Showing **10** of **13** results

- Yang, Yi 14
- Aad, Georges 13
- Abramowicz, Halina 13
- Abreu, Henso 13
- Acharya, Bobby Samir 13
- Adelman, Jahred 13
- Adye, Tim 13
- Aielli, Giulio 13
- Akimov, Andrei 13
- Albert, Justin 13
- Aleksa, Martin 13
- Alexa, Calin 13
- Alexopoulos, Theodoros 13
- Alhroob, Muhammad 13
- Alimonti, Gianluca 13
- Aloisio, Alberto 13
- Alviggi, Mariagrazia 13
- Amelung, Christoph 13
- Anastopoulos, Christos 13
- Andreazza, Attilio 13

The
ATLAS
collaboration
Aad, Georges
;
Abbott, Brad
;
Abdallah, Jalal
;
*et al. *

Eur.Phys.J.C 74 (2014) 3157, 2014.

https://inspirehep.net/literature/1311487
Inspire Record
1311487
DOI
10.17182/hepdata.65771
https://doi.org/10.17182/hepdata.65771
ATLAS measurements of the azimuthal anisotropy in lead-lead collisions at $\sqrt{s_{NN}}=2.76$ TeV are shown using a dataset of approximately 7 $\mu$b$^{-1}$ collected at the LHC in 2010. The measurements are performed for charged particles with transverse momenta $0.5<p_T<20$ GeV and in the pseudorapidity range $|\eta|<2.5$. The anisotropy is characterized by the Fourier coefficients, $v_n$, of the charged-particle azimuthal angle distribution for n = 2-4. The Fourier coefficients are evaluated using multi-particle cumulants calculated with the generating function method. Results on the transverse momentum, pseudorapidity and centrality dependence of the $v_n$ coefficients are presented. The elliptic flow, $v_2$, is obtained from the two-, four-, six- and eight-particle cumulants while higher-order coefficients, $v_3$ and $v_4$, are determined with two- and four-particle cumulants. Flow harmonics $v_n$ measured with four-particle cumulants are significantly reduced compared to the measurement involving two-particle cumulants. A comparison to $v_n$ measurements obtained using different analysis methods and previously reported by the LHC experiments is also shown. Results of measurements of flow fluctuations evaluated with multi-particle cumulants are shown as a function of transverse momentum and the collision centrality. Models of the initial spatial geometry and its fluctuations fail to describe the flow fluctuations measurements.

220
data tables
match query

The second flow harmonic measured with the two-particle cumulats as a function of <Npart>.

The second flow harmonic measured with the four-particle cumulats as a function of <Npart>.

The second flow harmonic measured with the six-particle cumulats as a function of <Npart>.

The second flow harmonic measured with the eight-particle cumulats as a function of <Npart>.

The second flow harmonic measured with the Event Plane method as a function of <Npart>.

The triangular flow harmonic measured with the Event Plane method as a function of <Npart>.

The triangular flow harmonic measured with the two-particle cumulants as a function of <Npart>.

The triangular flow harmonic measured with the two-particle cumulants as a function of <Npart>.

The quadrangular flow harmonic measured with the Event Plane method as a function of <Npart>.

The quadrangular flow harmonic measured with the two-particle cumulants as a function of <Npart>.

The quadrangular flow harmonic measured with the two-particle cumulants as a function of <Npart>.

The second flow harmonic fluctuations, F(v2), as a function of <Npart>.

The triangular flow harmonic fluctuations, F(v3), as a function of <Npart>.

The triangular flow harmonic fluctuations, F(v4), as a function of <Npart>.

The second flow harmonic measured with the two-particle cumulats as a function of <Npart>.

The second flow harmonic measured with the four-particle cumulats as a function of <Npart>.

The second flow harmonic measured with the six-particle cumulats as a function of <Npart>.

The second flow harmonic measured with the eight-particle cumulats as a function of <Npart>.

The triangular flow harmonic measured with the two-particle cumulants as a function of <Npart>.

The quadrangular flow harmonic measured with the Event Plane method as a function of <Npart>.

The second flow harmonic fluctuations, F(v2), as a function of <Npart>.

The triangular flow harmonic fluctuations, F(v3), as a function of <Npart>.

The triangular flow harmonic fluctuations, F(v4), as a function of <Npart>.

The
ATLAS
collaboration
Aad, Georges
;
Abbott, Brad
;
Abdallah, Jalal
;
*et al. *

Phys.Rev.C 92 (2015) 034903, 2015.

https://inspirehep.net/literature/1357991
Inspire Record
1357991
DOI
10.17182/hepdata.68950
https://doi.org/10.17182/hepdata.68950
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.

212
data tables
match query

$v_{2}$ 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.

Version 2

Measurement of charged-particle spectra in Pb+Pb collisions at $\sqrt{{s}_\mathsf{{NN}}} = 2.76$ TeV with the ATLAS detector at the LHC
The
ATLAS
collaboration
Aad, Georges
;
Abbott, Brad
;
Abdallah, Jalal
;
*et al. *

JHEP 09 (2015) 050, 2015.

https://inspirehep.net/literature/1360290
Inspire Record
1360290
DOI
10.17182/hepdata.67531
https://doi.org/10.17182/hepdata.67531
Charged-particle spectra obtained in 0.15 nb${}^{-1}$ of Pb+Pb interactions at $\sqrt{{s}_\mathsf{{NN}}}=2.76$TeV and 4.2 pb${}^{-1}$ of pp interactions at $\sqrt{s}=2.76$ TeV with the ATLAS detector at the LHC are presented in a wide transverse momentum ($0.5 < p_{\mathrm{T}} < 150$ GeV) and pseudorapidity ($|\eta|<2$) range. For Pb+Pb collisions, the spectra are presented as a function of collision centrality, which is determined by the response of the forward calorimeter located on both sides of the interaction point. The nuclear modification factors $R_{\mathrm{AA}}$ and $R_{\mathrm{CP}}$ are presented in detail as function of centrality, $p_{\mathrm{T}}$ and $\eta$. They show a distinct $p_{\mathrm{T}}$-dependence with a pronounced minimum at about 7 GeV. Above 60 GeV, $R_{\mathrm{AA}}$ is consistent with a plateau at a centrality-dependent value, within the uncertainties. The value is $0.55\pm0.01(stat.)\pm0.04(syst.)$ in the most central collisions. The $R_{\mathrm{AA}}$ distribution is consistent with flat $|\eta|$ dependence over the whole transverse momentum range in all centrality classes.

121
data tables
match query

Charged-particle spectra for pp.

Charged-particle spectra in different centrality intervals for Pb+Pb.

Charged-particle spectra in different centrality intervals for Pb+Pb (not shown in Fig. 10).

Charged-particle spectra in different centrality intervals for Pb+Pb.

Charged-particle spectra in different centrality intervals for Pb+Pb (not shown in Fig. 10).

Charged-particle spectra in different centrality intervals for Pb+Pb.

Charged-particle spectra in different centrality intervals for Pb+Pb (not shown in Fig. 10).

Charged-particle spectra in different centrality intervals for Pb+Pb.

Charged-particle spectra in different centrality intervals for Pb+Pb.

Charged-particle spectra in different eta intervals for pp.

Charged-particle spectra in different eta intervals for Pb+Pb.

Charged-particle spectra in different eta intervals for pp.

Charged-particle spectra in different eta intervals for Pb+Pb.

Charged-particle spectra in different eta intervals for pp.

Charged-particle spectra in different eta intervals for Pb+Pb.

Charged-particle spectra in different eta intervals for pp.

Charged-particle spectra in different eta intervals for Pb+Pb.

Charged-particle spectra in different eta intervals for pp.

Charged-particle spectra in different eta intervals for Pb+Pb.

Charged-particle spectra in different eta intervals for pp.

Charged-particle spectra in different eta intervals for Pb+Pb.

Charged-particle spectra in different eta intervals for pp.

Charged-particle spectra in different eta intervals for Pb+Pb.

Charged-particle spectra in different eta intervals for pp.

Charged-particle spectra in different eta intervals for Pb+Pb.

Rcp in different centrality intervals.

Rcp in different centrality intervals (not shown in Fig. 12).

Rcp in different centrality intervals.

Rcp in different centrality intervals (not shown in Fig. 12).

Rcp in different centrality intervals.

Rcp in different centrality intervals (not shown in Fig. 12).

Rcp in different centrality intervals.

Raa in different centrality intervals.

Raa in different centrality intervals (not shown in Fig. 13).

Raa in different centrality intervals.

Raa in different centrality intervals (not shown in Fig. 13).

Raa in different centrality intervals.

Raa in different centrality intervals (not shown in Fig. 13).

Raa in different centrality intervals.

Raa in different centrality intervals.

Raa in different eta intervals.

Raa in different eta intervals.

Raa in different eta intervals.

Raa in different eta intervals.

Raa in different eta intervals.

Raa in different eta intervals.

Raa in different eta intervals.

Raa in different eta intervals.

Raa.

Raa as a function of <Npart>.

Raa as a function of <Npart>.

Raa as a function of <Npart>.

Raa as a function of <Npart>.

Charged-particle spectra in different eta intervals for pp.

Charged-particle spectra in different eta intervals for Pb+Pb.

Charged-particle spectra in different eta intervals for Pb+Pb (not shown in Fig. 17).

Charged-particle spectra in different eta intervals for Pb+Pb.

Charged-particle spectra in different eta intervals for Pb+Pb (not shown in Fig. 17).

Charged-particle spectra in different eta intervals for Pb+Pb.

Charged-particle spectra in different eta intervals for Pb+Pb (not shown in Fig. 17).

Charged-particle spectra in different eta intervals for Pb+Pb.

Charged-particle spectra in different eta intervals for Pb+Pb.

Charged-particle spectra in different eta intervals for pp.

Charged-particle spectra in different eta intervals for Pb+Pb.

Charged-particle spectra in different eta intervals for Pb+Pb (not shown in Fig. 17).

Charged-particle spectra in different eta intervals for Pb+Pb.

Charged-particle spectra in different eta intervals for Pb+Pb (not shown in Fig. 17).

Charged-particle spectra in different eta intervals for Pb+Pb.

Charged-particle spectra in different eta intervals for Pb+Pb (not shown in Fig. 17).

Charged-particle spectra in different eta intervals for Pb+Pb.

Charged-particle spectra in different eta intervals for Pb+Pb.

Charged-particle spectra in different eta intervals for pp.

Charged-particle spectra in different eta intervals for Pb+Pb.

Charged-particle spectra in different eta intervals for Pb+Pb (not shown in Fig. 17).

Charged-particle spectra in different eta intervals for Pb+Pb.

Charged-particle spectra in different eta intervals for Pb+Pb (not shown in Fig. 17).

Charged-particle spectra in different eta intervals for Pb+Pb.

Charged-particle spectra in different eta intervals for Pb+Pb (not shown in Fig. 17).

Charged-particle spectra in different eta intervals for Pb+Pb.

Charged-particle spectra in different eta intervals for Pb+Pb.

Charged-particle spectra in different eta intervals for pp.

Charged-particle spectra in different eta intervals for Pb+Pb.

Charged-particle spectra in different eta intervals for Pb+Pb (not shown in Fig. 17).

Charged-particle spectra in different eta intervals for Pb+Pb.

Charged-particle spectra in different eta intervals for Pb+Pb (not shown in Fig. 17).

Charged-particle spectra in different eta intervals for Pb+Pb.

Charged-particle spectra in different eta intervals for Pb+Pb (not shown in Fig. 17).

Charged-particle spectra in different eta intervals for Pb+Pb.

Charged-particle spectra in different eta intervals for Pb+Pb.

Raa in different eta intervals.

Raa in different eta intervals (not shown in Fig. 18).

Raa in different eta intervals.

Raa in different eta intervals (not shown in Fig. 18).

Raa in different eta intervals.

Raa in different eta intervals (not shown in Fig. 18).

Raa in different eta intervals.

Raa in different eta intervals.

Raa in different eta intervals.

Raa in different eta intervals (not shown in Fig. 18).

Raa in different eta intervals.

Raa in different eta intervals (not shown in Fig. 18).

Raa in different eta intervals.

Raa in different eta intervals (not shown in Fig. 18).

Raa in different eta intervals.

Raa in different eta intervals.

Raa in different eta intervals.

Raa in different eta intervals (not shown in Fig. 18).

Raa in different eta intervals.

Raa in different eta intervals (not shown in Fig. 18).

Raa in different eta intervals.

Raa in different eta intervals (not shown in Fig. 18).

Raa in different eta intervals.

Raa in different eta intervals.

Raa in different eta intervals.

Raa in different eta intervals (not shown in Fig. 18).

Raa in different eta intervals.

Raa in different eta intervals (not shown in Fig. 18).

Raa in different eta intervals.

Raa in different eta intervals (not shown in Fig. 18).

Raa in different eta intervals.

Raa in different eta intervals.

The
ATLAS
collaboration
Aad, Georges
;
Abbott, Brad
;
Abdallah, Jalal
;
*et al. *

Phys.Lett.B 739 (2014) 320-342, 2014.

https://inspirehep.net/literature/1300152
Inspire Record
1300152
DOI
10.17182/hepdata.64272
https://doi.org/10.17182/hepdata.64272
Measurements of charged-particle fragmentation functions of jets produced in ultra-relativistic nuclear collisions can provide insight into the modification of parton showers in the hot, dense medium created in the collisions. ATLAS has measured jets in $\sqrt{s_{NN}} = 2.76$ TeV Pb+Pb collisions at the LHC using a data set recorded in 2011 with an integrated luminosity of 0.14 nb$^{-1}$. Jets were reconstructed using the anti-$k_{t}$ algorithm with distance parameter values $R$ = 0.2, 0.3, and 0.4. Distributions of charged-particle transverse momentum and longitudinal momentum fraction are reported for seven bins in collision centrality for $R=0.4$ jets with $p_{{T}}^{\mathrm{jet}}> 100$ GeV. Commensurate minimum $p_{\mathrm{T}}$ values are used for the other radii. Ratios of fragment distributions in each centrality bin to those measured in the most peripheral bin are presented. These ratios show a reduction of fragment yield in central collisions relative to peripheral collisions at intermediate $z$ values, $0.04 \lesssim z \lesssim 0.2$ and an enhancement in fragment yield for $z \lesssim 0.04$. A smaller, less significant enhancement is observed at large $z$ and large $p_{\mathrm{T}}$ in central collisions.

80
data tables
match query

D(z) distribution for R=0.4 jets.

D(z) distribution for R=0.4 jets.

D(z) distribution for R=0.4 jets.

D(z) distribution for R=0.4 jets.

D(z) distribution for R=0.4 jets.

D(z) distribution for R=0.4 jets.

D(z) distribution for R=0.4 jets.

D(z) distribution for R=0.3 jets.

D(z) distribution for R=0.3 jets.

D(z) distribution for R=0.3 jets.

D(z) distribution for R=0.3 jets.

D(z) distribution for R=0.3 jets.

D(z) distribution for R=0.3 jets.

D(z) distribution for R=0.3 jets.

D(z) distribution for R=0.2 jets.

D(z) distribution for R=0.2 jets.

D(z) distribution for R=0.2 jets.

D(z) distribution for R=0.2 jets.

D(z) distribution for R=0.2 jets.

D(z) distribution for R=0.2 jets.

D(z) distribution for R=0.2 jets.

D(pt) distribution for R=0.4 jets.

D(pt) distribution for R=0.4 jets.

D(pt) distribution for R=0.4 jets.

D(pt) distribution for R=0.4 jets.

D(pt) distribution for R=0.4 jets.

D(pt) distribution for R=0.4 jets.

D(pt) distribution for R=0.4 jets.

D(pt) distribution for R=0.3 jets.

D(pt) distribution for R=0.3 jets.

D(pt) distribution for R=0.3 jets.

D(pt) distribution for R=0.3 jets.

D(pt) distribution for R=0.3 jets.

D(pt) distribution for R=0.3 jets.

D(pt) distribution for R=0.3 jets.

D(pt) distribution for R=0.2 jets.

D(pt) distribution for R=0.2 jets.

D(pt) distribution for R=0.2 jets.

D(pt) distribution for R=0.2 jets.

D(pt) distribution for R=0.2 jets.

D(pt) distribution for R=0.2 jets.

D(pt) distribution for R=0.2 jets.

Ratio of D(z) distributions for R=0.4 jets for central to peripheral events.

Ratio of D(z) distributions for R=0.4 jets for central to peripheral events.

Ratio of D(z) distributions for R=0.4 jets for central to peripheral events.

Ratio of D(z) distributions for R=0.4 jets for central to peripheral events.

Ratio of D(z) distributions for R=0.4 jets for central to peripheral events.

Ratio of D(z) distributions for R=0.4 jets for central to peripheral events.

Ratio of D(z) distributions for R=0.3 jets for central to peripheral events.

Ratio of D(z) distributions for R=0.3 jets for central to peripheral events.

Ratio of D(z) distributions for R=0.3 jets for central to peripheral events.

Ratio of D(z) distributions for R=0.3 jets for central to peripheral events.

Ratio of D(z) distributions for R=0.3 jets for central to peripheral events.

Ratio of D(z) distributions for R=0.3 jets for central to peripheral events.

Ratio of D(z) distributions for R=0.2 jets for central to peripheral events.

Ratio of D(z) distributions for R=0.2 jets for central to peripheral events.

Ratio of D(z) distributions for R=0.2 jets for central to peripheral events.

Ratio of D(z) distributions for R=0.2 jets for central to peripheral events.

Ratio of D(z) distributions for R=0.2 jets for central to peripheral events.

Ratio of D(z) distributions for R=0.2 jets for central to peripheral events.

Ratio of D(pt) distributions for R=0.4 jets for central to peripheral events.

Ratio of D(pt) distributions for R=0.4 jets for central to peripheral events.

Ratio of D(pt) distributions for R=0.4 jets for central to peripheral events.

Ratio of D(pt) distributions for R=0.4 jets for central to peripheral events.

Ratio of D(pt) distributions for R=0.4 jets for central to peripheral events.

Ratio of D(pt) distributions for R=0.4 jets for central to peripheral events.

Ratio of D(pt) distributions for R=0.3 jets for central to peripheral events.

Ratio of D(pt) distributions for R=0.3 jets for central to peripheral events.

Ratio of D(pt) distributions for R=0.3 jets for central to peripheral events.

Ratio of D(pt) distributions for R=0.3 jets for central to peripheral events.

Ratio of D(pt) distributions for R=0.3 jets for central to peripheral events.

Ratio of D(pt) distributions for R=0.3 jets for central to peripheral events.

Ratio of D(pt) distributions for R=0.2 jets for central to peripheral events.

Ratio of D(pt) distributions for R=0.2 jets for central to peripheral events.

Ratio of D(pt) distributions for R=0.2 jets for central to peripheral events.

Ratio of D(pt) distributions for R=0.2 jets for central to peripheral events.

Ratio of D(pt) distributions for R=0.2 jets for central to peripheral events.

Ratio of D(pt) distributions for R=0.2 jets for central to peripheral events.

The
ATLAS
collaboration
Aad, Georges
;
Abbott, Brad
;
Abdallah, Jalal
;
*et al. *

Phys.Rev.C 90 (2014) 024905, 2014.

https://inspirehep.net/literature/1283339
Inspire Record
1283339
DOI
10.17182/hepdata.66137
https://doi.org/10.17182/hepdata.66137
A measurement of event-plane correlations involving two or three event planes of different order is presented as a function of centrality for 7 ub-1 Pb+Pb collision data at sqrt(s_NN)=2.76 TeV, recorded by the ATLAS experiment at the LHC. Fourteen correlators are measured using a standard event-plane method and a scalar-product method, and the latter method is found to give a systematically larger correlation signal. Several different trends in the centrality dependence of these correlators are observed. These trends are not reproduced by predictions based on the Glauber model, which includes only the correlations from the collision geometry in the initial state. Calculations that include the final-state collective dynamics are able to describe qualitatively, and in some cases also quantitatively, the centrality dependence of the measured correlators. These observations suggest that both the fluctuations in the initial geometry and non-linear mixing between different harmonics in the final state are important for creating these correlations in momentum space.

28
data tables
match query

Two-plane EP correlation data from SP method and EP method.

Two-plane EP correlation from Glauber model from SP method and EP method.

Two-plane EP correlation data from SP method and EP method.

Two-plane EP correlation from Glauber model from SP method and EP method.

Two-plane EP correlation data from SP method and EP method.

Two-plane EP correlation from Glauber model from SP method and EP method.

Two-plane EP correlation data from SP method and EP method.

Two-plane EP correlation from Glauber model from SP method and EP method.

Two-plane EP correlation data from SP method and EP method.

Two-plane EP correlation from Glauber model from SP method and EP method.

Two-plane EP correlation data from SP method and EP method.

Two-plane EP correlation from Glauber model from SP method and EP method.

Two-plane EP correlation data from SP method and EP method.

Two-plane EP correlation from Glauber model from SP method and EP method.

Two-plane EP correlation data from SP method and EP method.

Two-plane EP correlation from Glauber model from SP method and EP method.

Three-plane EP correlation data from SP method and EP method.

Three-plane EP correlation from Glauber model from SP method and EP method.

Three-plane EP correlation data from SP method and EP method.

Three-plane EP correlation from Glauber model from SP method and EP method.

Three-plane EP correlation data from SP method and EP method.

Three-plane EP correlation from Glauber model from SP method and EP method.

Three-plane EP correlation data from SP method and EP method.

Three-plane EP correlation from Glauber model from SP method and EP method.

Three-plane EP correlation data from SP method and EP method.

Three-plane EP correlation from Glauber model from SP method and EP method.

Three-plane EP correlation data from SP method and EP method.

Three-plane EP correlation from Glauber model from SP method and EP method.

The
ATLAS
collaboration
Aad, Georges
;
Abajyan, Tatevik
;
Abbott, Brad
;
*et al. *

JHEP 11 (2013) 183, 2013.

https://inspirehep.net/literature/1233359
Inspire Record
1233359
DOI
10.17182/hepdata.62783
https://doi.org/10.17182/hepdata.62783
The distributions of event-by-event harmonic flow coefficients v_n for n=2-4 are measured in sqrt(s_NN)=2.76 TeV Pb+Pb collisions using the ATLAS detector at the LHC. The measurements are performed using charged particles with transverse momentum pT> 0.5 GeV and in the pseudorapidity range |eta|<2.5 in a dataset of approximately 7 ub^-1 recorded in 2010. The shapes of the v_n distributions are described by a two-dimensional Gaussian function for the underlying flow vector in central collisions for v_2 and over most of the measured centrality range for v_3 and v_4. Significant deviations from this function are observed for v_2 in mid-central and peripheral collisions, and a small deviation is observed for v_3 in mid-central collisions. It is shown that the commonly used multi-particle cumulants are insensitive to the deviations for v_2. The v_n distributions are also measured independently for charged particles with 0.5<pT<1 GeV and pT>1 GeV. When these distributions are rescaled to the same mean values, the adjusted shapes are found to be nearly the same for these two pT ranges. The v_n distributions are compared with the eccentricity distributions from two models for the initial collision geometry: a Glauber model and a model that includes corrections to the initial geometry due to gluon saturation effects. Both models fail to describe the experimental data consistently over most of the measured centrality range.

201
data tables
match query

The relationship between centrality intervals and MEAN(Npart) estimated from the Glauber model.

Eccentricity curves for EPSILON2 in Figure 12.

Eccentricity curves for EPSILON3 in Figure 12.

Eccentricity curves for EPSILON4 in Figure 12.