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Measurements of inclusive jet suppression in heavy ion collisions at the LHC provide direct sensitivity to the physics of jet quenching. In a sample of lead-lead collisions at $\sqrt{s_{NN}}$ = 2.76 TeV corresponding to an integrated luminosity of approximately 7 inverse microbarns, ATLAS has measured jets with a calorimeter over the pseudorapidity interval |$\eta$| < 2.1 and over the transverse momentum range 38 < pT < 210 GeV. Jets were reconstructed using the anti-$k_t$ algorithm with values for the distance parameter that determines the nominal jet radius of R = 0.2, 0.3, 0.4 and 0.5. The centrality dependence of the jet yield is characterized by the jet "central-to-peripheral ratio," $R_{cp}$. Jet production is found to be suppressed by approximately a factor of two in the 10% most central collisions relative to peripheral collisions. $R_{cp}$ varies smoothly with centrality as characterized by the number of participating nucleons. The observed suppression is only weakly dependent on jet radius and transverse momentum. These results provide the first direct measurement of inclusive jet suppression in heavy ion collisions and complement previous measurements of dijet transverse energy imbalance at the LHC.
Glauber model calculation of the mean numbers of Npart and its associated errors, the mean Ncoll ratios, and Rcoll with fractional errors as a function of the centrality bins.
The Rcp values as a function of jet PT for the four R values, 0.2, 0.3, 0.4 and 0.5 for the collision centrality in the range 0 - 10 %.
The Rcp values as a function of jet PT for the four R values, 0.2, 0.3, 0.4 and 0.5 for the collision centrality in the range 10 - 20 %.
The Rcp values as a function of jet PT for the four R values, 0.2, 0.3, 0.4 and 0.5 for the collision centrality in the range 20 - 30 %.
The Rcp values as a function of jet PT for the four R values, 0.2, 0.3, 0.4 and 0.5 for the collision centrality in the range 30 - 40 %.
The Rcp values as a function of jet PT for the four R values, 0.2, 0.3, 0.4 and 0.5 for the collision centrality in the range 40 - 50 %.
The Rcp values as a function of jet PT for the four R values, 0.2, 0.3, 0.4 and 0.5 for the collision centrality in the range 50 - 60 %.
The Rcp values as a function of the mean number of participating nucleons, NPART, for the four R values, 0.2, 0.3, 0.4 and 0.5 for the jet PT range 38.36 - 44.21 GeV.
The Rcp values as a function of the mean number of participating nucleons, NPART, for the four R values, 0.2, 0.3, 0.4 and 0.5 for the jet PT range 44.21 - 50.94 GeV.
The Rcp values as a function of the mean number of participating nucleons, NPART, for the four R values, 0.2, 0.3, 0.4 and 0.5 for the jet PT range 50.94 - 58.70 GeV.
The Rcp values as a function of the mean number of participating nucleons, NPART, for the four R values, 0.2, 0.3, 0.4 and 0.5 for the jet PT range 58.70 - 67.64 GeV.
The Rcp values as a function of the mean number of participating nucleons, NPART, for the four R values, 0.2, 0.3, 0.4 and 0.5 for the jet PT range 67.64 - 77.94 GeV.
The Rcp values as a function of the mean number of participating nucleons, NPART, for the four R values, 0.2, 0.3, 0.4 and 0.5 for the jet PT range 77.94 - 89.81 GeV.
The Rcp values as a function of the mean number of participating nucleons, NPART, for the four R values, 0.2, 0.3, 0.4 and 0.5 for the jet PT range 89.81 - 103.5 GeV.
The Rcp values as a function of the mean number of participating nucleons, NPART, for the four R values, 0.2, 0.3, 0.4 and 0.5 for the jet PT range 103.5 - 119.3 GeV.
The Rcp values as a function of the mean number of participating nucleons, NPART, for the four R values, 0.2, 0.3, 0.4 and 0.5 for the jet PT range 119.3 - 137.4 GeV.
The Rcp values as a function of the mean number of participating nucleons, NPART, for the four R values, 0.2, 0.3, 0.4 and 0.5 for the jet PT range 137.4 - 158.3 GeV.
The Rcp values as a function of the mean number of participating nucleons, NPART, for the four R values, 0.2, 0.3, 0.4 and 0.5 for the jet PT range 158.3 - 182.5 GeV.
The Rcp values as a function of the mean number of participating nucleons, NPART, for the four R values, 0.2, 0.3, 0.4 and 0.5 for the jet PT range 182.5 - 210.3 GeV.
The Rcp values as a function of R for the three centrality ranges 0 - 10 %, 10 - 20 % and 20 - 30 % for the jet PT range 38.36 - 44.21 GeV.
The Rcp values as a function of R for the three centrality ranges 30 - 40 %, 40 - 50 % and 50 - 60 % for the jet PT range 38.36 - 44.21 GeV.
The Rcp values as a function of R for the three centrality ranges 0 - 10 %, 10 - 20 % and 20 - 30 % for the jet PT range 44.21 - 50.94 GeV.
The Rcp values as a function of R for the three centrality ranges 30 - 40 %, 40 - 50 % and 50 - 60 % for the jet PT range 44.21 - 50.94 GeV.
The Rcp values as a function of R for the three centrality ranges 0 - 10 %, 10 - 20 % and 20 - 30 % for the jet PT range 50.94 - 58.70 GeV.
The Rcp values as a function of R for the three centrality ranges 30 - 40 %, 40 - 50 % and 50 - 60 % for the jet PT range 50.94 - 58.70 GeV.
The Rcp values as a function of R for the three centrality ranges 0 - 10 %, 10 - 20 % and 20 - 30 % for the jet PT range 58.70 - 67.64 GeV.
The Rcp values as a function of R for the three centrality ranges 30 - 40 %, 40 - 50 % and 50 - 60 % for the jet PT range 58.70 - 67.64 GeV.
The Rcp values as a function of R for the three centrality ranges 0 - 10 %, 10 - 20 % and 20 - 30 % for the jet PT range 67.64 - 77.94 GeV.
The Rcp values as a function of R for the three centrality ranges 30 - 40 %, 40 - 50 % and 50 - 60 % for the jet PT range 67.64 - 77.94 GeV.
The Rcp values as a function of R for the three centrality ranges 0 - 10 %, 10 - 20 % and 20 - 30 % for the jet PT range 77.94 - 89.81 GeV.
The Rcp values as a function of R for the three centrality ranges 30 - 40 %, 40 - 50 % and 50 - 60 % for the jet PT range 77.94 - 89.81 GeV.
The Rcp values as a function of R for the three centrality ranges 0 - 10 %, 10 - 20 % and 20 - 30 % for the jet PT range 89.81 - 103.5 GeV.
The Rcp values as a function of R for the three centrality ranges 30 - 40 %, 40 - 50 % and 50 - 60 % for the jet PT range 89.81 - 103.5 GeV.
The Rcp values as a function of R for the three centrality ranges 0 - 10 %, 10 - 20 % and 20 - 30 % for the jet PT range 103.5 - 119.3 GeV.
The Rcp values as a function of R for the three centrality ranges 30 - 40 %, 40 - 50 % and 50 - 60 % for the jet PT range 103.5 - 119.3 GeV.
The Rcp values as a function of R for the three centrality ranges 0 - 10 %, 10 - 20 % and 20 - 30 % for the jet PT range 119.3 - 137.4 GeV.
The Rcp values as a function of R for the three centrality ranges 30 - 40 %, 40 - 50 % and 50 - 60 % for the jet PT range 119.3 - 137.4 GeV.
The Rcp values as a function of R for the three centrality ranges 0 - 10 %, 10 - 20 % and 20 - 30 % for the jet PT range 137.4 - 158.3 GeV.
The Rcp values as a function of R for the three centrality ranges 30 - 40 %, 40 - 50 % and 50 - 60 % for the jet PT range 137.4 - 158.3 GeV.
The Rcp values as a function of R for the three centrality ranges 0 - 10 %, 10 - 20 % and 20 - 30 % for the jet PT range 158.3 - 182.5 GeV.
The Rcp values as a function of R for the three centrality ranges 30 - 40 %, 40 - 50 % and 50 - 60 % for the jet PT range 158.3 - 182.5 GeV.
The Rcp values as a function of R for the three centrality ranges 0 - 10 %, 10 - 20 % and 20 - 30 % for the jet PT range 182.5 - 210.3 GeV.
The Rcp values as a function of R for the three centrality ranges 30 - 40 %, 40 - 50 % and 50 - 60 % for the jet PT range 182.5 - 210.3 GeV.
The ratios of Rcp between R=0.3, 0.4 and 0.5 and R=0.2 jets as a function of the jet PT for the centrality range 0 - 10 %.
The ratios of Rcp between R=0.3, 0.4 and 0.5 and R=0.2 jets as a function of the jet PT for the centrality range 10 - 20 %.
The ratios of Rcp between R=0.3, 0.4 and 0.5 and R=0.2 jets as a function of the jet PT for the centrality range 20 - 30 %.
The ratios of Rcp between R=0.3, 0.4 and 0.5 and R=0.2 jets as a function of the jet PT for the centrality range 30 - 40 %.
The ratios of Rcp between R=0.3, 0.4 and 0.5 and R=0.2 jets as a function of the jet PT for the centrality range 40 - 50 %.
The ratios of Rcp between R=0.3, 0.4 and 0.5 and R=0.2 jets as a function of the jet PT for the centrality range 50 - 60 %.
The covariance matrix for statistcal correlations for R = 0.2 and centrality range 0 - 10 %.
The covariance matrix for statistcal correlations for R = 0.3 and centrality range 0 - 10 %.
The covariance matrix for statistcal correlations for R = 0.4 and centrality range 0 - 10 %.
The covariance matrix for statistcal correlations for R = 0.5 and centrality range 0 - 10 %.
The covariance matrix for statistcal correlations for R = 0.2 and centrality range 10 - 20 %.
The covariance matrix for statistcal correlations for R = 0.3 and centrality range 10 - 20 %.
The covariance matrix for statistcal correlations for R = 0.4 and centrality range 10 - 20 %.
The covariance matrix for statistcal correlations for R = 0.5 and centrality range 10 - 20 %.
The covariance matrix for statistcal correlations for R = 0.2 and centrality range 20 - 30 %.
The covariance matrix for statistcal correlations for R = 0.3 and centrality range 20 - 30 %.
The covariance matrix for statistcal correlations for R = 0.4 and centrality range 20 - 30 %.
The covariance matrix for statistcal correlations for R = 0.5 and centrality range 20 - 30 %.
The covariance matrix for statistcal correlations for R = 0.2 and centrality range 30 - 40 %.
The covariance matrix for statistcal correlations for R = 0.3 and centrality range 30 - 40 %.
The covariance matrix for statistcal correlations for R = 0.4 and centrality range 30 - 40 %.
The covariance matrix for statistcal correlations for R = 0.5 and centrality range 30 - 40 %.
The covariance matrix for statistcal correlations for R = 0.2 and centrality range 40 - 50 %.
The covariance matrix for statistcal correlations for R = 0.3 and centrality range 40 - 50 %.
The covariance matrix for statistcal correlations for R = 0.4 and centrality range 40 - 50 %.
The covariance matrix for statistcal correlations for R = 0.5 and centrality range 40 - 50 %.
The covariance matrix for statistcal correlations for R = 0.2 and centrality range 50 - 60 %.
The covariance matrix for statistcal correlations for R = 0.3 and centrality range 50 - 60 %.
The covariance matrix for statistcal correlations for R = 0.4 and centrality range 50 - 60 %.
The covariance matrix for statistcal correlations for R = 0.5 and centrality range 50 - 60 %.
Correlations between the elliptic or triangular flow coefficients $v_m$ ($m$=2 or 3) and other flow harmonics $v_n$ ($n$=2 to 5) are measured using $\sqrt{s_{NN}}=2.76$ TeV Pb+Pb collision data collected in 2010 by the ATLAS experiment at the LHC, corresponding to an integrated lumonisity of 7 $\mu$b$^{-1}$. The $v_m$-$v_n$ correlations are measured in midrapidity as a function of centrality, and, for events within the same centrality interval, as a function of event ellipticity or triangularity defined in a forward rapidity region. For events within the same centrality interval, $v_3$ is found to be anticorrelated with $v_2$ and this anticorrelation is consistent with similar anticorrelations between the corresponding eccentricities $\epsilon_2$ and $\epsilon_3$. On the other hand, it is observed that $v_4$ increases strongly with $v_2$, and $v_5$ increases strongly with both $v_2$ and $v_3$. The trend and strength of the $v_m$-$v_n$ correlations for $n$=4 and 5 are found to disagree with $\epsilon_m$-$\epsilon_n$ correlations predicted by initial-geometry models. Instead, these correlations are found to be consistent with the combined effects of a linear contribution to $v_n$ and a nonlinear term that is a function of $v_2^2$ or of $v_2v_3$, as predicted by hydrodynamic models. A simple two-component fit is used to separate these two contributions. The extracted linear and nonlinear contributions to $v_4$ and $v_5$ are found to be consistent with previously measured event-plane correlations.
$v_{2}$ data for various $q_2$ bins, Centrality 0-5%.
$v_{3}$ data for various $q_2$ bins, Centrality 0-5%.
$v_{4}$ data for various $q_2$ bins, Centrality 0-5%.
$v_{5}$ data for various $q_2$ bins, Centrality 0-5%.
$v_{2}$ data for various $q_2$ bins, Centrality 5-10%.
$v_{3}$ data for various $q_2$ bins, Centrality 5-10%.
$v_{4}$ data for various $q_2$ bins, Centrality 5-10%.
$v_{5}$ data for various $q_2$ bins, Centrality 5-10%.
$v_{2}$ data for various $q_2$ bins, Centrality 10-15%.
$v_{3}$ data for various $q_2$ bins, Centrality 10-15%.
$v_{4}$ data for various $q_2$ bins, Centrality 10-15%.
$v_{5}$ data for various $q_2$ bins, Centrality 10-15%.
$v_{2}$ data for various $q_2$ bins, Centrality 15-20%.
$v_{3}$ data for various $q_2$ bins, Centrality 15-20%.
$v_{4}$ data for various $q_2$ bins, Centrality 15-20%.
$v_{5}$ data for various $q_2$ bins, Centrality 15-20%.
$v_{2}$ data for various $q_2$ bins, Centrality 20-25%.
$v_{3}$ data for various $q_2$ bins, Centrality 20-25%.
$v_{4}$ data for various $q_2$ bins, Centrality 20-25%.
$v_{5}$ data for various $q_2$ bins, Centrality 20-25%.
$v_{2}$ data for various $q_2$ bins, Centrality 25-30%.
$v_{3}$ data for various $q_2$ bins, Centrality 25-30%.
$v_{4}$ data for various $q_2$ bins, Centrality 25-30%.
$v_{5}$ data for various $q_2$ bins, Centrality 25-30%.
$v_{2}$ data for various $q_2$ bins, Centrality 30-35%.
$v_{3}$ data for various $q_2$ bins, Centrality 30-35%.
$v_{4}$ data for various $q_2$ bins, Centrality 30-35%.
$v_{5}$ data for various $q_2$ bins, Centrality 30-35%.
$v_{2}$ data for various $q_2$ bins, Centrality 35-40%.
$v_{3}$ data for various $q_2$ bins, Centrality 35-40%.
$v_{4}$ data for various $q_2$ bins, Centrality 35-40%.
$v_{5}$ data for various $q_2$ bins, Centrality 35-40%.
$v_{2}$ data for various $q_2$ bins, Centrality 40-45%.
$v_{3}$ data for various $q_2$ bins, Centrality 40-45%.
$v_{4}$ data for various $q_2$ bins, Centrality 40-45%.
$v_{5}$ data for various $q_2$ bins, Centrality 40-45%.
$v_{2}$ data for various $q_2$ bins, Centrality 45-50%.
$v_{3}$ data for various $q_2$ bins, Centrality 45-50%.
$v_{4}$ data for various $q_2$ bins, Centrality 45-50%.
$v_{5}$ data for various $q_2$ bins, Centrality 45-50%.
$v_{2}$ data for various $q_2$ bins, Centrality 50-55%.
$v_{3}$ data for various $q_2$ bins, Centrality 50-55%.
$v_{4}$ data for various $q_2$ bins, Centrality 50-55%.
$v_{5}$ data for various $q_2$ bins, Centrality 50-55%.
$v_{2}$ data for various $q_2$ bins, Centrality 55-60%.
$v_{3}$ data for various $q_2$ bins, Centrality 55-60%.
$v_{4}$ data for various $q_2$ bins, Centrality 55-60%.
$v_{5}$ data for various $q_2$ bins, Centrality 55-60%.
$v_{2}$ data for various $q_2$ bins, Centrality 60-65%.
$v_{3}$ data for various $q_2$ bins, Centrality 60-65%.
$v_{4}$ data for various $q_2$ bins, Centrality 60-65%.
$v_{5}$ data for various $q_2$ bins, Centrality 60-65%.
$v_{2}$ data for various $q_2$ bins, Centrality 65-70%.
$v_{3}$ data for various $q_2$ bins, Centrality 65-70%.
$v_{4}$ data for various $q_2$ bins, Centrality 65-70%.
$v_{5}$ data for various $q_2$ bins, Centrality 65-70%.
$v_{2}$ data for various $q_2$ bins, Centrality 0-10%.
$v_{3}$ data for various $q_2$ bins, Centrality 0-10%.
$v_{4}$ data for various $q_2$ bins, Centrality 0-10%.
$v_{5}$ data for various $q_2$ bins, Centrality 0-10%.
$v_{2}$ data for various $q_2$ bins, Centrality 10-20%.
$v_{3}$ data for various $q_2$ bins, Centrality 10-20%.
$v_{4}$ data for various $q_2$ bins, Centrality 10-20%.
$v_{5}$ data for various $q_2$ bins, Centrality 10-20%.
$v_{2}$ data for various $q_2$ bins, Centrality 20-30%.
$v_{3}$ data for various $q_2$ bins, Centrality 20-30%.
$v_{4}$ data for various $q_2$ bins, Centrality 20-30%.
$v_{5}$ data for various $q_2$ bins, Centrality 20-30%.
$v_{2}$ data for various $q_2$ bins, Centrality 30-40%.
$v_{3}$ data for various $q_2$ bins, Centrality 30-40%.
$v_{4}$ data for various $q_2$ bins, Centrality 30-40%.
$v_{5}$ data for various $q_2$ bins, Centrality 30-40%.
$v_{2}$ data for various $q_2$ bins, Centrality 40-50%.
$v_{3}$ data for various $q_2$ bins, Centrality 40-50%.
$v_{4}$ data for various $q_2$ bins, Centrality 40-50%.
$v_{5}$ data for various $q_2$ bins, Centrality 40-50%.
$v_{2}$ data for various $q_3$ bins, Centrality 0-5%.
$v_{3}$ data for various $q_3$ bins, Centrality 0-5%.
$v_{4}$ data for various $q_3$ bins, Centrality 0-5%.
$v_{5}$ data for various $q_3$ bins, Centrality 0-5%.
$v_{2}$ data for various $q_3$ bins, Centrality 5-10%.
$v_{3}$ data for various $q_3$ bins, Centrality 5-10%.
$v_{4}$ data for various $q_3$ bins, Centrality 5-10%.
$v_{5}$ data for various $q_3$ bins, Centrality 5-10%.
$v_{2}$ data for various $q_3$ bins, Centrality 10-15%.
$v_{3}$ data for various $q_3$ bins, Centrality 10-15%.
$v_{4}$ data for various $q_3$ bins, Centrality 10-15%.
$v_{5}$ data for various $q_3$ bins, Centrality 10-15%.
$v_{2}$ data for various $q_3$ bins, Centrality 15-20%.
$v_{3}$ data for various $q_3$ bins, Centrality 15-20%.
$v_{4}$ data for various $q_3$ bins, Centrality 15-20%.
$v_{5}$ data for various $q_3$ bins, Centrality 15-20%.
$v_{2}$ data for various $q_3$ bins, Centrality 20-25%.
$v_{3}$ data for various $q_3$ bins, Centrality 20-25%.
$v_{4}$ data for various $q_3$ bins, Centrality 20-25%.
$v_{5}$ data for various $q_3$ bins, Centrality 20-25%.
$v_{2}$ data for various $q_3$ bins, Centrality 25-30%.
$v_{3}$ data for various $q_3$ bins, Centrality 25-30%.
$v_{4}$ data for various $q_3$ bins, Centrality 25-30%.
$v_{5}$ data for various $q_3$ bins, Centrality 25-30%.
$v_{2}$ data for various $q_3$ bins, Centrality 30-35%.
$v_{3}$ data for various $q_3$ bins, Centrality 30-35%.
$v_{4}$ data for various $q_3$ bins, Centrality 30-35%.
$v_{5}$ data for various $q_3$ bins, Centrality 30-35%.
$v_{2}$ data for various $q_3$ bins, Centrality 35-40%.
$v_{3}$ data for various $q_3$ bins, Centrality 35-40%.
$v_{4}$ data for various $q_3$ bins, Centrality 35-40%.
$v_{5}$ data for various $q_3$ bins, Centrality 35-40%.
$v_{2}$ data for various $q_3$ bins, Centrality 40-45%.
$v_{3}$ data for various $q_3$ bins, Centrality 40-45%.
$v_{4}$ data for various $q_3$ bins, Centrality 40-45%.
$v_{5}$ data for various $q_3$ bins, Centrality 40-45%.
$v_{2}$ data for various $q_3$ bins, Centrality 45-50%.
$v_{3}$ data for various $q_3$ bins, Centrality 45-50%.
$v_{4}$ data for various $q_3$ bins, Centrality 45-50%.
$v_{5}$ data for various $q_3$ bins, Centrality 45-50%.
$v_{2}$ data for various $q_3$ bins, Centrality 50-55%.
$v_{3}$ data for various $q_3$ bins, Centrality 50-55%.
$v_{4}$ data for various $q_3$ bins, Centrality 50-55%.
$v_{5}$ data for various $q_3$ bins, Centrality 50-55%.
$v_{2}$ data for various $q_3$ bins, Centrality 55-60%.
$v_{3}$ data for various $q_3$ bins, Centrality 55-60%.
$v_{4}$ data for various $q_3$ bins, Centrality 55-60%.
$v_{5}$ data for various $q_3$ bins, Centrality 55-60%.
$v_{2}$ data for various $q_3$ bins, Centrality 60-65%.
$v_{3}$ data for various $q_3$ bins, Centrality 60-65%.
$v_{4}$ data for various $q_3$ bins, Centrality 60-65%.
$v_{5}$ data for various $q_3$ bins, Centrality 60-65%.
$v_{2}$ data for various $q_3$ bins, Centrality 65-70%.
$v_{3}$ data for various $q_3$ bins, Centrality 65-70%.
$v_{4}$ data for various $q_3$ bins, Centrality 65-70%.
$v_{5}$ data for various $q_3$ bins, Centrality 65-70%.
$v_{2}$ data for various $q_3$ bins, Centrality 0-10%.
$v_{3}$ data for various $q_3$ bins, Centrality 0-10%.
$v_{4}$ data for various $q_3$ bins, Centrality 0-10%.
$v_{5}$ data for various $q_3$ bins, Centrality 0-10%.
$v_{2}$ data for various $q_3$ bins, Centrality 10-20%.
$v_{3}$ data for various $q_3$ bins, Centrality 10-20%.
$v_{4}$ data for various $q_3$ bins, Centrality 10-20%.
$v_{5}$ data for various $q_3$ bins, Centrality 10-20%.
$v_{2}$ data for various $q_3$ bins, Centrality 20-30%.
$v_{3}$ data for various $q_3$ bins, Centrality 20-30%.
$v_{4}$ data for various $q_3$ bins, Centrality 20-30%.
$v_{5}$ data for various $q_3$ bins, Centrality 20-30%.
$v_{2}$ data for various $q_3$ bins, Centrality 30-40%.
$v_{3}$ data for various $q_3$ bins, Centrality 30-40%.
$v_{4}$ data for various $q_3$ bins, Centrality 30-40%.
$v_{5}$ data for various $q_3$ bins, Centrality 30-40%.
$v_{2}$ data for various $q_3$ bins, Centrality 40-50%.
$v_{3}$ data for various $q_3$ bins, Centrality 40-50%.
$v_{4}$ data for various $q_3$ bins, Centrality 40-50%.
$v_{5}$ data for various $q_3$ bins, Centrality 40-50%.
$v_{2}$ - $v_{2}$ inclusive correlation in 5% centrality intervals.
$v_{2}$ - $v_{2}$ correlation within each centrality.
$v_{2}$ - $v_{2}$ inclusive correlation in 5% centrality intervals.
$v_{2}$ - $v_{2}$ correlation within each centrality.
$v_{2}$ - $v_{2}$ inclusive correlation in 5% centrality intervals.
$v_{2}$ - $v_{2}$ correlation within each centrality.
$v_{2}$ - $v_{2}$ inclusive correlation in 5% centrality intervals.
$v_{2}$ - $v_{2}$ correlation within each centrality.
$v_{2}$ - $v_{2}$ inclusive correlation in 5% centrality intervals.
$v_{2}$ - $v_{2}$ correlation within each centrality.
$v_{3}$ - $v_{3}$ inclusive correlation in 5% centrality intervals.
$v_{3}$ - $v_{3}$ correlation within each centrality.
$v_{3}$ - $v_{3}$ inclusive correlation in 5% centrality intervals.
$v_{3}$ - $v_{3}$ correlation within each centrality.
$v_{3}$ - $v_{3}$ inclusive correlation in 5% centrality intervals.
$v_{3}$ - $v_{3}$ correlation within each centrality.
$v_{3}$ - $v_{3}$ inclusive correlation in 5% centrality intervals.
$v_{3}$ - $v_{3}$ correlation within each centrality.
$v_{2}$ - $v_{3}$ inclusive correlation in 5% centrality intervals.
$v_{2}$ - $v_{3}$ correlation for various q2 bins within each centrality.
$v_{2}$ - $v_{3}$ inclusive correlation in 5% centrality intervals.
$v_{2}$ - $v_{3}$ correlation for various q2 bins within each centrality.
$v_{2}$ - $v_{3}$ inclusive correlation in 5% centrality intervals.
$v_{2}$ - $v_{3}$ correlation for various q2 bins within each centrality.
$v_{2}$ - $v_{3}$ inclusive correlation in 5% centrality intervals.
$v_{2}$ - $v_{3}$ correlation for various q2 bins within each centrality.
linear fit result of $v_{2}$ - $v_{3}$ correlation within each centrality.
$v_{3}$ - $v_{2}$ inclusive correlation in 5% centrality intervals.
$v_{3}$ - $v_{2}$ correlation for various q3 bins within each centrality.
$v_{3}$ - $v_{2}$ inclusive correlation in 5% centrality intervals.
$v_{3}$ - $v_{2}$ correlation for various q3 bins within each centrality.
$v_{3}$ - $v_{2}$ inclusive correlation in 5% centrality intervals.
$v_{3}$ - $v_{2}$ correlation for various q3 bins within each centrality.
$v_{3}$ - $v_{2}$ inclusive correlation in 5% centrality intervals.
$v_{3}$ - $v_{2}$ correlation for various q3 bins within each centrality.
$v_{2}$ - $v_{4}$ inclusive correlation in 5% centrality intervals.
$v_{2}$ - $v_{4}$ correlation for various q2 bins within each centrality.
$v_{2}$ - $v_{4}$ inclusive correlation in 5% centrality intervals.
$v_{2}$ - $v_{4}$ correlation for various q2 bins within each centrality.
$v_{2}$ - $v_{4}$ inclusive correlation in 5% centrality intervals.
$v_{2}$ - $v_{4}$ correlation for various q2 bins within each centrality.
$v_{2}$ - $v_{4}$ inclusive correlation in 5% centrality intervals.
$v_{2}$ - $v_{4}$ correlation for various q2 bins within each centrality.
$v_{3}$ - $v_{4}$ inclusive correlation in 5% centrality intervals.
$v_{3}$ - $v_{4}$ correlation within each centrality.
$v_{3}$ - $v_{4}$ inclusive correlation in 5% centrality intervals.
$v_{3}$ - $v_{4}$ correlation within each centrality.
$v_4$ decomposed into linear and nonlinear contributions based on q2 event-shape selection.
$v_4$ decomposed into linear and nonlinear contributions based on q2 event-shape selection.
$v_4$ decomposed into linear and nonlinear contributions based on q2 event-shape selection.
$v_4$ decomposed into linear and nonlinear contributions based on q2 event-shape selection.
$v_4$ decomposed into linear and nonlinear contributions based on q2 event-shape selection.
$v_5$ decomposed into linear and nonlinear contributions based on q2 event-shape selection.
$v_5$ decomposed into linear and nonlinear contributions based on q3 event-shape selection.
RMS eccentricity scaled v_n.
RMS eccentricity scaled v_n.
$v_{2}$ - $v_{5}$ inclusive correlation in 5% centrality intervals.
$v_{2}$ - $v_{5}$ correlation for various q2 bins within each centrality.
$v_{3}$ - $v_{5}$ inclusive correlation in 5% centrality intervals.
$v_{3}$ - $v_{5}$ correlation for various q2 bins within each centrality.
Differential measurements of charged particle azimuthal anisotropy are presented for lead-lead collisions at sqrt(s_NN) = 2.76 TeV with the ATLAS detector at the LHC, based on an integrated luminosity of approximately 8 mb^-1. This anisotropy is characterized via a Fourier expansion of the distribution of charged particles in azimuthal angle (phi), with the coefficients v_n denoting the magnitude of the anisotropy. Significant v_2-v_6 values are obtained as a function of transverse momentum (0.5<pT<20 GeV), pseudorapidity (|eta|<2.5) and centrality using an event plane method. The v_n values for n>=3 are found to vary weakly with both eta and centrality, and their pT dependencies are found to follow an approximate scaling relation, v_n^{1/n}(pT) \propto v_2^{1/2}(pT). A Fourier analysis of the charged particle pair distribution in relative azimuthal angle (Dphi=phi_a-phi_b) is performed to extract the coefficients v_{n,n}=<cos (n Dphi)>. For pairs of charged particles with a large pseudorapidity gap (|Deta=eta_a-eta_b|>2) and one particle with pT<3 GeV, the v_{2,2}-v_{6,6} values are found to factorize as v_{n,n}(pT^a,pT^b) ~ v_n(pT^a)v_n(pT^b) in central and mid-central events. Such factorization suggests that these values of v_{2,2}-v_{6,6} are primarily due to the response of the created matter to the fluctuations in the geometry of the initial state. A detailed study shows that the v_{1,1}(pT^a,pT^b) data are consistent with the combined contributions from a rapidity-even v_1 and global momentum conservation. A two-component fit is used to extract the v_1 contribution. The extracted v_1 is observed to cross zero at pT\sim1.0 GeV, reaches a maximum at 4-5 GeV with a value comparable to that for v_3, and decreases at higher pT.
The EP Resolution Factor vs. Centrality for n values from2 to 6.
The Chi Reolution Factor vs. Centrality for n values from 2 to 6.
The one-dimensional Delta(PHI) correlation function vs Delta(PHI) for |DETARAP| in the range 2 to 5 summed over all n values from 1 to 6.
The Fourier coefficient V_n,n vs. |Delta(ETARAP)| for individual n values.
The Fourier coefficient V_n vs. |Delta(ETARAP)| from the 2PC anaysis for individual n values from 2 to n.
The Fourier coefiiciant V_n vs eta for PT 0.5 TO 1 GeV and centrality 0 TO 5%.
The Fourier coefiiciant V_n vs eta for PT 0.5 TO 1 GeV and centrality 5 TO 10%.
The Fourier coefiiciant V_n vs eta for PT 0.5 TO 1 GeV and centrality 10 TO 20%.
The Fourier coefiiciant V_n vs eta for PT 0.5 TO 1 GeV and centrality 20 TO 30%.
The Fourier coefiiciant V_n vs eta for PT 0.5 TO 1 GeV and centrality 30 TO 40%.
The Fourier coefiiciant V_n vs eta for PT 0.5 TO 1 GeV and centrality 40 TO 50%.
The Fourier coefiiciant V_n vs eta for PT 0.5 TO 1 GeV and centrality 50 TO 60%.
The Fourier coefiiciant V_n vs eta for PT 0.5 TO 1 GeV and centrality 60 TO 70%.
The Fourier coefiiciant V_n vs eta for PT 1 TO 2 GeV and centrality 0 TO 5%.
The Fourier coefiiciant V_n vs eta for PT 1 TO 2 GeV and centrality 5 TO 10%.
The Fourier coefiiciant V_n vs eta for PT 1 TO 2 GeV and centrality 10 TO 20%.
The Fourier coefiiciant V_n vs eta for PT 1 TO 2 GeV and centrality 20 TO 30%.
The Fourier coefiiciant V_n vs eta for PT 1 TO 2 GeV and centrality 30 TO 40%.
The Fourier coefiiciant V_n vs eta for PT 1 TO 2 GeV and centrality 40 TO 50%.
The Fourier coefiiciant V_n vs eta for PT 1 TO 2 GeV and centrality 50 TO 60%.
The Fourier coefiiciant V_n vs eta for PT 1 TO 2 GeV and centrality 60 TO 70%.
The Fourier coefiiciant V_n vs eta for PT 2 TO 3 GeV and centrality 0 TO 5%.
The Fourier coefiiciant V_n vs eta for PT 2 TO 3 GeV and centrality 5 TO 10%.
The Fourier coefiiciant V_n vs eta for PT 2 TO 3 GeV and centrality 10 TO 20%.
The Fourier coefiiciant V_n vs eta for PT 2 TO 3 GeV and centrality 20 TO 30%.
The Fourier coefiiciant V_n vs eta for PT 2 TO 3 GeV and centrality 30 TO 40%.
The Fourier coefiiciant V_n vs eta for PT 2 TO 3 GeV and centrality 40 TO 50%.
The Fourier coefiiciant V_n vs eta for PT 2 TO 3 GeV and centrality 50 TO 60%.
The Fourier coefiiciant V_n vs eta for PT 2 TO 3 GeV and centrality 60 TO 70%.
The Fourier coefiiciant V_n vs eta for PT 3 TO 4 GeV and centrality 0 TO 5%.
The Fourier coefiiciant V_n vs eta for PT 3 TO 4 GeV and centrality 5 TO 10%.
The Fourier coefiiciant V_n vs eta for PT 3 TO 4 GeV and centrality 10 TO 20%.
The Fourier coefiiciant V_n vs eta for PT 3 TO 4 GeV and centrality 20 TO 30%.
The Fourier coefiiciant V_n vs eta for PT 3 TO 4 GeV and centrality 30 TO 40%.
The Fourier coefiiciant V_n vs eta for PT 3 TO 4 GeV and centrality 40 TO 50%.
The Fourier coefiiciant V_n vs eta for PT 3 TO 4 GeV and centrality 50 TO 60%.
The Fourier coefiiciant V_n vs eta for PT 3 TO 4 GeV and centrality 60 TO 70%.
The Fourier coefiiciant V_n vs eta for PT 4 TO 8 GeV and centrality 0 TO 5%.
The Fourier coefiiciant V_n vs eta for PT 4 TO 8 GeV and centrality 5 TO 10%.
The Fourier coefiiciant V_n vs eta for PT 4 TO 8 GeV and centrality 10 TO 20%.
The Fourier coefiiciant V_n vs eta for PT 4 TO 8 GeV and centrality 20 TO 30%.
The Fourier coefiiciant V_n vs eta for PT 4 TO 8 GeV and centrality 30 TO 40%.
The Fourier coefiiciant V_n vs eta for PT 4 TO 8 GeV and centrality 40 TO 50%.
The Fourier coefiiciant V_n vs eta for PT 4 TO 8 GeV and centrality 50 TO 60%.
The Fourier coefiiciant V_n vs eta for PT 4 TO 8 GeV and centrality 60 TO 70%.
V_n vs PT for centrality 0 TO 5%.
V_n vs PT for centrality 5 TO 10%.
V_n vs PT for centrality 10 TO 20%.
V_n vs PT for centrality 20 TO 30%.
V_n vs PT for centrality 30 TO 40%.
V_n vs PT for centrality 40 TO 50%.
V_n vs PT for centrality 50 TO 60%.
V_n vs PT for centrality 60 TO 70%.
V_n vs Centrality for PT 1 TO 2 GeV.
V_n vs Centrality for PT 2 TO 3 GeV.
V_n vs Centrality for PT 3 TO 4 GeV.
V_n vs Centrality for PT 4 TO 8 GeV.
V_n vs Centrality for PT 8 TO 12 GeV.
V_n vs Centrality for PT 12 TO 20 GeV.
2PC.V_n vs n for Centrality 0 TO 1 %.
2PC.V_n vs n for Centrality 0 TO 5 %.
2PC.V_n vs n for Centrality 5 TO 10 %.
2PC.V_n vs n for Centrality 0 TO 10 %.
2PC.V_n vs n for Centrality 10 TO 20 %.
2PC.V_n vs n for Centrality 20 TO 30 %.
2PC.V_n vs n for Centrality 30 TO 40 %.
2PC.V_n vs n for Centrality 40 TO 50 %.
2PC.V_n vs n for Centrality 50 TO 60 %.
2PC.V_n vs n for Centrality 60 TO 70 %.
2PC.V_n vs n for Centrality 70 TO 80 %.
V_nn vs n for Centrality 0 TO 1 %.
V_nn vs n for Centrality 0 TO 5 %.
V_nn vs n for Centrality 5 TO 10 %.
V_nn vs n for Centrality 0 TO 10 %.
V_nn vs n for Centrality 10 TO 20 %.
V_nn vs n for Centrality 20 TO 30 %.
V_nn vs n for Centrality 30 TO 40 %.
V_nn vs n for Centrality 40 TO 50 %.
V_nn vs n for Centrality 50 TO 60 %.
V_nn vs n for Centrality 60 TO 70 %.
V_nn vs n for Centrality 70 TO 80 %.
correlation funcitons in various pT bins.
correlation funcitons in various pT bins.
correlation funcitons in various pT bins.
correlation funcitons in various pT bins.
v_{1,1} vs eta for different combinations of pTa and pTb. Figure 18.
v_{1,1} vs eta for different combinations of pTa and pTb. Figure 18.
v_{1,1} vs eta for different combinations of pTa and pTb. Figure 18.
v_{1,1} vs eta for different combinations of pTa and pTb. Figure 18.
v_{1} vs pT for different centrality selections, Figure 21.
v_n extracted from 2PC method utilizing the factorization relation.
v_n extracted from 2PC method utilizing the factorization relation.
v_n extracted from 2PC method utilizing the factorization relation.
v_n extracted from 2PC method utilizing the factorization relation.
v_n extracted from 2PC method utilizing the factorization relation.
v_n extracted from 2PC method utilizing the factorization relation.
v_n extracted from 2PC method utilizing the factorization relation.
v_n extracted from 2PC method utilizing the factorization relation.
v_n extracted from 2PC method utilizing the factorization relation.
v_n extracted from 2PC method utilizing the factorization relation.
v_n extracted from 2PC method utilizing the factorization relation.
v_n extracted from 2PC method utilizing the factorization relation.
v_n extracted from 2PC method utilizing the factorization relation.
v_n extracted from 2PC method utilizing the factorization relation.
v_n extracted from 2PC method utilizing the factorization relation.
v_n extracted from 2PC method utilizing the factorization relation.
v_n extracted from 2PC method utilizing the factorization relation.
v_n extracted from 2PC method utilizing the factorization relation.
v_n extracted from 2PC method utilizing the factorization relation.
v_n extracted from 2PC method utilizing the factorization relation.
v_n extracted from 2PC method utilizing the factorization relation.
v_n extracted from 2PC method utilizing the factorization relation.
v_n extracted from 2PC method utilizing the factorization relation.
v_n extracted from 2PC method utilizing the factorization relation.
v_ vs pta for various centrality pta combinations.
v_ vs pta for various centrality pta combinations.
v_ vs pta for various centrality pta combinations.
v_ vs pta for various centrality pta combinations.
v_ vs pta for various centrality pta combinations.
v_ vs pta for various centrality pta combinations.
v_ vs pta for various centrality pta combinations.
v_ vs pta for various centrality pta combinations.
v_ vs pta for various centrality pta combinations.
v_ vs pta for various centrality pta combinations.
v_ vs pta for various centrality pta combinations.
v_ vs pta for various centrality pta combinations.
v_ vs pta for various centrality pta combinations.
v_ vs pta for various centrality pta combinations.
v_ vs pta for various centrality pta combinations.
v_ vs pta for various centrality pta combinations.
v_ vs pta for various centrality pta combinations.
v_ vs pta for various centrality pta combinations.
v_ vs pta for various centrality pta combinations.
v_ vs pta for various centrality pta combinations.
v_ vs pta for various centrality pta combinations.
v_ vs pta for various centrality pta combinations.
v_ vs pta for various centrality pta combinations.
v_ vs pta for various centrality pta combinations.
v_ vs pta for various centrality pta combinations.
v_ vs pta for various centrality pta combinations.
v_ vs pta for various centrality pta combinations.
v_ vs pta for various centrality pta combinations.
v_ vs pta for various centrality pta combinations.
v_ vs pta for various centrality pta combinations.
v_ vs pta for various centrality pta combinations.
v_ vs pta for various centrality pta combinations.
v_ vs pta for various centrality pta combinations.
v_ vs pta for various centrality pta combinations.
v_ vs pta for various centrality pta combinations.
v_ vs pta for various centrality pta combinations.
v_ vs pta for various centrality pta combinations.
v_ vs pta for various centrality pta combinations.
v_ vs pta for various centrality pta combinations.
v_ vs pta for various centrality pta combinations.
v_ vs pta for various centrality pta combinations.
v_ vs pta for various centrality pta combinations.
v_ vs pta for various centrality pta combinations.
v_ vs pta for various centrality pta combinations.
v_ vs pta for various centrality pta combinations.
v_ vs pta for various centrality pta combinations.
v_ vs pta for various centrality pta combinations.
v_ vs pta for various centrality pta combinations.
v_ vs pta for various centrality pta combinations.
v_ vs pta for various centrality pta combinations.
v_ vs pta for various centrality pta combinations.
v_ vs pta for various centrality pta combinations.
v_ vs pta for various centrality pta combinations.
v_ vs pta for various centrality pta combinations.
v_ vs pta for various centrality pta combinations.
v_ vs pta for various centrality pta combinations.
v_ vs pta for various centrality pta combinations.
v_ vs pta for various centrality pta combinations.
v_ vs pta for various centrality pta combinations.
v_ vs pta for various centrality pta combinations.
v_ vs pta for various centrality pta combinations.
v_ vs pta for various centrality pta combinations.
v_ vs pta for various centrality pta combinations.
v_ vs pta for various centrality pta combinations.
v_ vs pta for various centrality pta combinations.
v_ vs pta for various centrality pta combinations.
v_ vs pta for various centrality pta combinations.
v_ vs pta for various centrality pta combinations.
v_ vs pta for various centrality pta combinations.
v_ vs pta for various centrality pta combinations.
v_ vs pta for various centrality pta combinations.
v_ vs pta for various centrality pta combinations.
v_ vs pta for various centrality pta combinations.
v_ vs pta for various centrality pta combinations.
v_ vs pta for various centrality pta combinations.
v_ vs pta for various centrality pta combinations.
v_ vs pta for various centrality pta combinations.
v_ vs pta for various centrality pta combinations.
v_ vs pta for various centrality pta combinations.
v_ vs pta for various centrality pta combinations.
v_ vs pta for various centrality pta combinations.
v_ vs pta for various centrality pta combinations.
v_ vs pta for various centrality pta combinations.
v_ vs pta for various centrality pta combinations.
v_ vs pta for various centrality pta combinations.
v_ vs pta for various centrality pta combinations.
v_ vs pta for various centrality pta combinations.
v_ vs pta for various centrality pta combinations.
v_ vs pta for various centrality pta combinations.
v_ vs pta for various centrality pta combinations.
v_ vs pta for various centrality pta combinations.
v_ vs pta for various centrality pta combinations.
v_ vs pta for various centrality pta combinations.
v_ vs pta for various centrality pta combinations.
v_ vs pta for various centrality pta combinations.
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