) exclusion limits at 95% CL on m($\tilde{t}$) as a function of $\tau(\tilde{t})$.

Observed exclusion limits at 95% CL on m($\tilde{t}$) as a function of $\tau(\tilde{t})$.

Observed exclusion limits at 95% CL on m($\tilde{t}$) as a function of $\tau(\tilde{t})$.

Observed (+1 sigma) exclusion limits at 95% CL on m($\tilde{t}$) as a function of $\tau(\tilde{t})$.

Observed (+1 sigma) exclusion limits at 95% CL on m($\tilde{t}$) as a function of $\tau(\tilde{t})$.

Observed (-1 sigma) exclusion limits at 95% CL on m($\tilde{t}$) as a function of $\tau(\tilde{t})$.

Observed (-1 sigma) exclusion limits at 95% CL on m($\tilde{t}$) as a function of $\tau(\tilde{t})$.

Exclusion limits on the production cross section as a function of m($\tilde{t}$) are shown for several values of $\tau(\tilde{t})$ along with the nominal signal production cross section and its theoretical uncertainty.

Exclusion limits on the production cross section as a function of m($\tilde{t}$) are shown for several values of $\tau(\tilde{t})$ along with the nominal signal production cross section and its theoretical uncertainty.

Parameterized event selection efficiencies for the $E_{T}^{miss}$ Trigger SR. The event-level efficiencies for each SR are extracted for all events passing the acceptance of the corresponding SR.

Parameterized event selection efficiencies for the $E_{T}^{miss}$ Trigger SR. The event-level efficiencies for each SR are extracted for all events passing the acceptance of the corresponding SR.

Parameterized event selection efficiencies for the Muon Trigger SR. The event-level efficiencies for each SR are extracted for all events passing the acceptance of the corresponding SR.

Parameterized event selection efficiencies for the Muon Trigger SR. The event-level efficiencies for each SR are extracted for all events passing the acceptance of the corresponding SR.

Parameterized muon-level reconstruction efficiencies as a function of the muon $p_{T}$ and $d_{0}$. The muon-level efficiencies are extracted using muons passing the muon acceptance criteria.

Parameterized muon-level reconstruction efficiencies as a function of the muon $p_{T}$ and $d_{0}$. The muon-level efficiencies are extracted using muons passing the muon acceptance criteria.

Parameterized vertex-level reconstruction efficiencies as a function of the radial position of the truth vertex. The efficiency is calculated independent of the muons originating from this truth vertex.

Parameterized vertex-level reconstruction efficiencies as a function of the radial position of the truth vertex. The efficiency is calculated independent of the muons originating from this truth vertex.

Parameterized vertex-level reconstruction efficiencies as a function of the radial position of the truth vertex. The efficiency is calculated only for truth vertices which have a muon originating from them which is matched to a reconstructed muon.

Parameterized vertex-level reconstruction efficiencies as a function of the radial position of the truth vertex. The efficiency is calculated only for truth vertices which have a muon originating from them which is matched to a reconstructed muon.

The $p_{T}$ distribution of all muons originating from LLP decays in the samples used to calculate and validate the efficiencies.

The $p_{T}$ distribution of all muons originating from LLP decays in the samples used to calculate and validate the efficiencies.

The invariant mass and multiplicity of selected decay products of all truth vertices used in the calculation and validation of the reconstructed efficiencies.

The invariant mass and multiplicity of selected decay products of all truth vertices used in the calculation and validation of the reconstructed efficiencies.

Upper limits at the 95% CL on the cross section for SSM $W^\prime$ production and decay to the muon+neutrino channel as a function of the $W^\prime$ pole mass.

Combined (electron and muon channels) upper limits at the 95% CL on the cross section for SSM $W^\prime$ production and decay to a single lepton generation as a function of the $W^\prime$ pole mass.

Observed upper limits at the 95% CL on the cross section for generic $W^\prime$ production and decay to the electron+neutrino channel as a function of the $W^\prime$ pole mass.

Observed upper limits at the 95% CL on the cross section for generic $W^\prime$ production and decay to the muon+neutrino channel as a function of the $W^\prime$ pole mass.

Combined (electron and muon channels) observed upper limits at the 95% CL on the cross section for generic $W^\prime$ production and decay to a single lepton generation as a function of the $W^\prime$ pole mass.

Observed upper limits at the 95% CL on the visible cross section in the electron+neutrino channel as a function of the transverse mass threshold.

Observed upper limits at the 95% CL on the visible cross section in the muon+neutrino channel as a function of the transverse mass threshold.

Product of acceptance and efficiency for the electron and muon selections as a function of the $W^\prime$ pole mass.

Expected upper limits at the 95% CL on the cross section for generic $W^\prime$ production and decay to the electron+neutrino channel as a function of the $W^\prime$ pole mass.

Expected upper limits at the 95% CL on the cross section for generic $W^\prime$ production and decay to the muon+neutrino channel as a function of the $W^\prime$ pole mass.

Combined (electron and muon channels) expected upper limits at the 95% CL on the cross section for generic $W^\prime$ production and decay to a single lepton generation as a function of the $W^\prime$ pole mass.

Distribution of the resonance mass in the c-tag Signal Region of the $ ce$ channel for the post-fit background, the observed data, and the expected signal with $m_{LQ} = 1$ TeV.

Distribution of the resonance mass in the b-tag Signal Region of the $ ce$ channel for the post-fit background, the observed data, and the expected signal with $m_{LQ} = 1$ TeV.

Distribution of the resonance mass in the untagged Signal Region of the $ c\mu$ channel for the post-fit background, the observed data, and the expected signal with $m_{LQ} = 1$ TeV.

Distribution of the resonance mass in the c-tag Signal Region of the $ c\mu$ channel for the post-fit background, the observed data, and the expected signal with $m_{LQ} = 1$ TeV.

Distribution of the resonance mass in the b-tag Signal Region of the $ c\mu$ channel for the post-fit background, the observed data, and the expected signal with $m_{LQ} = 1$ TeV.

Distribution of the resonance mass in the 0-tag Signal Region of the $ be$ channel for the post-fit background, the observed data, and the expected signal with $m_{LQ} = 1$ TeV.

Distribution of the resonance mass in the 1-tag Signal Region of the $ be$ channel for the post-fit background, the observed data, and the expected signal with $m_{LQ} = 1$ TeV.

Distribution of the resonance mass in the 2-tag Signal Region of the $ be$ channel for the post-fit background, the observed data, and the expected signal with $m_{LQ} = 1$ TeV.

Distribution of the resonance mass in the 0-tag Signal Region of the $ b\mu$ channel for the post-fit background, the observed data, and the expected signal with $m_{LQ} = 1$ TeV.

Distribution of the resonance mass in the 1-tag Signal Region of the $ b\mu$ channel for the post-fit background, the observed data, and the expected signal with $m_{LQ} = 1$ TeV.

Distribution of the resonance mass in the 2-tag Signal Region of the $ b\mu$ channel for the post-fit background, the observed data, and the expected signal with $m_{LQ} = 1$ TeV.

The observed and expected limits on the leptoquark pair production cross-section at 95% CL for $\mathcal{B}=1$ into electrons, shown as a function of $m_{LQ}$ for the $qe$ channel.

The observed and expected limits on the leptoquark pair production cross-section at 95% CL for $\mathcal{B}=1$ into muons, shown as a function of $m_{LQ}$ for the $q\mu$ channel.

The observed and expected limits on the leptoquark pair production cross-section at 95% CL for $\mathcal{B}=1$ into electrons, shown as a function of $m_{LQ}$ for the $ce$ channel.

The observed and expected limits on the leptoquark pair production cross-section at 95% CL for $\mathcal{B}=1$ into muons, shown as a function of $m_{LQ}$ for the $c\mu$ channel.

The observed and expected limits on the leptoquark pair production cross-section at 95% CL for $\mathcal{B}=1$ into electrons, shown as a function of $m_{LQ}$ for the $be$ channel.

The observed and expected limits on the leptoquark pair production cross-section at 95% CL for $\mathcal{B}=1$ into muons, shown as a function of $m_{LQ}$ for the $b\mu$ channel.

The observed and expected limits on the leptoquark branching ratio at 95% CL, shown as a function of $m_{LQ}$ for the $qe$ channel.

The observed and expected limits on the leptoquark branching ratio at 95% CL, shown as a function of $m_{LQ}$ for the $q\mu$ channel.

The observed and expected limits on the leptoquark branching ratio at 95% CL, shown as a function of $m_{LQ}$ for the $ce$ channel.

The observed and expected limits on the leptoquark branching ratio at 95% CL, shown as a function of $m_{LQ}$ for the $c\mu$ channel.

The observed and expected limits on the leptoquark branching ratio at 95% CL, shown as a function of $m_{LQ}$ for the $be$ channel.

The observed and expected limits on the leptoquark branching ratio at 95% CL, shown as a function of $m_{LQ}$ for the $b\mu$ channel.

The signal selection efficiency x acceptance summed over all signal regions, for all masses and LQ decay channels considered.

The observed and expected limits for all masses and LQ decay channels considered.

Cutflow Table in the electron channel, considering signal samples with LQ mass of 1 TeV.

Cutflow Table in the muon channel, considering signal samples with LQ mass of 1 TeV.

Observed $CL_S$ limits on $\sigma \times BR$ for $m_{\Phi} = 200-400$ GeV.

Observed $CL_S$ limits on $\sigma \times BR$ for $m_{\Phi} = 600-1000$ GeV.

Combined limits from this analysis (ID) and the CR and MS analyses for $m_{H} = 125$ GeV, $m_s = 15$ GeV.

Combined limits from this analysis (ID) and the CR and MS analyses for $m_{H} = 125$ GeV, $m_s = 25$ GeV.

Combined limits from this analysis (ID) and the CR and MS analyses for $m_{H} = 125$ GeV, $m_s = 40$ GeV.

Combined limits from this analysis (ID) and the CR and MS analyses for $m_{H} = 125$ GeV, $m_s = 55$ GeV.

Combined limits from this analysis (ID) and the CR and MS analyses for $m_{\Phi} = 200$ GeV, $m_s = 25$ GeV.

Combined limits from this analysis (ID) and the CR and MS analyses for $m_{\Phi} = 200$ GeV, $m_s = 50$ GeV.

Comparison of the IDVx reconstruction and selection efficiency for a Higgs with a mass of 125 GeV decaying to an LLP with a mass of 8 GeV, using only standard tracking versus using standard and large radius tracking for all ID vertices in the signal MC sample.

Comparison of the IDVx reconstruction and selection efficiency for a Higgs with a mass of 125 GeV decaying to an LLP with a mass of 8 GeV, using only standard tracking versus using standard and large radius tracking for ID vertices passing the full IDVx selection criteria.

Comparison of the IDVx reconstruction and selection efficiency for a $\Phi$ with a mass of 1000 GeV decaying to an LLP with a mass of 150 GeV, using only standard tracking versus using standard and large radius tracking for all ID vertices in the signal MC sample.

Comparison of the IDVx reconstruction and selection efficiency for a $\Phi$ with a mass of 1000 GeV decaying to an LLP with a mass of 150 GeV, using only standard tracking versus using standard and large radius tracking for ID vertices passing the full IDVx selection criteria.

The IDVx selection efficiency as a function of long-lived particle decay $z$ position for MC signal samples with a 125 GeV Higgs boson decaying to LLPs with masses of 8, 25, and 55 GeV. The efficiency for the 55 GeV LLP sample is shown both with and without the material veto applied.

The IDVx selection efficiency as a function of long-lived particle decay $z$ position for MC signal samples with mediators of masses 200, 400, and 600 GeV decaying to LLPs with a mass of 50 GeV. The efficiency for the 200 GeV mediator sample is shown both with and without the material veto applied.

The impact of the IDVx selections on the selection efficiency for the MC signal sample with a Higgs with a mass of 125 GeV decaying to an LLP with a mass of 8 GeV.

The impact of the IDVx selections on the selection efficiency for the MC signal sample with a Higgs with a mass of 125 GeV decaying to an LLP with a mass of 8 GeV.

The impact of the IDVx selections on the selection efficiency for the MC signal sample with a Higgs with a mass of 125 GeV decaying to an LLP with a mass of 8 GeV.

The impact of the IDVx selections on the selection efficiency for the MC signal sample with a $\Phi$ with a mass of 400 GeV decaying to an LLP with a mass of 50 GeV.

The impact of the IDVx selections on the selection efficiency for the MC signal sample with a $\Phi$ with a mass of 400 GeV decaying to an LLP with a mass of 50 GeV.

The impact of the IDVx selections on the selection efficiency for the MC signal sample with a $\Phi$ with a mass of 400 GeV decaying to an LLP with a mass of 50 GeV.

The impact of the IDVx selections on the selection efficiency for the MC signal sample with a $\Phi$ with a mass of 1000 GeV decaying to an LLP with a mass of 150 GeV.

The impact of the IDVx selections on the selection efficiency for the MC signal sample with a $\Phi$ with a mass of 1000 GeV decaying to an LLP with a mass of 150 GeV.

The impact of the IDVx selections on the selection efficiency for the MC signal sample with a $\Phi$ with a mass of 1000 GeV decaying to an LLP with a mass of 150 GeV.

$CL_S$ limits on $B_{H\rightarrow ss}$ for $m_{H} = 125$ GeV, $m_s = 8$ GeV.

$CL_S$ limits on $B_{H\rightarrow ss}$ for $m_{H} = 125$ GeV, $m_s = 15$ GeV.

$CL_S$ limits on $B_{H\rightarrow ss}$ for $m_{H} = 125$ GeV, $m_s = 25$ GeV.

$CL_S$ limits on $B_{H\rightarrow ss}$ for $m_{H} = 125$ GeV, $m_s = 40$ GeV.

$CL_S$ limits on $B_{H\rightarrow ss}$ for $m_{H} = 125$ GeV, $m_s = 55$ GeV.

$CL_S$ limits on $\sigma \times B_{\Phi\rightarrow ss}$ for $m_{\Phi} = 200$ GeV, $m_s = 8$ GeV.

$CL_S$ limits on $\sigma \times B_{\Phi\rightarrow ss}$ for $m_{\Phi} = 200$ GeV, $m_s = 25$ GeV.

$CL_S$ limits on $\sigma \times B_{\Phi\rightarrow ss}$ for $m_{\Phi} = 200$ GeV, $m_s = 50$ GeV.

$CL_S$ limits on $\sigma \times B_{\Phi\rightarrow ss}$ for $m_{\Phi} = 400$ GeV, $m_s = 50$ GeV.

$CL_S$ limits on $\sigma \times B_{\Phi\rightarrow ss}$ for $m_{\Phi} = 400$ GeV, $m_s = 100$ GeV.

$CL_S$ limits on $\sigma \times B_{\Phi\rightarrow ss}$ for $m_{\Phi} = 600$ GeV, $m_s = 50$ GeV.

$CL_S$ limits on $\sigma \times B_{\Phi\rightarrow ss}$ for $m_{\Phi} = 600$ GeV, $m_s = 150$ GeV.

$CL_S$ limits on $\sigma \times B_{\Phi\rightarrow ss}$ for $m_{\Phi} = 1000$ GeV, $m_s = 50$ GeV.

$CL_S$ limits on $\sigma \times B_{\Phi\rightarrow ss}$ for $m_{\Phi} = 1000$ GeV, $m_s = 150$ GeV.

$CL_S$ limits on $\sigma \times B_{\Phi\rightarrow ss}$ for $m_{\Phi} = 1000$ GeV, $m_s = 400$ GeV.

Distributions in SR-low of the data and post-fit background prediction for H^boost. The SR-low event selections are applied for each distribution except for the variable shown, where the selection is indicated by a red arrow. The normalization factor for the WZ background is derived from the background-only estimation described in Section 7. The expected distribution for a benchmark signal model is included for comparison. The first (last) bin includes underflow (overflow). The "Top-quark like" category contains the tt̄, Wt, and WW processes while the "Others" category contains backgrounds from triboson production and processes that include a Higgs boson, 3 or more tops, and tops produced in association with W or Z bosons. The bottom panel shows the ratio of the data to the post-fit background prediction. The hatched bands indicate the combined theoretical, experimental, and MC statistical uncertainties.

Distributions in SR-low of the data and post-fit background prediction for m_eff^3ℓ/H^boost. The SR-low event selections are applied for each distribution except for the variable shown, where the selection is indicated by a red arrow. The normalization factor for the WZ background is derived from the background-only estimation described in Section 7. The expected distribution for a benchmark signal model is included for comparison. The first (last) bin includes underflow (overflow). The "Top-quark like" category contains the tt̄, Wt, and WW processes while the "Others" category contains backgrounds from triboson production and processes that include a Higgs boson, 3 or more tops, and tops produced in association with W or Z bosons. The bottom panel shows the ratio of the data to the post-fit background prediction. The hatched bands indicate the combined theoretical, experimental, and MC statistical uncertainties.

Distributions in SR-low of the data and post-fit background prediction for m_eff^3ℓ/H^boost. The SR-low event selections are applied for each distribution except for the variable shown, where the selection is indicated by a red arrow. The normalization factor for the WZ background is derived from the background-only estimation described in Section 7. The expected distribution for a benchmark signal model is included for comparison. The first (last) bin includes underflow (overflow). The "Top-quark like" category contains the tt̄, Wt, and WW processes while the "Others" category contains backgrounds from triboson production and processes that include a Higgs boson, 3 or more tops, and tops produced in association with W or Z bosons. The bottom panel shows the ratio of the data to the post-fit background prediction. The hatched bands indicate the combined theoretical, experimental, and MC statistical uncertainties.

Distributions in SR-low of the data and post-fit background prediction for p_T^soft/(p_T^soft + m_eff^3ℓ). The SR-low event selections are applied for each distribution except for the variable shown, where the selection is indicated by a red arrow. The normalization factor for the WZ background is derived from the background-only estimation described in Section 7. The expected distribution for a benchmark signal model is included for comparison. The first (last) bin includes underflow (overflow). The "Top-quark like" category contains the tt̄, Wt, and WW processes while the "Others" category contains backgrounds from triboson production and processes that include a Higgs boson, 3 or more tops, and tops produced in association with W or Z bosons. The bottom panel shows the ratio of the data to the post-fit background prediction. The hatched bands indicate the combined theoretical, experimental, and MC statistical uncertainties.

Distributions in SR-low of the data and post-fit background prediction for p_T^soft/(p_T^soft + m_eff^3ℓ). The SR-low event selections are applied for each distribution except for the variable shown, where the selection is indicated by a red arrow. The normalization factor for the WZ background is derived from the background-only estimation described in Section 7. The expected distribution for a benchmark signal model is included for comparison. The first (last) bin includes underflow (overflow). The "Top-quark like" category contains the tt̄, Wt, and WW processes while the "Others" category contains backgrounds from triboson production and processes that include a Higgs boson, 3 or more tops, and tops produced in association with W or Z bosons. The bottom panel shows the ratio of the data to the post-fit background prediction. The hatched bands indicate the combined theoretical, experimental, and MC statistical uncertainties.

Distributions in SR-ISR of the data and post-fit background prediction for m_T. The SR-ISR event selections are applied for each distribution except for the variable shown, where the selection is indicated by a red arrow. The normalization factor for the WZ background is derived from the background-only estimation described in Section 7. The expected distribution for a benchmark signal model is included for comparison. The first (last) bin includes underflow (overflow). The "Top-quark like" category contains the tt̄, Wt, and WW processes while the "Others" category contains backgrounds from triboson production and processes that include a Higgs boson, 3 or more tops, and tops produced in association with W or Z bosons. The bottom panel shows the ratio of the data to the post-fit background prediction. The hatched bands indicate the combined theoretical, experimental, and MC statistical uncertainties.

Distributions in SR-ISR of the data and post-fit background prediction for m_T. The SR-ISR event selections are applied for each distribution except for the variable shown, where the selection is indicated by a red arrow. The normalization factor for the WZ background is derived from the background-only estimation described in Section 7. The expected distribution for a benchmark signal model is included for comparison. The first (last) bin includes underflow (overflow). The "Top-quark like" category contains the tt̄, Wt, and WW processes while the "Others" category contains backgrounds from triboson production and processes that include a Higgs boson, 3 or more tops, and tops produced in association with W or Z bosons. The bottom panel shows the ratio of the data to the post-fit background prediction. The hatched bands indicate the combined theoretical, experimental, and MC statistical uncertainties.

Distributions in SR-ISR of the data and post-fit background prediction for R(E_T^miss,jets). The SR-ISR event selections are applied for each distribution except for the variable shown, where the selection is indicated by a red arrow. The normalization factor for the WZ background is derived from the background-only estimation described in Section 7. The expected distribution for a benchmark signal model is included for comparison. The first (last) bin includes underflow (overflow). The "Top-quark like" category contains the tt̄, Wt, and WW processes while the "Others" category contains backgrounds from triboson production and processes that include a Higgs boson, 3 or more tops, and tops produced in association with W or Z bosons. The bottom panel shows the ratio of the data to the post-fit background prediction. The hatched bands indicate the combined theoretical, experimental, and MC statistical uncertainties.

Distributions in SR-ISR of the data and post-fit background prediction for R(E_T^miss,jets). The SR-ISR event selections are applied for each distribution except for the variable shown, where the selection is indicated by a red arrow. The normalization factor for the WZ background is derived from the background-only estimation described in Section 7. The expected distribution for a benchmark signal model is included for comparison. The first (last) bin includes underflow (overflow). The "Top-quark like" category contains the tt̄, Wt, and WW processes while the "Others" category contains backgrounds from triboson production and processes that include a Higgs boson, 3 or more tops, and tops produced in association with W or Z bosons. The bottom panel shows the ratio of the data to the post-fit background prediction. The hatched bands indicate the combined theoretical, experimental, and MC statistical uncertainties.

Distributions in SR-ISR of the data and post-fit background prediction for p_T^soft. The SR-ISR event selections are applied for each distribution except for the variable shown, where the selection is indicated by a red arrow. The normalization factor for the WZ background is derived from the background-only estimation described in Section 7. The expected distribution for a benchmark signal model is included for comparison. The first (last) bin includes underflow (overflow). The "Top-quark like" category contains the tt̄, Wt, and WW processes while the "Others" category contains backgrounds from triboson production and processes that include a Higgs boson, 3 or more tops, and tops produced in association with W or Z bosons. The bottom panel shows the ratio of the data to the post-fit background prediction. The hatched bands indicate the combined theoretical, experimental, and MC statistical uncertainties.

Distributions in SR-ISR of the data and post-fit background prediction for p_T^soft. The SR-ISR event selections are applied for each distribution except for the variable shown, where the selection is indicated by a red arrow. The normalization factor for the WZ background is derived from the background-only estimation described in Section 7. The expected distribution for a benchmark signal model is included for comparison. The first (last) bin includes underflow (overflow). The "Top-quark like" category contains the tt̄, Wt, and WW processes while the "Others" category contains backgrounds from triboson production and processes that include a Higgs boson, 3 or more tops, and tops produced in association with W or Z bosons. The bottom panel shows the ratio of the data to the post-fit background prediction. The hatched bands indicate the combined theoretical, experimental, and MC statistical uncertainties.

Distributions in SR-ISR of the data and post-fit background prediction for p_T^jets. The SR-ISR event selections are applied for each distribution except for the variable shown, where the selection is indicated by a red arrow. The normalization factor for the WZ background is derived from the background-only estimation described in Section 7. The expected distribution for a benchmark signal model is included for comparison. The first (last) bin includes underflow (overflow). The "Top-quark like" category contains the tt̄, Wt, and WW processes while the "Others" category contains backgrounds from triboson production and processes that include a Higgs boson, 3 or more tops, and tops produced in association with W or Z bosons. The bottom panel shows the ratio of the data to the post-fit background prediction. The hatched bands indicate the combined theoretical, experimental, and MC statistical uncertainties.

Distributions in SR-ISR of the data and post-fit background prediction for p_T^jets. The SR-ISR event selections are applied for each distribution except for the variable shown, where the selection is indicated by a red arrow. The normalization factor for the WZ background is derived from the background-only estimation described in Section 7. The expected distribution for a benchmark signal model is included for comparison. The first (last) bin includes underflow (overflow). The "Top-quark like" category contains the tt̄, Wt, and WW processes while the "Others" category contains backgrounds from triboson production and processes that include a Higgs boson, 3 or more tops, and tops produced in association with W or Z bosons. The bottom panel shows the ratio of the data to the post-fit background prediction. The hatched bands indicate the combined theoretical, experimental, and MC statistical uncertainties.

Observed exclusion contour on C1N2 production assuming on-shell $W/Z$ decays as a function of the C1/N2 and N1 masses, and derived from the combined fit of low-mass and ISR regions.

Observed exclusion contour on C1N2 production assuming on-shell $W/Z$ decays as a function of the C1/N2 and N1 masses, and derived from the combined fit of low-mass and ISR regions.

Expected exclusion contour on C1N2 production assuming on-shell $W/Z$ decays as a function of the C1/N2 and N1 masses, and derived from the combined fit of low-mass and ISR regions.

Expected exclusion contour on C1N2 production assuming on-shell $W/Z$ decays as a function of the C1/N2 and N1 masses, and derived from the combined fit of low-mass and ISR regions.

Plus 1$\sigma$ uncertainty, varying the signal cross section within its uncertainty, on the observed exclusion contour on C1N2 production assuming on-shell $W/Z$ decays as a function of the C1/N2 and N1 masses, and derived from the combined fit of low-mass and ISR regions.

Plus 1$\sigma$ uncertainty, varying the signal cross section within its uncertainty, on the observed exclusion contour on C1N2 production assuming on-shell $W/Z$ decays as a function of the C1/N2 and N1 masses, and derived from the combined fit of low-mass and ISR regions.

Minus 1$\sigma$ uncertainty, varying the signal cross section within its uncertainty, on the observed exclusion contour on C1N2 production assuming on-shell $W/Z$ decays as a function of the C1/N2 and N1 masses, and derived from the combined fit of low-mass and ISR regions.

Minus 1$\sigma$ uncertainty, varying the signal cross section within its uncertainty, on the observed exclusion contour on C1N2 production assuming on-shell $W/Z$ decays as a function of the C1/N2 and N1 masses, and derived from the combined fit of low-mass and ISR regions.

Plus 1$\sigma$ uncertainty, due to uncertainties in the background prediction and experimental uncertainties affecting the signal, on the expected exclusion contour on C1N2 production assuming on-shell $W/Z$ decays as a function of the C1/N2 and N1 masses, and derived from the combined fit of low-mass and ISR regions.

Plus 1$\sigma$ uncertainty, due to uncertainties in the background prediction and experimental uncertainties affecting the signal, on the expected exclusion contour on C1N2 production assuming on-shell $W/Z$ decays as a function of the C1/N2 and N1 masses, and derived from the combined fit of low-mass and ISR regions.

Minus 1$\sigma$ uncertainty, due to uncertainties in the background prediction and experimental uncertainties affecting the signal, on the expected exclusion contour on C1N2 production assuming on-shell $W/Z$ decays as a function of the C1/N2 and N1 masses, and derived from the combined fit of low-mass and ISR regions.

Minus 1$\sigma$ uncertainty, due to uncertainties in the background prediction and experimental uncertainties affecting the signal, on the expected exclusion contour on C1N2 production assuming on-shell $W/Z$ decays as a function of the C1/N2 and N1 masses, and derived from the combined fit of low-mass and ISR regions.

Upper limits on observed wino-bino simplified model signal cross section $\sigma_\text{obs}^\text{95}$.

Upper limits on observed wino-bino simplified model signal cross section $\sigma_\text{obs}^\text{95}$.

Upper limits on expected wino-bino simplified model signal cross section $\sigma_\text{exp}^\text{95}$.

Upper limits on expected wino-bino simplified model signal cross section $\sigma_\text{exp}^\text{95}$.

Signal acceptance in SR-low, for signals with $m(\widetilde{\chi}^{\pm}_{1}/\widetilde{\chi}^{0}_{2}) - m\widetilde{\chi}^{0}_{1} \geq 100$ GeV.

Signal acceptance in SR-low, for signals with $m(\widetilde{\chi}^{\pm}_{1}/\widetilde{\chi}^{0}_{2}) - m\widetilde{\chi}^{0}_{1} \geq 100$ GeV.

Signal efficiency in SR-low, for signals with $m(\widetilde{\chi}^{\pm}_{1}/\widetilde{\chi}^{0}_{2}) - m\widetilde{\chi}^{0}_{1} \geq 100$ GeV.

Signal efficiency in SR-low, for signals with $m(\widetilde{\chi}^{\pm}_{1}/\widetilde{\chi}^{0}_{2}) - m\widetilde{\chi}^{0}_{1} \geq 100$ GeV.

Signal acceptance in SR-ISR, for signals with $m(\widetilde{\chi}^{\pm}_{1}/\widetilde{\chi}^{0}_{2}) - m\widetilde{\chi}^{0}_{1} \geq 100$ GeV.

Signal acceptance in SR-ISR, for signals with $m(\widetilde{\chi}^{\pm}_{1}/\widetilde{\chi}^{0}_{2}) - m\widetilde{\chi}^{0}_{1} \geq 100$ GeV.

Signal efficiency in SR-ISR, for signals with $m(\widetilde{\chi}^{\pm}_{1}/\widetilde{\chi}^{0}_{2}) - m\widetilde{\chi}^{0}_{1} \geq 100$ GeV.

Signal efficiency in SR-ISR, for signals with $m(\widetilde{\chi}^{\pm}_{1}/\widetilde{\chi}^{0}_{2}) - m\widetilde{\chi}^{0}_{1} \geq 100$ GeV.

Signal acceptance in SR-low, for signals with $m(\widetilde{\chi}^{\pm}_{1}/\widetilde{\chi}^{0}_{2}) - m\widetilde{\chi}^{0}_{1} < 100$ GeV.

Signal acceptance in SR-low, for signals with $m(\widetilde{\chi}^{\pm}_{1}/\widetilde{\chi}^{0}_{2}) - m\widetilde{\chi}^{0}_{1} < 100$ GeV.

Signal efficiency in SR-low, for signals with $m(\widetilde{\chi}^{\pm}_{1}/\widetilde{\chi}^{0}_{2}) - m\widetilde{\chi}^{0}_{1} < 100$ GeV.

Signal efficiency in SR-low, for signals with $m(\widetilde{\chi}^{\pm}_{1}/\widetilde{\chi}^{0}_{2}) - m\widetilde{\chi}^{0}_{1} < 100$ GeV.

Signal acceptance in SR-ISR, for signals with $m(\widetilde{\chi}^{\pm}_{1}/\widetilde{\chi}^{0}_{2}) - m\widetilde{\chi}^{0}_{1} < 100$ GeV.

Signal acceptance in SR-ISR, for signals with $m(\widetilde{\chi}^{\pm}_{1}/\widetilde{\chi}^{0}_{2}) - m\widetilde{\chi}^{0}_{1} < 100$ GeV.

Signal efficiency in SR-ISR, for signals with $m(\widetilde{\chi}^{\pm}_{1}/\widetilde{\chi}^{0}_{2}) - m\widetilde{\chi}^{0}_{1} < 100$ GeV.

Signal efficiency in SR-ISR, for signals with $m(\widetilde{\chi}^{\pm}_{1}/\widetilde{\chi}^{0}_{2}) - m\widetilde{\chi}^{0}_{1} < 100$ GeV.

The observed and expected yields after the background-only fit in the SRs. The normalization factors of the $WZ$ sample for the low-mass and ISR regions are different and are treated separately in the combined fit. \The "Top-quark like" category contains the tt&#772;, Wt, and WW processes while the "Others" category contains backgrounds from triboson production and processes that include a Higgs boson, 3 or more tops, and tops produced in association with W or Z bosons. Combined statistical and systematic uncertainties are presented. The individual uncertainties can be correlated and do not necessarily add in quadrature to equal the total background uncertainty.

The observed and expected yields after the background-only fit in the SRs. The normalization factors of the $WZ$ sample for the low-mass and ISR regions are different and are treated separately in the combined fit. \The "Top-quark like" category contains the tt&#772;, Wt, and WW processes while the "Others" category contains backgrounds from triboson production and processes that include a Higgs boson, 3 or more tops, and tops produced in association with W or Z bosons. Combined statistical and systematic uncertainties are presented. The individual uncertainties can be correlated and do not necessarily add in quadrature to equal the total background uncertainty.

Summary of the expected background and data yields in $\text{SR-low}$ and $\text{SR-ISR}$. The second and third columns show the data and total expected background with systematic uncertainties. The fourth column gives the model-independent upper limits at 95\% CL on the visible cross section ($\sigma_\text{vis}$). The fifth and sixth columns give the visible number of observed ($S^{95}_\text{obs}$) and expected ($S^{95}_\text{exp}$) events of a generic beyond-the-SM process, where uncertainties on $S^{95}_\text{exp}$ reflect the $\pm 1 \sigma$ uncertainties on the background estimation. The last column shows the discovery $p$-value and Gaussian significance $Z$ assuming no signal.

Summary of the expected background and data yields in $\text{SR-low}$ and $\text{SR-ISR}$. The second and third columns show the data and total expected background with systematic uncertainties. The fourth column gives the model-independent upper limits at 95\% CL on the visible cross section ($\sigma_\text{vis}$). The fifth and sixth columns give the visible number of observed ($S^{95}_\text{obs}$) and expected ($S^{95}_\text{exp}$) events of a generic beyond-the-SM process, where uncertainties on $S^{95}_\text{exp}$ reflect the $\pm 1 \sigma$ uncertainties on the background estimation. The last column shows the discovery $p$-value and Gaussian significance $Z$ assuming no signal.

Upper limits on observed (expected) wino-bino simplified model signal cross section $\sigma_\text{obs(exp)}^\text{95}$.

Upper limits on observed (expected) wino-bino simplified model signal cross section $\sigma_\text{obs(exp)}^\text{95}$.

Full list of event selections and MC generator-weighted yields and in $\text{SR-ISR}$ for the main $WZ$ background and a representative $\tilde{\chi}^{\pm}_{1}\tilde{\chi}^{0}_{2}$ signal point of mass 200 GeV and mass splitting $\Delta m = 100$ GeV normalized to 139 fb$^{-1}$. 40000 events were generated.

Full list of event selections and MC generator-weighted yields and in $\text{SR-ISR}$ for the main $WZ$ background and a representative $\tilde{\chi}^{\pm}_{1}\tilde{\chi}^{0}_{2}$ signal point of mass 200 GeV and mass splitting $\Delta m = 100$ GeV normalized to 139 fb$^{-1}$. 40000 events were generated.

Full list of event selections and MC generator-weighted yields and in $\text{SR-low}$ for the main $WZ$ background and a representative $\tilde{\chi}^{\pm}_{1}\tilde{\chi}^{0}_{2}$ signal point of mass 200 GeV and mass splitting $\Delta m = 100$ GeV normalized to 139 fb$^{-1}$. 40000 events were generated.

Full list of event selections and MC generator-weighted yields and in $\text{SR-low}$ for the main $WZ$ background and a representative $\tilde{\chi}^{\pm}_{1}\tilde{\chi}^{0}_{2}$ signal point of mass 200 GeV and mass splitting $\Delta m = 100$ GeV normalized to 139 fb$^{-1}$. 40000 events were generated.

The observed upper limits on the model cross-section in units of pb for simplified models with ${\tilde{\tau}}_L {\tilde{\tau}}_L$ only production. Three points at $M({\tilde{\chi}}^{0}_{1})=200GeV$ were removed from the plot but kept in the table because they overlapped with the plot's legend and are far from the exclusion contour.

The observed 95\% CL exclusion contours for the combined fit of SR-lowMass and SR-highMass for simplified models with combined ${\tilde{\tau}}^{+}_{R,L} {\tilde{\tau}}^{-}_{R,L}$ production.

The observed 95\% CL exclusion contours for the combined fit of SR-lowMass and SR-highMass for simplified models with combined ${\tilde{\tau}}^{+}_{R,L} {\tilde{\tau}}^{-}_{R,L}$ production.

The expected 95% CL exclusion contours for the combined fit of SR-lowMass and SR-highMass for simplified models with combined ${\tilde{\tau}}^{+}_{R,L} {\tilde{\tau}}^{-}_{R,L}$ production.

The expected 95% CL exclusion contours for the combined fit of SR-lowMass and SR-highMass for simplified models with combined ${\tilde{\tau}}^{+}_{R,L} {\tilde{\tau}}^{-}_{R,L}$ production.

The observed 95% CL exclusion contours for the combined fit of SR-lowMass and SR-highMass for simplified models with ${\tilde{\tau}}_L {\tilde{\tau}}_L$ only production.

The observed 95% CL exclusion contours for the combined fit of SR-lowMass and SR-highMass for simplified models with ${\tilde{\tau}}_L {\tilde{\tau}}_L$ only production.

The expected 95% CL exclusion contours for the combined fit of SR-lowMass and SR-highMass for simplified models with ${\tilde{\tau}}_L {\tilde{\tau}}_L$ only production.

The expected 95% CL exclusion contours for the combined fit of SR-lowMass and SR-highMass for simplified models with ${\tilde{\tau}}_L {\tilde{\tau}}_L$ only production.

Observed 95% CL exclusion limits for simplified models with direct stau pair production in SR-lowMass.

Observed 95% CL exclusion limits for simplified models with direct stau pair production in SR-lowMass.

Expected 95% CL exclusion limits for simplified models with direct stau pair production in SR-lowMass.

Expected 95% CL exclusion limits for simplified models with direct stau pair production in SR-lowMass.

Observed 95% CL exclusion limits for simplified models with direct stau pair production in SR-highMass.

Observed 95% CL exclusion limits for simplified models with direct stau pair production in SR-highMass.

Expected 95% CL exclusion limits for simplified models with direct stau pair production in SR-highMass.

Expected 95% CL exclusion limits for simplified models with direct stau pair production in SR-highMass.

Signal acceptance in SR highMass for combined stau final states

Signal acceptance in SR highMass for combined stau final states

Signal acceptance in SR lowMass for combined stau final states

Signal acceptance in SR lowMass for combined stau final states

Signal efficiency in SR highMass for combined stau final states

Signal efficiency in SR highMass for combined stau final states

Signal efficiency in SR lowMass for combined stau final states

Signal efficiency in SR lowMass for combined stau final states

Signal acceptance*efficiency in SR highMass for combined stau final states

Signal acceptance*efficiency in SR highMass for combined stau final states

Signal acceptance*efficiency in SR lowMass for combined stau final states

Signal acceptance*efficiency in SR lowMass for combined stau final states

Cutflow for two reference points (${\tilde{\tau}}^{+}_{R,L} {\tilde{\tau}}^{-}_{R,L}$ production) in SR. The column labelled $N_{weighted}$ shows the results including all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$, while $N_{raw}$ in brackets shows the results for the generated number of events. The quoted uncertainties are statistical only. The "Generator filter" includes the requirements that two $\tau$ in the event have ${p}_{T} > 15$ GeV and $|\eta| <$ 2.6. The "Baseline Cut" includes the requirement of two baseline $\tau$ with a minimum value at 0.01 of the boosted decision tree discriminant (JetBDTSigTransMin $>$ 0.01) and ${p}_{T, \tau_{1}} > 50$ GeV and ${p}_{T, \tau_{2}} > 40$ GeV. At the step "Trigger & offline cuts", the following requirements are applied: the event is recorded using the asymmetric di-$\tau$ trigger (di-$\tau$ $E_{T}^{miss}$ trigger) in SR-lowMass (SR-highMass), and the lepton $p_{T}$ and $E_{T}^{miss}$ are required at plateau.

Cutflow for two reference points (${\tilde{\tau}}^{+}_{R,L} {\tilde{\tau}}^{-}_{R,L}$ production) in SR. The column labelled $N_{weighted}$ shows the results including all correction factors applied to simulation, and is normalised to 139 fb$^{-1}$, while $N_{raw}$ in brackets shows the results for the generated number of events. The quoted uncertainties are statistical only. The "Generator filter" includes the requirements that two $\tau$ in the event have ${p}_{T} > 15$ GeV and $|\eta| <$ 2.6. The "Baseline Cut" includes the requirement of two baseline $\tau$ with a minimum value at 0.01 of the boosted decision tree discriminant (JetBDTSigTransMin $>$ 0.01) and ${p}_{T, \tau_{1}} > 50$ GeV and ${p}_{T, \tau_{2}} > 40$ GeV. At the step "Trigger & offline cuts", the following requirements are applied: the event is recorded using the asymmetric di-$\tau$ trigger (di-$\tau$ $E_{T}^{miss}$ trigger) in SR-lowMass (SR-highMass), and the lepton $p_{T}$ and $E_{T}^{miss}$ are required at plateau.

Observed and expected numbers of events in the control and signal regions where all control and signal region bins are included as constraints in the likelihood. The expected event yields of SM processes are given after the background-only fit. The entries marked as "--" are negligible. The uncertainties correspond to the sum in quadrature of statistical and systematic uncertainties. The correlation of systematic uncertainties among control regions and among background processes is fully taken into account.

Observed and expected numbers of events in the control and signal regions where all control and signal region bins are included as constraints in the likelihood. The expected event yields of SM processes are given after the background-only fit. The entries marked as "--" are negligible. The uncertainties correspond to the sum in quadrature of statistical and systematic uncertainties. The correlation of systematic uncertainties among control regions and among background processes is fully taken into account.

The post-fit $m_{T2}$ distribution for SR-lowMass. The stacked histograms show the expected SM backgrounds. The multi-jet contribution is estimated from data using the ABCD method. The contributions of multi-jet and $W$+jets events are scaled with the corresponding normalization factors derived from the background-only fit. The hatched bands represent the sum in quadrature of systematic and statistical uncertainties of the total SM background. For illustration, the distributions from the SUSY reference points are also shown as dashed lines. The last bin includes the overflow events.

The post-fit $m_{T2}$ distribution for SR-lowMass. The stacked histograms show the expected SM backgrounds. The multi-jet contribution is estimated from data using the ABCD method. The contributions of multi-jet and $W$+jets events are scaled with the corresponding normalization factors derived from the background-only fit. The hatched bands represent the sum in quadrature of systematic and statistical uncertainties of the total SM background. For illustration, the distributions from the SUSY reference points are also shown as dashed lines. The last bin includes the overflow events.

The post-fit $m_{T2}$ distribution for SR-highMass. The stacked histograms show the expected SM backgrounds. The multi-jet contribution is estimated from data using the ABCD method. The contributions of multi-jet and $W$+jets events are scaled with the corresponding normalization factors derived from the background-only fit. The hatched bands represent the sum in quadrature of systematic and statistical uncertainties of the total SM background. For illustration, the distributions from the SUSY reference points are also shown as dashed lines. The last bin includes the overflow events.

The post-fit $m_{T2}$ distribution for SR-highMass. The stacked histograms show the expected SM backgrounds. The multi-jet contribution is estimated from data using the ABCD method. The contributions of multi-jet and $W$+jets events are scaled with the corresponding normalization factors derived from the background-only fit. The hatched bands represent the sum in quadrature of systematic and statistical uncertainties of the total SM background. For illustration, the distributions from the SUSY reference points are also shown as dashed lines. The last bin includes the overflow events.

The $m_{T2}$ post-fit distributions in the multi-jet background validation region VR-F (lowMass). The stacked histograms show the contribution of each relevant SM process. The multi-jet shape is taken from VR-E in the ABCD method and the normalization is determined by the transfer factor $T$ and rescaled by a correction factor determined by the fit. The hatched bands represent the combined statistical and systematic uncertainties in the sum of the SM backgrounds shown. For illustration, the distributions of the SUSY reference points are also shown as dashed lines.

The $m_{T2}$ post-fit distributions in the multi-jet background validation region VR-F (lowMass). The stacked histograms show the contribution of each relevant SM process. The multi-jet shape is taken from VR-E in the ABCD method and the normalization is determined by the transfer factor $T$ and rescaled by a correction factor determined by the fit. The hatched bands represent the combined statistical and systematic uncertainties in the sum of the SM backgrounds shown. For illustration, the distributions of the SUSY reference points are also shown as dashed lines.

The $m_{T2}$ post-fit distributions in the multi-jet background validation VR-F (highMass). The stacked histograms show the contribution of each relevant SM process. The multi-jet shape is taken from VR-E in the ABCD method and the normalization is determined by the transfer factor $T$ and rescaled by a correction factor determined by the fit. The hatched bands represent the combined statistical and systematic uncertainties in the sum of the SM backgrounds shown. For illustration, the distributions of the SUSY reference points are also shown as dashed lines.

The $m_{T2}$ post-fit distributions in the multi-jet background validation VR-F (highMass). The stacked histograms show the contribution of each relevant SM process. The multi-jet shape is taken from VR-E in the ABCD method and the normalization is determined by the transfer factor $T$ and rescaled by a correction factor determined by the fit. The hatched bands represent the combined statistical and systematic uncertainties in the sum of the SM backgrounds shown. For illustration, the distributions of the SUSY reference points are also shown as dashed lines.

The $E_{T}^{miss}$ post-fit distributions in the multi-jet background validation region VR-F (lowMass). The stacked histograms show the contribution of each relevant SM process. The multi-jet shape is taken from VR-E in the ABCD method and the normalization is determined by the transfer factor $T$ and rescaled by a correction factor determined by the fit. The hatched bands represent the combined statistical and systematic uncertainties in the sum of the SM backgrounds shown. For illustration, the distributions of the SUSY reference points are also shown as dashed lines.

The $E_{T}^{miss}$ post-fit distributions in the multi-jet background validation region VR-F (lowMass). The stacked histograms show the contribution of each relevant SM process. The multi-jet shape is taken from VR-E in the ABCD method and the normalization is determined by the transfer factor $T$ and rescaled by a correction factor determined by the fit. The hatched bands represent the combined statistical and systematic uncertainties in the sum of the SM backgrounds shown. For illustration, the distributions of the SUSY reference points are also shown as dashed lines.

The $E_{T}^{miss}$ post-fit distributions in the multi-jet background validation region VR-F (highMass). The stacked histograms show the contribution of each relevant SM process. The multi-jet shape is taken from VR-E in the ABCD method and the normalization is determined by the transfer factor $T$ and rescaled by a correction factor determined by the fit. The hatched bands represent the combined statistical and systematic uncertainties in the sum of the SM backgrounds shown. For illustration, the distributions of the SUSY reference points are also shown as dashed lines.

The $E_{T}^{miss}$ post-fit distributions in the multi-jet background validation region VR-F (highMass). The stacked histograms show the contribution of each relevant SM process. The multi-jet shape is taken from VR-E in the ABCD method and the normalization is determined by the transfer factor $T$ and rescaled by a correction factor determined by the fit. The hatched bands represent the combined statistical and systematic uncertainties in the sum of the SM backgrounds shown. For illustration, the distributions of the SUSY reference points are also shown as dashed lines.

The pre-fit $m_{T2}$ distribution in the $WCR$. The SM backgrounds other than multi-jet production are estimated from MC simulation. The multi-jet contribution is estimated from data using the OS--SS method. The hatched bands represent the combined statistical and systematic uncertainties of the total SM background. For illustration, the distributions of the SUSY reference points are also shown as dashed lines.

The pre-fit $m_{T2}$ distribution in the $WCR$. The SM backgrounds other than multi-jet production are estimated from MC simulation. The multi-jet contribution is estimated from data using the OS--SS method. The hatched bands represent the combined statistical and systematic uncertainties of the total SM background. For illustration, the distributions of the SUSY reference points are also shown as dashed lines.

The post-fit yields in the $WVR$, $TVRs$, $ZVRs$ and $VVVRs$. The SM backgrounds other than multi-jet production are estimated from MC simulation. The multi-jet contribution is negligible and is estimated from data using the ABCD method, using CRs obtained with the same technique used for the SRs. The hatched bands represent the combined statistical and systematic uncertainties of the total SM background. For illustration, the distributions of the SUSY reference points are also shown as dashed lines. The lower panels show the ratio of data to the SM background estimate.

The post-fit yields in the $WVR$, $TVRs$, $ZVRs$ and $VVVRs$. The SM backgrounds other than multi-jet production are estimated from MC simulation. The multi-jet contribution is negligible and is estimated from data using the ABCD method, using CRs obtained with the same technique used for the SRs. The hatched bands represent the combined statistical and systematic uncertainties of the total SM background. For illustration, the distributions of the SUSY reference points are also shown as dashed lines. The lower panels show the ratio of data to the SM background estimate.