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CMS-HIN-19-013 ; CERN-EP-2020-247
In-medium modification of dijets in PbPb collisions at ${\sqrt {\smash [b]{s_{_{\mathrm {NN}}}}}} = $ 5.02 TeV
JHEP 05 (2021) 116
Abstract: Modifications to the distribution of charged particles with respect to high transverse momentum (${p_{\mathrm{T}}}$) jets passing through a quark-gluon plasma are explored using the CMS detector. Back-to-back dijets are analyzed in lead-lead and proton-proton collisions at ${\sqrt {\smash [b]{s_{_{\mathrm {NN}}}}}} = $ 5.02 TeV via correlations of charged particles in bins of relative pseudorapidity and angular distance from the leading and subleading jet axes. In comparing the lead-lead and proton-proton collision results, modifications to the charged-particle relative distance distribution and to the momentum distributions around the jet axis are found to depend on the dijet momentum balance ${x_{j}} $, which is the ratio between the subleading and leading jet ${p_{\mathrm{T}}}$. For events with ${x_{j}} \approx $ 1, these modifications are observed for both the leading and subleading jets. However, while subleading jets show significant modifications for events with a larger dijet momentum imbalance, much smaller modifications are found for the leading jets in these events.
Figures & Tables Summary Additional Figures References CMS Publications
Figures

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Figure 1:
Distributions of charged-particle yields correlated to leading jets in the region $ {| \Delta \varphi |} < $ 1 as a function of $ {| \Delta \eta |}$ for pp (first column) and PbPb (second to fifth columns) collisions in different centrality bins, shown differentially for all ${{p_{\mathrm {T}}} ^{\mathrm {ch}}}$ bins. The first row shows the charged-particle yields without any selection on ${x_{j}}$, while other rows show the charged-particle yields in different bins of ${x_{j}}$, starting with the most unbalanced 0 $ < {x_{j}} < $ 0.6 (second row) to the most balanced 0.8 $ < {x_{j}} < $ 1.0 (fourth row) dijet events.

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Figure 2:
Distributions of charged-particle yields correlated to subleading jets in the region $ {| \Delta \varphi |} < $ 1 as a function of $ {| \Delta \eta |}$ for pp (first column) and PbPb (second to fifth columns) collisions in different centrality bins, shown differentially for all ${{p_{\mathrm {T}}} ^{\mathrm {ch}}}$ bins. The first row shows the charged-particle yields without any selection on ${x_{j}}$, while other rows show the charged-particle yields in different bins of ${x_{j}}$, starting with the most unbalanced 0 $ < {x_{j}} < $ 0.6 (second row) to the most balanced 0.8 $ < {x_{j}} < $ 1.0 (fourth row) dijet events.

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Figure 3:
Jet radial momentum profile $\mathrm {P}(\Delta r)$ for pp (first column) and PbPb (second to fifth columns) collisions in different centrality bins as a function of $\Delta r$, shown differentially in ${{p_{\mathrm {T}}} ^{\mathrm {ch}}}$ for leading (upper row) and subleading (lower row) jets.

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Figure 4:
The PbPb to pp ratio of the jet radial momentum distributions as a function of $\Delta r$, $\mathrm {P} (\Delta r)_{\mathrm {PbPb}}/\mathrm {P} (\Delta r)_{{{\mathrm{p}} {\mathrm{p}}}}$, for different centrality bins for the leading jets (upper row) and subleading jets (lower row).

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Figure 5:
Leading jet shapes $\rho (\Delta r)$ (normalized to unity over $\Delta r < $ 1) for pp (first column) and PbPb (second to fifth columns) collisions in different centrality bins as a function of $\Delta r$, shown differentially in ${{p_{\mathrm {T}}} ^{\mathrm {ch}}}$ for the inclusive ${x_{j}}$ bin (first row) and in differential bins 0 $ < {x_{j}} < $ 0.6 (second row), 0.6 $ < {x_{j}} < $ 0.8 (third row), and 0.8 $ < {x_{j}} < $ 1.0 (fourth row).

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Figure 6:
The PbPb to pp ratio as a function of $\Delta r$ for leading jet shapes, $ {\rho (\Delta r)_{\mathrm {PbPb}}} / {\rho (\Delta r)_{\mathrm {{{\mathrm{p}} {\mathrm{p}}}}}} $, in different centrality bins for 0 $ < {x_{j}} < $ 0.6 (upper row), 0.6 $ < {x_{j}} < $ 0.8 (middle row) and 0.8 $ < {x_{j}} < $ 1.0 (lower row) dijet selections. The leading jet shape ratio for all dijets, i.e., without any selection on the dijet momentum balance are also shown in each row for comparison. The error bars represent the statistical uncertainties and the shaded areas the systematic uncertainties.

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Figure 7:
Subleading jet shapes $\rho (\Delta r)$ (normalized to unity over $\Delta r < $ 1) for pp (first column) and PbPb (second to fifth columns) collisions in different centrality bins as a function of $\Delta r$, shown differentially in ${{p_{\mathrm {T}}} ^{\mathrm {ch}}}$ for the inclusive ${x_{j}}$ bin (first row) and in differential bins 0 $ < {x_{j}} < $ 0.6 (second row), 0.6 $ < {x_{j}} < $ 0.8 (third row), and 0.8 $ < {x_{j}} < $ 1.0 (fourth row).

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Figure 8:
The PbPb to pp ratio as a function of $\Delta r$ for subleading jet shapes, $ {\rho (\Delta r)_{\mathrm {PbPb}}} / {\rho (\Delta r)_{\mathrm {{{\mathrm{p}} {\mathrm{p}}}}}} $, in different centrality bins for 0 $ < {x_{j}} < $ 0.6 (upper row), 0.6 $ < {x_{j}} < $ 0.8 (middle row) and 0.8 $ < {x_{j}} < $ 1.0 (lower row) dijet selections. The subleading jet shape ratio for all dijets, i.e., without any selection on the dijet momentum balance are also shown in each row for comparison. The error bars represent the statistical uncertainties and the shaded areas the systematic uncertainties.

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Figure 9:
Ratio of momentum-unbalanced (0.0 $ < {x_{j}} < $ 0.6, upper row) and balanced (0.8 $ < {x_{j}} < $ 1.0, lower row) jet shapes to ${x_{j}}$ integrated jet shapes for leading jets in pp collisions and different PbPb centrality bins as a function of $\Delta r$. The error bars represent the statistical uncertainties and the shaded areas the systematic uncertainties.

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Figure 10:
Ratio of momentum-unbalanced (0.0 $ < {x_{j}} < $ 0.6, upper row) and balanced (0.8 $ < {x_{j}} < $ 1.0, lower row) jet shapes to ${x_{j}}$ integrated jet shapes for subleading jets in pp collisions and different PbPb centrality bins as a function of $\Delta r$. The error bars represent the statistical uncertainties and the shaded areas the systematic uncertainties.

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Figure 11:
Generator-level vs. reconstructed ${x_{j}}$ values in the analysis ${x_{j}}$ bins. The plots show the probability to find a generator level ${x_{j}}$ for a given reconstructed ${x_{j}}$. The PYTHIA 8 simulation is shown in the upper-left plot while the most central PYTHIA+HYDJET is shown in the lower-right plot.

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Figure 11-a:
Generator-level vs. reconstructed ${x_{j}}$ values in the analysis ${x_{j}}$ bins. The plots show the probability to find a generator level ${x_{j}}$ for a given reconstructed ${x_{j}}$. The PYTHIA 8 simulation is shown.

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Figure 11-b:
Generator-level vs. reconstructed ${x_{j}}$ values in the analysis ${x_{j}}$ bins. The plots show the probability to find a generator level ${x_{j}}$ for a given reconstructed ${x_{j}}$. The PYTHIA+HYDJET simulation is shown in the 50-90% centrality bin.

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Figure 11-c:
Generator-level vs. reconstructed ${x_{j}}$ values in the analysis ${x_{j}}$ bins. The plots show the probability to find a generator level ${x_{j}}$ for a given reconstructed ${x_{j}}$. The PYTHIA+HYDJET simulation is shown in the 30-50% centrality bin.

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Figure 11-d:
Generator-level vs. reconstructed ${x_{j}}$ values in the analysis ${x_{j}}$ bins. The plots show the probability to find a generator level ${x_{j}}$ for a given reconstructed ${x_{j}}$. The PYTHIA+HYDJET simulation is shown in the 10-30% centrality bin.

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Figure 11-e:
Generator-level vs. reconstructed ${x_{j}}$ values in the analysis ${x_{j}}$ bins. The plots show the probability to find a generator level ${x_{j}}$ for a given reconstructed ${x_{j}}$. The PYTHIA+HYDJET simulation is shown in the 0-10% centrality bin.

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Figure 12:
Generator-level vs. reconstructed ${x_{j}}$ values in the analysis ${x_{j}}$ bins. The plots show the probability to find a reconstructed ${x_{j}}$ for a given generator level ${x_{j}}$. The PYTHIA 8 simulation is shown in the upper-left plot while the most central PYTHIA+HYDJET is shown in the lower-right plot.

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Figure 12-a:
Generator-level vs. reconstructed ${x_{j}}$ values in the analysis ${x_{j}}$ bins. The plots show the probability to find a reconstructed ${x_{j}}$ for a given generator level ${x_{j}}$. The PYTHIA 8 simulation is shown.

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Figure 12-b:
Generator-level vs. reconstructed ${x_{j}}$ values in the analysis ${x_{j}}$ bins. The plots show the probability to find a reconstructed ${x_{j}}$ for a given generator level ${x_{j}}$. The PYTHIA+HYDJET simulation is shown in the 50-90% centrality bin.

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Figure 12-c:
Generator-level vs. reconstructed ${x_{j}}$ values in the analysis ${x_{j}}$ bins. The plots show the probability to find a reconstructed ${x_{j}}$ for a given generator level ${x_{j}}$. The PYTHIA+HYDJET simulation is shown in the 30-50% centrality bin.

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Figure 12-d:
Generator-level vs. reconstructed ${x_{j}}$ values in the analysis ${x_{j}}$ bins. The plots show the probability to find a reconstructed ${x_{j}}$ for a given generator level ${x_{j}}$. The PYTHIA+HYDJET simulation is shown in the 10-30% centrality bin.

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Figure 12-e:
Generator-level vs. reconstructed ${x_{j}}$ values in the analysis ${x_{j}}$ bins. The plots show the probability to find a reconstructed ${x_{j}}$ for a given generator level ${x_{j}}$. The PYTHIA+HYDJET simulation is shown in the 0-10% centrality bin.
Tables

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Table 1:
The total number of events for pp and for different PbPb centrality bins are shown in the top row. The other rows show the percentage of all events that falls within a given ${x_{j}}$ bin.

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Table 2:
Systematic uncertainties for the leading jet shape components, integrated over ${x_{j}}$, and $\Delta r$, and shown for pp and centrality-binned PbPb collisions. The ranges correspond to the ${p_{\mathrm {T}}}$ dependence of the uncertainty. If some ${p_{\mathrm {T}}}$ bins have an uncertainty smaller than 0.5%, the range is presented with a "$ < $" symbol and the upper bound.

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Table 3:
Systematic uncertainties for the subleading jet shape components, integrated over ${x_{j}}$, and $\Delta r$, and shown for pp and centrality-binned PbPb collisions. The ranges correspond to the ${p_{\mathrm {T}}}$ dependence of the uncertainty. If some ${p_{\mathrm {T}}}$ bins have an uncertainty smaller than 0.5%, the range is presented with a "$ < $" symbol and the upper bound.
Summary
The CMS experiment has measured charged-particle yields and jet shapes in events containing back-to-back dijet pairs around the jet axes using data from proton-proton (pp) and lead-lead (PbPb) collisions at ${\sqrt {\smash [b]{s_{_{\mathrm {NN}}}}}} = $ 5.02 TeV collected in 2017 and 2018. When comparing the charged-particle yields around the jet axes, an excess of low transverse momentum (${p_{\mathrm{T}}}$) particles is observed in PbPb with respect to pp collisions. This excess is larger for subleading jets compared to leading jets. The excess is also found to have a different dijet momentum balance ${x_{j}} = {p_{\mathrm{T}}^{\text{subleading}}} / {p_{\mathrm{T}}^{\text{leading}}}$ dependence for the leading and subleading jets. A possible cause for ${x_{j}} $ imbalance is that the leading jet is produced near the surface of the quark-gluon plasma while the subleading jet needs to traverse a longer distance through the plasma. The leading jets show the strongest modifications in balanced events (${x_{j}} \approx $ 1), while subleading jets experience the most pronounced modifications in events with a large jet momentum imbalance. A possible explanation is that in balanced events both jets lose a comparable amount of energy, while in events with a momentum imbalance the leading jet loses significantly less energy. Furthermore, jet quenching could lead to the reversal of the apparent leading/subleading ordering.

For the jet shapes, which are normalized distributions of charged-particle transverse momentum as a function of the distance from the jet axis, a redistribution of energy is observed from small angles with respect to the jet axis to larger angles when comparing PbPb and pp events. The difference between the PbPb and pp results is larger for the leading jets compared to the subleading jets, which can be explained by the subleading jet distribution in pp collisions being significantly wider than that for leading jets. When studying the unbalanced ${x_{j}} $ selection for the subleading jets in pp collisions, a fragmentation pattern consistent with the presence of a third jet is observed. Such a pattern is not observed in balanced pp events or in the PbPb sample. As a result, the enhancement of the PbPb to pp ratio for unbalanced events peaks around $\Delta r = 0.4$ and becomes smaller at larger $\Delta r$.

When comparing the jet shapes corresponding to different dijet momentum balance conditions, the distributions for leading jets are found to be the widest for events with balanced jet momenta. For subleading jets, the situation is the opposite, and the widest distributions are found from events having a significant momentum imbalance. These observations are consistent with the interpretation of the charged-particle yield measurements, namely that the average path length inside the medium for leading jets is larger for momentum balanced events, while for subleading jets it is larger in unbalanced events. By studying the charged-particle yields correlated to jets and jet shapes for the first time as a function of dijet momentum balance, this study provides new constraints to the theoretical models and provides a unique way to explore the transition between the domains of weakly and strongly interacting QCD matter.
Additional Figures

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Additional Figure 1:
Distributions of charged particle yields correlated to leading jets as a function of $|\Delta \eta |$ for pp (first column) and 0-10 % central PbPb collisions (second column), shown differentially for all charged particle transverse momentum $(p_{\rm T}^{\rm ch})$ bins. The first row shows the charged particle yields without any selection on $x_{j} = {{p_{\mathrm {T}}} {}^{\mathrm {subleading}}}/{{p_{\mathrm {T}}} {}^{\mathrm {leading}}}$, while other rows show the charged particle yields in different bins of $x_{j}$, starting with the most unbalanced 0 $ < x_{j} < $ 0.6 (second row) to the most balanced 0.8 $ < x_{j} < $ 1.0 (third row) dijet events.

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Additional Figure 2:
Distributions of charged particle yields correlated to subleading jets as a function of $|\Delta \eta |$ for pp (first column) and 0-10 % central PbPb collisions (second column), shown differentially for all charged particle transverse momentum $(p_{\rm T}^{\rm ch})$ bins. The first row shows the charged particle yields without any selection on $x_{j} = {{p_{\mathrm {T}}} {}^{\mathrm {subleading}}}/{{p_{\mathrm {T}}} {}^{\mathrm {leading}}}$, while other rows show the charged particle yields in different bins of $x_{j}$, starting with the most unbalanced 0 $ < x_{j} < $ 0.6 (second row) to the most balanced 0.8 $ < x_{j} < $ 1.0 (third row) dijet events.

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Additional Figure 3:
Leading jet shapes $\rho (\Delta r)$ (normalized to unity over $\Delta r < $ 1) for pp (first column) and 0-10 % central PbPb collisions (second column), shown differentially in charged particle transverse momentum $(p_{\rm T}^{\rm ch})$ for inclusive $x_{j} = {{p_{\mathrm {T}}} {}^{\mathrm {subleading}}}/{{p_{\mathrm {T}}} {}^{\mathrm {leading}}}$ bin (first row) and in $x_{j}$ bins 0 $ < x_{j} < $ 0.6 (second row) and 0.8 $ < x_{j} < $ 1.0 (third row).

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Additional Figure 4:
Subleading jet shapes $\rho (\Delta r)$ (normalized to unity over $\Delta r < $ 1) for pp (first column) and 0-10 % central PbPb collisions (second column), shown differentially in charged particle transverse momentum $(p_{\rm T}^{\rm ch})$ for inclusive $x_{j} = {{p_{\mathrm {T}}} {}^{\mathrm {subleading}}}/{{p_{\mathrm {T}}} {}^{\mathrm {leading}}}$ bin (first row) and in $x_{j}$ bins 0 $ < x_{j} < $ 0.6 (second row) and 0.8 $ < x_{j} < $ 1.0 (third row).

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Additional Figure 5:
PbPb to pp ratio for leading jet shapes $\rho (\Delta r)_{\rm PbPb} / \rho (\Delta r)_{\rm pp}$ for events in the 0-10 % centrality class. The gray points show $x_{j} = {{p_{\mathrm {T}}} {}^{\mathrm {subleading}}}/{{p_{\mathrm {T}}} {}^{\mathrm {leading}}}$ integrated ratio, while the red points are the ratio in 0 $ < x_{j} < $ 0.6 bin and blue points in 0.8 $ < x_{j} < $ 1.0 bin. Error bars are statistical uncertainties while the shaded areas show systematic uncertainties.

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Additional Figure 6:
PbPb to pp ratio for subleading jet shapes $\rho (\Delta r)_{\rm PbPb} / \rho (\Delta r)_{\rm pp}$ for events in the 0-10 % centrality class. The gray points show $x_{j} = {{p_{\mathrm {T}}} {}^{\mathrm {subleading}}}/{{p_{\mathrm {T}}} {}^{\mathrm {leading}}}$ integrated ratio, while the red points are the ratio in 0 $ < x_{j} < $ 0.6 bin and blue points in 0.8 $ < x_{j} < $ 1.0 bin. Error bars are statistical uncertainties while the shaded areas show systematic uncertainties.
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