Normalized $\sigma_{\psi(2S)}/\sigma_{J/\psi}$ in $6.5<p_T<30.0\,GeV$ and $ 1 < y_{CM} < 1.935$ as functions of normalized $\text{N}^{{\text{corr.}}}_{\text{track}}$

Normalized $\sigma_{\psi(2S)}/\sigma_{J/\psi}$ in $6.5<p_T<30.0\,GeV$ and $ -2.865 < y_{CM} < 1.935$ as functions of normalized $\text{N}^{{\text{corr.}}}_{\text{track}}$

Normalized $\sigma_{\psi(2S)}/\sigma_{J/\psi}$ in $3.0<p_T<6.5\,GeV$ and $ -2.865 < y_{CM} < -2 $ as functions of normalized $\text{N}^{{\text{corr.}}}_{\text{track}}$

Normalized $\sigma_{\psi(2S)}/\sigma_{J/\psi}$ in $3.0<p_T<6.5\,GeV$ and $ 1 < y_{CM} < 1.935 $ as functions of normalized $\text{N}^{{\text{corr.}}}_{\text{track}}$

The ratio of data to the sum of the SM backgrounds. The uncertainties of simulated data are only the statistical unvertainty in the simulation predictions.

Distribution of $p_{T}^{miss}$ after the preselection from 2017 data (black points) and simulation (colored lines). The simulated distribution of two signal points are represented by colored lines, while not being stacked on the background distributions: $(m(\mathrm{\widetilde{t}}_{1}),m(\widetilde{\chi}^{0}_{1}))$ = (500, 490) and (500, 420) GeV. The last bin includes the overflow events.

The ratio of data to the sum of the SM backgrounds. The uncertainties of simulated data are only the statistical unvertainty in the simulation predictions.

Distribution of $p_{T}^{miss}$ after the preselection from 2018 data (black points) and simulation (colored lines). The simulated distribution of two signal points are represented by colored lines, while not being stacked on the background distributions: $(m(\mathrm{\widetilde{t}}_{1}),m(\widetilde{\chi}^{0}_{1}))$ = (500, 490) and (500, 420) GeV. The last bin includes the overflow events.

The ratio of data to the sum of the SM backgrounds. The uncertainties of simulated data are only the statistical unvertainty in the simulation predictions.

Distribution of $N_{Jet}$ after the preselection from 2017 data (black points) and simulation (colored lines). The simulated distribution of two signal points are represented by colored lines, while not being stacked on the background distributions: $(m(\mathrm{\widetilde{t}}_{1}),m(\widetilde{\chi}^{0}_{1}))$ = (500, 490) and (500, 420) GeV. The last bin includes the overflow events.

The ratio of data to the sum of the SM backgrounds. The uncertainties of simulated data are only the statistical unvertainty in the simulation predictions.

Distribution of $N_{Jet}$ after the preselection from 2018 data (black points) and simulation (colored lines). The simulated distribution of two signal points are represented by colored lines, while not being stacked on the background distributions: $(m(\mathrm{\widetilde{t}}_{1}),m(\widetilde{\chi}^{0}_{1}))$ = (500, 490) and (500, 420) GeV. The last bin includes the overflow events.

The ratio of data to the sum of the SM backgrounds. The uncertainties of simulated data are only the statistical unvertainty in the simulation predictions.

Simulated distribution of $p_{T}(l)$ after the preselection. The total background distribution and the signal distributions are all normalized to unit area. The signal distributions are given for a top squark mass of 300 GeV and $\Delta {m}=10$, 30,50, and 80 GeV.

Simulated distribution of $p_{T}(l)$ after the preselection. The total background distribution and the signal distributions are all normalized to unit area. The signal distributions are shown for four different $(m(\mathrm{\widetilde{t}}_{1}),m(\widetilde{\chi}^{0}_{1}))$ values, all corresponding to the same $\Delta {m} = 30$ GeV.

Simulated distribution of $p_{T}^{miss}$ after the preselection. The total background distribution and the signal distributions are all normalized to unit area. The signal distributions are given for a top squark mass of 300 GeV and $\Delta {m}=10$, 30,50, and 80 GeV.

Simulated distribution of $p_{T}^{miss}$ after the preselection. The total background distribution and the signal distributions are all normalized to unit area. The signal distributions are shown for four different $(m(\mathrm{\widetilde{t}}_{1}),m(\widetilde{\chi}^{0}_{1}))$ values, all corresponding to the same $\Delta {m} = 30$ GeV.

Simulated distribution of $N_{Jet}$ after the preselection. The total background distribution and the signal distributions are all normalized to unit area. The signal distributions are given for a top squark mass of 300 GeV and $\Delta {m}=10$, 30,50, and 80 GeV.

Simulated distribution of $N_{Jet}$ after the preselection. The total background distribution and the signal distributions are all normalized to unit area. The signal distributions are shown for four different $(m(\mathrm{\widetilde{t}}_{1}),m(\widetilde{\chi}^{0}_{1}))$ values, all corresponding to the same $\Delta {m} = 30$ GeV.

BDT output distributions from data (black points) and simulation (colored lines) after the preselection for $\Delta {m} = 10$ GeV for 2017 data. The last bin corresponds to the SR. A representative $(m(\mathrm{\widetilde{t}}_{1}),m(\widetilde{\chi}^{0}_{1}))$ signal point is also presented, while not added to the SM background.

The ratio of data to the sum of the SM backgrounds. Only the statistical uncertainty in the data and the simulated background are represented.

BDT output distributions from data (black points) and simulation (colored lines) after the preselection for $\Delta {m} = 20$ GeV for 2017 data. The last bin corresponds to the SR. A representative $(m(\mathrm{\widetilde{t}}_{1}),m(\widetilde{\chi}^{0}_{1}))$ signal point is also presented, while not added to the SM background.

The ratio of data to the sum of the SM backgrounds. Only the statistical uncertainty in the data and the simulated background are represented.

BDT output distributions from data (black points) and simulation (colored lines) after the preselection for $\Delta {m} = 30$ GeV for 2017 data. The last bin corresponds to the SR. A representative $(m(\mathrm{\widetilde{t}}_{1}),m(\widetilde{\chi}^{0}_{1}))$ signal point is also presented, while not added to the SM background.

The ratio of data to the sum of the SM backgrounds. Only the statistical uncertainty in the data and the simulated background are represented.

BDT output distributions from data (black points) and simulation (colored lines) after the preselection for $\Delta {m} = 40$ GeV for 2017 data. The last bin corresponds to the SR. A representative $(m(\mathrm{\widetilde{t}}_{1}),m(\widetilde{\chi}^{0}_{1}))$ signal point is also presented, while not added to the SM background.

The ratio of data to the sum of the SM backgrounds. Only the statistical uncertainty in the data and the simulated background are represented.

BDT output distributions from data (black points) and simulation (colored lines) after the preselection for $\Delta {m} = 50$ GeV for 2017 data. The last bin corresponds to the SR. A representative $(m(\mathrm{\widetilde{t}}_{1}),m(\widetilde{\chi}^{0}_{1}))$ signal point is also presented, while not added to the SM background.

The ratio of data to the sum of the SM backgrounds. Only the statistical uncertainty in the data and the simulated background are represented.

BDT output distributions from data (black points) and simulation (colored lines) after the preselection for $\Delta {m} = 60$ GeV for 2017 data. The last bin corresponds to the SR. A representative $(m(\mathrm{\widetilde{t}}_{1}),m(\widetilde{\chi}^{0}_{1}))$ signal point is also presented, while not added to the SM background.

The ratio of data to the sum of the SM backgrounds. Only the statistical uncertainty in the data and the simulated background are represented.

BDT output distributions from data (black points) and simulation (colored lines) after the preselection for $\Delta {m} = 70$ GeV for 2017 data. The last bin corresponds to the SR. A representative $(m(\mathrm{\widetilde{t}}_{1}),m(\widetilde{\chi}^{0}_{1}))$ signal point is also presented, while not added to the SM background.

The ratio of data to the sum of the SM backgrounds. Only the statistical uncertainty in the data and the simulated background are represented.

BDT output distributions from data (black points) and simulation (colored lines) after the preselection for $\Delta {m} = 80$ GeV for 2017 data. The last bin corresponds to the SR. A representative $(m(\mathrm{\widetilde{t}}_{1}),m(\widetilde{\chi}^{0}_{1}))$ signal point is also presented, while not added to the SM background.

The ratio of data to the sum of the SM backgrounds. Only the statistical uncertainty in the data and the simulated background are represented.

BDT output distributions from data (black points) and simulation (colored lines) after the preselection for $\Delta {m} = 10$ GeV for 2018 data. The last bin corresponds to the SR. A representative $(m(\mathrm{\widetilde{t}}_{1}),m(\widetilde{\chi}^{0}_{1}))$ signal point is also presented, while not added to the SM background.

The ratio of data to the sum of the SM backgrounds. Only the statistical uncertainty in the data and the simulated background are represented.

BDT output distributions from data (black points) and simulation (colored lines) after the preselection for $\Delta {m} = 20$ GeV for 2018 data. The last bin corresponds to the SR. A representative $(m(\mathrm{\widetilde{t}}_{1}),m(\widetilde{\chi}^{0}_{1}))$ signal point is also presented, while not added to the SM background.

The ratio of data to the sum of the SM backgrounds. Only the statistical uncertainty in the data and the simulated background are represented.

BDT output distributions from data (black points) and simulation (colored lines) after the preselection for $\Delta {m} = 30$ GeV for 2018 data. The last bin corresponds to the SR. A representative $(m(\mathrm{\widetilde{t}}_{1}),m(\widetilde{\chi}^{0}_{1}))$ signal point is also presented, while not added to the SM background.

The ratio of data to the sum of the SM backgrounds. Only the statistical uncertainty in the data and the simulated background are represented.

BDT output distributions from data (black points) and simulation (colored lines) after the preselection for $\Delta {m} = 40$ GeV for 2018 data. The last bin corresponds to the SR. A representative $(m(\mathrm{\widetilde{t}}_{1}),m(\widetilde{\chi}^{0}_{1}))$ signal point is also presented, while not added to the SM background.

The ratio of data to the sum of the SM backgrounds. Only the statistical uncertainty in the data and the simulated background are represented.

BDT output distributions from data (black points) and simulation (colored lines) after the preselection for $\Delta {m} = 50$ GeV for 2018 data. The last bin corresponds to the SR. A representative $(m(\mathrm{\widetilde{t}}_{1}),m(\widetilde{\chi}^{0}_{1}))$ signal point is also presented, while not added to the SM background.

The ratio of data to the sum of the SM backgrounds. Only the statistical uncertainty in the data and the simulated background are represented.

BDT output distributions from data (black points) and simulation (colored lines) after the preselection for $\Delta {m} = 60$ GeV for 2018 data. The last bin corresponds to the SR. A representative $(m(\mathrm{\widetilde{t}}_{1}),m(\widetilde{\chi}^{0}_{1}))$ signal point is also presented, while not added to the SM background.

The ratio of data to the sum of the SM backgrounds. Only the statistical uncertainty in the data and the simulated background are represented.

BDT output distributions from data (black points) and simulation (colored lines) after the preselection for $\Delta {m} = 70$ GeV for 2018 data. The last bin corresponds to the SR. A representative $(m(\mathrm{\widetilde{t}}_{1}),m(\widetilde{\chi}^{0}_{1}))$ signal point is also presented, while not added to the SM background.

The ratio of data to the sum of the SM backgrounds. Only the statistical uncertainty in the data and the simulated background are represented.

BDT output distributions from data (black points) and simulation (colored lines) after the preselection for $\Delta {m} = 80$ GeV for 2018 data. The last bin corresponds to the SR. A representative $(m(\mathrm{\widetilde{t}}_{1}),m(\widetilde{\chi}^{0}_{1}))$ signal point is also presented, while not added to the SM background.

The ratio of data to the sum of the SM backgrounds. Only the statistical uncertainty in the data and the simulated background are represented.

Distribution of $p_{T}(l)$ for $\Delta {m} = 10$ in the VR where $200 < p_{T}^{miss} < 280$ GeV from 2017 data (black points) and simulation (colored lines). The predicted signal distribution is shown for $(m(\mathrm{\widetilde{t}}_{1}),m(\widetilde{\chi}^{0}_{1}))$ = (475, 465).

The ratio of data to the sum of the SM backgrounds. Only the statistical uncertainty in the data and the simulated background are represented.

Distribution of $p_{T}(l)$ for $\Delta {m} = 10$ in the VR where $200 < p_{T}^{miss} < 280$ GeV from 2018 data (black points) and simulation (colored lines). The predicted signal distribution is shown for $(m(\mathrm{\widetilde{t}}_{1}),m(\widetilde{\chi}^{0}_{1}))$ = (475, 465).

The ratio of data to the sum of the SM backgrounds. Only the statistical uncertainty in the data and the simulated background are represented.

Distribution of BDT for $\Delta {m} = 10$ in the VR where $200 < p_{T}^{miss} < 280$ GeV from 2017 data (black points) and simulation (colored lines). The predicted signal distribution is shown for $(m(\mathrm{\widetilde{t}}_{1}),m(\widetilde{\chi}^{0}_{1}))$ = (475, 465).

The ratio of data to the sum of the SM backgrounds. Only the statistical uncertainty in the data and the simulated background are represented.

Distribution of BDT for $\Delta {m} = 10$ in the VR where $200 < p_{T}^{miss} < 280$ GeV from 2018 data (black points) and simulation (colored lines). The predicted signal distribution is shown for $(m(\mathrm{\widetilde{t}}_{1}),m(\widetilde{\chi}^{0}_{1}))$ = (475, 465).

The ratio of data to the sum of the SM backgrounds. Only the statistical uncertainty in the data and the simulated background are represented.

Distribution of $p_{T}^{miss}$ for $\Delta {m} = 60$ in the VR where $p_{T}(l) > 30$ GeV from 2017 data (black points) and simulation (colored lines). The predicted signal distribution is shown for $(m(\mathrm{\widetilde{t}}_{1}),m(\widetilde{\chi}^{0}_{1}))$ = (576, 516).

The ratio of data to the sum of the SM backgrounds. Only the statistical uncertainty in the data and the simulated background are represented.

Distribution of $p_{T}^{miss}$ for $\Delta {m} = 60$ in the VR where $p_{T}(l) > 30$ GeV from 2018 data (black points) and simulation (colored lines). The predicted signal distribution is shown for $(m(\mathrm{\widetilde{t}}_{1}),m(\widetilde{\chi}^{0}_{1}))$ = (576, 516).

The ratio of data to the sum of the SM backgrounds. Only the statistical uncertainty in the data and the simulated background are represented.

Distribution of BDT for $\Delta {m} = 60$ in the VR where $p_{T}(l) > 30$ GeV from 2017 data (black points) and simulation (colored lines). The predicted signal distribution is shown for $(m(\mathrm{\widetilde{t}}_{1}),m(\widetilde{\chi}^{0}_{1}))$ = (576, 516).

The ratio of data to the sum of the SM backgrounds. Only the statistical uncertainty in the data and the simulated background are represented.

Distribution of BDT for $\Delta {m} = 60$ in the VR where $p_{T}(l) > 30$ GeV from 2018 data (black points) and simulation (colored lines). The predicted signal distribution is shown for $(m(\mathrm{\widetilde{t}}_{1}),m(\widetilde{\chi}^{0}_{1}))$ = (576, 516).

The ratio of data to the sum of the SM backgrounds. Only the statistical uncertainty in the data and the simulated background are represented.

Data yield and prediction of the total background and its composition in the eight SRs as defined in for the 2017 analysis. The predictions of W + jets, ttbar, and nonprompt lepton processes are based on data, while those of rare backgrounds are based on simulation. The predictions and their associated uncertainties are pre-fit. The uncertainties are the quadratic sum of the statistical and systematic uncertainties for background processes predicted from data, and the uncertainties on the cross section for the backgrounds predicted from simulation. The yields for two signal points, with $\Delta {m} = 10$ GeV and $\Delta {m} = 80$ GeV, are also shown. The bins corresponding to different $\Delta {m}$ trainings are not statistically independent.

The ratio of the number of observed events over the predicted total background per SR.

Data yield and prediction of the total background and its composition in the eight SRs as defined in for the 2018 analysis. The predictions of W + jets, ttbar, and nonprompt lepton processes are based on data, while those of rare backgrounds are based on simulation. The predictions and their associated uncertainties are pre-fit. The uncertainties are the quadratic sum of the statistical and systematic uncertainties for background processes predicted from data, and the uncertainties on the cross section for the backgrounds predicted from simulation. The yields for two signal points, with $\Delta {m} = 10$ GeV and $\Delta {m} = 80$ GeV, are also shown. The bins corresponding to different $\Delta {m}$ trainings are not statistically independent.

The ratio of the number of observed events over the predicted total background per SR.

Exclusion limit at 95% CL for the four-body decay of the top squark as a function of $m(\mathrm{\widetilde{t}}_{1})$ and $\Delta {m}$, for the combined data of the years 2016, 2017, and 2018. The lines represent the observed limits. These limits are derived using the expected top squark pair production cross section. The green line represents the central values, and the other lines the variations due to the theoretical or experimental uncertainties.

Exclusion limit at 95% CL for the four-body decay of the top squark as a function of $m(\mathrm{\widetilde{t}}_{1})$ and $\Delta {m}$, for the combined data of the years 2016, 2017, and 2018. The lines represent the expected limits. These limits are derived using the expected top squark pair production cross section. The green line represents the central values, and the other lines the variations due to the theoretical or experimental uncertainties.

Exclusion limit at 95% CL for the four-body decay of the top squark as a function of $m(\mathrm{\widetilde{t}}_{1})$ and $\Delta {m}$, for the combined data of the years 2016, 2017, and 2018.

Left panel.The vertical bars (boxes) represent the statistical (bin-to-bin systematic) uncertainties, while the horizontal bars give the bin widths. The global uncertainty (of 5.7%) is not graphically represented. The blue line represents the average of all the points. $ 12 < \mathrm{B} \, p_T < 70$ GeV and $ 0 < |y| < 2.4 $. Global uncertanties are not included in the table (5.7%)

Right panel.The vertical bars (boxes) represent the statistical (bin-to-bin systematic) uncertainties, while the horizontal bars give the bin widths. The global uncertainty (of 5.7%) is not graphically represented. The blue line represents the average of all the points. $ 12 < \mathrm{B} \, p_T < 70$ GeV and $ 0 < |y| < 2.4 $. Global uncertanties are not included in the table (5.7%)

A comparison of the measured cell $E_T^{miss}$ distribution with that predicted by the two-component model for two pile-up scenarios. The magenta points extend the measured distribution using L1 $E_T^{miss} > 30\,$GeV and L1 $E_T^{miss} > 50\,$GeV data. The red curve is the prediction from the calorimeter-resolution part of the model. The green curve is the high $E_T^{miss}$ tail's probability distribution for the mean number of $pp$ interactions $\mu$ in each figure. The blue curve is the full model prediction computed by combining the $E_T^{miss}$ from these two individual sources shown in red and green, each calculated for $\mu=\langle\mu\rangle$. The black points show the unbiased $E_T^{miss}$ distribution measured in data. Corresponds to a prediction for $\langle\mu\rangle = 25$.

A comparison of the measured cell $E_T^{miss}$ distribution with that predicted by the two-component model for two pile-up scenarios. The magenta points extend the measured distribution using L1 $E_T^{miss} > 30\,$GeV and L1 $E_T^{miss} > 50\,$GeV data. The red curve is the prediction from the calorimeter-resolution part of the model. The green curve is the high $E_T^{miss}$ tail's probability distribution for the mean number of $pp$ interactions $\mu$ in each figure. The blue curve is the full model prediction computed by combining the $E_T^{miss}$ from these two individual sources shown in red and green, each calculated for $\mu=\langle\mu\rangle$. The black points show the unbiased $E_T^{miss}$ distribution measured in data. Corresponds to a prediction for $\langle\mu\rangle = 25$.

A comparison of the measured cell $E_T^{miss}$ distribution with that predicted by the two-component model for two pile-up scenarios. The magenta points extend the measured distribution using L1 $E_T^{miss} > 30\,$GeV and L1 $E_T^{miss} > 50\,$GeV data. The red curve is the prediction from the calorimeter-resolution part of the model. The green curve is the high $E_T^{miss}$ tail's probability distribution for the mean number of $pp$ interactions $\mu$ in each figure. The blue curve is the full model prediction computed by combining the $E_T^{miss}$ from these two individual sources shown in red and green, each calculated for $\mu=\langle\mu\rangle$. The black points show the unbiased $E_T^{miss}$ distribution measured in data. Corresponds to a prediction for $\langle\mu\rangle = 55$.

A comparison of the measured cell $E_T^{miss}$ distribution with that predicted by the two-component model for two pile-up scenarios. The magenta points extend the measured distribution using L1 $E_T^{miss} > 30\,$GeV and L1 $E_T^{miss} > 50\,$GeV data. The red curve is the prediction from the calorimeter-resolution part of the model. The green curve is the high $E_T^{miss}$ tail's probability distribution for the mean number of $pp$ interactions $\mu$ in each figure. The blue curve is the full model prediction computed by combining the $E_T^{miss}$ from these two individual sources shown in red and green, each calculated for $\mu=\langle\mu\rangle$. The black points show the unbiased $E_T^{miss}$ distribution measured in data. Corresponds to a prediction for $\langle\mu\rangle = 55$.

A comparison of the measured cell $E_T^{miss}$ distribution with that predicted by the two-component model for two pile-up scenarios. The magenta points extend the measured distribution using L1 $E_T^{miss} > 30\,$GeV and L1 $E_T^{miss} > 50\,$GeV data. The red curve is the prediction from the calorimeter-resolution part of the model. The green curve is the high $E_T^{miss}$ tail's probability distribution for the mean number of $pp$ interactions $\mu$ in each figure. The blue curve is the full model prediction computed by combining the $E_T^{miss}$ from these two individual sources shown in red and green, each calculated for $\mu=\langle\mu\rangle$. The black points show the unbiased $E_T^{miss}$ distribution measured in data. Corresponds to a prediction for $\langle\mu\rangle = 55$.

A comparison of the measured cell $E_T^{miss}$ distribution with that predicted by the two-component model for two pile-up scenarios. The magenta points extend the measured distribution using L1 $E_T^{miss} > 30\,$GeV and L1 $E_T^{miss} > 50\,$GeV data. The red curve is the prediction from the calorimeter-resolution part of the model. The green curve is the high $E_T^{miss}$ tail's probability distribution for the mean number of $pp$ interactions $\mu$ in each figure. The blue curve is the full model prediction computed by combining the $E_T^{miss}$ from these two individual sources shown in red and green, each calculated for $\mu=\langle\mu\rangle$. The black points show the unbiased $E_T^{miss}$ distribution measured in data. Corresponds to a prediction for $\langle\mu\rangle = 55$.

A comparison of the measured cell $E_T^{miss}$ distribution with that predicted by the two-component model for two pile-up scenarios. The magenta points extend the measured distribution using L1 $E_T^{miss} > 30\,$GeV and L1 $E_T^{miss} > 50\,$GeV data. The red curve is the prediction from the calorimeter-resolution part of the model. The green curve is the high $E_T^{miss}$ tail's probability distribution for the mean number of $pp$ interactions $\mu$ in each figure. The blue curve is the full model prediction computed by combining the $E_T^{miss}$ from these two individual sources shown in red and green, each calculated for $\mu=\langle\mu\rangle$. The black points show the unbiased $E_T^{miss}$ distribution measured in data. Corresponds to a prediction for $\langle\mu\rangle = 55$.

The $E_T^{miss}$ model predicted trigger rate as a function of $\mu$ for the cell $E_T^{miss}$ algorithm with a threshold of $80\,$GeV and $120\,$GeV, assuming no additional pile-up mitigation.

The L1 $E_T^{miss}$ trigger efficiency, shown as a function of $p_T(\mu\mu)$ in $Z\rightarrow\mu\mu$ events.

The efficiencies in the plot are shown for events satisfying a $Z\rightarrow\mu\mu$ selection and with $p_T(\mu\mu)$ larger than $150\,$GeV vs pile-up for each of the four years of data taking.

The L1 $E_T^{miss}$ trigger rate as a function of $\langle\mu\rangle$ for runs in three different periods ($\alpha$, $\beta$, $\gamma$) in the year 2017.

The L1 $E_T^{miss}$ trigger rate as a function of $\langle\mu\rangle$ for runs in three different periods ($\alpha$, $\beta$, $\gamma$) in the year 2017.

The L1 $E_T^{miss}$ trigger rate as a function of $\langle\mu\rangle$ for runs in three different periods ($\alpha$, $\beta$, $\gamma$) in the year 2017.

The L1 $E_T^{miss}$ trigger efficiency is shown as a function of mean pile-up for events satisfying a $Z\rightarrow\mu\mu$ selection and with $p_T(\mu\mu)$ larger than $150\,$GeV in three periods during the year 2017.

Background acceptance vs signal efficiency for each of four individual HLT $E_T^{miss}$ algorithms for a $Z\rightarrow\mu\mu$ selection with $p_T(\mu\mu) > 175\,$GeV for data recorded in the year 2017. The diamond indicates the performance of the pufit $E_T^{miss}>110\,$GeV trigger. $0\leq\langle\mu\rangle < 20$.

Background acceptance vs signal efficiency for each of four individual HLT $E_T^{miss}$ algorithms for a $Z\rightarrow\mu\mu$ selection with $p_T(\mu\mu) > 175\,$GeV for data recorded in the year 2017. The diamond indicates the performance of the pufit $E_T^{miss}>110\,$GeV trigger. $0\leq\langle\mu\rangle < 20$.

Background acceptance vs signal efficiency for each of four individual HLT $E_T^{miss}$ algorithms for a $Z\rightarrow\mu\mu$ selection with $p_T(\mu\mu) > 175\,$GeV for data recorded in the year 2017. The diamond indicates the performance of the pufit $E_T^{miss}>110\,$GeV trigger. $0\leq\langle\mu\rangle < 20$.

Background acceptance vs signal efficiency for each of four individual HLT $E_T^{miss}$ algorithms for a $Z\rightarrow\mu\mu$ selection with $p_T(\mu\mu) > 175\,$GeV for data recorded in the year 2017. The diamond indicates the performance of the pufit $E_T^{miss}>110\,$GeV trigger. $0\leq\langle\mu\rangle < 20$.

Background acceptance vs signal efficiency for each of four individual HLT $E_T^{miss}$ algorithms for a $Z\rightarrow\mu\mu$ selection with $p_T(\mu\mu) > 175\,$GeV for data recorded in the year 2017. The diamond indicates the performance of the pufit $E_T^{miss}>110\,$GeV trigger. $0\leq\langle\mu\rangle < 20$.

Background acceptance vs signal efficiency for each of four individual HLT $E_T^{miss}$ algorithms for a $Z\rightarrow\mu\mu$ selection with $p_T(\mu\mu) > 175\,$GeV for data recorded in the year 2017. The diamond indicates the performance of the pufit $E_T^{miss}>110\,$GeV trigger. $20\leq\langle\mu\rangle < 30$.

Background acceptance vs signal efficiency for each of four individual HLT $E_T^{miss}$ algorithms for a $Z\rightarrow\mu\mu$ selection with $p_T(\mu\mu) > 175\,$GeV for data recorded in the year 2017. The diamond indicates the performance of the pufit $E_T^{miss}>110\,$GeV trigger. $20\leq\langle\mu\rangle < 30$.

Background acceptance vs signal efficiency for each of four individual HLT $E_T^{miss}$ algorithms for a $Z\rightarrow\mu\mu$ selection with $p_T(\mu\mu) > 175\,$GeV for data recorded in the year 2017. The diamond indicates the performance of the pufit $E_T^{miss}>110\,$GeV trigger. $20\leq\langle\mu\rangle < 30$.

Background acceptance vs signal efficiency for each of four individual HLT $E_T^{miss}$ algorithms for a $Z\rightarrow\mu\mu$ selection with $p_T(\mu\mu) > 175\,$GeV for data recorded in the year 2017. The diamond indicates the performance of the pufit $E_T^{miss}>110\,$GeV trigger. $20\leq\langle\mu\rangle < 30$.

Background acceptance vs signal efficiency for each of four individual HLT $E_T^{miss}$ algorithms for a $Z\rightarrow\mu\mu$ selection with $p_T(\mu\mu) > 175\,$GeV for data recorded in the year 2017. The diamond indicates the performance of the pufit $E_T^{miss}>110\,$GeV trigger. $20\leq\langle\mu\rangle < 30$.

Background acceptance vs signal efficiency for each of four individual HLT $E_T^{miss}$ algorithms for a $Z\rightarrow\mu\mu$ selection with $p_T(\mu\mu) > 175\,$GeV for data recorded in the year 2017. The diamond indicates the performance of the pufit $E_T^{miss}>110\,$GeV trigger. $30\leq\langle\mu\rangle < 40$.

Background acceptance vs signal efficiency for each of four individual HLT $E_T^{miss}$ algorithms for a $Z\rightarrow\mu\mu$ selection with $p_T(\mu\mu) > 175\,$GeV for data recorded in the year 2017. The diamond indicates the performance of the pufit $E_T^{miss}>110\,$GeV trigger. $30\leq\langle\mu\rangle < 40$.

Background acceptance vs signal efficiency for each of four individual HLT $E_T^{miss}$ algorithms for a $Z\rightarrow\mu\mu$ selection with $p_T(\mu\mu) > 175\,$GeV for data recorded in the year 2017. The diamond indicates the performance of the pufit $E_T^{miss}>110\,$GeV trigger. $30\leq\langle\mu\rangle < 40$.

Background acceptance vs signal efficiency for each of four individual HLT $E_T^{miss}$ algorithms for a $Z\rightarrow\mu\mu$ selection with $p_T(\mu\mu) > 175\,$GeV for data recorded in the year 2017. The diamond indicates the performance of the pufit $E_T^{miss}>110\,$GeV trigger. $30\leq\langle\mu\rangle < 40$.

Background acceptance vs signal efficiency for each of four individual HLT $E_T^{miss}$ algorithms for a $Z\rightarrow\mu\mu$ selection with $p_T(\mu\mu) > 175\,$GeV for data recorded in the year 2017. The diamond indicates the performance of the pufit $E_T^{miss}>110\,$GeV trigger. $30\leq\langle\mu\rangle < 40$.

Background acceptance vs signal efficiency for each of four individual HLT $E_T^{miss}$ algorithms for a $Z\rightarrow\mu\mu$ selection with $p_T(\mu\mu) > 175\,$GeV for data recorded in the year 2017. The diamond indicates the performance of the pufit $E_T^{miss}>110\,$GeV trigger. $40\leq\langle\mu\rangle$.

Background acceptance vs signal efficiency for each of four individual HLT $E_T^{miss}$ algorithms for a $Z\rightarrow\mu\mu$ selection with $p_T(\mu\mu) > 175\,$GeV for data recorded in the year 2017. The diamond indicates the performance of the pufit $E_T^{miss}>110\,$GeV trigger. $40\leq\langle\mu\rangle$.

Background acceptance vs signal efficiency for each of four individual HLT $E_T^{miss}$ algorithms for a $Z\rightarrow\mu\mu$ selection with $p_T(\mu\mu) > 175\,$GeV for data recorded in the year 2017. The diamond indicates the performance of the pufit $E_T^{miss}>110\,$GeV trigger. $40\leq\langle\mu\rangle$.

Background acceptance vs signal efficiency for each of four individual HLT $E_T^{miss}$ algorithms for a $Z\rightarrow\mu\mu$ selection with $p_T(\mu\mu) > 175\,$GeV for data recorded in the year 2017. The diamond indicates the performance of the pufit $E_T^{miss}>110\,$GeV trigger. $40\leq\langle\mu\rangle$.

Background acceptance vs signal efficiency for each of four individual HLT $E_T^{miss}$ algorithms for a $Z\rightarrow\mu\mu$ selection with $p_T(\mu\mu) > 175\,$GeV for data recorded in the year 2017. The diamond indicates the performance of the pufit $E_T^{miss}>110\,$GeV trigger. $40\leq\langle\mu\rangle$.

Relative background acceptance fraction vs. relative efficiency for two different $p_T(\mu\mu)>150\,$GeV threshold for data recorded in the year 2018. Two of the curves show the performance of the stand-alone cell algorithm and the stand-alone pufit algorithm. The other two show combined algorithms each formed by requiring that the event satisfy both a fixed threshold (either 65 GeV or 70 GeV as shown in the legend) for the cell algorithm and a pufit threshold which varies along the curve. In each plot the background acceptance fractions and the efficiencies are relative to those of the pufit $E_T^{miss} > 110\,$GeV trigger and thus can be greater than one. The diamond indicates the performance of the pufit $E_T^{miss}>110\,$GeV trigger while the cross indicates the performance of the combined (pufit $E_T^{miss}>110\,$GeV and cell $E_T^{miss}>50\,$GeV) trigger.

Relative background acceptance fraction vs. relative efficiency for two different $p_T(\mu\mu)>150\,$GeV threshold for data recorded in the year 2018. Two of the curves show the performance of the stand-alone cell algorithm and the stand-alone pufit algorithm. The other two show combined algorithms each formed by requiring that the event satisfy both a fixed threshold (either 65 GeV or 70 GeV as shown in the legend) for the cell algorithm and a pufit threshold which varies along the curve. In each plot the background acceptance fractions and the efficiencies are relative to those of the pufit $E_T^{miss} > 110\,$GeV trigger and thus can be greater than one. The diamond indicates the performance of the pufit $E_T^{miss}>110\,$GeV trigger while the cross indicates the performance of the combined (pufit $E_T^{miss}>110\,$GeV and cell $E_T^{miss}>50\,$GeV) trigger.

Relative background acceptance fraction vs. relative efficiency for two different $p_T(\mu\mu)>150\,$GeV threshold for data recorded in the year 2018. Two of the curves show the performance of the stand-alone cell algorithm and the stand-alone pufit algorithm. The other two show combined algorithms each formed by requiring that the event satisfy both a fixed threshold (either 65 GeV or 70 GeV as shown in the legend) for the cell algorithm and a pufit threshold which varies along the curve. In each plot the background acceptance fractions and the efficiencies are relative to those of the pufit $E_T^{miss} > 110\,$GeV trigger and thus can be greater than one. The diamond indicates the performance of the pufit $E_T^{miss}>110\,$GeV trigger while the cross indicates the performance of the combined (pufit $E_T^{miss}>110\,$GeV and cell $E_T^{miss}>50\,$GeV) trigger.

Relative background acceptance fraction vs. relative efficiency for two different $p_T(\mu\mu)>150\,$GeV threshold for data recorded in the year 2018. Two of the curves show the performance of the stand-alone cell algorithm and the stand-alone pufit algorithm. The other two show combined algorithms each formed by requiring that the event satisfy both a fixed threshold (either 65 GeV or 70 GeV as shown in the legend) for the cell algorithm and a pufit threshold which varies along the curve. In each plot the background acceptance fractions and the efficiencies are relative to those of the pufit $E_T^{miss} > 110\,$GeV trigger and thus can be greater than one. The diamond indicates the performance of the pufit $E_T^{miss}>110\,$GeV trigger while the cross indicates the performance of the combined (pufit $E_T^{miss}>110\,$GeV and cell $E_T^{miss}>50\,$GeV) trigger.

Relative background acceptance fraction vs. relative efficiency for two different $p_T(\mu\mu)>150\,$GeV threshold for data recorded in the year 2018. Two of the curves show the performance of the stand-alone cell algorithm and the stand-alone pufit algorithm. The other two show combined algorithms each formed by requiring that the event satisfy both a fixed threshold (either 65 GeV or 70 GeV as shown in the legend) for the cell algorithm and a pufit threshold which varies along the curve. In each plot the background acceptance fractions and the efficiencies are relative to those of the pufit $E_T^{miss} > 110\,$GeV trigger and thus can be greater than one. The diamond indicates the performance of the pufit $E_T^{miss}>110\,$GeV trigger while the cross indicates the performance of the combined (pufit $E_T^{miss}>110\,$GeV and cell $E_T^{miss}>50\,$GeV) trigger.

Relative background acceptance fraction vs. relative efficiency for two different $p_T(\mu\mu)>150\,$GeV threshold for data recorded in the year 2018. Two of the curves show the performance of the stand-alone cell algorithm and the stand-alone pufit algorithm. The other two show combined algorithms each formed by requiring that the event satisfy both a fixed threshold (either 65 GeV or 70 GeV as shown in the legend) for the cell algorithm and a pufit threshold which varies along the curve. In each plot the background acceptance fractions and the efficiencies are relative to those of the pufit $E_T^{miss} > 110\,$GeV trigger and thus can be greater than one. The diamond indicates the performance of the pufit $E_T^{miss}>110\,$GeV trigger while the cross indicates the performance of the combined (pufit $E_T^{miss}>110\,$GeV and cell $E_T^{miss}>50\,$GeV) trigger.

Relative background acceptance fraction vs. relative efficiency for two different $p_T(\mu\mu)>150\,$GeV threshold for data recorded in the year 2018. Two of the curves show the performance of the stand-alone cell algorithm and the stand-alone pufit algorithm. The other two show combined algorithms each formed by requiring that the event satisfy both a fixed threshold (either 65 GeV or 70 GeV as shown in the legend) for the cell algorithm and a pufit threshold which varies along the curve. In each plot the background acceptance fractions and the efficiencies are relative to those of the pufit $E_T^{miss} > 110\,$GeV trigger and thus can be greater than one. The diamond indicates the performance of the pufit $E_T^{miss}>110\,$GeV trigger while the cross indicates the performance of the combined (pufit $E_T^{miss}>110\,$GeV and cell $E_T^{miss}>50\,$GeV) trigger.

Relative background acceptance fraction vs. relative efficiency for two different $p_T(\mu\mu)>150\,$GeV threshold for data recorded in the year 2018. Two of the curves show the performance of the stand-alone cell algorithm and the stand-alone pufit algorithm. The other two show combined algorithms each formed by requiring that the event satisfy both a fixed threshold (either 65 GeV or 70 GeV as shown in the legend) for the cell algorithm and a pufit threshold which varies along the curve. In each plot the background acceptance fractions and the efficiencies are relative to those of the pufit $E_T^{miss} > 110\,$GeV trigger and thus can be greater than one. The diamond indicates the performance of the pufit $E_T^{miss}>110\,$GeV trigger while the cross indicates the performance of the combined (pufit $E_T^{miss}>110\,$GeV and cell $E_T^{miss}>50\,$GeV) trigger.

Relative background acceptance fraction vs. relative efficiency for two different $p_T(\mu\mu)>150\,$GeV threshold for data recorded in the year 2018. Two of the curves show the performance of the stand-alone cell algorithm and the stand-alone pufit algorithm. The other two show combined algorithms each formed by requiring that the event satisfy both a fixed threshold (either 65 GeV or 70 GeV as shown in the legend) for the cell algorithm and a pufit threshold which varies along the curve. In each plot the background acceptance fractions and the efficiencies are relative to those of the pufit $E_T^{miss} > 110\,$GeV trigger and thus can be greater than one. The diamond indicates the performance of the pufit $E_T^{miss}>110\,$GeV trigger while the cross indicates the performance of the combined (pufit $E_T^{miss}>110\,$GeV and cell $E_T^{miss}>50\,$GeV) trigger.

Relative background acceptance fraction vs. relative efficiency for two different $p_T(\mu\mu)>150\,$GeV threshold for data recorded in the year 2018. Two of the curves show the performance of the stand-alone cell algorithm and the stand-alone pufit algorithm. The other two show combined algorithms each formed by requiring that the event satisfy both a fixed threshold (either 65 GeV or 70 GeV as shown in the legend) for the cell algorithm and a pufit threshold which varies along the curve. In each plot the background acceptance fractions and the efficiencies are relative to those of the pufit $E_T^{miss} > 110\,$GeV trigger and thus can be greater than one. The diamond indicates the performance of the pufit $E_T^{miss}>110\,$GeV trigger while the cross indicates the performance of the combined (pufit $E_T^{miss}>110\,$GeV and cell $E_T^{miss}>50\,$GeV) trigger.

Relative background acceptance fraction vs. relative efficiency for two different $p_T(\mu\mu)>150\,$GeV threshold for data recorded in the year 2018. Two of the curves show the performance of the stand-alone cell algorithm and the stand-alone pufit algorithm. The other two show combined algorithms each formed by requiring that the event satisfy both a fixed threshold (either 65 GeV or 70 GeV as shown in the legend) for the cell algorithm and a pufit threshold which varies along the curve. In each plot the background acceptance fractions and the efficiencies are relative to those of the pufit $E_T^{miss} > 110\,$GeV trigger and thus can be greater than one. The diamond indicates the performance of the pufit $E_T^{miss}>110\,$GeV trigger while the cross indicates the performance of the combined (pufit $E_T^{miss}>110\,$GeV and cell $E_T^{miss}>50\,$GeV) trigger.

Relative background acceptance fraction vs. relative efficiency for two different $p_T(\mu\mu)>150\,$GeV threshold for data recorded in the year 2018. Two of the curves show the performance of the stand-alone cell algorithm and the stand-alone pufit algorithm. The other two show combined algorithms each formed by requiring that the event satisfy both a fixed threshold (either 65 GeV or 70 GeV as shown in the legend) for the cell algorithm and a pufit threshold which varies along the curve. In each plot the background acceptance fractions and the efficiencies are relative to those of the pufit $E_T^{miss} > 110\,$GeV trigger and thus can be greater than one. The diamond indicates the performance of the pufit $E_T^{miss}>110\,$GeV trigger while the cross indicates the performance of the combined (pufit $E_T^{miss}>110\,$GeV and cell $E_T^{miss}>50\,$GeV) trigger.

Turn-on efficiency curves are shown for $Z\rightarrow\mu\mu$ events for three algorithms: the cell algorithm alone, the pufit algorithm alone and the combined cell+pufit algorithm. The thresholds are set such that the algorithms have equal rates, and the data were recorded in the year 2018. Here the trigger efficiency is shown with respect to $p_T(\mu\mu)$.

Turn-on efficiency curves are shown for $Z\rightarrow\mu\mu$ events for three algorithms: the cell algorithm alone, the pufit algorithm alone and the combined cell+pufit algorithm. The thresholds are set such that the algorithms have equal rates, and the data were recorded in the year 2018. Here the trigger efficiency is shown with respect to the offline $E_T^{miss}$ calculation with muons treated as being invisible.

Efficiencies for $Z\rightarrow\mu\mu$ events are shown for the L1 $E_T^{miss}>50\,$GeV trigger and for the complete L1+HLT trigger chain that also requires pufit > 110 GeV. Each is shown as a function of $\langle\mu\rangle$ for a $p_T(\mu\mu)$ threshold of 150 GeV.

Efficiencies for $Z\rightarrow\mu\mu$ events are shown for the L1 $E_T^{miss}>50\,$GeV trigger and for the complete L1+HLT trigger chain that also requires pufit > 110 GeV. Each is shown as a function of $\langle\mu\rangle$ for an offline $E_T^{miss}$ threshold of 150 GeV.

Efficiencies for $Z\rightarrow\mu\mu$ events are shown for the L1 $E_T^{miss}>50\,$GeV trigger and for the complete L1+HLT trigger chain that also requires pufit > 110 GeV. Each is shown as a function of $\langle\mu\rangle$ for an $p_T(\mu\mu)$ threshold of 175 GeV.

Efficiencies for $Z\rightarrow\mu\mu$ events are shown for the L1 $E_T^{miss}>50\,$GeV trigger and for the complete L1+HLT trigger chain that also requires pufit > 110 GeV. Each is shown as a function of $\langle\mu\rangle$ for an offline $E_T^{miss}$ threshold of 175 GeV.

HLT_xe70_mht trigger output rate as a function of $\langle\mu\rangle$ shown for an example run in year 2015.

HLT_xe90_mht trigger output rate as a function of $\langle\mu\rangle$ shown for an example run in year 2016.

HLT_xe110_mht trigger output rate as a function of $\langle\mu\rangle$ shown for an example run in year 2016.

HLT_xe110_pufit trigger output rate as a function of $\langle\mu\rangle$ shown for an example run in year 2017. This trigger included an implicit requirement of cell $E_T^{miss}>50\,$GeV.

HLT_xe110_pufit_xe65 trigger output rate as a function of $\langle\mu\rangle$ shown for an example run in year 2018.

HLT_xe110_pufit_xe70 trigger output rate as a function of $\langle\mu\rangle$ shown for an example run in year 2018.

Full-chain trigger efficiencies for each year as a function of $p_T(\mu\mu)$. The efficiency corresponds to that of the lowest unprescaled trigger that is adjusted throughout each year.

Full-chain trigger efficiencies for 2015, 2016, 2017 and 2018 as a function of $\langle\mu\rangle$ for $p_T(\mu\mu)>150$ GeV. The efficiency corresponds to that of the lowest unprescaled trigger that is adjusted throughout the year.

Efficiencies for the first-level trigger L1XE50 and the combined L1+HLT trigger chain HLT_xe110_pufit_xe65_L1XE50 in data recorded in the year 2018 are shown as a function of $\langle\mu\rangle$ for $W\rightarrow e\nu$ and $Z\rightarrow\mu\mu$ selections with offline $E_T^{miss}>150\,$GeV.

Efficiencies for the first-level trigger L1XE50 and the combined L1+HLT trigger chain HLT_xe110_pufit_xe65_L1XE50 in data recorded in the year 2018 are shown as a function of $\langle\mu\rangle$ for $t\bar t$ with offline $E_T^{miss}>150\,$GeV.

Efficiencies for the first-level trigger L1XE50 and the combined L1+HLT trigger chain HLT_xe110_pufit_xe65_L1XE50 in data recorded in the year 2018 are shown as a function of $\langle\mu\rangle$ for $W\rightarrow e\nu$ and $Z\rightarrow\mu\mu$ selections with offline $E_T^{miss}>175\,$GeV.

Efficiencies for the first-level trigger L1XE50 and the combined L1+HLT trigger chain HLT_xe110_pufit_xe65_L1XE50 in data recorded in the year 2018 are shown as a function of $\langle\mu\rangle$ for $t\bar t$ with offline $E_T^{miss}>175\,$GeV.

p-T differential invariant yield of Lambda + anti-Lambda in pp collisions with center-of-mass energy/nucleon = 7 TeV.

p-T differential invariant yield of Xi+ + Xi- in pp collisions with center-of-mass energy/nucleon = 7 TeV.

p-T differential invariant yield of Xi+ + Xi- in pp collisions with center-of-mass energy/nucleon = 7 TeV.

p-T differential invariant yield of K0s in pPb collisions with center-of-mass energy/nucleon = 5.02 TeV.

p-T differential invariant yield of K0s in pPb collisions with center-of-mass energy/nucleon = 5.02 TeV.

p-T differential invariant yield of Lambda + anti-Lambda in pPb collisions with center-of-mass energy/nucleon = 5.02 TeV.

p-T differential invariant yield of Lambda + anti-Lambda in pPb collisions with center-of-mass energy/nucleon = 5.02 TeV.

p-T differential invariant yield of Xi+ + Xi- in pPb collisions with center-of-mass energy/nucleon = 5.02 TeV.

p-T differential invariant yield of Xi+ + Xi- in pPb collisions with center-of-mass energy/nucleon = 5.02 TeV.

p-T differential invariant yield of K0s in PbPb collisions with center-of-mass energy/nucleon = 2.76 TeV.

p-T differential invariant yield of K0s in PbPb collisions with center-of-mass energy/nucleon = 2.76 TeV.

p-T differential invariant yield of Lambda + anti-Lambda in PbPb collisions with center-of-mass energy/nucleon = 2.76 TeV.

p-T differential invariant yield of Lambda + anti-Lambda in PbPb collisions with center-of-mass energy/nucleon = 2.76 TeV.

p-T differential invariant yield of Xi+ + Xi- in PbPb collisions with center-of-mass energy/nucleon = 2.76 TeV.

p-T differential invariant yield of Xi+ + Xi- in PbPb collisions with center-of-mass energy/nucleon = 2.76 TeV.

pT-dependent Lambda+anti-Lambda to K0S production ratios in pp collisions with centre-of-mass energy = 7 TeV.

pT-dependent Lambda+anti-Lambda to K0S production ratios in pPb collisions with centre-of-mass energy/nucleon = 5.02 TeV.

pT-dependent Lambda+anti-Lambda to K0S production ratios in PbPb collisions with centre-of-mass energy/nucleon = 2.76 TeV.

pT-dependent Xi+ + Xi- to Lambda+anti-Lambda production ratios in pp collisions with centre-of-mass energy = 7 TeV.

pT-dependent Xi+ + Xi- to Lambda+anti-Lambda production ratios in pPb collisions with centre-of-mass energy/nucleon = 5.02 TeV.

pT-dependent Xi+ + Xi- to Lambda+anti-Lambda production ratios in PbPb collisions with centre-of-mass energy/nucleon = 2.76 TeV.

Simultenous blast-wave fits parameters in pp collisions with center-of-mass energy/nucleon = 7 TeV.

Simultenous blast-wave fits parameters in pPb collisions with center-of-mass energy/nucleon = 5.02 TeV.

Simultenous blast-wave fits parameters in PbPb collisions with center-of-mass energy/nucleon = 2.76 TeV.

Mean transverse kinetic energy MEAN(KE_T) of K0s in pp collisions with centre-of-mass energy/nucleon=7 TeV.

Mean transverse kinetic energy MEAN(KE_T) of Lambda+anti-Lambda in pp collisions with centre-of-mass energy/nucleon=7 TeV.

Mean transverse kinetic energy MEAN(KE_T) of K0s in pPb collisions with centre-of-mass energy/nucleon=5.02 TeV.

Mean transverse kinetic energy MEAN(KE_T) of Lambda+anti-Lambda in pPb collisions with centre-of-mass energy/nucleon=5.02 TeV.

Mean transverse kinetic energy MEAN(KE_T) of Xi+ + Xi- in pPb collisions with centre-of-mass energy/nucleon=5.02 TeV.

Mean transverse kinetic energy MEAN(KE_T) of K0s in PbPb collisions with centre-of-mass energy/nucleon=2.76 TeV.

Mean transverse kinetic energy MEAN(KE_T) of Lambda + anti-Lambda in PbPb collisions with centre-of-mass energy/nucleon=2.76 TeV.

p-T differential invariant yield of K0s in pPb collisions with center-of-mass energy/nucleon = 5.02 TeV.

p-T differential invariant yield of Lambda + anti-Lambda in pPb collisions with center-of-mass energy/nucleon = 5.02 TeV.

p-T differential invariant yield of K0s in pPb collisions with center-of-mass energy/nucleon = 5.02 TeV.

p-T differential invariant yield of Lambda + anti-Lambda in pPb collisions with center-of-mass energy/nucleon = 5.02 TeV.

p-T differential invariant yield of K0s in pPb collisions with center-of-mass energy/nucleon = 5.02 TeV.

p-T differential invariant yield of Lambda + anti-Lambda in pPb collisions with center-of-mass energy/nucleon = 5.02 TeV.

pT-dependent Lambda+Anti-Lambda to K0S production ratios in pPb collisions with centre-of-mass energy/nucleon = 5.02 TeV.

Mean transverse kinetic energy MEAN(KE_T) of K0s in pPb collisions with centre-of-mass energy/nucleon=5.02 TeV.

Mean transverse kinetic energy MEAN(KE_T) of Lambda + anti-Lambda in pPb collisions with centre-of-mass energy/nucleon=5.02 TeV.

Forward-backward asymmetry, $N_{\ell}(+\eta_{\textrm{lab}})/ N_{\ell}(-\eta_{\textrm{lab}})$, for the negative leptons.

Forward-backward asymmetry, $N_{\ell}(+\eta_{\textrm{lab}})/ N_{\ell}(-\eta_{\textrm{lab}})$, for the positive leptons.

Forward-backward asymmetry, $N_{\ell}(+\eta_{\textrm{lab}})/ N_{\ell}(-\eta_{\textrm{lab}})$, for the charged-summed W bosons, as a function of the lepton pseudorapidity.

Nuclear modification factor RAA of psi(2S) integrated over centrality, for the midrapidity and forward rapidity analysis bins.

Normalised differential TOP TOPBAR production cross section as a function of the number of additional jets with PT(JET) > 30 GeV in the lepton+jets channel. The total uncertainties are shown and include the statistical and systematic uncertainties.

Measured gap fraction in the dilepton channel as a function of the additional jet transverse momentum, GAPFRAC(PT) = N(PT)/N(TOTAL) where N(PT) is the number of events that do not contain additional jets above the given PT threshold.

Measured gap fraction in the dilepton channel as a function of the scalar sum of the transverse momentum of the additional jets, HT = SUM(PT(ADD. JETS)), GAPFRAC(HT) = N(HT)/N(TOTAL) where N(HT) is the number of events in which HT (with PT > 30 GeV) is less than the given threshold.

The transverse momentum production spectra per NSD event spectra for LAMBDA mesons at 0.9 and 7 TeV.

The rapidity production spectra per NSD event spectra for XI- mesons at 0.9 and 7 TeV.

The transverse momentum production spectra per NSD event spectra for XI- mesons at 0.9 and 7 TeV.

Ratio of production rates per NSD event for LAMBDA and KS events as a function of transverse momentum.

Ratio of production rates per NSD event for XI- and LAMBDA events as a function of transverse momentum.

Ratio of production rates per NSD event for LAMBDA and KS events as a function of rapidity.

Ratio of production rates per NSD event for XI- and LAMBDA events as a function of rapidity.

Results from fitting the Tsallis function to the data at 0.9 TeV. See text of paper for details.

Results from fitting the Tsallis function to the data at 7 TeV. See text of paper for details.

Mean PT obtained from the DN/DPT distributions.

The central rapidity production rate and the integrated yields per NSD event at 0.9 TeV for |rapidity| < 2.

The central rapidity production rate and the integrated yields per NSD event at 7 TeV for |rapidity| < 2.