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| CMS-EXO-23-007 ; CERN-EP-2024-068 | ||
| Enriching the physics program of the CMS experiment via data scouting and data parking | ||
| CMS Collaboration | ||
| 24 March 2024 | ||
| Accepted for publication in Physics Reports | ||
| Abstract: Specialized data-taking and data-processing techniques were introduced by the CMS experiment in Run 1 of the CERN LHC to enhance the sensitivity of searches for new physics and the precision of standard model measurements. These techniques, termed data scouting and data parking, extend the data-taking capabilities of CMS beyond the original design specifications. The novel data-scouting strategy trades complete event information for higher event rates, while keeping the data bandwidth within limits. Data parking involves storing a large amount of raw detector data collected by algorithms with low trigger thresholds to be processed when sufficient computational power is available to handle such data. The research program of the CMS Collaboration is greatly expanded with these techniques. The implementation, performance, and physics results obtained with data scouting and data parking in CMS over the last decade are discussed in this Report, along with new developments aimed at further improving low-mass physics sensitivity over the next years of data taking. | ||
| Links: e-print arXiv:2403.16134 [hep-ex] (PDF) ; CDS record ; inSPIRE record ; Physics Briefing ; CADI line (restricted) ; | ||
| Figures | |
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Figure 1:
A schematic view of the typical Run 2 data flow during 2018 showing the data acquisition strategy with scouting and parking data streams, along with the standard data stream. A value of $ {\mathcal{L}_{\text{inst}}} = $ 1.2 $\times$ 10$^{34}$ cm$^{-2}$s$^{-1}$ over a typical 2018 fill, corresponding to an average pileup of 38, is considered. |
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Figure 2:
Comparison of the typical HLT rates of the standard, parking, and scouting data streams from Run 1 to Run 3. The $ \mathcal{L}_{\text{inst}} $ averaged over one typical fill of a given data-taking year is shown in pink. |
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Figure 3:
The efficiency of the Run 2 Calo scouting (left) and standard (right) jet triggers as a function of the reconstructed mass of the dijet system. Figures taken from Ref. [65]. |
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Figure 3-a:
The efficiency of the Run 2 Calo scouting (left) and standard (right) jet triggers as a function of the reconstructed mass of the dijet system. Figures taken from Ref. [65]. |
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Figure 3-b:
The efficiency of the Run 2 Calo scouting (left) and standard (right) jet triggers as a function of the reconstructed mass of the dijet system. Figures taken from Ref. [65]. |
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Figure 4:
The efficiency of the Run 2 PF scouting jet triggers as a function of $ H_{\mathrm{T}} $ (left) and as a function of the leading large-radius jet $ p_{\mathrm{T}} $ and trimmed jet mass (right). |
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Figure 4-a:
The efficiency of the Run 2 PF scouting jet triggers as a function of $ H_{\mathrm{T}} $ (left) and as a function of the leading large-radius jet $ p_{\mathrm{T}} $ and trimmed jet mass (right). |
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Figure 4-b:
The efficiency of the Run 2 PF scouting jet triggers as a function of $ H_{\mathrm{T}} $ (left) and as a function of the leading large-radius jet $ p_{\mathrm{T}} $ and trimmed jet mass (right). |
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Figure 5:
The observed percent difference between the $ p_{\mathrm{T}} $ of Calo jets at the HLT and the $ p_{\mathrm{T}} $ of PF jets reconstructed offline (points), fitted to a smooth function (curve), vs. the Calo jet $ p_{\mathrm{T}} $. Both Calo and PF jets are calibrated with corrections derived from simulation. Figure taken from Ref. [65]. |
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Figure 6:
The distribution of $ m_{\mathrm{jjj}} $ for the resolved three-jet search (left), and average jet mass {($ \tilde{m} = (m_1 + m_2)/ $ 2)} for the merged three-parton search (right), adapted from Ref. [67]. Both analyses use PF jets. The peak around 170 GeV in both distributions corresponds to the all-hadronic decay of the top quark. The data (points) are compared to the background-only prediction (blue) and the full background fit including simulations of the top quark resonance (red). |
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Figure 6-a:
The distribution of $ m_{\mathrm{jjj}} $ for the resolved three-jet search (left), and average jet mass {($ \tilde{m} = (m_1 + m_2)/ $ 2)} for the merged three-parton search (right), adapted from Ref. [67]. Both analyses use PF jets. The peak around 170 GeV in both distributions corresponds to the all-hadronic decay of the top quark. The data (points) are compared to the background-only prediction (blue) and the full background fit including simulations of the top quark resonance (red). |
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Figure 6-b:
The distribution of $ m_{\mathrm{jjj}} $ for the resolved three-jet search (left), and average jet mass {($ \tilde{m} = (m_1 + m_2)/ $ 2)} for the merged three-parton search (right), adapted from Ref. [67]. Both analyses use PF jets. The peak around 170 GeV in both distributions corresponds to the all-hadronic decay of the top quark. The data (points) are compared to the background-only prediction (blue) and the full background fit including simulations of the top quark resonance (red). |
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Figure 7:
Left: output of the QGD for quark (orange) and gluon (blue) jets. The corresponding receiver operating characteristic (ROC) curve is also shown. Right: observation of fully hadronic top quark decays in the invariant mass of three jets with a QCD multijet background, for an inclusive selection (no QGD), and for a selection including the QGD score. Figure adapted from Ref. [67]. |
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Figure 7-a:
Left: output of the QGD for quark (orange) and gluon (blue) jets. The corresponding receiver operating characteristic (ROC) curve is also shown. Right: observation of fully hadronic top quark decays in the invariant mass of three jets with a QCD multijet background, for an inclusive selection (no QGD), and for a selection including the QGD score. Figure adapted from Ref. [67]. |
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Figure 7-b:
Left: output of the QGD for quark (orange) and gluon (blue) jets. The corresponding receiver operating characteristic (ROC) curve is also shown. Right: observation of fully hadronic top quark decays in the invariant mass of three jets with a QCD multijet background, for an inclusive selection (no QGD), and for a selection including the QGD score. Figure adapted from Ref. [67]. |
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Figure 8:
Dimuon invariant mass distribution of events selected with the standard muon triggers (blue, dashed) and scouting muon triggers (pink, solid) in the mass range 11--240 GeV, normalized to $ {\mathcal{L}_{\text{int}}} = $ 96.6 fb$^{-1}$, corresponding to the scouting data collected in 2017 and 2018. The selection applied to obtain each distribution is described in Ref. [68]. |
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Figure 9:
Dimuon invariant mass spectrum and event rate of each L1 seed (legend) obtained with the scouting stream reconstructed at the HLT, using data collected in 2018 corresponding to $ {\mathcal{L}_{\text{int}}} = $ 60 fb$^{-1}$. Well-known dimuon resonances from various meson decays or from Z boson decays are indicated above each peak. |
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Figure 10:
Dimuon invariant mass distributions in bins of transverse displacement from the PV ($ l_{\text{xy}} $). Figure adapted from Ref. [69]. |
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Figure 11:
Efficiency of the dimuon scouting trigger and logical ``OR'' of all L1 triggers measured with 2017 and 2018 data. The efficiency is shown as a function of the angular separation between the two muons $ {\Delta}R_{\mu\mu} $ and the dimuon mass $ m_{\mu\mu} $ (left), and as a function of $ {\Delta}R_{\mu\mu} $ and the subleading muon $ p_{\mathrm{T}} $ (right). The selection in the right plot also requires 0.45 $ < m_{\mu\mu} < $ 0.65 GeV to focus on the $ \eta $ meson resonance region. The statistical uncertainty in the measured values is generally less than 3% per bin on the left plot and less than 15% per bin on the right plot. |
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Figure 11-a:
Efficiency of the dimuon scouting trigger and logical ``OR'' of all L1 triggers measured with 2017 and 2018 data. The efficiency is shown as a function of the angular separation between the two muons $ {\Delta}R_{\mu\mu} $ and the dimuon mass $ m_{\mu\mu} $ (left), and as a function of $ {\Delta}R_{\mu\mu} $ and the subleading muon $ p_{\mathrm{T}} $ (right). The selection in the right plot also requires 0.45 $ < m_{\mu\mu} < $ 0.65 GeV to focus on the $ \eta $ meson resonance region. The statistical uncertainty in the measured values is generally less than 3% per bin on the left plot and less than 15% per bin on the right plot. |
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Figure 11-b:
Efficiency of the dimuon scouting trigger and logical ``OR'' of all L1 triggers measured with 2017 and 2018 data. The efficiency is shown as a function of the angular separation between the two muons $ {\Delta}R_{\mu\mu} $ and the dimuon mass $ m_{\mu\mu} $ (left), and as a function of $ {\Delta}R_{\mu\mu} $ and the subleading muon $ p_{\mathrm{T}} $ (right). The selection in the right plot also requires 0.45 $ < m_{\mu\mu} < $ 0.65 GeV to focus on the $ \eta $ meson resonance region. The statistical uncertainty in the measured values is generally less than 3% per bin on the left plot and less than 15% per bin on the right plot. |
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Figure 12:
Background rejection vs. signal efficiency of the new MVA-based muon ID strategies evaluated on scouting data: $ \Upsilon{\textrm{(1S)}} $-trained MVA (black line), $ \mathrm{J}/\psi $-trained MVA (red line). A comparison with the performance of the previous cut-based selection, which was optimised for signals at the mass range higher than 11 GeV, is also shown for the $ \Upsilon{\textrm{(1S)}} $ (blue triangle) and $ \mathrm{J}/\psi $ (brown square) signals. Figure adapted from Ref. [71]. |
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Figure 13:
Relative width of dimuon resonances as a function of mass, measured in 2017 and 2018 scouting data. The fits are performed separately for the 2017 and 2018 data sets. The values shown are the average width of each fit, weighted by the $ \mathcal{L}_{\text{int}} $ value corresponding to the data accumulated in each year. From left to right, the $ \eta $, $ \phi $, $ \mathrm{J}/\psi $, \PGyP2S, $ \Upsilon{\textrm{(1S)}} $, $ \Upsilon{\textrm{(2S)}} $, and $ \Upsilon{\textrm{(3S)}} $ resonances are shown. The inserts display fits of the $ \mathrm{J}/\psi $ and $ \Upsilon $ peaks obtained with scouting data (black markers) separately for the signal (green) and background (red) components, and for their sum (blue). |
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Figure 14:
Left: dijet mass spectra (points) compared to a fitted parametrization of the background (solid curve) for the inclusive search performed in Ref. [65]. Right: dijet mass spectrum (points) compared to a fitted parametrization of the background (solid curve) for the three-jet analysis performed in Ref. [77]. The lower panel shows the difference between the data and the fitted parametrization, divided by the statistical uncertainty of the data. Examples of predicted signals from narrow gluon-gluon, quark-gluon, and quark-quark resonances are shown with cross sections equal to the observed upper limits at 95% CL. Figures taken from Refs. [65] (left) and [77] (right). |
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Figure 14-a:
Left: dijet mass spectra (points) compared to a fitted parametrization of the background (solid curve) for the inclusive search performed in Ref. [65]. Right: dijet mass spectrum (points) compared to a fitted parametrization of the background (solid curve) for the three-jet analysis performed in Ref. [77]. The lower panel shows the difference between the data and the fitted parametrization, divided by the statistical uncertainty of the data. Examples of predicted signals from narrow gluon-gluon, quark-gluon, and quark-quark resonances are shown with cross sections equal to the observed upper limits at 95% CL. Figures taken from Refs. [65] (left) and [77] (right). |
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Figure 14-b:
Left: dijet mass spectra (points) compared to a fitted parametrization of the background (solid curve) for the inclusive search performed in Ref. [65]. Right: dijet mass spectrum (points) compared to a fitted parametrization of the background (solid curve) for the three-jet analysis performed in Ref. [77]. The lower panel shows the difference between the data and the fitted parametrization, divided by the statistical uncertainty of the data. Examples of predicted signals from narrow gluon-gluon, quark-gluon, and quark-quark resonances are shown with cross sections equal to the observed upper limits at 95% CL. Figures taken from Refs. [65] (left) and [77] (right). |
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Figure 15:
Observed limits on the universal coupling $ g^\prime_\mathrm{q} $ between a leptophobic $ \mathrm{Z}^{'} $ boson and quarks [76] from various CMS dijet analyses. Regions above the lines are excluded at 95% CL. The grey dashed lines show the $ g^\prime_\mathrm{q} $ values at fixed values of $ {\Gamma_{\mathrm{Z}^{'}}/m_{\mathrm{Z}^{'}}} $. Limits from scouting-based analyses are indicated with bold lines. |
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Figure 16:
Comparison of limits from searches for RPV gluinos decaying to three partons. Regions above the lines are excluded at 95% CL. The two CMS analyses that use data scouting are also indicated with bold lines. |
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Figure 17:
Observed (points) and expected (dashes) limits on the product of production cross section, branching fraction, and acceptance for pair-produced merged two-quark resonances. The variations at the one and two standard deviation levels in the expected limits are displayed with shaded bands. A comparison with the theoretical predictions for top squark production (red) is also shown. Figure taken from Ref. [67]. |
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Figure 18:
Expected and observed upper limits at 90% CL on the square of the kinetic mixing coefficient ($ \epsilon^2 $) as a function of dark photon mass. Results obtained with scouting triggers are displayed to the left of the vertical purple line, while those obtained with standard triggers are shown to the right. Limits at 90% CL obtained from the search performed by the LHCb Collaboration [90] are shown in red, and constraints at 95% CL from the measurements of electroweak observables are shown in light blue [91]. Figure taken from Ref. [68]. |
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Figure 19:
The $ m_{\mu\mu} $ distribution obtained with the scouting data collected during 2017 and 2018 with two sets of selections: the $ \mathrm{J}/\psi $-trained (red) and the $ \Upsilon{\textrm{(1S)}} $-trained (blue) MVA-based muon identification algorithms. Figure taken from Ref. [71]. |
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Figure 20:
Upper limits at 90% CL on the square of the kinetic mixing coefficient ($ \epsilon^2 $) in the minimal dark photon model obtained as a recast of model-independent limits on the production rates of dimuon resonances for the inclusive category. The CMS limits (pink) are compared with the existing limits at 90% CL provided by LHCb [92] (blue) and BaBar [93] (gray). Figure taken from Ref. [71]. |
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Figure 21:
Left: diagram illustrating an SM-like Higgs boson (H) decay to four leptons ($ \ell $) via two intermediate dark photons ($\mathrm{Z_D}$). Right: diagram illustrating the production of a scalar resonance $ \phi $ in a b hadron decay, through mixing with an SM-like Higgs boson. Figure taken from Ref. [69]. |
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Figure 21-a:
Left: diagram illustrating an SM-like Higgs boson (H) decay to four leptons ($ \ell $) via two intermediate dark photons ($\mathrm{Z_D}$). Right: diagram illustrating the production of a scalar resonance $ \phi $ in a b hadron decay, through mixing with an SM-like Higgs boson. Figure taken from Ref. [69]. |
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Figure 21-b:
Left: diagram illustrating an SM-like Higgs boson (H) decay to four leptons ($ \ell $) via two intermediate dark photons ($\mathrm{Z_D}$). Right: diagram illustrating the production of a scalar resonance $ \phi $ in a b hadron decay, through mixing with an SM-like Higgs boson. Figure taken from Ref. [69]. |
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Figure 22:
Observed limits at 95% CL on (upper) the branching fraction $ \mathcal{B}(\mathrm{H} \to \mathrm{Z_D}\mathrm{Z_D}) $ and (lower) the branching fraction product $ {\mathcal{B}(\mathrm{h}_{\mathrm{b}}\to\phi \mathrm{X})\cdot\mathcal{B}(\phi\to\mu\mu)} $ as contours in the parameter space containing the signal mass ($ m_{\mathrm{Z_D}} $ or $ m_{\phi} $, respectively) and the signal lifetime $ c\tau_0 $. The vertical gray bands indicate mass ranges containing known SM resonances, which are masked for this search. The limits are obtained using the combination of all dimuon and four-muon event categories. Figures taken from Ref. [69]. |
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Figure 22-a:
Observed limits at 95% CL on (upper) the branching fraction $ \mathcal{B}(\mathrm{H} \to \mathrm{Z_D}\mathrm{Z_D}) $ and (lower) the branching fraction product $ {\mathcal{B}(\mathrm{h}_{\mathrm{b}}\to\phi \mathrm{X})\cdot\mathcal{B}(\phi\to\mu\mu)} $ as contours in the parameter space containing the signal mass ($ m_{\mathrm{Z_D}} $ or $ m_{\phi} $, respectively) and the signal lifetime $ c\tau_0 $. The vertical gray bands indicate mass ranges containing known SM resonances, which are masked for this search. The limits are obtained using the combination of all dimuon and four-muon event categories. Figures taken from Ref. [69]. |
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Figure 22-b:
Observed limits at 95% CL on (upper) the branching fraction $ \mathcal{B}(\mathrm{H} \to \mathrm{Z_D}\mathrm{Z_D}) $ and (lower) the branching fraction product $ {\mathcal{B}(\mathrm{h}_{\mathrm{b}}\to\phi \mathrm{X})\cdot\mathcal{B}(\phi\to\mu\mu)} $ as contours in the parameter space containing the signal mass ($ m_{\mathrm{Z_D}} $ or $ m_{\phi} $, respectively) and the signal lifetime $ c\tau_0 $. The vertical gray bands indicate mass ranges containing known SM resonances, which are masked for this search. The limits are obtained using the combination of all dimuon and four-muon event categories. Figures taken from Ref. [69]. |
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Figure 23:
The four-muon invariant mass ($ m_{4\mu} $) distribution in the range 0.46--0.90 GeV, obtained with pp collision data collected during 2017--2018. The observed distribution (points) is compared to the background-only prediction (green dashed) and to the full background fit including simulations of the signal (solid blue). The peak observed in the mass window 0.53--0.57 GeV corresponds to the $ \eta $ meson. The pull distribution in the lower panel is shown relative to the background component of the fit model and defined as $ {(\mathrm{Data} - \mathrm{Fit})/ \mathrm{Uncertainty}} $, where the uncertainty is statistical only. Figure taken from Ref. [95]. |
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Figure 24:
The product of acceptance ($ A $) and efficiency as a function of the generated $ \eta $ meson $ p_{\mathrm{T}} $ for the two-muon (red circles and blue squares) and four-muon (orange up triangles and green down triangles) $ \eta $ meson decays. The product is evaluated using simulated samples. Figure taken from Ref. [95]. |
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Figure 25:
Relative rate of each L1 algorithm category, shown as the fraction of the total rate based on the 2022 (orange) and 2023 (blue) configurations. The proportional rate of each category with respect to the total is shown on the right. The values are computed using reference runs with average pileup of 60. |
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Figure 26:
Trigger efficiency as a function of AK4 jet $ p_{\mathrm{T}} $ (left), AK8 jet $ p_{\mathrm{T}} $ (center), and $ H_{\mathrm{T}} $ (right). The efficiency is computed from collision data recorded in 2022 by the scouting (black points) and standard (red points) streams. |
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Figure 26-a:
Trigger efficiency as a function of AK4 jet $ p_{\mathrm{T}} $ (left), AK8 jet $ p_{\mathrm{T}} $ (center), and $ H_{\mathrm{T}} $ (right). The efficiency is computed from collision data recorded in 2022 by the scouting (black points) and standard (red points) streams. |
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Figure 26-b:
Trigger efficiency as a function of AK4 jet $ p_{\mathrm{T}} $ (left), AK8 jet $ p_{\mathrm{T}} $ (center), and $ H_{\mathrm{T}} $ (right). The efficiency is computed from collision data recorded in 2022 by the scouting (black points) and standard (red points) streams. |
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Figure 26-c:
Trigger efficiency as a function of AK4 jet $ p_{\mathrm{T}} $ (left), AK8 jet $ p_{\mathrm{T}} $ (center), and $ H_{\mathrm{T}} $ (right). The efficiency is computed from collision data recorded in 2022 by the scouting (black points) and standard (red points) streams. |
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Figure 27:
The JES as a function of mean scouting jet $ p_{\mathrm{T}} $ derived from simulated (left) and recorded (right) events. The red and black points correspond to events where the two leading jets have $ {|\eta| } < $ 1.3 and 1.3 $ < |\eta| < $ 2.5, respectively. |
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Figure 27-a:
The JES as a function of mean scouting jet $ p_{\mathrm{T}} $ derived from simulated (left) and recorded (right) events. The red and black points correspond to events where the two leading jets have $ {|\eta| } < $ 1.3 and 1.3 $ < |\eta| < $ 2.5, respectively. |
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Figure 27-b:
The JES as a function of mean scouting jet $ p_{\mathrm{T}} $ derived from simulated (left) and recorded (right) events. The red and black points correspond to events where the two leading jets have $ {|\eta| } < $ 1.3 and 1.3 $ < |\eta| < $ 2.5, respectively. |
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Figure 28:
The JER as a function of average $ p_{\mathrm{T}} $. The JER is computed from simulated (upper) and recorded (lower) events, by requiring the two leading jets to have $ |\eta| < $ 1.3 (left) and 1.3 $ < |\eta| < $ 2.5 (right). The red and black data points denote 2022 collision data of the scouting and offline reconstructions, respectively. |
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Figure 28-a:
The JER as a function of average $ p_{\mathrm{T}} $. The JER is computed from simulated (upper) and recorded (lower) events, by requiring the two leading jets to have $ |\eta| < $ 1.3 (left) and 1.3 $ < |\eta| < $ 2.5 (right). The red and black data points denote 2022 collision data of the scouting and offline reconstructions, respectively. |
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Figure 28-b:
The JER as a function of average $ p_{\mathrm{T}} $. The JER is computed from simulated (upper) and recorded (lower) events, by requiring the two leading jets to have $ |\eta| < $ 1.3 (left) and 1.3 $ < |\eta| < $ 2.5 (right). The red and black data points denote 2022 collision data of the scouting and offline reconstructions, respectively. |
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Figure 28-c:
The JER as a function of average $ p_{\mathrm{T}} $. The JER is computed from simulated (upper) and recorded (lower) events, by requiring the two leading jets to have $ |\eta| < $ 1.3 (left) and 1.3 $ < |\eta| < $ 2.5 (right). The red and black data points denote 2022 collision data of the scouting and offline reconstructions, respectively. |
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Figure 28-d:
The JER as a function of average $ p_{\mathrm{T}} $. The JER is computed from simulated (upper) and recorded (lower) events, by requiring the two leading jets to have $ |\eta| < $ 1.3 (left) and 1.3 $ < |\eta| < $ 2.5 (right). The red and black data points denote 2022 collision data of the scouting and offline reconstructions, respectively. |
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Figure 29:
The ratio of the JER derived from scouting events to the JER derived from offline events as a function of average $ p_{\mathrm{T}} $. The ratio is computed from simulated (left) and recorded (right) events, by requiring the two leading jets to have $ |\eta| < $ 1.3 (red points) and 1.3 $ < |\eta| < $ 2.5 (black points). |
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Figure 29-a:
The ratio of the JER derived from scouting events to the JER derived from offline events as a function of average $ p_{\mathrm{T}} $. The ratio is computed from simulated (left) and recorded (right) events, by requiring the two leading jets to have $ |\eta| < $ 1.3 (red points) and 1.3 $ < |\eta| < $ 2.5 (black points). |
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Figure 29-b:
The ratio of the JER derived from scouting events to the JER derived from offline events as a function of average $ p_{\mathrm{T}} $. The ratio is computed from simulated (left) and recorded (right) events, by requiring the two leading jets to have $ |\eta| < $ 1.3 (red points) and 1.3 $ < |\eta| < $ 2.5 (black points). |
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Figure 30:
Trigger efficiency for ggF boosted $ \mathrm{H} \to \mathrm{b} \overline{\mathrm{b}} $ events as a function of the highest particle-level Higgs boson $ p_{\mathrm{T}} $ (left) and highest offline-reconstructed AK8 jet $ p_{\mathrm{T}} $ (right), as determined from simulation. The black and red points correspond to the scouting and the standard trigger selection, respectively. |
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Figure 30-a:
Trigger efficiency for ggF boosted $ \mathrm{H} \to \mathrm{b} \overline{\mathrm{b}} $ events as a function of the highest particle-level Higgs boson $ p_{\mathrm{T}} $ (left) and highest offline-reconstructed AK8 jet $ p_{\mathrm{T}} $ (right), as determined from simulation. The black and red points correspond to the scouting and the standard trigger selection, respectively. |
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Figure 30-b:
Trigger efficiency for ggF boosted $ \mathrm{H} \to \mathrm{b} \overline{\mathrm{b}} $ events as a function of the highest particle-level Higgs boson $ p_{\mathrm{T}} $ (left) and highest offline-reconstructed AK8 jet $ p_{\mathrm{T}} $ (right), as determined from simulation. The black and red points correspond to the scouting and the standard trigger selection, respectively. |
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Figure 31:
Number of ggF boosted $ \mathrm{H} \to \mathrm{b} \overline{\mathrm{b}} $ events as a function of the highest particle-level Higgs boson $ p_{\mathrm{T}} $ (left) and highest offline-reconstructed AK8 jet $ p_{\mathrm{T}} $ (right). The large-radius jet with highest $ p_{\mathrm{T}} $ in each event is required to have a maximum angular distance $ {\Delta}R < $ 0.8 from the two final-state b quarks (blue). The events are then required to pass either the standard (red) or scouting (black) trigger selection. The number of events is computed from simulation with projected $ {\mathcal{L}_{\text{int}}} = $ 100 fb$^{-1}$. |
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Figure 31-a:
Number of ggF boosted $ \mathrm{H} \to \mathrm{b} \overline{\mathrm{b}} $ events as a function of the highest particle-level Higgs boson $ p_{\mathrm{T}} $ (left) and highest offline-reconstructed AK8 jet $ p_{\mathrm{T}} $ (right). The large-radius jet with highest $ p_{\mathrm{T}} $ in each event is required to have a maximum angular distance $ {\Delta}R < $ 0.8 from the two final-state b quarks (blue). The events are then required to pass either the standard (red) or scouting (black) trigger selection. The number of events is computed from simulation with projected $ {\mathcal{L}_{\text{int}}} = $ 100 fb$^{-1}$. |
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Figure 31-b:
Number of ggF boosted $ \mathrm{H} \to \mathrm{b} \overline{\mathrm{b}} $ events as a function of the highest particle-level Higgs boson $ p_{\mathrm{T}} $ (left) and highest offline-reconstructed AK8 jet $ p_{\mathrm{T}} $ (right). The large-radius jet with highest $ p_{\mathrm{T}} $ in each event is required to have a maximum angular distance $ {\Delta}R < $ 0.8 from the two final-state b quarks (blue). The events are then required to pass either the standard (red) or scouting (black) trigger selection. The number of events is computed from simulation with projected $ {\mathcal{L}_{\text{int}}} = $ 100 fb$^{-1}$. |
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Figure 32:
Invariant mass distribution of opposite-sign muon pairs obtained with the scouting triggers, collected during 2022 with all Run 3 dimuon algorithms (blue curve), and with each individual algorithm (remaining colors). |
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Figure 33:
Comparison between the $ l_{\text{xy}} $ distribution for Run 2 (orange) and Run 3 (blue) events in data that contain dimuon pairs with a common displaced vertex and a minimal selection on the vertex quality. The dashed vertical lines, placed at radii of 29, 68, 109, and 160$ \,\text{mm} $, correspond to the positions of the pixel layers where photons undergo conversion processes in the material, causing the observed peaks in the $ l_{\text{xy}} $ distribution. |
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Figure 34:
Resolution on the transverse momentum of scouting muons compared to offline muons using data collected in 2022. Differences in muon momentum scale between the scouting and offline reconstruction algorithms are studied in bins of 1 GeV (10 GeV) for muon $ p_{\mathrm{T}} $ smaller (larger) than 60 GeV. Values for the barrel (blue circles) and endcap (orange triangles) sections are shown separately. |
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Figure 35:
Comparison of the dimuon spectra obtained with scouting (pink filled histogram) and offline (blue solid line) muons during the 2022 data-taking period. The ratio between the two distributions with a wider binning is also shown in the bottom panel as a gray band. |
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Figure 36:
Trigger efficiencies for the scouting trigger paths seeded by L1 algorithms targeting either single-electron events (left) or single-photon events (right), as a function of the respective object $ p_{\mathrm{T}} $ reconstructed offline. To be considered for scouting, the leading electron or photon must have $ {p_{\mathrm{T}}} > $ 30 GeV. Results are only shown for electrons or photons detected in the barrel region. |
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Figure 36-a:
Trigger efficiencies for the scouting trigger paths seeded by L1 algorithms targeting either single-electron events (left) or single-photon events (right), as a function of the respective object $ p_{\mathrm{T}} $ reconstructed offline. To be considered for scouting, the leading electron or photon must have $ {p_{\mathrm{T}}} > $ 30 GeV. Results are only shown for electrons or photons detected in the barrel region. |
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Figure 36-b:
Trigger efficiencies for the scouting trigger paths seeded by L1 algorithms targeting either single-electron events (left) or single-photon events (right), as a function of the respective object $ p_{\mathrm{T}} $ reconstructed offline. To be considered for scouting, the leading electron or photon must have $ {p_{\mathrm{T}}} > $ 30 GeV. Results are only shown for electrons or photons detected in the barrel region. |
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Figure 37:
Dielectron mass distribution observed with Run 3 scouting data collected during the 2023 data-taking period. The $ \mathrm{J}/\psi $ and two of the $ \Upsilon $ meson peaks are visible. |
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Figure 38:
The L1 trigger rate and the amount of pileup as a function of time, shown for representative LHC fills during 2017 (left) and 2018 (right). Occasional lower rates are observed due to transient effects, such as the throttling of the trigger system in response to subdetector dead time [9]. Changes in the trigger configuration are indicated by vertical green dashed lines. |
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Figure 38-a:
The L1 trigger rate and the amount of pileup as a function of time, shown for representative LHC fills during 2017 (left) and 2018 (right). Occasional lower rates are observed due to transient effects, such as the throttling of the trigger system in response to subdetector dead time [9]. Changes in the trigger configuration are indicated by vertical green dashed lines. |
png pdf |
Figure 38-b:
The L1 trigger rate and the amount of pileup as a function of time, shown for representative LHC fills during 2017 (left) and 2018 (right). Occasional lower rates are observed due to transient effects, such as the throttling of the trigger system in response to subdetector dead time [9]. Changes in the trigger configuration are indicated by vertical green dashed lines. |
png pdf |
Figure 39:
Left: an example scenario in which the $ {\mathrm{B}}\,\text{parking} $ data throughput per L1 trigger setting is adjusted to maintain an average of approximately 2\unitGB/s throughout an LHC fill. The dotted red and dashed blue lines trace the $ {\mathrm{B}}\,\text{parking} $ data throughput and maximum allowed HLT rate, respectively, determined for each trigger configuration. Changes in the trigger configuration are indicated by vertical green dashed lines. The trigger logic is adjusted to operate close to the permitted HLT rate. Right: rate of $ \mathrm{b} \overline{\mathrm{b}} $ events in acceptance versus HLT rate for a parameter scan over $ p_{\mathrm{T}} $ and $ \text{IP}_{\text{sig}} $ thresholds imposed in the HLT logic for the $ \mathcal{L}_{\text{inst}} $ and L1 requirements indicated in the legend; each point (blue circle) represents a unique pair of thresholds and the red star indicates the optimal pairing of $ {p_{\mathrm{T}}} > $ 12 GeV and $ {\text{IP}_{\text{sig}} > 6} $ at a peak $ {\mathcal{L}_{\text{inst}}} = $ 1.7 $\times$ 10$^{34}$ cm$^{-2}$s$^{-1}$ and an HLT rate close to the maximum allowed value of $ {\approx} $1.5 kHz. |
png pdf |
Figure 39-a:
Left: an example scenario in which the $ {\mathrm{B}}\,\text{parking} $ data throughput per L1 trigger setting is adjusted to maintain an average of approximately 2\unitGB/s throughout an LHC fill. The dotted red and dashed blue lines trace the $ {\mathrm{B}}\,\text{parking} $ data throughput and maximum allowed HLT rate, respectively, determined for each trigger configuration. Changes in the trigger configuration are indicated by vertical green dashed lines. The trigger logic is adjusted to operate close to the permitted HLT rate. Right: rate of $ \mathrm{b} \overline{\mathrm{b}} $ events in acceptance versus HLT rate for a parameter scan over $ p_{\mathrm{T}} $ and $ \text{IP}_{\text{sig}} $ thresholds imposed in the HLT logic for the $ \mathcal{L}_{\text{inst}} $ and L1 requirements indicated in the legend; each point (blue circle) represents a unique pair of thresholds and the red star indicates the optimal pairing of $ {p_{\mathrm{T}}} > $ 12 GeV and $ {\text{IP}_{\text{sig}} > 6} $ at a peak $ {\mathcal{L}_{\text{inst}}} = $ 1.7 $\times$ 10$^{34}$ cm$^{-2}$s$^{-1}$ and an HLT rate close to the maximum allowed value of $ {\approx} $1.5 kHz. |
png pdf |
Figure 39-b:
Left: an example scenario in which the $ {\mathrm{B}}\,\text{parking} $ data throughput per L1 trigger setting is adjusted to maintain an average of approximately 2\unitGB/s throughout an LHC fill. The dotted red and dashed blue lines trace the $ {\mathrm{B}}\,\text{parking} $ data throughput and maximum allowed HLT rate, respectively, determined for each trigger configuration. Changes in the trigger configuration are indicated by vertical green dashed lines. The trigger logic is adjusted to operate close to the permitted HLT rate. Right: rate of $ \mathrm{b} \overline{\mathrm{b}} $ events in acceptance versus HLT rate for a parameter scan over $ p_{\mathrm{T}} $ and $ \text{IP}_{\text{sig}} $ thresholds imposed in the HLT logic for the $ \mathcal{L}_{\text{inst}} $ and L1 requirements indicated in the legend; each point (blue circle) represents a unique pair of thresholds and the red star indicates the optimal pairing of $ {p_{\mathrm{T}}} > $ 12 GeV and $ {\text{IP}_{\text{sig}} > 6} $ at a peak $ {\mathcal{L}_{\text{inst}}} = $ 1.7 $\times$ 10$^{34}$ cm$^{-2}$s$^{-1}$ and an HLT rate close to the maximum allowed value of $ {\approx} $1.5 kHz. |
png pdf |
Figure 40:
HLT trigger rates and the number of pileup events shown as a function of time during a representative LHC fill in 2018. The rates for the promptly reconstructed core physics (black solid markers) and $ {\mathrm{B}}\,\text{parking} $ (blue open markers) data streams are shown separately. Occasional lower rates are observed due to transient effects, such as the throttling of the trigger system in response to subdetector dead time [9]. Changes in the trigger configuration are indicated by vertical green dashed lines. |
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Figure 41:
Mass difference between reconstructed $ \mathrm{D}^{*+} $ and $ \mathrm{D^0} $ candidates from the production mode $ {\mathrm{B}^0} \to \mathrm{D}^{*+} \mu^{-} \overline{\nu} $ and the subsequent decay chain $ \mathrm{D}^{*+} \to \mathrm{D^0}\pi^{+}_{\text{soft}} \to (\mathrm{K^-}\pi^{+})\pi^{+}_{\text{soft}} $. Events containing kaon and muon candidates with same-sign (opposite-sign) charges are indicated by solid (open) markers. |
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Figure 42:
The left panel shows the $ p_{\mathrm{T}}^{\text{gen}} $ spectra of the leading and subleading electrons (dashed and solid green histograms) from $ {\mathrm{B}^{+}}\to\mathrm{K^+}\mathrm{e}^+\mathrm{e}^- $ decays, and the efficiency to identify genuine electrons as a function of $ p_{\mathrm{T}}^{\text{gen}} $ for PF (solid red squares) and low-$ p_{\mathrm{T}} $ electron candidates (solid blue circles). Efficiencies for the low-$ p_{\mathrm{T}} $ electron candidates that satisfy an ID score threshold, tuned to give the same misidentification probability as for PF electron candidates, are also shown (open markers). The right panel shows the performance of the PF (solid red square) and low-$ p_{\mathrm{T}} $ (solid blue circle) reconstruction algorithms and their corresponding ID algorithms (curves). The efficiencies and misidentification probabilities are determined relative to charged-particle tracks obtained from simulation, for both $ {\mathrm{B}^{+}}\to\mathrm{K^+}\mathrm{e}^+\mathrm{e}^- $ decays and background processes, and satisfying $ {p_{\mathrm{T}}} > $ 2 GeV and $ |\eta| < $ 2.5. |
png pdf |
Figure 42-a:
The left panel shows the $ p_{\mathrm{T}}^{\text{gen}} $ spectra of the leading and subleading electrons (dashed and solid green histograms) from $ {\mathrm{B}^{+}}\to\mathrm{K^+}\mathrm{e}^+\mathrm{e}^- $ decays, and the efficiency to identify genuine electrons as a function of $ p_{\mathrm{T}}^{\text{gen}} $ for PF (solid red squares) and low-$ p_{\mathrm{T}} $ electron candidates (solid blue circles). Efficiencies for the low-$ p_{\mathrm{T}} $ electron candidates that satisfy an ID score threshold, tuned to give the same misidentification probability as for PF electron candidates, are also shown (open markers). The right panel shows the performance of the PF (solid red square) and low-$ p_{\mathrm{T}} $ (solid blue circle) reconstruction algorithms and their corresponding ID algorithms (curves). The efficiencies and misidentification probabilities are determined relative to charged-particle tracks obtained from simulation, for both $ {\mathrm{B}^{+}}\to\mathrm{K^+}\mathrm{e}^+\mathrm{e}^- $ decays and background processes, and satisfying $ {p_{\mathrm{T}}} > $ 2 GeV and $ |\eta| < $ 2.5. |
png pdf |
Figure 42-b:
The left panel shows the $ p_{\mathrm{T}}^{\text{gen}} $ spectra of the leading and subleading electrons (dashed and solid green histograms) from $ {\mathrm{B}^{+}}\to\mathrm{K^+}\mathrm{e}^+\mathrm{e}^- $ decays, and the efficiency to identify genuine electrons as a function of $ p_{\mathrm{T}}^{\text{gen}} $ for PF (solid red squares) and low-$ p_{\mathrm{T}} $ electron candidates (solid blue circles). Efficiencies for the low-$ p_{\mathrm{T}} $ electron candidates that satisfy an ID score threshold, tuned to give the same misidentification probability as for PF electron candidates, are also shown (open markers). The right panel shows the performance of the PF (solid red square) and low-$ p_{\mathrm{T}} $ (solid blue circle) reconstruction algorithms and their corresponding ID algorithms (curves). The efficiencies and misidentification probabilities are determined relative to charged-particle tracks obtained from simulation, for both $ {\mathrm{B}^{+}}\to\mathrm{K^+}\mathrm{e}^+\mathrm{e}^- $ decays and background processes, and satisfying $ {p_{\mathrm{T}}} > $ 2 GeV and $ |\eta| < $ 2.5. |
png pdf |
Figure 43:
The pileup distribution obtained from the $ {\mathrm{B}}\,\text{parking} $ data set. Contributions from each trigger combination are shown, with the histogram areas normalized to the number of events recorded by each trigger. |
png pdf |
Figure 44:
The invariant mass distribution for pairs of oppositely charged muons originating from a common vertex, obtained from a subset of the $ {\mathrm{B}}\,\text{parking} $ data. |
png pdf |
Figure 45:
Results of an unbinned likelihood fit to the invariant mass distributions for the the $ {\mathrm{B}^{+}}\to{\mathrm{J}/\psi} (\to\mu^{+}\mu^{-})\mathrm{K^+} $ (left) and the $ {\mathrm{B}^{+}}\to\mathrm{K^+}\mu^{+}\mu^{-} $ (right) channels. Various functions are used to parametrize the contributions from the signal and various background processes. Taken from Ref. [152]. |
png pdf |
Figure 45-a:
Results of an unbinned likelihood fit to the invariant mass distributions for the the $ {\mathrm{B}^{+}}\to{\mathrm{J}/\psi} (\to\mu^{+}\mu^{-})\mathrm{K^+} $ (left) and the $ {\mathrm{B}^{+}}\to\mathrm{K^+}\mu^{+}\mu^{-} $ (right) channels. Various functions are used to parametrize the contributions from the signal and various background processes. Taken from Ref. [152]. |
png pdf |
Figure 45-b:
Results of an unbinned likelihood fit to the invariant mass distributions for the the $ {\mathrm{B}^{+}}\to{\mathrm{J}/\psi} (\to\mu^{+}\mu^{-})\mathrm{K^+} $ (left) and the $ {\mathrm{B}^{+}}\to\mathrm{K^+}\mu^{+}\mu^{-} $ (right) channels. Various functions are used to parametrize the contributions from the signal and various background processes. Taken from Ref. [152]. |
png pdf |
Figure 46:
Results of an unbinned likelihood fit to the invariant mass distributions for the $ {\mathrm{B}^{+}}\to{\mathrm{J}/\psi} (\to\mathrm{e}^+\mathrm{e}^-)\mathrm{K^+} $ (left) and the $ {\mathrm{B}^{+}}\to\mathrm{K^+}\mathrm{e}^+\mathrm{e}^- $ (right) channels. The upper (lower) panels are for candidates using the PF-PF (PF-LP) category. Various functions are used to parametrize the contributions from the signal and various background processes. Taken from Ref. [152]. |
png pdf |
Figure 46-a:
Results of an unbinned likelihood fit to the invariant mass distributions for the $ {\mathrm{B}^{+}}\to{\mathrm{J}/\psi} (\to\mathrm{e}^+\mathrm{e}^-)\mathrm{K^+} $ (left) and the $ {\mathrm{B}^{+}}\to\mathrm{K^+}\mathrm{e}^+\mathrm{e}^- $ (right) channels. The upper (lower) panels are for candidates using the PF-PF (PF-LP) category. Various functions are used to parametrize the contributions from the signal and various background processes. Taken from Ref. [152]. |
png pdf |
Figure 46-b:
Results of an unbinned likelihood fit to the invariant mass distributions for the $ {\mathrm{B}^{+}}\to{\mathrm{J}/\psi} (\to\mathrm{e}^+\mathrm{e}^-)\mathrm{K^+} $ (left) and the $ {\mathrm{B}^{+}}\to\mathrm{K^+}\mathrm{e}^+\mathrm{e}^- $ (right) channels. The upper (lower) panels are for candidates using the PF-PF (PF-LP) category. Various functions are used to parametrize the contributions from the signal and various background processes. Taken from Ref. [152]. |
png pdf |
Figure 46-c:
Results of an unbinned likelihood fit to the invariant mass distributions for the $ {\mathrm{B}^{+}}\to{\mathrm{J}/\psi} (\to\mathrm{e}^+\mathrm{e}^-)\mathrm{K^+} $ (left) and the $ {\mathrm{B}^{+}}\to\mathrm{K^+}\mathrm{e}^+\mathrm{e}^- $ (right) channels. The upper (lower) panels are for candidates using the PF-PF (PF-LP) category. Various functions are used to parametrize the contributions from the signal and various background processes. Taken from Ref. [152]. |
png pdf |
Figure 46-d:
Results of an unbinned likelihood fit to the invariant mass distributions for the $ {\mathrm{B}^{+}}\to{\mathrm{J}/\psi} (\to\mathrm{e}^+\mathrm{e}^-)\mathrm{K^+} $ (left) and the $ {\mathrm{B}^{+}}\to\mathrm{K^+}\mathrm{e}^+\mathrm{e}^- $ (right) channels. The upper (lower) panels are for candidates using the PF-PF (PF-LP) category. Various functions are used to parametrize the contributions from the signal and various background processes. Taken from Ref. [152]. |
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Figure 47:
Comparison of the measured differential $ {\mathrm{B}^{+}}\to\mathrm{K^+}\mu^{+}\mu^{-} $ branching fraction with the theoretical predictions obtained using HEPFIT, SUPERISO, FLAVIO, and EOS packages. Reliable predictions are not available between the $ \mathrm{J}/\psi $ and \PGyP2S resonance regions. The HEPFIT predictions are available only for $ q^2 < $ 8 GeV$^{2} $. Taken from Ref. [152]. |
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Figure 48:
Feynman diagram of a semileptonic decay of a $ {\mathrm{B}} $ meson into the primary lepton ($ \ell_{P} $), a hadronic system (X), and a SM neutrino, which then mixes with a heavy neutrino ($ \mathrm{N} $). The $ \mathrm{N} $ decays weakly into a charged lepton $ \ell^\pm $ and a charged pion $ \pi^{\mp} $, forming a vertex displaced from the $ {\mathrm{p}}{\mathrm{p}} $ interaction point. Taken from Ref. [158]. |
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Figure 49:
Expected and observed 95% CL limits on $ |V_{\mathrm{N}}|^2 $ as a function of $ m_\mathrm{N} $ in the Majorana scenario, for the coupling hypotheses ($ r_\mathrm{e} $, $ r_\mu $, $ r_\tau $) = (0, 1, 0) on the left and ($ r_\mathrm{e} $, $ r_\mu $, $ r_\tau $) = (1/3, 1/3, 1/3) on the right. The mass range with no limits shown corresponds to the $ \mathrm{D^0} $ meson veto employed by the search. Taken from Ref. [158]. |
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Figure 49-a:
Expected and observed 95% CL limits on $ |V_{\mathrm{N}}|^2 $ as a function of $ m_\mathrm{N} $ in the Majorana scenario, for the coupling hypotheses ($ r_\mathrm{e} $, $ r_\mu $, $ r_\tau $) = (0, 1, 0) on the left and ($ r_\mathrm{e} $, $ r_\mu $, $ r_\tau $) = (1/3, 1/3, 1/3) on the right. The mass range with no limits shown corresponds to the $ \mathrm{D^0} $ meson veto employed by the search. Taken from Ref. [158]. |
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Figure 49-b:
Expected and observed 95% CL limits on $ |V_{\mathrm{N}}|^2 $ as a function of $ m_\mathrm{N} $ in the Majorana scenario, for the coupling hypotheses ($ r_\mathrm{e} $, $ r_\mu $, $ r_\tau $) = (0, 1, 0) on the left and ($ r_\mathrm{e} $, $ r_\mu $, $ r_\tau $) = (1/3, 1/3, 1/3) on the right. The mass range with no limits shown corresponds to the $ \mathrm{D^0} $ meson veto employed by the search. Taken from Ref. [158]. |
png pdf |
Figure 50:
Observed limits on $ c\tau_{\mathrm{N}} $ as a function of the coupling ratios ($ r_\mathrm{e} $, $ r_\mu $, $ r_\tau $) for fixed $ \mathrm{N} $ masses of 1 GeV (upper left), 1.5 GeV (upper right), and 2 GeV (lower center), in the Majorana scenario. A red cross indicates that no exclusion limit was set for that point. The tick orientation indicates the direction of reading. Taken from Ref. [158]. |
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Figure 50-a:
Observed limits on $ c\tau_{\mathrm{N}} $ as a function of the coupling ratios ($ r_\mathrm{e} $, $ r_\mu $, $ r_\tau $) for fixed $ \mathrm{N} $ masses of 1 GeV (upper left), 1.5 GeV (upper right), and 2 GeV (lower center), in the Majorana scenario. A red cross indicates that no exclusion limit was set for that point. The tick orientation indicates the direction of reading. Taken from Ref. [158]. |
png pdf |
Figure 50-b:
Observed limits on $ c\tau_{\mathrm{N}} $ as a function of the coupling ratios ($ r_\mathrm{e} $, $ r_\mu $, $ r_\tau $) for fixed $ \mathrm{N} $ masses of 1 GeV (upper left), 1.5 GeV (upper right), and 2 GeV (lower center), in the Majorana scenario. A red cross indicates that no exclusion limit was set for that point. The tick orientation indicates the direction of reading. Taken from Ref. [158]. |
png pdf |
Figure 50-c:
Observed limits on $ c\tau_{\mathrm{N}} $ as a function of the coupling ratios ($ r_\mathrm{e} $, $ r_\mu $, $ r_\tau $) for fixed $ \mathrm{N} $ masses of 1 GeV (upper left), 1.5 GeV (upper right), and 2 GeV (lower center), in the Majorana scenario. A red cross indicates that no exclusion limit was set for that point. The tick orientation indicates the direction of reading. Taken from Ref. [158]. |
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Figure 51:
Dimuon mass spectra obtained from data recorded in 2022 during Run 3, corresponding to $ {\mathcal{L}_{\text{int}}} = $ 3.2 fb$^{-1}$. In the range $ {2m_{\mu}^{\text{PDG}}} < m_{\mu\mu} < $ 8.5 GeV, the light blue distribution represents the subset of dimuon events triggered by the inclusive low-mass trigger algorithm, while the dark blue distribution shows the subset of dimuon events triggered by the displaced low-mass trigger path. In the range 8.5 $ < m_{\mu\mu} < $ 11.5 GeV, dimuon events are instead triggered by the HLT paths targeting the $ \Upsilon{\textrm{(nS)}} $ resonances, which are shown by the pink distribution. |
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Figure 52:
Left: invariant mass distribution for candidate $ {\mathrm{B}^0}\to\mathrm{J}/\psi\mathrm{K^0_S} $ decays. Right: proper decay length (ct) distribution obtained from candidate $ {\mathrm{B}^{+}}\to{\mathrm{J}/\psi} (\to\mu^{+}\mu^{-})\mathrm{K^+} $ decays. Both types of candidates are reconstructed from events recorded using the dimuon triggers. |
png pdf |
Figure 52-a:
Left: invariant mass distribution for candidate $ {\mathrm{B}^0}\to\mathrm{J}/\psi\mathrm{K^0_S} $ decays. Right: proper decay length (ct) distribution obtained from candidate $ {\mathrm{B}^{+}}\to{\mathrm{J}/\psi} (\to\mu^{+}\mu^{-})\mathrm{K^+} $ decays. Both types of candidates are reconstructed from events recorded using the dimuon triggers. |
png pdf |
Figure 52-b:
Left: invariant mass distribution for candidate $ {\mathrm{B}^0}\to\mathrm{J}/\psi\mathrm{K^0_S} $ decays. Right: proper decay length (ct) distribution obtained from candidate $ {\mathrm{B}^{+}}\to{\mathrm{J}/\psi} (\to\mu^{+}\mu^{-})\mathrm{K^+} $ decays. Both types of candidates are reconstructed from events recorded using the dimuon triggers. |
png pdf |
Figure 53:
Dimuon invariant mass distributions in the $ \eta $ (left) and $ \mathrm{J}/\psi $ (right) mass regions, as obtained from data recorded by the inclusive low-mass dimuon trigger algorithm. |
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Figure 53-a:
Dimuon invariant mass distributions in the $ \eta $ (left) and $ \mathrm{J}/\psi $ (right) mass regions, as obtained from data recorded by the inclusive low-mass dimuon trigger algorithm. |
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Figure 53-b:
Dimuon invariant mass distributions in the $ \eta $ (left) and $ \mathrm{J}/\psi $ (right) mass regions, as obtained from data recorded by the inclusive low-mass dimuon trigger algorithm. |
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Figure 54:
Total L1 (left) and HLT (right) trigger rates, and the number of pileup interactions, shown as a function of time for a representative LHC fill during 2022. The rates for the promptly reconstructed core physics (black solid markers) and $ {\mathrm{B}}\,\text{parking} $ (blue open markers) data streams are shown separately in the right panel. Occasional lower rates are observed due to transient effects, such as the throttling of the trigger system in response to subdetector dead time [9]. Changes in the trigger configuration are indicated by vertical green dashed lines. |
png pdf |
Figure 54-a:
Total L1 (left) and HLT (right) trigger rates, and the number of pileup interactions, shown as a function of time for a representative LHC fill during 2022. The rates for the promptly reconstructed core physics (black solid markers) and $ {\mathrm{B}}\,\text{parking} $ (blue open markers) data streams are shown separately in the right panel. Occasional lower rates are observed due to transient effects, such as the throttling of the trigger system in response to subdetector dead time [9]. Changes in the trigger configuration are indicated by vertical green dashed lines. |
png pdf |
Figure 54-b:
Total L1 (left) and HLT (right) trigger rates, and the number of pileup interactions, shown as a function of time for a representative LHC fill during 2022. The rates for the promptly reconstructed core physics (black solid markers) and $ {\mathrm{B}}\,\text{parking} $ (blue open markers) data streams are shown separately in the right panel. Occasional lower rates are observed due to transient effects, such as the throttling of the trigger system in response to subdetector dead time [9]. Changes in the trigger configuration are indicated by vertical green dashed lines. |
png pdf |
Figure 55:
Pileup distribution measured in the dielectron data set. Contributions from each trigger combination are shown, with the histogram areas normalized to the number of events recorded by each trigger. |
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Figure 56:
The invariant mass distribution for pairs of oppositely charged electrons originating from a common vertex, reconstructed from the dielectron data set. |
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Figure 57:
The invariant mass distribution for candidate $ {\mathrm{B}^{+}}\to{\mathrm{J}/\psi} (\to\mathrm{e}^+\mathrm{e}^-)\mathrm{K^+} $ decays, reconstructed from the dielectron data set. The histogram is normalized to unit area. |
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Figure 58:
The L1 (blue) and L1+HLT (red) efficiencies as a function of $ m_{\mathrm{jj}} $ for the VBF inclusive (left) and VBF+$ p_{\mathrm{T}}^\text{miss} $ (right) parking triggers. In the right figure, $ p_{\mathrm{T}}^\text{miss} $(no-$ \mu $) refers to the event $ p_{\mathrm{T}}^\text{miss} $ corrected for muons. |
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Figure 58-a:
The L1 (blue) and L1+HLT (red) efficiencies as a function of $ m_{\mathrm{jj}} $ for the VBF inclusive (left) and VBF+$ p_{\mathrm{T}}^\text{miss} $ (right) parking triggers. In the right figure, $ p_{\mathrm{T}}^\text{miss} $(no-$ \mu $) refers to the event $ p_{\mathrm{T}}^\text{miss} $ corrected for muons. |
png pdf |
Figure 58-b:
The L1 (blue) and L1+HLT (red) efficiencies as a function of $ m_{\mathrm{jj}} $ for the VBF inclusive (left) and VBF+$ p_{\mathrm{T}}^\text{miss} $ (right) parking triggers. In the right figure, $ p_{\mathrm{T}}^\text{miss} $(no-$ \mu $) refers to the event $ p_{\mathrm{T}}^\text{miss} $ corrected for muons. |
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Figure 59:
Distributions of $ m_{\mathrm{jj}} $ for VBF $ \mathrm{H} \to \text{invisible} $ events passing the triggers used in the Run 2 analysis (blue), compared to events passing either one of the Run 2 triggers, the VBF+$ p_{\mathrm{T}}^\text{miss} $ parking trigger, or the VBF inclusive parking trigger implemented in Run 3 (black). The Run 2 trigger selection includes the VBF+$ p_{\mathrm{T}}^\text{miss} $ trigger algorithm introduced in Run 2, plus the standard trigger requiring $ p_{\mathrm{T}}^\text{miss} > $ 120 GeV and $ H_{\mathrm{T}}^{\text{miss}} > $ 120 GeV. In all cases, loose, offline selections are applied to match the trigger-level requirements. |
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Figure 60:
Trigger efficiency for selecting signal HH events, plotted as a function of the reconstructed invariant mass of the two Higgs bosons, as measured in simulated $ {\mathrm{H}\mathrm{H} \to 4\mathrm{b}} $ (left) and $ {\mathrm{H}\mathrm{H} \to 2\mathrm{b}2\tau} $ (right) samples corresponding to nominal Run 3 conditions. |
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Figure 60-a:
Trigger efficiency for selecting signal HH events, plotted as a function of the reconstructed invariant mass of the two Higgs bosons, as measured in simulated $ {\mathrm{H}\mathrm{H} \to 4\mathrm{b}} $ (left) and $ {\mathrm{H}\mathrm{H} \to 2\mathrm{b}2\tau} $ (right) samples corresponding to nominal Run 3 conditions. |
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Figure 60-b:
Trigger efficiency for selecting signal HH events, plotted as a function of the reconstructed invariant mass of the two Higgs bosons, as measured in simulated $ {\mathrm{H}\mathrm{H} \to 4\mathrm{b}} $ (left) and $ {\mathrm{H}\mathrm{H} \to 2\mathrm{b}2\tau} $ (right) samples corresponding to nominal Run 3 conditions. |
| Tables | |
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Table 1:
Comparison of the typical HLT trigger rates of the standard, parking, and scouting data streams during Run 1 and Run 2. The average $ \mathcal{L}_{\text{inst}} $ over one typical fill of a given data-taking year and the average pileup (PU) are also reported, consistent with the scenarios reported in Fig. 2. |
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Table 2:
List of L1 and HLT thresholds for the most relevant scouting triggers in Run 2. The list corresponds to the 2018 thresholds that were valid for the overall Run 2 data-taking period. Differences with respect to the 2016 or 2017 scenario are reported in parentheses. Muons and photons are annotated as $ \mu $ and $ \gamma $, respectively, while OS stands for opposite-sign muon pairs. In cases where the same threshold is applied to all selected objects in an event, a single number is shown, while if different thresholds are applied to the objects, they are shown separated by slashes from the highest to the lowest. |
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Table 3:
Comparisons of the event rate, event size, and total bandwidth between the standard and scouting trigger strategies, for an LHC fill corresponding to data collected in 2018 with $ \mathcal{L}_{\text{inst}} \approx $ 1.8 $\times$ 10$^{34}$ cm$^{-2}$s$^{-1}$ at the start of the fill, one of the highest at the LHC in Run 2, and pileup around 50. |
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Table 4:
List of observables saved in the scouting output during Run 2. The upper part of the table lists the observables present in the Calo stream and the lower part lists the contents of the PF stream. The PF candidates are sorted into charged and neutral hadrons, muons, electron and photons, hadronic and electromagnetic deposits in HF. |
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Table 5:
Time required for the object reconstruction and selection criteria in the scouting paths seeded by different L1 algorithms, using only a CPU or accelerating certain steps with a GPU. For the comparison, we pick a representative run recorded in 2023. The time needed to run the full HLT menu reconstruction including non-scouting paths is also shown for reference. |
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Table 6:
List of L1 and HLT thresholds for the lowest unprescaled scouting triggers during Run 3. The list corresponds to the 2022 and 2023 data-taking periods. Conditions that changed between the two years are annotated in bold face. The four separate HLT paths and corresponding thresholds were only present in 2023. No thresholds were applied at the HLT in 2022. In cases where the same threshold is applied to all selected objects in an event, a single number is shown, while if different thresholds are applied to the objects, they are shown separated by slashes from the highest to the lowest. The notation ``OS'' stands for opposite-sign muon pairs and ``SC'' for calorimeter superclusters. |
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Table 7:
Comparison of the typical HLT trigger rates of the standard, parking, and scouting data streams during 2018 (Run 2), 2022, and 2023 (Run 3). The average $ \mathcal{L}_{\text{inst}} $ value over one typical fill of a given data-taking year and the average pileup (PU) are also reported, coherently with the scenarios reported in Fig. 2. |
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Table 8:
HLT rates for the scouting paths seeded by different L1 algorithms during two reference runs with an average pileup of 60 recorded in 2022 and 2023. |
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Table 9:
List of observables related to the newly introduced scouting electrons (e) and photons ($ \gamma $) stored in the scouting data set in Run 3. The calorimeter observables shared by $ \mathrm{e}/\gamma $ objects are listed in the upper part of the table, while the track features specific to electrons are reported in the lower part. Tracker-based isolation for photons is to be computed offline from the stored PF candidates. |
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Table 10:
Summary of the main physics targets of the Run 1 parking strategy and the corresponding parking triggers in 2012. The average HLT rates reserved for Higgs boson measurements, $ {\mathrm{B}} $ physics measurements, and new-physics searches are also quoted for $ {\mathcal{L}_{\text{inst}}} \approx $ 4 $\times$ 10$^{34}$ cm$^{-2}$s$^{-1}$. |
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Table 11:
Single-muon trigger settings used during a typical LHC fill. The kinematical thresholds are changed when $ \mathcal{L}_{\text{inst}} $ (and consequently pileup) fall below the listed values. Also listed are the lower-bound thresholds on the tag-side muon $ p_{\mathrm{T}} $ (L1 and HLT) and $ \text{IP}_{\text{sig}} $ (HLT only), the corresponding L1 and HLT peak trigger rates, and the HLT data stream purity estimated from simulated events. No dedicated trigger is enabled at the start of each LHC fill when $ \mathcal{L}_{\text{inst}} $ is typically above 1.7 $\times$ 10$^{34}$ cm$^{-2}$s$^{-1}$. |
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Table 12:
Trigger configurations, defined by unique combinations of L1 $ p_{\mathrm{T}} $, HLT $ p_{\mathrm{T}} $, and $ \text{IP}_{\text{sig}} $ thresholds, used to record events containing $ \mathrm{b}\to\mu\mathrm{X} $ decays. The $ \mathcal{L}_{\text{int}} $ value, the mean number of pileup interactions, and the number of events recorded by each combination are aggregated over the periods for which each combination provided the lowest enabled L1 $ p_{\mathrm{T}} $ threshold. |
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Table 13:
Trigger configurations, defined by unique combinations of L1 and HLT $ E_{\text{T}} $ thresholds (applied to each electron candidate) and L1 $ {\Delta}R $, used to record events containing dielectron final states. The thresholds on the L1 and HLT $ E_{\text{T}} $ (L1 $ {\Delta}R $) values are lower (upper) bounds. The $ \mathcal{L}_{\text{int}} $ value and the mean number of pileup interactions recorded by each trigger combination are aggregated over periods for which each combination provided the lowest enabled L1 $ E_{\text{T}} $ threshold. Representative peak L1 and HLT trigger rates are given for each setting. |
png pdf |
Table 14:
Definition and rates of the VBF algorithms at L1. The quoted rates are for $ {\mathcal{L}_{\text{inst}}} = $ 2 $\times$ 10$^{34}$ cm$^{-2}$s$^{-1}$ and do not account for overlaps with other seeds. |
png pdf |
Table 15:
Definition and rates of the VBF paths at the HLT. The quoted rates are for $ {\mathcal{L}_{\text{inst}}} = $ 2 $\times$ 10$^{34}$ cm$^{-2}$s$^{-1}$ and do not account for overlaps with other seeds. |
png pdf |
Table 16:
Gains in signal acceptance from the new individual VBF parking paths for a selection of benchmark signals with respect to the relevant Run 2 triggers used to collect data in the standard data set. |
| Summary |
| The extreme collision rate of the LHC, coupled with the data size needed to characterize complex interactions, poses a fundamental problem for collider experiments. Trigger, data acquisition, and downstream computing systems have finite resources that the experiments carefully allocate to the various parts of their physics programs. Searches for new physics at the energy frontier and measurements of electroweak-scale particles are one of the centerpieces of the CMS physics program, and therefore a significant portion of data acquisition resources is dedicated to triggers focusing on heavy-mass and high-$ p_{\mathrm{T}} $ particles. There are, however, compelling reasons that new physics might manifest itself in other ways, for example in the existence of new light particles or, indirectly, as unexpected anomalies in precision measurements of benchmark standard model (SM) processes. This review has highlighted two innovative techniques employed by CMS to extend the physics reach of the experiment beyond the one achieved with the original design of the detector and of the data processing pipeline: data parking and data scouting. After their inception in Run 1, both data scouting and data parking were expanded in Run 2, and became established and widely employed techniques in Run 3. Data scouting records high-rate streams of data at the cost of a reduced event content utilizing physics objects such as jets, muons, and electrons reconstructed at the trigger level. The momentum and energy resolutions of these objects approach those achieved by the full offline event reconstruction, thereby facilitating searches for new physics in previously unexplored regions of phase space at the LHC. Notably, data scouting has enabled groundbreaking searches employing jet and muon objects that have already been published, while ongoing efforts involving electron and tau lepton objects further promise to extend the potential of this innovative technique. The incorporation of jet objects obtained with data scouting has proven instrumental in pushing the boundaries of resonance searches in both dijet and multijet channels. These objects, which closely mirror nominal jets in energy resolution, have extended the reach of resonance searches to previously unexplored low-mass regions, overcoming limitations faced by standard analyses. Notably, the search for dijet resonances has been extended from the conventional lower limit of 1.5 TeV to 600 GeV. Moreover, multijet searches employing data scouting exhibit remarkable sensitivity down to masses as low as 70 GeV for both two- and three-parton decays, leveraging resolved and merged jets. The cross section sensitivity achieved in multijet analyses with data scouting surpasses previous searches by an order of magnitude, enabling probes of new physics sectors with electroweak couplings, such as Higgsinos, in fully hadronic final states. Furthermore, muon scouting has allowed the triggering of very low $ p_{\mathrm{T}} $ dimuon events, including both prompt- and displaced-decay scenarios. These events have significantly extended the searches for new particles decaying to muon pairs, reaching the 2 $ m_\mu $ kinematic limit. In the pursuit of long-lived dimuon resonances, the results are competitive with those from LHCb and $ {\mathrm{B}} $ factories within certain mass and lifetime ranges. Additionally, decays to four muons have been successfully studied, culminating in the groundbreaking first-time observation of the rare decay of the $ \eta $ meson into four muons, $ \eta \to \mu^{+} \mu^{-} \mu^{+} \mu^{-} $, with a measured branching fraction on the order of 10$^{-9} $. The result is in agreement with theoretical predictions and improves on the precision of previous upper-limit measurements by more than 5 orders of magnitude. In the data-parking technique, data are recorded by high-rate triggers and temporarily stored (i.e.,, parked) in raw format until computing resources become available for full event reconstruction, for example during the end of the year or even during long shutdown periods. This contrasts with the typical reconstruction workflow, where data reconstruction begins within 48 hours after it is recorded. Throughout Run 1, different parking strategies were explored. Data collected with parking triggers during 2012 served the publication of impactful physics analyses including Higgs boson measurements, with a focus on vector boson fusion production, as well as precise measurements of various b quark hadron lifetimes in final states involving pairs of muons. Searches for beyond SM physics ranged from multijet searches to investigations into dark matter and supersymmetry in hadronic final states. The 2018 data-parking analyses emphasized $ {\mathrm{B}} $ physics. CMS collected a large data set comprising $ \mathcal{O}$(10$^{10}$) $\mathrm{b} \overline{\mathrm{b}} $ events using a broad collection of single, displaced muon triggers. One distinctive feature of the parking data set is that different triggers with progressively lower thresholds were activated at different times during an LHC fill to maintain approximately constant (and high) L1 trigger rates, as the instantaneous luminosity decreased over the duration of the fill. With this method, we maximized the number of $ \mathrm{b} \overline{\mathrm{b}} $ events recorded. At the start of Run 3, the parking strategy for $ {\mathrm{B}} $ physics was expanded, notably including dedicated low-$ p_{\mathrm{T}} $ triggers to collect dimuon and dielectron events. The single- and double-muon, and double-electron parked data sets enable CMS to perform a variety of $ {\mathrm{B}} $ physics analyses of rare SM processes, as well as precision tests of lepton flavor universality. In addition, the $ {\mathrm{B}}\,\text{parking} $ data enables several innovative searches beyond heavy flavor physics, such as for long-lived heavy neutrinos. Ongoing efforts in Run 3 have also seen the enhancement of alternative parking strategies inspired by the experience acquired in Runs 1 and 2. Dedicated parking triggers targeting vector boson fusion and double Higgs boson production have been designed to augment sensitivity to tests of the Higgs sector. Moreover, the implementation of distinct parking triggers dedicated to the exploration of long-lived particles decaying into jets, leptons, and photons offers the opportunity to substantially expand the CMS physics capabilities, extending its role at the forefront of high priority searches in the field. These two techniques, scouting and parking, serve complementary purposes: data scouting is designed to accommodate searches akin to those conducted with standard triggers and data sets while overcoming the restrictions of stringent trigger thresholds, while data parking is particularly beneficial for analyses focused on optimal precision and accuracy but that also require a higher rate of data collection. Searches for new physics with low-$ p_{\mathrm{T}} $ jets in the final state require scouting data streams. The parking data sets provide sensitivity to processes involving low-$ p_{\mathrm{T}} $ single-muon and dilepton final states. Both scouting and parking data can be utilized to design comprehensive search and measurement strategies for low-$ p_{\mathrm{T}} $ dimuon final states. The comprehensive investigations conducted via scouting and parking analyses have significantly expanded the parameter space boundaries of CMS sensitivity, pushing beyond the anticipated limits of hadron colliders. The improvements achieved in trigger thresholds and data collection bandwidth, coupled with the implementation of pioneering methods for the reconstruction of essential entities such as jets, electrons, muons, tau leptons decaying hadronically, and photons, stand poised to elevate not only the quantity but also the caliber of data amassed with these sophisticated techniques. This breakthrough promises to deepen our understanding and unlock novel insights into the underlying physics, marking a significant advancement in the capabilities of the CMS experiment. The importance of the data scouting and data parking approaches goes beyond enriching the current CMS physics program. The classic data acquisition model is not guaranteed to remain sustainable with the luminosity expected to be delivered by the LHC at the end of Run 3, and, crucially, during the high-luminosity LHC era. Moreover, computing and data acquisition resources will be designed to cope with peak luminosity demands, leaving significant spare computing power and data bandwidth at non-peak times. It is therefore essential to devise ways to develop real-time physics selection and analysis within the trigger and data acquisition systems themselves, and to optimize the utilization of idle computing resources during periods when the LHC is not operating at its maximum capacity. The CMS experience with data scouting and data parking in the last decade will prove decisive to tackling these challenges for particle physics experiments in the coming years. |
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