Observation of a diffractive contribution to dijet production in proton-proton collisions at $\sqrt{s}=7\mathrm{TeV}$

The cross section for dijet production in proton-proton collisions at $\sqrt{s}=7\mathrm{TeV}$ is presented as a function of $\tilde{\xi }$, a variable that approximates the fractional momentum loss of the scattered proton in single-diffractive events. The analysis is based on an integrated luminosity of $2.7{\mathrm{nb}}^{-1}$ collected with the CMS detector at the LHC at low instantaneous luminosities, and uses events with jet transverse momentum of at least 20 GeV. The dijet cross section results are compared to the predictions of diffractive and nondiffractive models. The low-$\tilde{\xi }$ data show a significant contribution from diffractive dijet production, observed for the first time at the LHC. The associated rapidity gap survival probability is estimated.

Figure 1

Schematic diagram of diffractive dijet production. The diagram shows the example of the $gg\to \mathrm{\text{jet}}$ process; the $qq$ and $gq$ initial states also contribute.

Figure 2

The generated $\xi$ versus generated (top panel) and reconstructed (bottom panel) ${\tilde{\xi }}^{+}$ correlations for single-diffractive dijet events simulated by PYTHIA8; events in the bottom panel are those passing the selection described in Sec. 4.

Figure 3

Reconstructed transverse-momentum distributions of the leading (left panel) and second-leading (right panel) jets (black dots) compared to detector-level MC simulations (histograms) generated with two nondiffractive models (PYTHIA6 Z2 and PYTHIA8 tune 1). The error bars indicate the statistical uncertainty. The MC distributions are normalized to the number of events in the corresponding distributions for the data. The ratios of the data and MC distributions are also shown.

Figure 4

Reconstructed pseudorapidity distributions of the leading (top panel) and second-leading (bottom panel) jets (black dots) compared to detector-level MC simulations (histograms) generated with two nondiffractive models (PYTHIA6 Z2 and PYTHIA8 tune 1). The statistical uncertainties are smaller than the data points. The MC distributions are normalized to the number of events in the corresponding distributions for the data.

Figure 5

Reconstructed pseudorapidity distributions of the leading (top panel) and second-leading (bottom panel) jets after the ${\eta }_{\max }<3$ selection (black dots) compared to three detector-level MC simulations (histograms). Events with the ${\eta }_{\min }>-3$ condition are also included in the figure with ${\eta }^{\mathrm{j}1,\mathrm{j}2}\to -{\eta }^{\mathrm{j}1,\mathrm{j}2}$. The error bars indicate the statistical uncertainty. The predictions of the nondiffractive (PYTHIA6 Z2) and diffractive (pompyt, scaled by the value quoted in the legend) contributions and their sum are also shown. The sum of the predictions of the two MC simulations is normalized to the number of events in the corresponding distributions for the data.

Figure 6

Reconstructed $\tilde{\xi }$ distribution compared to detector-level MC predictions with and without diffractive dijet production. The predictions of (a) $\mathrm{PYTHIA}6\mathrm{Z}2+\mathrm{POMPYT}$, (b) $\mathrm{PYTHIA}6\mathrm{D}6\mathrm{T}+\mathrm{POMPYT}$, and (c) PYTHIA8 tune 1 are shown (in all the cases the relative diffractive contributions from the MC simulation are scaled by the values given in the legend). The error bars indicate the statistical uncertainty; the band represents the calorimeter energy scale uncertainty. The sum of the predictions of the two MC simulations is normalized to the number of events in the corresponding distributions for the data.

Figure 7

Reconstructed $\tilde{\xi }$ distributions with (open symbols) and without (closed symbols) the ${\eta }_{\max }<3$ (or ${\eta }_{\min }>-3$) condition are compared to detector-level MC predictions including diffractive dijet production ($\mathrm{PYTHIA}6\mathrm{Z}2+\mathrm{POMPYT}$). The error bars indicate the statistical uncertainty; the band represents the calorimeter energy scale uncertainty. The relative diffractive dijet contribution from the MC simulation has been scaled by the factor 0.23. The sum of the predictions of the two MC simulations is normalized to the number of events in the corresponding distributions for the data.

Figure 8

The differential cross section for inclusive dijet production as a function of $\tilde{\xi }$ for jets with axes in the range $|{\eta }^{\mathrm{j}1,\mathrm{j}2}|<4.4$ and $p_{\mathrm{T}}^{\mathrm{j}1,\mathrm{j}2}>20\mathrm{GeV}$. The points are plotted at the center of the bins. The error bars indicate the statistical uncertainty and the band represents the systematic uncertainties added in quadrature. The predictions of nondiffractive (PYTHIA6 Z2 and PYTHIA8 tune 1) and diffractive (pompyt SD, pomwig SD and PYTHIA8 $\mathrm{SD}+\mathrm{DD}$) MC generators are also shown, along with that of the NLO calculation based on POWHEG (first bin only). The predictions of pompyt and pomwig in the first $\tilde{\xi }$ bin are identical.