Summary of results for cross-section of non-prompt $\psi(2S)$ decaying to a muon pair for 13 TeV data in fb/GeV. Uncertainties are statistical and systematic, respectively.

Summary of results for non-prompt fraction of $J/\psi$ decaying to a muon pair as a percentage for 13 TeV data. Uncertainties are statistical and systematic, respectively.

Summary of results for non-prompt fraction of $\psi(2S)$ decaying to a muon pair as a percentage for 13 TeV data. Uncertainties are statistical and systematic, respectively.

Summary of results for prompt $\psi(2S)$ over $J/\psi$ ratio as a percentage for 13 TeV data. Uncertainties are statistical and systematic, respectively.

Summary of results for non-prompt $\psi(2S)$ over $J/\psi$ ratio as a percentage for 13 TeV data. Uncertainties are statistical and systematic, respectively.

Correction factors for an assumed angular dependence $\propto 1+\lambda_{\theta} \cos^2\theta $ in the helicity frame, for several values of the parameter $\lambda_{\theta}$. The correction factors were found to be the same (within 1% - 2%) for $J/\psi$ and $\psi(2S)$ mesons, for prompt and non-prompt production mechanisms, and for the three rapidity intervals considered in this paper.

Measured $p_T$ cross sections in full-lepton phase space for 1.2 < |y| < 1.6.

Measured $p_T$ cross sections in full-lepton phase space for 1.6 < |y| < 2.0.

Measured $p_T$ cross sections in full-lepton phase space for 2.0 < |y| < 2.4.

Measured $p_T$ cross sections in full-lepton phase space for 2.4 < |y| < 2.8.

Measured $p_T$ cross sections in full-lepton phase space for 2.8 < |y| < 3.6.

Measured cross sections in full-lepton phase space as a function of |y|.

Normalised measured $p_T$ cross sections in full-lepton phase space for |y| < 1.6.

Figure 2a, full-event

Figure 2b, subevent

Figure 3

Figure 4

$R_\text{CP}$ plotted as a function of approximated $x_p$ for $-1.0 < y_b < 0.0$ and $0.0 < y^* < 1.0$, constructed using $\langle y_{\text{b}} \rangle$ and $\langle y^{*} \rangle$. The proton-going direction is defined by $y_{\text{b}} > 0$.

$R_\text{CP}$ plotted as a function of approximated $x_p$ for $-1.0 < y_b < 0.0$ and $1.0 < y^* < 2.0$, constructed using $\langle y_{\text{b}} \rangle$ and $\langle y^{*} \rangle$. The proton-going direction is defined by $y_{\text{b}} > 0$.

$R_\text{CP}$ plotted as a function of approximated $x_p$ for $-1.0 < y_b < 0.0$ and $2.0 < y^* < 3.0$, constructed using $\langle y_{\text{b}} \rangle$ and $\langle y^{*} \rangle$. The proton-going direction is defined by $y_{\text{b}} > 0$.

$R_\text{CP}$ plotted as a function of approximated $x_p$ for $0.0 < y_b < 1.0$ and $0.0 < y^* < 1.0$, constructed using $\langle y_{\text{b}} \rangle$ and $\langle y^{*} \rangle$. The proton-going direction is defined by $y_{\text{b}} > 0$.

$R_\text{CP}$ plotted as a function of approximated $x_p$ for $0.0 < y_b < 1.0$ and $1.0 < y^* < 2.0$, constructed using $\langle y_{\text{b}} \rangle$ and $\langle y^{*} \rangle$. The proton-going direction is defined by $y_{\text{b}} > 0$.

$R_\text{CP}$ plotted as a function of approximated $x_p$ for $0.0 < y_b < 1.0$ and $2.0 < y^* < 4.0$, constructed using $\langle y_{\text{b}} \rangle$ and $\langle y^{*} \rangle$. The proton-going direction is defined by $y_{\text{b}} > 0$.

$R_\text{CP}$ plotted as a function of approximated $x_p$ for $1.0 < y_b < 2.0$ and $0.0 < y^* < 1.0$, constructed using $\langle y_{\text{b}} \rangle$ and $\langle y^{*} \rangle$. The proton-going direction is defined by $y_{\text{b}} > 0$.

$R_\text{CP}$ plotted as a function of approximated $x_p$ for $1.0 < y_b < 2.0$ and $1.0 < y^* < 2.0$, constructed using $\langle y_{\text{b}} \rangle$ and $\langle y^{*} \rangle$. The proton-going direction is defined by $y_{\text{b}} > 0$.

$R_\text{CP}$ plotted as a function of approximated $x_p$ for $1.0 < y_b < 2.0$ and $2.0 < y^* < 3.0$, constructed using $\langle y_{\text{b}} \rangle$ and $\langle y^{*} \rangle$. The proton-going direction is defined by $y_{\text{b}} > 0$.

$R_\text{CP}$ plotted as a function of approximated $x_p$ for $2.0 < y_b < 3.0$ and $0.0 < y^* < 1.0$, constructed using $\langle y_{\text{b}} \rangle$ and $\langle y^{*} \rangle$. The proton-going direction is defined by $y_{\text{b}} > 0$.

$R_\text{CP}$ plotted as a function of approximated $x_p$ for $2.0 < y_b < 3.0$ and $1.0 < y^* < 2.0$, constructed using $\langle y_{\text{b}} \rangle$ and $\langle y^{*} \rangle$. The proton-going direction is defined by $y_{\text{b}} > 0$.

$R_\text{CP}$ plotted as a function of approximated $x_p$ for $3.0 < y_b < 4.0$ and $0.0 < y^* < 1.0$, constructed using $\langle y_{\text{b}} \rangle$ and $\langle y^{*} \rangle$. The proton-going direction is defined by $y_{\text{b}} > 0$.

$R_\text{CP}$ plotted as a function of approximated $x_{Pb}$ for $-3.0 < y_b < -2.0$ and $0.0 < y^* < 1.0$, constructed using $\langle y_{\text{b}} \rangle$ and $\langle y^{*} \rangle$. The proton-going direction is defined by $y_{\text{b}} > 0$.

$R_\text{CP}$ plotted as a function of approximated $x_{Pb}$ for $-2.0 < y_b < -1.0$ and $0.0 < y^* < 1.0$, constructed using $\langle y_{\text{b}} \rangle$ and $\langle y^{*} \rangle$. The proton-going direction is defined by $y_{\text{b}} > 0$.

$R_\text{CP}$ plotted as a function of approximated $x_{Pb}$ for $-2.0 < y_b < -1.0$ and $1.0 < y^* < 2.0$, constructed using $\langle y_{\text{b}} \rangle$ and $\langle y^{*} \rangle$. The proton-going direction is defined by $y_{\text{b}} > 0$.

$R_\text{CP}$ plotted as a function of approximated $x_{Pb}$ for $-1.0 < y_b < 0.0$ and $0.0 < y^* < 1.0$, constructed using $\langle y_{\text{b}} \rangle$ and $\langle y^{*} \rangle$. The proton-going direction is defined by $y_{\text{b}} > 0$.

$R_\text{CP}$ plotted as a function of approximated $x_{Pb}$ for $-1.0 < y_b < 0.0$ and $1.0 < y^* < 2.0$, constructed using $\langle y_{\text{b}} \rangle$ and $\langle y^{*} \rangle$. The proton-going direction is defined by $y_{\text{b}} > 0$.

$R_\text{CP}$ plotted as a function of approximated $x_{Pb}$ for $-1.0 < y_b < 0.0$ and $2.0 < y^* < 3.0$, constructed using $\langle y_{\text{b}} \rangle$ and $\langle y^{*} \rangle$. The proton-going direction is defined by $y_{\text{b}} > 0$.

$R_\text{CP}$ plotted as a function of approximated $x_{Pb}$ for $0.0 < y_b < 1.0$ and $0.0 < y^* < 1.0$, constructed using $\langle y_{\text{b}} \rangle$ and $\langle y^{*} \rangle$. The proton-going direction is defined by $y_{\text{b}} > 0$.

$R_\text{CP}$ plotted as a function of approximated $x_{Pb}$ for $0.0 < y_b < 1.0$ and $1.0 < y^* < 2.0$, constructed using $\langle y_{\text{b}} \rangle$ and $\langle y^{*} \rangle$. The proton-going direction is defined by $y_{\text{b}} > 0$.

$R_\text{CP}$ plotted as a function of approximated $x_{Pb}$ for $0.0 < y_b < 1.0$ and $2.0 < y^* < 4.0$, constructed using $\langle y_{\text{b}} \rangle$ and $\langle y^{*} \rangle$. The proton-going direction is defined by $y_{\text{b}} > 0$.

$R_\text{CP}$ plotted as a function of approximated $x_{Pb}$ for $1.0 < y_b < 2.0$ and $0.0 < y^* < 1.0$, constructed using $\langle y_{\text{b}} \rangle$ and $\langle y^{*} \rangle$. The proton-going direction is defined by $y_{\text{b}} > 0$.

$R_\text{CP}$ plotted as a function of approximated $x_{Pb}$ for $1.0 < y_b < 2.0$ and $1.0 < y^* < 2.0$, constructed using $\langle y_{\text{b}} \rangle$ and $\langle y^{*} \rangle$. The proton-going direction is defined by $y_{\text{b}} > 0$.

$R_\text{CP}$ plotted as a function of approximated $x_{Pb}$ for $1.0 < y_b < 2.0$ and $2.0 < y^* < 3.0$, constructed using $\langle y_{\text{b}} \rangle$ and $\langle y^{*} \rangle$. The proton-going direction is defined by $y_{\text{b}} > 0$.

$R_\text{CP}$ plotted as a function of approximated $x_{Pb}$ for $2.0 < y_b < 3.0$ and $0.0 < y^* < 1.0$, constructed using $\langle y_{\text{b}} \rangle$ and $\langle y^{*} \rangle$. The proton-going direction is defined by $y_{\text{b}} > 0$.

$R_\text{CP}$ plotted as a function of approximated $x_{Pb}$ for $2.0 < y_b < 3.0$ and $1.0 < y^* < 2.0$, constructed using $\langle y_{\text{b}} \rangle$ and $\langle y^{*} \rangle$. The proton-going direction is defined by $y_{\text{b}} > 0$.

$R_\text{CP}$ plotted as a function of approximated $x_{Pb}$ for $3.0 < y_b < 4.0$ and $0.0 < y^* < 1.0$, constructed using $\langle y_{\text{b}} \rangle$ and $\langle y^{*} \rangle$. The proton-going direction is defined by $y_{\text{b}} > 0$.

$R_\text{CP}$ plotted as a function of approximated $x_\mathrm{F}$ for $-3.0 < y_b < -2.0$ and $0.0 < y^* < 1.0$, constructed using $\langle y_{\text{b}} \rangle$ and $\langle y^{*} \rangle$. The proton-going direction is defined by $y_{\text{b}} > 0$.

$R_\text{CP}$ plotted as a function of approximated $x_\mathrm{F}$ for $-2.0 < y_b < -1.0$ and $0.0 < y^* < 1.0$, constructed using $\langle y_{\text{b}} \rangle$ and $\langle y^{*} \rangle$. The proton-going direction is defined by $y_{\text{b}} > 0$.

$R_\text{CP}$ plotted as a function of approximated $x_\mathrm{F}$ for $-2.0 < y_b < -1.0$ and $1.0 < y^* < 2.0$, constructed using $\langle y_{\text{b}} \rangle$ and $\langle y^{*} \rangle$. The proton-going direction is defined by $y_{\text{b}} > 0$.

$R_\text{CP}$ plotted as a function of approximated $x_\mathrm{F}$ for $-1.0 < y_b < 0.0$ and $0.0 < y^* < 1.0$, constructed using $\langle y_{\text{b}} \rangle$ and $\langle y^{*} \rangle$. The proton-going direction is defined by $y_{\text{b}} > 0$.

$R_\text{CP}$ plotted as a function of approximated $x_\mathrm{F}$ for $-1.0 < y_b < 0.0$ and $1.0 < y^* < 2.0$, constructed using $\langle y_{\text{b}} \rangle$ and $\langle y^{*} \rangle$. The proton-going direction is defined by $y_{\text{b}} > 0$.

$R_\text{CP}$ plotted as a function of approximated $x_\mathrm{F}$ for $-1.0 < y_b < 0.0$ and $2.0 < y^* < 3.0$, constructed using $\langle y_{\text{b}} \rangle$ and $\langle y^{*} \rangle$. The proton-going direction is defined by $y_{\text{b}} > 0$.

$R_\text{CP}$ plotted as a function of approximated $x_\mathrm{F}$ for $0.0 < y_b < 1.0$ and $0.0 < y^* < 1.0$, constructed using $\langle y_{\text{b}} \rangle$ and $\langle y^{*} \rangle$. The proton-going direction is defined by $y_{\text{b}} > 0$.

$R_\text{CP}$ plotted as a function of approximated $x_\mathrm{F}$ for $0.0 < y_b < 1.0$ and $1.0 < y^* < 2.0$, constructed using $\langle y_{\text{b}} \rangle$ and $\langle y^{*} \rangle$. The proton-going direction is defined by $y_{\text{b}} > 0$.

$R_\text{CP}$ plotted as a function of approximated $x_\mathrm{F}$ for $0.0 < y_b < 1.0$ and $2.0 < y^* < 4.0$, constructed using $\langle y_{\text{b}} \rangle$ and $\langle y^{*} \rangle$. The proton-going direction is defined by $y_{\text{b}} > 0$.

$R_\text{CP}$ plotted as a function of approximated $x_\mathrm{F}$ for $1.0 < y_b < 2.0$ and $0.0 < y^* < 1.0$, constructed using $\langle y_{\text{b}} \rangle$ and $\langle y^{*} \rangle$. The proton-going direction is defined by $y_{\text{b}} > 0$.

$R_\text{CP}$ plotted as a function of approximated $x_\mathrm{F}$ for $1.0 < y_b < 2.0$ and $1.0 < y^* < 2.0$, constructed using $\langle y_{\text{b}} \rangle$ and $\langle y^{*} \rangle$. The proton-going direction is defined by $y_{\text{b}} > 0$.

$R_\text{CP}$ plotted as a function of approximated $x_\mathrm{F}$ for $1.0 < y_b < 2.0$ and $2.0 < y^* < 3.0$, constructed using $\langle y_{\text{b}} \rangle$ and $\langle y^{*} \rangle$. The proton-going direction is defined by $y_{\text{b}} > 0$.

$R_\text{CP}$ plotted as a function of approximated $x_\mathrm{F}$ for $2.0 < y_b < 3.0$ and $0.0 < y^* < 1.0$, constructed using $\langle y_{\text{b}} \rangle$ and $\langle y^{*} \rangle$. The proton-going direction is defined by $y_{\text{b}} > 0$.

$R_\text{CP}$ plotted as a function of approximated $x_\mathrm{F}$ for $2.0 < y_b < 3.0$ and $1.0 < y^* < 2.0$, constructed using $\langle y_{\text{b}} \rangle$ and $\langle y^{*} \rangle$. The proton-going direction is defined by $y_{\text{b}} > 0$.

$R_\text{CP}$ plotted as a function of approximated $x_\mathrm{F}$ for $3.0 < y_b < 4.0$ and $0.0 < y^* < 1.0$, constructed using $\langle y_{\text{b}} \rangle$ and $\langle y^{*} \rangle$. The proton-going direction is defined by $y_{\text{b}} > 0$.

$\langle T_{\text{AB}} \rangle$ normalized per-event dijet yields in $0-10$% collisions as a function of $\langle y_{\text{b}} \rangle$ for $40.0 < p_{\text{T,Avg}} \text{ [GeV]} < 49.6$ and $0.0 < y^{*} < 1.0$.

$\langle T_{\text{AB}} \rangle$ normalized per-event dijet yields in $60-90$% collisions as a function of $\langle y_{\text{b}} \rangle$ for $40.0 < p_{\text{T,Avg}} \text{ [GeV]} < 49.6$ and $0.0 < y^{*} < 1.0$.

$\langle T_{\text{AB}} \rangle$ normalized per-event dijet yields in $0-10$% collisions as a function of $\langle y_{\text{b}} \rangle$ for $49.6 < p_{\text{T,Avg}} \text{ [GeV]} < 61.4$ and $0.0 < y^{*} < 1.0$.

$\langle T_{\text{AB}} \rangle$ normalized per-event dijet yields in $60-90$% collisions as a function of $\langle y_{\text{b}} \rangle$ for $49.6 < p_{\text{T,Avg}} \text{ [GeV]} < 61.4$ and $0.0 < y^{*} < 1.0$.

$\langle T_{\text{AB}} \rangle$ normalized per-event dijet yields in $0-10$% collisions as a function of $\langle y_{\text{b}} \rangle$ for $61.4 < p_{\text{T,Avg}} \text{ [GeV]} < 76.1$ and $0.0 < y^{*} < 1.0$.

$\langle T_{\text{AB}} \rangle$ normalized per-event dijet yields in $60-90$% collisions as a function of $\langle y_{\text{b}} \rangle$ for $61.4 < p_{\text{T,Avg}} \text{ [GeV]} < 76.1$ and $0.0 < y^{*} < 1.0$.

$\langle T_{\text{AB}} \rangle$ normalized per-event dijet yields in $0-10$% collisions as a function of $\langle y_{\text{b}} \rangle$ for $40.0 < p_{\text{T,Avg}} \text{ [GeV]} < 49.6$ and $1.0 < y^{*} < 2.0$.

$\langle T_{\text{AB}} \rangle$ normalized per-event dijet yields in $60-90$% collisions as a function of $\langle y_{\text{b}} \rangle$ for $40.0 < p_{\text{T,Avg}} \text{ [GeV]} < 49.6$ and $1.0 < y^{*} < 2.0$.

$\langle T_{\text{AB}} \rangle$ normalized per-event dijet yields in $0-10$% collisions as a function of $\langle y_{\text{b}} \rangle$ for $49.6 < p_{\text{T,Avg}} \text{ [GeV]} < 61.4$ and $1.0 < y^{*} < 2.0$.

$\langle T_{\text{AB}} \rangle$ normalized per-event dijet yields in $60-90$% collisions as a function of $\langle y_{\text{b}} \rangle$ for $49.6 < p_{\text{T,Avg}} \text{ [GeV]} < 61.4$ and $1.0 < y^{*} < 2.0$.

$\langle T_{\text{AB}} \rangle$ normalized per-event dijet yields in $0-10$% collisions as a function of $\langle y_{\text{b}} \rangle$ for $61.4 < p_{\text{T,Avg}} \text{ [GeV]} < 76.1$ and $1.0 < y^{*} < 2.0$.

$\langle T_{\text{AB}} \rangle$ normalized per-event dijet yields in $60-90$% collisions as a function of $\langle y_{\text{b}} \rangle$ for $61.4 < p_{\text{T,Avg}} \text{ [GeV]} < 76.1$ and $1.0 < y^{*} < 2.0$.

$\langle T_{\text{AB}} \rangle$ normalized per-event dijet yields in $0-10$% collisions as a function of $\langle y_{\text{b}} \rangle$ for $40.0 < p_{\text{T,Avg}} \text{ [GeV]} < 49.6$ and $2.0 < y^{*} < 4.0$.

$\langle T_{\text{AB}} \rangle$ normalized per-event dijet yields in $60-90$% collisions as a function of $\langle y_{\text{b}} \rangle$ for $40.0 < p_{\text{T,Avg}} \text{ [GeV]} < 49.6$ and $2.0 < y^{*} < 4.0$.

$\langle T_{\text{AB}} \rangle$ normalized per-event dijet yields in $0-10$% collisions as a function of $\langle y_{\text{b}} \rangle$ for $49.6 < p_{\text{T,Avg}} \text{ [GeV]} < 61.4$ and $2.0 < y^{*} < 4.0$.

$\langle T_{\text{AB}} \rangle$ normalized per-event dijet yields in $60-90$% collisions as a function of $\langle y_{\text{b}} \rangle$ for $49.6 < p_{\text{T,Avg}} \text{ [GeV]} < 61.4$ and $2.0 < y^{*} < 4.0$.

$\langle T_{\text{AB}} \rangle$ normalized per-event dijet yields in $0-10$% collisions as a function of $\langle y_{\text{b}} \rangle$ for $61.4 < p_{\text{T,Avg}} \text{ [GeV]} < 76.1$ and $2.0 < y^{*} < 4.0$.

$\langle T_{\text{AB}} \rangle$ normalized per-event dijet yields in $60-90$% collisions as a function of $\langle y_{\text{b}} \rangle$ for $61.4 < p_{\text{T,Avg}} \text{ [GeV]} < 76.1$ and $2.0 < y^{*} < 4.0$.

Observed 95% CL upper limits on the cross section for all masses and charges of photon-fusion pair-produced spin-1/2 monopoles as a function of mass for magnetic charges $|g|=1g_D$ and $|g|=2g_D$.

Observed 95% CL upper limits on the cross section for all masses and charges of Drell-Yan spin-0 HECOs production as a function of mass for various values of electric charge in the range $20\le|z|\le100$.

Observed 95% CL upper limits on the cross section for all masses and charges of Drell-Yan spin-1/2 HECOs production as a function of mass for various values of electric charge in the range $20\le|z|\le100$.

Observed 95% CL upper limits on the cross section for all masses and charges of photon-fusion pair-produced spin-0 HECOs as a function of mass for various values of electric charge in the range $20\le|z|\le100$.

Observed 95% CL upper limits on the cross section for all masses and charges of photon-fusion pair-produced spin-1/2 HECOs as a function of mass for various values of electric charge in the range $20\le|z|\le100$.

Selection efficiency as a function of transverse kinetic energy $E^\text{kin}_\text{T}=E_\text{kin}\sin\theta$ and pseudorapidity $|\eta|$ for $g=1g_\textrm{D}$ monopoles of mass 200 GeV.

Selection efficiency as a function of transverse kinetic energy $E^\text{kin}_\text{T}=E_\text{kin}\sin\theta$ and pseudorapidity $|\eta|$ for $g=1g_\textrm{D}$ monopoles of mass 500 GeV.

Selection efficiency as a function of transverse kinetic energy $E^\text{kin}_\text{T}=E_\text{kin}\sin\theta$ and pseudorapidity $|\eta|$ for $g=1g_\textrm{D}$ monopoles of mass 1000 GeV.

Selection efficiency as a function of transverse kinetic energy $E^\text{kin}_\text{T}=E_\text{kin}\sin\theta$ and pseudorapidity $|\eta|$ for $g=1g_\textrm{D}$ monopoles of mass 1500 GeV.

Selection efficiency as a function of transverse kinetic energy $E^\text{kin}_\text{T}=E_\text{kin}\sin\theta$ and pseudorapidity $|\eta|$ for $g=1g_\textrm{D}$ monopoles of mass 2000 GeV.

Selection efficiency as a function of transverse kinetic energy $E^\text{kin}_\text{T}=E_\text{kin}\sin\theta$ and pseudorapidity $|\eta|$ for $g=1g_\textrm{D}$ monopoles of mass 2500 GeV.

Selection efficiency as a function of transverse kinetic energy $E^\text{kin}_\text{T}=E_\text{kin}\sin\theta$ and pseudorapidity $|\eta|$ for $g=1g_\textrm{D}$ monopoles of mass 3000 GeV.

Selection efficiency as a function of transverse kinetic energy $E^\text{kin}_\text{T}=E_\text{kin}\sin\theta$ and pseudorapidity $|\eta|$ for $g=1g_\textrm{D}$ monopoles of mass 4000 GeV.

Selection efficiency as a function of transverse kinetic energy $E^\text{kin}_\text{T}=E_\text{kin}\sin\theta$ and pseudorapidity $|\eta|$ for $g=2g_\textrm{D}$ monopoles of mass 200 GeV.

Selection efficiency as a function of transverse kinetic energy $E^\text{kin}_\text{T}=E_\text{kin}\sin\theta$ and pseudorapidity $|\eta|$ for $g=2g_\textrm{D}$ monopoles of mass 500 GeV.

Selection efficiency as a function of transverse kinetic energy $E^\text{kin}_\text{T}=E_\text{kin}\sin\theta$ and pseudorapidity $|\eta|$ for $g=2g_\textrm{D}$ monopoles of mass 1000 GeV.

Selection efficiency as a function of transverse kinetic energy $E^\text{kin}_\text{T}=E_\text{kin}\sin\theta$ and pseudorapidity $|\eta|$ for $g=2g_\textrm{D}$ monopoles of mass 1500 GeV.

Selection efficiency as a function of transverse kinetic energy $E^\text{kin}_\text{T}=E_\text{kin}\sin\theta$ and pseudorapidity $|\eta|$ for $g=2g_\textrm{D}$ monopoles of mass 2000 GeV.

Selection efficiency as a function of transverse kinetic energy $E^\text{kin}_\text{T}=E_\text{kin}\sin\theta$ and pseudorapidity $|\eta|$ for $g=2g_\textrm{D}$ monopoles of mass 2500 GeV.

Selection efficiency as a function of transverse kinetic energy $E^\text{kin}_\text{T}=E_\text{kin}\sin\theta$ and pseudorapidity $|\eta|$ for $g=2g_\textrm{D}$ monopoles of mass 3000 GeV.

Selection efficiency as a function of transverse kinetic energy $E^\text{kin}_\text{T}=E_\text{kin}\sin\theta$ and pseudorapidity $|\eta|$ for $g=2g_\textrm{D}$ monopoles of mass 4000 GeV.

Selection efficiency as a function of transverse kinetic energy $E^\text{kin}_\text{T}=E_\text{kin}\sin\theta$ and pseudorapidity $|\eta|$ for HECOs of charge $|z|=20$ of mass 200 GeV.

Selection efficiency as a function of transverse kinetic energy $E^\text{kin}_\text{T}=E_\text{kin}\sin\theta$ and pseudorapidity $|\eta|$ for HECOs of charge $|z|=20$ of mass 500 GeV.

Selection efficiency as a function of transverse kinetic energy $E^\text{kin}_\text{T}=E_\text{kin}\sin\theta$ and pseudorapidity $|\eta|$ for HECOs of charge $|z|=20$ of mass 1000 GeV.

Selection efficiency as a function of transverse kinetic energy $E^\text{kin}_\text{T}=E_\text{kin}\sin\theta$ and pseudorapidity $|\eta|$ for HECOs of charge $|z|=20$ of mass 1500 GeV.

Selection efficiency as a function of transverse kinetic energy $E^\text{kin}_\text{T}=E_\text{kin}\sin\theta$ and pseudorapidity $|\eta|$ for HECOs of charge $|z|=20$ of mass 2000 GeV.

Selection efficiency as a function of transverse kinetic energy $E^\text{kin}_\text{T}=E_\text{kin}\sin\theta$ and pseudorapidity $|\eta|$ for HECOs of charge $|z|=20$ of mass 2500 GeV.

Selection efficiency as a function of transverse kinetic energy $E^\text{kin}_\text{T}=E_\text{kin}\sin\theta$ and pseudorapidity $|\eta|$ for HECOs of charge $|z|=20$ of mass 3000 GeV.

Selection efficiency as a function of transverse kinetic energy $E^\text{kin}_\text{T}=E_\text{kin}\sin\theta$ and pseudorapidity $|\eta|$ for HECOs of charge $|z|=20$ of mass 4000 GeV.

Selection efficiency as a function of transverse kinetic energy $E^\text{kin}_\text{T}=E_\text{kin}\sin\theta$ and pseudorapidity $|\eta|$ for HECOs of charge $|z|=40$ of mass 200 GeV.

Selection efficiency as a function of transverse kinetic energy $E^\text{kin}_\text{T}=E_\text{kin}\sin\theta$ and pseudorapidity $|\eta|$ for HECOs of charge $|z|=40$ of mass 500 GeV.

Selection efficiency as a function of transverse kinetic energy $E^\text{kin}_\text{T}=E_\text{kin}\sin\theta$ and pseudorapidity $|\eta|$ for HECOs of charge $|z|=40$ of mass 1000 GeV.

Selection efficiency as a function of transverse kinetic energy $E^\text{kin}_\text{T}=E_\text{kin}\sin\theta$ and pseudorapidity $|\eta|$ for HECOs of charge $|z|=40$ of mass 1500 GeV.

Selection efficiency as a function of transverse kinetic energy $E^\text{kin}_\text{T}=E_\text{kin}\sin\theta$ and pseudorapidity $|\eta|$ for HECOs of charge $|z|=40$ of mass 2000 GeV.

Selection efficiency as a function of transverse kinetic energy $E^\text{kin}_\text{T}=E_\text{kin}\sin\theta$ and pseudorapidity $|\eta|$ for HECOs of charge $|z|=40$ of mass 2500 GeV.

Selection efficiency as a function of transverse kinetic energy $E^\text{kin}_\text{T}=E_\text{kin}\sin\theta$ and pseudorapidity $|\eta|$ for HECOs of charge $|z|=40$ of mass 3000 GeV.

Selection efficiency as a function of transverse kinetic energy $E^\text{kin}_\text{T}=E_\text{kin}\sin\theta$ and pseudorapidity $|\eta|$ for HECOs of charge $|z|=40$ of mass 4000 GeV.

Selection efficiency as a function of transverse kinetic energy $E^\text{kin}_\text{T}=E_\text{kin}\sin\theta$ and pseudorapidity $|\eta|$ for HECOs of charge $|z|=60$ of mass 200 GeV.

Selection efficiency as a function of transverse kinetic energy $E^\text{kin}_\text{T}=E_\text{kin}\sin\theta$ and pseudorapidity $|\eta|$ for HECOs of charge $|z|=60$ of mass 500 GeV.

Selection efficiency as a function of transverse kinetic energy $E^\text{kin}_\text{T}=E_\text{kin}\sin\theta$ and pseudorapidity $|\eta|$ for HECOs of charge $|z|=60$ of mass 1000 GeV.

Selection efficiency as a function of transverse kinetic energy $E^\text{kin}_\text{T}=E_\text{kin}\sin\theta$ and pseudorapidity $|\eta|$ for HECOs of charge $|z|=60$ of mass 1500 GeV.

Selection efficiency as a function of transverse kinetic energy $E^\text{kin}_\text{T}=E_\text{kin}\sin\theta$ and pseudorapidity $|\eta|$ for HECOs of charge $|z|=60$ of mass 2000 GeV.

Selection efficiency as a function of transverse kinetic energy $E^\text{kin}_\text{T}=E_\text{kin}\sin\theta$ and pseudorapidity $|\eta|$ for HECOs of charge $|z|=60$ of mass 2500 GeV.

Selection efficiency as a function of transverse kinetic energy $E^\text{kin}_\text{T}=E_\text{kin}\sin\theta$ and pseudorapidity $|\eta|$ for HECOs of charge $|z|=60$ of mass 3000 GeV.

Selection efficiency as a function of transverse kinetic energy $E^\text{kin}_\text{T}=E_\text{kin}\sin\theta$ and pseudorapidity $|\eta|$ for HECOs of charge $|z|=60$ of mass 4000 GeV.

Selection efficiency as a function of transverse kinetic energy $E^\text{kin}_\text{T}=E_\text{kin}\sin\theta$ and pseudorapidity $|\eta|$ for HECOs of charge $|z|=80$ of mass 200 GeV.

Selection efficiency as a function of transverse kinetic energy $E^\text{kin}_\text{T}=E_\text{kin}\sin\theta$ and pseudorapidity $|\eta|$ for HECOs of charge $|z|=80$ of mass 500 GeV.

Selection efficiency as a function of transverse kinetic energy $E^\text{kin}_\text{T}=E_\text{kin}\sin\theta$ and pseudorapidity $|\eta|$ for HECOs of charge $|z|=80$ of mass 1000 GeV.

Selection efficiency as a function of transverse kinetic energy $E^\text{kin}_\text{T}=E_\text{kin}\sin\theta$ and pseudorapidity $|\eta|$ for HECOs of charge $|z|=80$ of mass 1500 GeV.

Selection efficiency as a function of transverse kinetic energy $E^\text{kin}_\text{T}=E_\text{kin}\sin\theta$ and pseudorapidity $|\eta|$ for HECOs of charge $|z|=80$ of mass 2000 GeV.

Selection efficiency as a function of transverse kinetic energy $E^\text{kin}_\text{T}=E_\text{kin}\sin\theta$ and pseudorapidity $|\eta|$ for HECOs of charge $|z|=80$ of mass 2500 GeV.

Selection efficiency as a function of transverse kinetic energy $E^\text{kin}_\text{T}=E_\text{kin}\sin\theta$ and pseudorapidity $|\eta|$ for HECOs of charge $|z|=80$ of mass 3000 GeV.

Selection efficiency as a function of transverse kinetic energy $E^\text{kin}_\text{T}=E_\text{kin}\sin\theta$ and pseudorapidity $|\eta|$ for HECOs of charge $|z|=80$ of mass 4000 GeV.

Selection efficiency as a function of transverse kinetic energy $E^\text{kin}_\text{T}=E_\text{kin}\sin\theta$ and pseudorapidity $|\eta|$ for HECOs of charge $|z|=100$ of mass 200 GeV.

Selection efficiency as a function of transverse kinetic energy $E^\text{kin}_\text{T}=E_\text{kin}\sin\theta$ and pseudorapidity $|\eta|$ for HECOs of charge $|z|=100$ of mass 500 GeV.

Selection efficiency as a function of transverse kinetic energy $E^\text{kin}_\text{T}=E_\text{kin}\sin\theta$ and pseudorapidity $|\eta|$ for HECOs of charge $|z|=100$ of mass 1000 GeV.

Selection efficiency as a function of transverse kinetic energy $E^\text{kin}_\text{T}=E_\text{kin}\sin\theta$ and pseudorapidity $|\eta|$ for HECOs of charge $|z|=100$ of mass 1500 GeV.

Selection efficiency as a function of transverse kinetic energy $E^\text{kin}_\text{T}=E_\text{kin}\sin\theta$ and pseudorapidity $|\eta|$ for HECOs of charge $|z|=100$ of mass 2000 GeV.

Selection efficiency as a function of transverse kinetic energy $E^\text{kin}_\text{T}=E_\text{kin}\sin\theta$ and pseudorapidity $|\eta|$ for HECOs of charge $|z|=100$ of mass 2500 GeV.

Selection efficiency as a function of transverse kinetic energy $E^\text{kin}_\text{T}=E_\text{kin}\sin\theta$ and pseudorapidity $|\eta|$ for HECOs of charge $|z|=100$ of mass 3000 GeV.

Selection efficiency as a function of transverse kinetic energy $E^\text{kin}_\text{T}=E_\text{kin}\sin\theta$ and pseudorapidity $|\eta|$ for HECOs of charge $|z|=100$ of mass 4000 GeV.

The average tetrajet invariant mass distributions in data, along with the fitted background estimates for 0.16 < $\alpha$ < 0.18.

The average tetrajet invariant mass distributions in data, along with the fitted background estimates for 0.18 < $\alpha$ < 0.20.

The average tetrajet invariant mass distributions in data, along with the fitted background estimates for 0.20 < $\alpha$ < 0.22.

The average tetrajet invariant mass distributions in data, along with the fitted background estimates for 0.22 < $\alpha$ < 0.24.

The average tetrajet invariant mass distributions in data, along with the fitted background estimates for 0.24 < $\alpha$ < 0.26.

The average tetrajet invariant mass distributions in data, along with the fitted background estimates for 0.26 < $\alpha$ < 0.28.

The average tetrajet invariant mass distributions in data, along with the fitted background estimates for 0.28 < $\alpha$ < 0.30.

The average tetrajet invariant mass distributions in data, along with the fitted background estimates for 0.30 < $\alpha$ < 0.32.

The average tetrajet invariant mass distributions in data, along with the fitted background estimates for 0.32 < $\alpha$ < 0.34.

The average dijet invariant mass distributions in data, along with the fitted background estimates for 0.10 < $\alpha$ < 0.12.

The average dijet invariant mass distributions in data, along with the fitted background estimates for 0.12 < $\alpha$ < 0.14.

The average dijet invariant mass distributions in data, along with the fitted background estimates for 0.14 < $\alpha$ < 0.16.

The average dijet invariant mass distributions in data, along with the fitted background estimates for 0.16 < $\alpha$ < 0.18.

The average dijet invariant mass distributions in data, along with the fitted background estimates for 0.18 < $\alpha$ < 0.20.

The average dijet invariant mass distributions in data, along with the fitted background estimates for 0.20 < $\alpha$ < 0.22.

The average dijet invariant mass distributions in data, along with the fitted background estimates for 0.22 < $\alpha$ < 0.24.

The average dijet invariant mass distributions in data, along with the fitted background estimates for 0.24 < $\alpha$ < 0.26.

The average dijet invariant mass distributions in data, along with the fitted background estimates for 0.26 < $\alpha$ < 0.28.

The average dijet invariant mass distributions in data, along with the fitted background estimates for 0.28 < $\alpha$ < 0.30.

The average dijet invariant mass distributions in data, along with the fitted background estimates for 0.30 < $\alpha$ < 0.32.

The average dijet invariant mass distributions in data, along with the fitted background estimates for 0.32 < $\alpha$ < 0.34.

The expected and observed limits on $m_{4j}$ for signal templates using a 4-parameter fit function for 0.10 < $\alpha$ < 0.12.

The expected and observed limits on $m_{4j}$ for signal templates using a 4-parameter fit function for 0.12 < $\alpha$ < 0.14.

The expected and observed limits on $m_{4j}$ for signal templates using a 4-parameter fit function for 0.14 < $\alpha$ < 0.16.

The expected and observed limits on $m_{4j}$ for signal templates using a 4-parameter fit function for 0.16 < $\alpha$ < 0.18.

The expected and observed limits on $m_{4j}$ for signal templates using a 4-parameter fit function for 0.18 < $\alpha$ < 0.20.

The expected and observed limits on $m_{4j}$ for signal templates using a 4-parameter fit function for 0.20 < $\alpha$ < 0.22.

The expected and observed limits on $m_{4j}$ for signal templates using a 4-parameter fit function for 0.22 < $\alpha$ < 0.24.

The expected and observed limits on $m_{4j}$ for signal templates using a 4-parameter fit function for 0.24 < $\alpha$ < 0.26.

The expected and observed limits on $m_{4j}$ for signal templates using a 4-parameter fit function for 0.26 < $\alpha$ < 0.28.

The expected and observed limits on $m_{4j}$ for signal templates using a 4-parameter fit function for 0.28 < $\alpha$ < 0.30.

The expected and observed limits on $m_{4j}$ for signal templates using a 4-parameter fit function for 0.30 < $\alpha$ < 0.32.

The expected and observed limits on $m_{4j}$ for signal templates using a 4-parameter fit function for 0.32 < $\alpha$ < 0.34.

The expected and observed limits on $m_{\langle 2j \rangle}$ for signal templates using a 4-parameter fit function for 0.10 < $\alpha$ < 0.12.

The expected and observed limits on $m_{\langle 2j \rangle}$ for signal templates using a 4-parameter fit function for 0.12 < $\alpha$ < 0.14.

The expected and observed limits on $m_{\langle 2j \rangle}$ for signal templates using a 4-parameter fit function for 0.14 < $\alpha$ < 0.16.

The expected and observed limits on $m_{\langle 2j \rangle}$ for signal templates using a 4-parameter fit function for 0.16 < $\alpha$ < 0.18.

The expected and observed limits on $m_{\langle 2j \rangle}$ for signal templates using a 4-parameter fit function for 0.18 < $\alpha$ < 0.20.

The expected and observed limits on $m_{\langle 2j \rangle}$ for signal templates using a 4-parameter fit function for 0.20 < $\alpha$ < 0.22.

The expected and observed limits on $m_{\langle 2j \rangle}$ for signal templates using a 4-parameter fit function for 0.22 < $\alpha$ < 0.24.

The expected and observed limits on $m_{\langle 2j \rangle}$ for signal templates using a 4-parameter fit function for 0.24 < $\alpha$ < 0.26.

The expected and observed limits on $m_{\langle 2j \rangle}$ for signal templates using a 4-parameter fit function for 0.26 < $\alpha$ < 0.28.

The expected and observed limits on $m_{\langle 2j \rangle}$ for signal templates using a 4-parameter fit function for 0.28 < $\alpha$ < 0.30.

The expected and observed limits on $m_{\langle 2j \rangle}$ for signal templates using a 4-parameter fit function for 0.30 < $\alpha$ < 0.32.

The expected and observed limits on $m_{\langle 2j \rangle}$ for signal templates using a 4-parameter fit function for 0.32 < $\alpha$ < 0.34.

The expected and observed limits on $m_{4j}$ for Gaussian signal templates using a 4-parameter fit function for 0.10 < $\alpha$ < 0.12.

The expected and observed limits on $m_{4j}$ for Gaussian signal templates using a 4-parameter fit function for 0.12 < $\alpha$ < 0.14.

The expected and observed limits on $m_{4j}$ for Gaussian signal templates using a 4-parameter fit function for 0.14 < $\alpha$ < 0.16.

The expected and observed limits on $m_{4j}$ for Gaussian signal templates using a 4-parameter fit function for 0.16 < $\alpha$ < 0.18.

The expected and observed limits on $m_{4j}$ for Gaussian signal templates using a 4-parameter fit function for 0.18 < $\alpha$ < 0.20.

The expected and observed limits on $m_{4j}$ for Gaussian signal templates using a 4-parameter fit function for 0.20 < $\alpha$ < 0.22.

The expected and observed limits on $m_{4j}$ for Gaussian signal templates using a 4-parameter fit function for 0.22 < $\alpha$ < 0.24.

The expected and observed limits on $m_{4j}$ for Gaussian signal templates using a 4-parameter fit function for 0.24 < $\alpha$ < 0.26.

The expected and observed limits on $m_{4j}$ for Gaussian signal templates using a 4-parameter fit function for 0.26 < $\alpha$ < 0.28.

The expected and observed limits on $m_{4j}$ for Gaussian signal templates using a 4-parameter fit function for 0.28 < $\alpha$ < 0.30.

The expected and observed limits on $m_{4j}$ for Gaussian signal templates using a 4-parameter fit function for 0.30 < $\alpha$ < 0.32.

The expected and observed limits on $m_{4j}$ for Gaussian signal templates using a 4-parameter fit function for 0.32 < $\alpha$ < 0.34.

The expected and observed limits on $m_{\langle 2j \rangle}$ for Gaussian signal templates using a 4-parameter fit function for 0.10 < $\alpha$ < 0.12.

The expected and observed limits on $m_{\langle 2j \rangle}$ for Gaussian signal templates using a 4-parameter fit function for 0.12 < $\alpha$ < 0.14.

The expected and observed limits on $m_{\langle 2j \rangle}$ for Gaussian signal templates using a 4-parameter fit function for 0.14 < $\alpha$ < 0.16.

The expected and observed limits on $m_{\langle 2j \rangle}$ for Gaussian signal templates using a 4-parameter fit function for 0.16 < $\alpha$ < 0.18.

The expected and observed limits on $m_{\langle 2j \rangle}$ for Gaussian signal templates using a 4-parameter fit function for 0.18 < $\alpha$ < 0.20.

The expected and observed limits on $m_{\langle 2j \rangle}$ for Gaussian signal templates using a 4-parameter fit function for 0.20 < $\alpha$ < 0.22.

The expected and observed limits on $m_{\langle 2j \rangle}$ for Gaussian signal templates using a 4-parameter fit function for 0.22 < $\alpha$ < 0.24.

The expected and observed limits on $m_{\langle 2j \rangle}$ for Gaussian signal templates using a 4-parameter fit function for 0.24 < $\alpha$ < 0.26.

The expected and observed limits on $m_{\langle 2j \rangle}$ for Gaussian signal templates using a 4-parameter fit function for 0.26 < $\alpha$ < 0.28.

The expected and observed limits on $m_{\langle 2j \rangle}$ for Gaussian signal templates using a 4-parameter fit function for 0.28 < $\alpha$ < 0.30.

The expected and observed limits on $m_{\langle 2j \rangle}$ for Gaussian signal templates using a 4-parameter fit function for 0.30 < $\alpha$ < 0.32.

The expected and observed limits on $m_{\langle 2j \rangle}$ for Gaussian signal templates using a 4-parameter fit function for 0.32 < $\alpha$ < 0.34.

The product of the analysis acceptance and selection efficiency for all analysis selection criteria is shown as a function of $m_Y$ and $m_{X}/m_{Y}$.

The cross-section of the signal samples is shown as a function of $m_Y$ and $m_{X}/m_{Y}$.

Event yields after a background-only fit and data in the three signal regions. All the backgrounds except the fake $\tau_{\mathrm{had}}$ are estimated using the real $\tau_{\mathrm{had}}$ contributions from MC. The fake $\tau_{\mathrm{had}}$ background contribution is data-driven. The top-quark, $V$+jets, and small backgrounds are included in "Others". The total uncertainties include both systematic and statistical. In the $WW1\ell2\tau_{\mathrm{had}}$ channel, the smaller uncertainty in the total background than the fake $\tau_{\mathrm{had}}$ is due to the anticorrelations between the nuisance parameters of the fake $\tau_{\mathrm{had}}$ and diboson backgrounds obtained in the fit.

Uncertainties on $\sigma(pp\to X\to SH)$ obtained from the combined fit to the data for the least and most sensitive mass points. The uncertainties are symmetrised and grouped into the categories described in the systematic section.

Summary of observed and expected 95\% CL upper limits on the production cross-section of $\sigma(pp\to X\to SH)$ in fb in the combination of $WW$+$ZZ$, and in the $WW$ and $ZZ$ individual channels. The best expected limit is set to 85 fb for $m_{X}$ = 1250 GeV and $_{S}$ = 300 GeV. The combination limits gain about 26--53% over the best individual limit from the $WW1\ell2\tau_{\mathrm{had}}$ channel.

Values of $\Delta Z$ for the discovery sensitivity, as defined in the text, as a function of the invariant mass $\textit{m}$. The j+b-jet invariant mass distribution is calculated in the 10 pb AR. Positive percentages indicate improvements in sensitivity. Horizontal dashed lines are drawn at 100% and 200% to guide the eye. The five benchmark BSM models are (1) charged Higgs boson production in association with a top quark, $tbH^{+}$ with $H^{+} \rightarrow t\bar{b}$; (2) a Kaluza-Klein gauge boson, $W_{KK}$, with the SM $W$ boson and a radion $\phi$; (3) a $Z'$ boson decaying to a composite lepton $E$ and $\ell$, with $E \rightarrow Z\ell$; (4) the sequential standard model $W' \rightarrow W Z' \rightarrow \ell\nu q\bar{q}$; (5) a simplified dark-matter model with an axial-vector mediator $Z' \rightarrow q\bar{q}$, where one of the quarks radiates a $W$ boson decaying to $\ell\nu$. The multiple markers shown for the composite-lepton model at the same invariant mass values correspond to different composite lepton ($E$) masses between 0.25 and 3.5 TeV. The center positions of the markers are set to the masses of the corresponding heavy particles.

Values of $\Delta Z$ for the discovery sensitivity, as defined in the text, as a function of the invariant mass $\textit{m}$. The 2b-jet invariant mass distribution is calculated in the 10 pb AR. Positive percentages indicate improvements in sensitivity. Horizontal dashed lines are drawn at 100% and 200% to guide the eye. The five benchmark BSM models are (1) charged Higgs boson production in association with a top quark, $tbH^{+}$ with $H^{+} \rightarrow t\bar{b}$; (2) a Kaluza-Klein gauge boson, $W_{KK}$, with the SM $W$ boson and a radion $\phi$; (3) a $Z'$ boson decaying to a composite lepton $E$ and $\ell$, with $E \rightarrow Z\ell$; (4) the sequential standard model $W' \rightarrow W Z' \rightarrow \ell\nu q\bar{q}$; (5) a simplified dark-matter model with an axial-vector mediator $Z' \rightarrow q\bar{q}$, where one of the quarks radiates a $W$ boson decaying to $\ell\nu$. The multiple markers shown for the composite-lepton model at the same invariant mass values correspond to different composite lepton ($E$) masses between 0.25 and 3.5 TeV. The center positions of the markers are set to the masses of the corresponding heavy particles.

Values of $\Delta Z$ for the discovery sensitivity, as defined in the text, as a function of the invariant mass $\textit{m}$. The j+e invariant mass distribution is calculated in the 10 pb AR. Positive percentages indicate improvements in sensitivity. Horizontal dashed lines are drawn at 100% and 200% to guide the eye. The five benchmark BSM models are (1) charged Higgs boson production in association with a top quark, $tbH^{+}$ with $H^{+} \rightarrow t\bar{b}$; (2) a Kaluza-Klein gauge boson, $W_{KK}$, with the SM $W$ boson and a radion $\phi$; (3) a $Z'$ boson decaying to a composite lepton $E$ and $\ell$, with $E \rightarrow Z\ell$; (4) the sequential standard model $W' \rightarrow W Z' \rightarrow \ell\nu q\bar{q}$; (5) a simplified dark-matter model with an axial-vector mediator $Z' \rightarrow q\bar{q}$, where one of the quarks radiates a $W$ boson decaying to $\ell\nu$. The multiple markers shown for the composite-lepton model at the same invariant mass values correspond to different composite lepton ($E$) masses between 0.25 and 3.5 TeV. The center positions of the markers are set to the masses of the corresponding heavy particles.

Values of $\Delta Z$ for the discovery sensitivity, as defined in the text, as a function of the invariant mass $\textit{m}$. The b-jet+e invariant mass distribution is calculated in the 10 pb AR. Positive percentages indicate improvements in sensitivity. Horizontal dashed lines are drawn at 100% and 200% to guide the eye. The five benchmark BSM models are (1) charged Higgs boson production in association with a top quark, $tbH^{+}$ with $H^{+} \rightarrow t\bar{b}$; (2) a Kaluza-Klein gauge boson, $W_{KK}$, with the SM $W$ boson and a radion $\phi$; (3) a $Z'$ boson decaying to a composite lepton $E$ and $\ell$, with $E \rightarrow Z\ell$; (4) the sequential standard model $W' \rightarrow W Z' \rightarrow \ell\nu q\bar{q}$; (5) a simplified dark-matter model with an axial-vector mediator $Z' \rightarrow q\bar{q}$, where one of the quarks radiates a $W$ boson decaying to $\ell\nu$. The multiple markers shown for the composite-lepton model at the same invariant mass values correspond to different composite lepton ($E$) masses between 0.25 and 3.5 TeV. The center positions of the markers are set to the masses of the corresponding heavy particles.

Values of $\Delta Z$ for the discovery sensitivity, as defined in the text, as a function of the invariant mass $\textit{m}$. The j+$\gamma$ invariant mass distribution is calculated in the 10 pb AR. Positive percentages indicate improvements in sensitivity. Horizontal dashed lines are drawn at 100% and 200% to guide the eye. The five benchmark BSM models are (1) charged Higgs boson production in association with a top quark, $tbH^{+}$ with $H^{+} \rightarrow t\bar{b}$; (2) a Kaluza-Klein gauge boson, $W_{KK}$, with the SM $W$ boson and a radion $\phi$; (3) a $Z'$ boson decaying to a composite lepton $E$ and $\ell$, with $E \rightarrow Z\ell$; (4) the sequential standard model $W' \rightarrow W Z' \rightarrow \ell\nu q\bar{q}$; (5) a simplified dark-matter model with an axial-vector mediator $Z' \rightarrow q\bar{q}$, where one of the quarks radiates a $W$ boson decaying to $\ell\nu$. The multiple markers shown for the composite-lepton model at the same invariant mass values correspond to different composite lepton ($E$) masses between 0.25 and 3.5 TeV. The center positions of the markers are set to the masses of the corresponding heavy particles.

Values of $\Delta Z$ for the discovery sensitivity, as defined in the text, as a function of the invariant mass $\textit{m}$. The j+$\mu$ invariant mass distribution is calculated in the 10 pb AR. Positive percentages indicate improvements in sensitivity. Horizontal dashed lines are drawn at 100% and 200% to guide the eye. The five benchmark BSM models are (1) charged Higgs boson production in association with a top quark, $tbH^{+}$ with $H^{+} \rightarrow t\bar{b}$; (2) a Kaluza-Klein gauge boson, $W_{KK}$, with the SM $W$ boson and a radion $\phi$; (3) a $Z'$ boson decaying to a composite lepton $E$ and $\ell$, with $E \rightarrow Z\ell$; (4) the sequential standard model $W' \rightarrow W Z' \rightarrow \ell\nu q\bar{q}$; (5) a simplified dark-matter model with an axial-vector mediator $Z' \rightarrow q\bar{q}$, where one of the quarks radiates a $W$ boson decaying to $\ell\nu$. The multiple markers shown for the composite-lepton model at the same invariant mass values correspond to different composite lepton ($E$) masses between 0.25 and 3.5 TeV. The center positions of the markers are set to the masses of the corresponding heavy particles.

Values of $\Delta Z$ for the discovery sensitivity, as defined in the text, as a function of the invariant mass $\textit{m}$. The b-jet+$\mu$ invariant mass distribution is calculated in the 10 pb AR. Positive percentages indicate improvements in sensitivity. Horizontal dashed lines are drawn at 100% and 200% to guide the eye. The five benchmark BSM models are (1) charged Higgs boson production in association with a top quark, $tbH^{+}$ with $H^{+} \rightarrow t\bar{b}$; (2) a Kaluza-Klein gauge boson, $W_{KK}$, with the SM $W$ boson and a radion $\phi$; (3) a $Z'$ boson decaying to a composite lepton $E$ and $\ell$, with $E \rightarrow Z\ell$; (4) the sequential standard model $W' \rightarrow W Z' \rightarrow \ell\nu q\bar{q}$; (5) a simplified dark-matter model with an axial-vector mediator $Z' \rightarrow q\bar{q}$, where one of the quarks radiates a $W$ boson decaying to $\ell\nu$. The multiple markers shown for the composite-lepton model at the same invariant mass values correspond to different composite lepton ($E$) masses between 0.25 and 3.5 TeV. The center positions of the markers are set to the masses of the corresponding heavy particles.

Values of $\Delta Z$ for the discovery sensitivity, as defined in the text, as a function of the invariant mass $\textit{m}$. The b-jet+$\gamma$ invariant mass distribution is calculated in the 10 pb AR. Positive percentages indicate improvements in sensitivity. Horizontal dashed lines are drawn at 100% and 200% to guide the eye. The five benchmark BSM models are (1) charged Higgs boson production in association with a top quark, $tbH^{+}$ with $H^{+} \rightarrow t\bar{b}$; (2) a Kaluza-Klein gauge boson, $W_{KK}$, with the SM $W$ boson and a radion $\phi$; (3) a $Z'$ boson decaying to a composite lepton $E$ and $\ell$, with $E \rightarrow Z\ell$; (4) the sequential standard model $W' \rightarrow W Z' \rightarrow \ell\nu q\bar{q}$; (5) a simplified dark-matter model with an axial-vector mediator $Z' \rightarrow q\bar{q}$, where one of the quarks radiates a $W$ boson decaying to $\ell\nu$. The multiple markers shown for the composite-lepton model at the same invariant mass values correspond to different composite lepton ($E$) masses between 0.25 and 3.5 TeV. The center positions of the markers are set to the masses of the corresponding heavy particles.

The 95% CL upper limits on the cross section times acceptance ($A$), efficiency ($\epsilon$), and branching ratio ($B$) for Gaussian-shaped signals with different signal widths. The limits are calculated for events with at least one lepton with pT > 60 GeV in the 10 pb AR. Two width hypotheses are shown; $\sigma X / mX = 0$ and $\sigma X / mX = 0.15$. In both cases, the detector resolution for jets is included in the simulation of signal samples. The $\pm1 \sigma$ and $\pm2 \sigma$ bands around the expected limit are shown for $\sigma X / mX = 0$ signals. Mass points are spaced 5% apart, relative to the preceding point, starting at 0.3 TeV.

Distributions of the anomaly score from the AE for data and five benchmark BSM models. Their legends, from top to bottom, are; (1) charged Higgs boson production in association with a top quark, $tbH^{+}$ with $H^{+} \rightarrow t\bar{b}$, with the $H^{+}$ mass of 2 TeV; (2) a 6 TeV Kaluza-Klein gauge boson, $W_{KK}$, with the SM $W$ boson and a radion $\phi$; (3) a 4 TeV $Z'$ boson decaying to a composite lepton $E$ with a mass of 0.5 TeV; (4) the sequential standard model $W' \rightarrow WZ' \rightarrow \ell\nu q\bar{q}$, with the $W'$ mass of 6.2 TeV and the $Z'$ mass of 6 TeV; (5) a simplified dark-matter model with an axial-vector mediator $Z' \rightarrow q\bar{q}$, where one of the quarks radiates a $W$ boson decaying to $\ell\nu$ with a mass of 6 TeV. The distributions for the BSM models are smoothed to remove fluctuations due to low MC event counts. The vertical lines indicate the start of the three anomaly regions (ARs). The labels of the three ARs indicate the visible cross section for hypothetical processes yielding the same number of events as observed in the 140 $fb^{-1}$ dataset. The AE is applied to preselected events without any requirements on invariant mass distributions.

Invariant mass distributions of $j+Y$ for $M_{jY}$ > 0.3 TeV after preselection along with the fit from Eq.(1). The fit is represented by red lines, and the associated uncertainties are indicated by yellow bands. The lower panels show the bin-by-bin significances of deviations from the fit, calculated as $(d_{\textit{i}} - f_{\textit{i}}) / \delta_{\textit{i}}$, where $d_{\textit{i}}$ is the data yield, $f_{\textit{i}}$ is the fit value, and $\delta_{i}$ is the uncertainty of data in the $\textit{i}$-th bin

Distributions of the anomaly score for data and several anomaly scenarios. The example BSM model (shown with the dashed blue lines) is the sequential standard model $W' \rightarrow WZ' \rightarrow \ell\nu q\bar{q}$. The mass of $W'$ is set to 2.2 TeV and the mass of the $Z'$ is set to 2 TeV. This model leads to the final state of one lepton, two jets, and small missing transverse energy that is similar to the SM backgrounds. The other histograms represent events from the same model with artificial modifications to represent anomalous events; (1) "Anomaly 1" is the case where all jets beyond the second jet ($N_{j}$ > 2) are replaced with photons; (2) "Anomaly 2" is the case where all jets beyond the second jet are replaced with $b$-jets; (3) "Anomaly 3" is the case of low-multiplicity events where all jets beyond the second jet are removed. The histograms are normalized to unit. The left peak near log(Loss) = -9 visible in Anomaly 1 and 2 is from the events without a third jet.