The European Physical Journal C

, 74:3108

Neutral pion production at midrapidity in pp and Pb–Pb collisions at \(\sqrt{s_{{\mathrm {NN}}}}= 2.76\,{\mathrm {TeV}}\)

  • The ALICE Collaboration
  • B. Abelev
  • J. Adam
  • D. Adamová
  • M. M. Aggarwal
  • M. Agnello
  • A. Agostinelli
  • N. Agrawal
  • Z. Ahammed
  • N. Ahmad
  • I. Ahmed
  • S. U. Ahn
  • S. A. Ahn
  • I. Aimo
  • S. Aiola
  • M. Ajaz
  • A. Akindinov
  • S. N. Alam
  • D. Aleksandrov
  • B. Alessandro
  • D. Alexandre
  • A. Alici
  • A. Alkin
  • J. Alme
  • T. Alt
  • S. Altinpinar
  • I. Altsybeev
  • C. Alves Garcia Prado
  • C. Andrei
  • A. Andronic
  • V. Anguelov
  • J. Anielski
  • T. Antičić
  • F. Antinori
  • P. Antonioli
  • L. Aphecetche
  • H. Appelshäuser
  • S. Arcelli
  • N. Armesto
  • R. Arnaldi
  • T. Aronsson
  • I. C. Arsene
  • M. Arslandok
  • A. Augustinus
  • R. Averbeck
  • T. C. Awes
  • M. D. Azmi
  • M. Bach
  • A. Badalà
  • Y. W. Baek
  • S. Bagnasco
  • R. Bailhache
  • R. Bala
  • A. Baldisseri
  • F. Baltasar Dos Santos Pedrosa
  • R. C. Baral
  • R. Barbera
  • F. Barile
  • G. G. Barnaföldi
  • L. S. Barnby
  • V. Barret
  • J. Bartke
  • M. Basile
  • N. Bastid
  • S. Basu
  • B. Bathen
  • G. Batigne
  • B. Batyunya
  • P. C. Batzing
  • C. Baumann
  • I. G. Bearden
  • H. Beck
  • C. Bedda
  • N. K. Behera
  • I. Belikov
  • F. Bellini
  • R. Bellwied
  • E. Belmont-Moreno
  • R. BelmontIII
  • V. Belyaev
  • G. Bencedi
  • S. Beole
  • I. Berceanu
  • A. Bercuci
  • Y. Berdnikov
  • D. Berenyi
  • M. E. Berger
  • R. A. Bertens
  • D. Berzano
  • L. Betev
  • A. Bhasin
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  • A. K. Bhati
  • B. Bhattacharjee
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  • N. Bianchi
  • C. Bianchin
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Open Access
Regular Article - Experimental Physics

DOI: 10.1140/epjc/s10052-014-3108-8

Cite this article as:
The ALICE Collaboration, Abelev, B., Adam, J. et al. Eur. Phys. J. C (2014) 74: 3108. doi:10.1140/epjc/s10052-014-3108-8

Abstract

Invariant yields of neutral pions at midrapidity in the transverse momentum range \(0.6 < {p_\mathrm{T}} < 12\,{\mathrm {GeV}}/\)c measured in Pb–Pb collisions at \({\sqrt{s_\mathrm{{NN}}}} =2.76\) TeV are presented for six centrality classes. The pp reference spectrum was measured in the range \(0.4 < {p_\mathrm{T}} < 10\,{\mathrm {GeV}}/\)c at the same center-of-mass energy. The nuclear modification factor, \(R_\mathrm{{AA}}\), shows a suppression of neutral pions in central Pb–Pb collisions by a factor of up to about \(8{-}10\) for \(5 \lesssim {p_\mathrm{T}} \lesssim 7\,{\mathrm {GeV}}/\)c. The presented measurements are compared with results at lower center-of-mass energies and with theoretical calculations.

1 Introduction

Quantum chromodynamics (QCD) predicts a transition from hadronic matter to a state of deconfined quarks and gluons, i.e., to the quark-gluon plasma (QGP), at a temperature of \(T_c \approx 150{-}160\,{\mathrm {MeV}}\) at vanishing net baryon number [1, 2]. Energy densities created in Pb–Pb collisions at the LHC are estimated to be sufficiently large to reach this state [3, 4]. At low transverse momenta (roughly \(p_\mathrm{T}\lesssim 3\,{\mathrm {GeV}}/\)c) it is expected that pressure gradients in the QGP produced in an ultrarelativistic collision of two nuclei give rise to a collective, outward-directed velocity profile, resulting in a characteristic modification of hadron spectra [5]. At sufficiently large \(p_\mathrm{T}\) (\(\gtrsim 3{-}8\,{\mathrm {GeV}}\)/c), hadrons in pp and Pb–Pb collisions originate from hard scattering as products of jet fragmentation. Hard-scattered quarks and gluons, produced in the initial stage of the heavy-ion collision, must traverse the QGP that is produced around them and lose energy in the process through interactions with that medium. This phenomenon (“jet quenching”) leads to a modification of hadron yields at high \(p_\mathrm{T}\) [6, 7]. By studying observables related to jet quenching one would like to better understand the mechanism of parton energy loss and to use hard probes as a tool to characterize the QGP.

The modification of the hadron yields for different \(p_\mathrm{T}\) intervals in heavy-ion (A–A) collisions with respect to pp collisions can be quantified with the nuclear modification factor
$$\begin{aligned} R_\mathrm{{AA}}(p_\mathrm{T})=\frac{{\mathrm {d}}^2N/{\mathrm {d}}p_\mathrm{T}{\mathrm {d}}y|_{{\mathrm {AA}}}}{\langle T_{\mathrm{AA}}\rangle \times {\mathrm {d}}^2\sigma /{\mathrm {d}}p_\mathrm{T}{\mathrm {d}}y|_{{\mathrm {pp}}}} \end{aligned}$$
(1)
where the nuclear overlap function \(\langle T_{\mathrm{AA}}\rangle \) is related to the average number of inelastic nucleon-nucleon collisions as \(\langle T_{\mathrm{AA}}\rangle = \langle N_{{\mathrm {coll}}} \rangle / \sigma _{{\mathrm {inel}}}^{\mathrm {pp}}\). In the factorization approach of a perturbative QCD calculation of particle production from hard scattering, the overlap function \(T_{\mathrm{AA}}\) can be interpreted as the increase of the parton flux in going from pp to A–A collisions. Without nuclear effects, \(R_\mathrm{{AA}}\) will be unity in the hard scattering regime.

Parton energy loss depends on a number of factors including the transport properties of the medium and its space-time evolution, the initial parton energy, and the parton type [8, 9, 10, 11, 12]. The nuclear modification factor, \(R_\mathrm{{AA}}\), is also affected by the slope of the initial parton transverse momentum spectrum prior to any interaction with the medium and by initial-state effects like the modifications of the parton distributions in nuclei. An important constraint for modeling these effects comes from the study of p–A collisions [13], but also from the study of A–A collisions at different center-of-mass energies (\(\sqrt{s_{{\mathrm {NN}}}}\)) and different centralities. For instance, the increase in \(\sqrt{s_{{\mathrm {NN}}}}\) from RHIC to LHC energies by about a factor 14 results in larger initial energy densities and less steeply falling initial parton spectra [14]. Moreover, at the LHC, pions with \(p_\mathrm{T}\lesssim 50\,{\mathrm {GeV}}/\)c are dominantly produced in the fragmentation of gluons [15], whereas the contribution from quark fragmentation in the same \(p_\mathrm{T}\) region is much larger and more strongly varying with \(p_\mathrm{T}\) at RHIC [16]. Therefore, the pion suppression results at the LHC will be dominated by gluon energy loss, and simpler to interpret than the results from RHIC. Compared to measurements of the \(R_\mathrm{{AA}}\) for inclusive charged hadrons, differences between the baryon and meson \(R_\mathrm{{AA}}\) provide additional information on the parton energy loss mechanism and/or on hadronization in A–A collisions [17, 18]. Experimentally, neutral pions are ideally suited for this as they can be cleanly identified (on a statistical basis) via the decay \(\pi ^0 \rightarrow \gamma \gamma \).

The suppression of neutral pions and charged hadrons at large transverse momentum [19, 20, 21, 22, 23] and the disappearance of azimuthal back-to-back correlations of charged hadrons in central Au–Au collision at RHIC [24, 25] (see also [26, 27, 28, 29]) were interpreted in terms of parton energy loss in hot QCD matter. Neutral pions in central Au–Au collisions at \(\sqrt{s_{{\mathrm {NN}}}}= 200\,{\mathrm {GeV}}\) were found to be suppressed by a factor of \(4{-}5\) for \(p_\mathrm{T}\gtrsim 4\,{\mathrm {GeV}}/\)c [30, 31]. The rather weak dependence of \(R_\mathrm{{AA}}\) on \(p_\mathrm{T}\) was described by a large number of jet quenching models [32]. The \(\sqrt{s_{{\mathrm {NN}}}}\) and system size dependence was studied in Cu-Cu collisions at \(\sqrt{s_{{\mathrm {NN}}}}= 19.4\), 62.4, and \(200\,{\mathrm {GeV}}\) [33] and in Au–Au collisions at \(\sqrt{s_{{\mathrm {NN}}}}= 39\), 62.4, and \(200\,{\mathrm {GeV}}\) [22, 34]. In central Cu-Cu collisions the onset of \(R_\mathrm{{AA}}< 1\) was found to occur between \(\sqrt{s_{{\mathrm {NN}}}}= 19.4\) and \(62.4\,{\mathrm {GeV}}\). For unidentified charged hadrons in central Pb–Pb collisions at the LHC, \(R_\mathrm{{AA}}\) was found to increase from \(R_\mathrm{{AA}}< 0.2\) at \(p_\mathrm{T}\approx 7\,{\mathrm {GeV}}/\)c to \(R_\mathrm{{AA}}\approx 0.5\) for \(p_\mathrm{T}\gtrsim 50\,{\mathrm {GeV}}/\)c, in line with a decrease of the relative energy loss with increasing parton \(p_\mathrm{T}\) [35, 36, 37].

The dependence of the neutral pion \(R_\mathrm{{AA}}\) on \(\sqrt{s_{{\mathrm {NN}}}}\) and \(p_\mathrm{T}\) in Au–Au collisions at RHIC energies for \(2 \lesssim p_\mathrm{T}\lesssim 7\,{\mathrm {GeV}}/\)c is not fully reproduced by jet quenching calculations in the GLV framework which is based on perturbative QCD [34, 38, 39]. This may indicate that, especially for this intermediate \(p_\mathrm{T}\) range, jet quenching calculations do not yet fully capture the relevant physics processes. With the large increase in \(\sqrt{s_{{\mathrm {NN}}}}\) the measurement of \(R_\mathrm{{AA}}\) at the LHC provides a large lever arm to further constrain parton energy loss models. Phenomena affecting pion production in the \(p_\mathrm{T}\) range \(0.6<p_\mathrm{T}< 12\,{\mathrm {GeV}}/\)c of this measurement include collective radial flow at low \(p_\mathrm{T}\) and parton energy loss at high \(p_\mathrm{T}\). The data are therefore well suited to test models aiming at a description of particle production over the full transverse momentum range, including the potentially complicated interplay between jets and the evolving medium.

2 Detector description

Neutral pions were reconstructed via the two-photon decay channel \(\pi ^0 \rightarrow \gamma \gamma \) which has a branching ratio of 98.8 % [40]. Two independent methods of photon detection were employed: with the photon spectrometer (PHOS) which is an electromagnetic calorimeter [41], and with photon conversions measured in the central tracking system using the inner tracking system (ITS) [42] and the time projection chamber (TPC) [43]. In the latter method, referred to as photon conversion method (PCM), conversions out to the middle of the TPC were reconstructed (radial distance \(R \approx 180\,{\mathrm {cm}}\)). The material in this range amounts to \((11.4 \pm 0.5)\) % of a radiation length \(X_0\) for \(|\eta | < 0.9\) corresponding to a plateau value of the photon conversion probability of \((8.6 \pm 0.4)\,\%\). The measurement of neutral pions with two independent methods with different systematics and with momentum resolutions having opposite dependence on momentum provides a valuable check of the systematic uncertainties and facilitates the measurements of neutral pions in a wide momentum range with small systematic uncertainty.

PHOS consists of three modules installed at a distance of \(4.6\,{\mathrm {m}}\) from the interaction point. PHOS subtends \(260^\circ <\varphi <320^\circ \) in azimuth and \(|\eta |<0.13\) in pseudorapidity. Each module has 3584 detection channels in a matrix of \(64\times 56\) cells made of lead tungstate (PbWO\(_4\)) crystals each of size \(2.2\times 2.2 \times 18\) cm\(^3\). The transverse dimensions of the cells are slightly larger than the PbWO\(_4\) Molière radius of \(2\,{\mathrm {cm}}\). The signals from the cells are measured by avalanche photodiodes with a low-noise charge-sensitive preamplifier. In order to increase the light yield and thus to improve energy resolution, PHOS crystals are cooled down to a temperature of \(-25~^\circ \)C. The PHOS cells were calibrated in pp collisions by equalizing the \(\pi ^0\) peak position for all cell combinations registering a hit by a decay photon.

The inner tracking system (ITS) [44] consists of two layers of silicon pixel detectors (SPD) positioned at a radial distance of \(3.9\,{\mathrm {a}}\)nd \(7.6\,{\mathrm {cm}}\), two layers of silicon drift detectors (SDD) at \(15.0\,{\mathrm {a}}\)nd \(23.9\,{\mathrm {cm}}\), and two layers of silicon strip detectors (SSD) at \(38.0\,{\mathrm {a}}\)nd \(43.0\,{\mathrm {cm}}\). The two SPD layers cover a pseudorapidity range of \(|\eta |<2\) and \(|\eta | < 1.4\), respectively. The SDD and the SSD subtend \(|\eta |<0.9\) and \(|\eta |<1.0\), respectively.

The time projection chamber (TPC) [43] is a large (85 m\(^3\)) cylindrical drift detector filled with a Ne/CO\(_2\)/N\(_2\) (85.7/9.5/4.8 %) gas mixture. It covers a pseudorapidity range of \(|\eta |<0.9\) over the full azimuthal angle for the maximum track length of 159 reconstructed space points. With the magnetic field of \(B=0.5\,{\mathrm {T}}\), electron and positron tracks were reconstructed down to transverse momenta of about \(50\,{\mathrm {MeV}}/\)c. In addition, the TPC provides particle identification via the measurement of the specific energy loss (d\(E\)/d\(x\)) with a resolution of 5.5 % [43]. The ITS and the TPC were aligned with respect to each other to a precision better than 100 \(\upmu \)m using tracks from cosmic rays and proton–proton collisions [42].

Two forward scintillator hodoscopes (VZERO-A and VZERO-C) [45] subtending \(2.8 < \eta < 5.1\) and \(-3.7 < \eta < -1.7\), respectively, were used in the minimum bias trigger in the pp and in the Pb–Pb run. The sum of the amplitudes of VZERO-A and VZERO-C served as a measure of centrality in Pb–Pb collisions [46]. Spectator (non-interacting) protons and neutrons were measured with zero degree calorimeters (ZDCs), located close to the beam pipe, \(114\,{\mathrm {m}}\) away from the interaction point on either side of the ALICE detector [44].

3 Data processing

3.1 Event selection

The pp sample at \(\sqrt{s}=2.76\) TeV was collected in the 2011 LHC run. The minimum bias trigger (MB\(_{{\mathrm {OR}}}\)) in the pp run required a hit in either VZERO hodoscope or a hit in the SPD. Based on a van der Meer scan the cross section for inelastic pp collisions was determined to be \(\sigma _{{\mathrm {inel}}} = (62.8^{+2.4}_{-4.0} \pm 1.2)\,{\mathrm {mb}}\) and the MB\(_{{\mathrm {OR}}}\) trigger had an efficiency of \(\sigma _{{\mathrm {MB_{OR}}}}/\sigma _{{\mathrm {inel}}} = 0.881^{+0.059}_{-0.035}\) [47]. The results were obtained from samples of \(34.7 \times 10^6\) (PHOS) and \(58 \times 10^6\) (PCM) minimum bias pp collisions corresponding to an integrated luminosity \(\mathcal{L}_\mathrm{int} = 0.63~\text{ nb }^{-1}\) and \(\mathcal{L}_\mathrm{int} = 1.05~\text{ nb }^{-1}\), respectively. PHOS and the central tracking detectors used in the PCM were in different readout partitions of the ALICE experiment which resulted in the different integrated luminosities.

The Pb–Pb data at \(\sqrt{s_{{\mathrm {NN}}}}= 2.76\,{\mathrm {TeV}}\) were recorded in the 2010 LHC run. At the ALICE interaction region up to 114 bunches, each containing about \(7 \times 10^7\)\(^{208}\)Pb ions, were collided. The rate of hadronic interactions was about 100 Hz, corresponding to a luminosity of about \(1.3 \times 10^{25}\,{\mathrm {cm^{-2} s^{-1}}}\). The detector readout was triggered by the LHC bunch-crossing signal and a minimum bias interaction trigger based on trigger signals from VZERO-A, VZERO-C, and SPD [46]. The efficiency for triggering on a hadronic Pb–Pb collision ranged between 98.4 and 99.7 %, depending on the minimum bias trigger configuration. For the centrality range 0-80 % studied in the Pb–Pb analyses \(16.1 \times 10^6\) events in the PHOS analysis and \(13.2 \times 10^6\) events in the PCM analysis passed the offline event selection.

In both pp and Pb–Pb analyses, the event selection was based on VZERO timing information and on the correlation between TPC tracks and hits in the SPD to reject background events coming from parasitic beam interactions. In addition, an energy deposit in the ZDCs of at least three standard deviations above the single-neutron peak was required for Pb–Pb collisions to further suppress electromagnetic interactions [46]. Only events with a reconstructed vertex in \(| z_{{\mathrm {vtx}}} | < 10\,{\mathrm {cm}}\) with respect to the nominal interaction vertex position along the beam direction were used.

3.2 Neutral pion reconstruction

The PHOS and PCM analyses presented here are based on methods previously used in pp collisions at \(\sqrt{s} = 0.9\) and \(7\,{\mathrm {TeV}}\) [48]. Neutral pions were reconstructed using the \(\pi ^0\rightarrow \gamma \gamma \) decay channel either with both photon candidates detected in PHOS or both photons converted into \(e^+e^-\) pairs and reconstructed in the central tracking system. For the photon measurement with PHOS adjacent lead tungstate cells with energy signals above a threshold (\(12\,{\mathrm {MeV}}\)) were grouped into clusters [49]. The energies of the cells in a cluster were summed up to determine the photon energy. The selection of the photon candidates in PHOS was different for pp and Pb–Pb collisions due to the large difference in detector occupancy. For pp collisions cluster overlap is negligible and combinatorial background small. Therefore, only relatively loose photon identification cuts on the cluster parameters were used in order to maximize the \(\pi ^0\) reconstruction efficiency: the cluster energy for pp collisions was required to be above the minimum ionizing energy \(E_{{\mathrm {cluster}}}>0.3\) GeV and the number of cells in a cluster was required to be greater than two to reduce the contribution of hadronic clusters. In the case of the most central Pb–Pb collisions about 80 clusters are reconstructed in PHOS, resulting in an occupancy of up to 1/5 of the 10,752 PHOS cells. This leads to a sizable probability of cluster overlap and to a high combinatorial background in the two-cluster invariant mass spectra. A local cluster maximum was defined as a cell with a signal at least \(30\,{\mathrm {MeV}}\) higher than the signal in each surrounding cell. A cluster with more than one local maximum was unfolded to several contributing clusters [49]. As the lateral width of showers resulting from hadrons is typically larger than the one of photon showers, non-photonic background was reduced by a \(p_\mathrm{T}\) dependent shower shape cut. This cut is based on the eigenvalues \(\lambda _0\), \(\lambda _1\) of the covariance matrix built from the cell coordinates and weights \(w_i=\max [0,w_0+\log (E_i/E_{{\mathrm {cluster}}})]\), \(w_0=4.5\) where \(E_i\) is the energy measured in cell \(i\). In the Pb–Pb case only cells with a distance to the cluster center of \(R_{{\mathrm {disp}}}=4.5\,{\mathrm {cm}}\) were used in the dispersion calculation. A 2D \(p_\mathrm{T}\)-dependent cut in the \(\lambda _0\)-\(\lambda _1\) plane was tuned to have an efficiency of \({\sim }0.95\) using pp data. In addition, clusters associated with a charged particle were rejected by application of a cut on the minimum distance from a PHOS cluster to the extrapolation of reconstructed tracks to the PHOS surface [49]. This distance cut depended on track momentum and was tuned by using real data to minimize false rejection of photon clusters. The corresponding loss of the \(\pi ^0\) yield was about 1 % in pp collisions (independent of \(p_\mathrm{T}\)). In Pb–Pb collisions the \(\pi ^0\) inefficiency due to the charged particle rejection is about 1 % in peripheral and increases to about 7 % in central Pb–Pb collisions. In addition, to reduce the effect of cluster overlap, the cluster energy was taken as the core energy of the cluster, summing over cells with centers within a radius \(R_{{\mathrm {core}}}=3.5\,{\mathrm {cm}}\) of the cluster center of gravity, rather than summing over all cells of the cluster. By using the core energy, the centrality dependence of the width and position of the \(\pi ^0\) peak is reduced, due to a reduction of overlap effects. The use of the core energy leads to an additional non-linearity due to energy leakage outside \(R_{{\mathrm {core}}}\): the difference between full and core energy is negligible at \(E_{{\mathrm {cluster}}} \lesssim 1\,{\mathrm {GeV}}\) and reaches \({\sim } 4\) % at \(E_{{\mathrm {cluster}}} \sim 10\,{\mathrm {GeV}}\). This non-linearity, however, is well reproduced in the GEANT3 Monte Carlo simulations [50] of the PHOS detector response (compare \(p_\mathrm{T}\) dependences of peak positions in data and Monte Carlo in Fig. 2) and is corrected for in the final spectra.

PHOS is sensitive to pile-up from multiple events that occur within the \(6\,{\mathrm {\upmu s}}\) readout interval of the PHOS front-end electronics. The shortest time interval between two bunch crossings in pp collisions was \(525\,{\mathrm {ns}}\). To suppress photons produced in other bunch crossings, a cut on arrival time \(|t|<265\,{\mathrm {ns}}\) was applied to reconstructed clusters which removed 16 % of the clusters. In the Pb–Pb collisions, the shortest time interval between bunch crossing was \(500\,{\mathrm {ns}}\), but the interaction probability per bunch crossing was much smaller than in pp collisions. To check for a contribution from other bunch crossings to the measured spectra, a timing cut was applied, and the pile-up contribution was found to be negligible in all centrality classes. Therefore, a timing cut was not applied in the final PHOS Pb–Pb analysis.
Fig. 1

(Color online) Invariant mass spectra in selected \(p_\mathrm{T}\) slices for PCM (upper row) and PHOS (lower row) in the \(\pi ^0\) mass region for pp (left column), \(60{-}80\,\%\) (middle column) and \(0{-}10\,\%\) (right column) Pb–Pb collisions. The histogram and the filled points show the data before and after background subtraction, respectively. For the \(0{-}10\,\%\) class the invariant mass distributions after background subtraction were scaled by a factor 15 and 5 for PCM and PHOS, respectively, for better visibility of the peak. The positions and widths of the \(\pi ^0\) peaks were determined from the fits, shown as blue curves, to the invariant mass spectra after background subtraction

The starting point of the conversion analysis is a sample of photon candidates corresponding to track pairs reconstructed by a secondary vertex (V0) finding algorithm [49, 51]. In this step, no constraints on the reconstructed invariant mass and pointing of the momentum vector to the collision vertex were applied. Both tracks of a V0 were required to contain reconstructed clusters (i.e., space points) in the TPC. V0’s were accepted as photon candidates if the ratio of the number of reconstructed TPC clusters over the number of findable clusters (taking into account track length, spatial location, and momentum) was larger than 0.6 for both tracks. In order to reject \(K_s^0\), \(\Lambda \), and \(\bar{\Lambda }\) decays, electron selection and pion rejection cuts were applied. V0’s used as photon candidates were required to have tracks with a specific energy loss in the TPC within a band of [\(-3 \sigma \), \(5 \sigma \)] around the average electron d\(E\)/d\(x\), and of more than \(3\sigma \) above the average pion d\(E\)/d\(x\) (where the second condition was only applied for tracks with measured momenta \(p > 0.4\,{\mathrm {GeV}}/\)c). Moreover, tracks with an associated signal in the TOF detector were only accepted as photon candidates if they were consistent with the electron hypothesis within a \(\pm 5 \sigma \) band. A generic particle decay model based on the Kalman filter method [52] was fitted to a reconstructed V0 assuming that the particle originated from the primary vertex and had a mass \(M_{V0}=0\). Remaining contamination in the photon sample was reduced by cutting on the \(\chi ^2\) of this fit. Furthermore, the transverse momentum \(q_T = p_e \sin \theta _{V0,e}\) [53] of the electron, \(p_e\), with respect to the V0 momentum was restricted to \(q_T < 0.05\,{\mathrm {GeV}}/\)c. As the photon is massless, the difference \(\Delta \theta = |\theta _{e^-} - \theta _{e^+}|\) of the polar angles of the electron and the positron from a photon conversion is small and the bending of the tracks in the magnetic field only results in a difference \(\Delta \varphi = |\varphi _{e^-} - \varphi _{e^+}|\) of the azimuthal angles of the two momentum vectors. Therefore, remaining random track combinations, reconstructed as a V0, were suppressed further by a cut on the ratio of \(\Delta \theta \) to the total opening angle of the \(e^+e^-\) pair calculated after propagating both the electron and the positron \(50\,{\mathrm {cm}}\) from the conversion point in the radial direction. In order to reject \(e^+e^-\) pairs from Dalitz decays the distance between the nominal interaction point and the reconstructed conversion point of a photon candidate had to be larger than \(5\,{\mathrm {cm}}\) in radial direction. The maximum allowed radial distance for reconstructed V0’s was \(180\,{\mathrm {cm}}\).

Pile-up of neutral pions coming from bunch crossings other than the triggered one also has an effect on the PCM measurement. At the level of reconstructed photons, this background is largest for photons for which both the electron and the positron were reconstructed with the TPC alone without tracking information from the ITS. These photons, which typically converted at large radii \(R\), constitute a significant fraction of the total PCM photon sample, which is about \(67\,\%\) in case of the pp analysis. This sample is affected because the TPC drift velocity of \(2.7\,{\mathrm {cm/\upmu s}}\) corresponds to a drift distance of \(1.41\,{\mathrm {cm}}\) between two bunch crossings in the pp run which is a relatively short distance compared to the width of \(\sigma _z \approx 5\,{\mathrm {cm}}\) of the distribution of the primary vertex in the \(z\) direction. The distribution of the distance of closest approach in the \(z\) direction (\({\mathrm {DCA}}_z\)) of the straight line defined by the reconstructed photon momentum is wider for photons from bunch crossings other than the triggered one. The \({\mathrm {DCA}}_z\) distribution of photons which had an invariant mass in the \(\pi ^0\) mass range along with a second photon was measured for each \(p_\mathrm{T}\) interval. Entries in the tails at large \({\mathrm {DCA}}_z\) were used to determine the background distribution and to correct the neutral pion yields for inter bunch pile-up. For the pp analysis, this was a \(5{-}7\,\%\) correction for \(p_\mathrm{T}\gtrsim 2\,{\mathrm {GeV}}/\)c and a correction of up to \(15\,\%\) at lower \(p_\mathrm{T}\) (\(p_\mathrm{T}\approx 1\,{\mathrm {GeV}}/\)c). In the Pb–Pb case the correction at low \(p_\mathrm{T}\) was about 10 %, and became smaller for higher \(p_\mathrm{T}\) and for more central collisions. For the \(20{-}40\,\%\) centrality class and more central classes the pile-up contribution was negligible and no pile-up correction was applied. In the PCM as well as in the PHOS analysis, events for which two or more pp or Pb–Pb interactions occurred in the same bunch crossing were rejected based on the number of primary vertices reconstructed with the SPD [49] which has an integration time of less than \(200\,{\mathrm {ns}}\).

In the PHOS as well as in the PCM analysis, the neutral pion yield was extracted from a peak above a combinatorial background in the two-photon invariant mass spectrum. Examples of invariant mass spectra, in the \(\pi ^0\) mass region, are shown in Fig. 1 for selected \(p_\mathrm{T}\) bins for pp collisions, and peripheral and central Pb–Pb collisions. The combinatorial background was determined by mixing photon candidates from different events. In the PCM measurement the combinatorial background was reduced by cutting on the energy asymmetry \(\alpha = |E_{\gamma _1}-E_{\gamma _2}|/(E_{\gamma _1}+E_{\gamma _2})\), where \(\alpha < 0.65\) was required for the central classes (\(0{-}5\), \(5{-}10\), \(10{-}20\), \(20{-}40~\%\)) and \(\alpha < 0.8\) for the two peripheral classes (\(40{-}60\), \(60{-}80\,\%\)). In both analyses the mixed-event background distributions were normalized to the right and left sides of the \(\pi ^0\) peak. A residual correlated background was taken into account using a linear or second order polynomial fit. The \(\pi ^0\) peak parameters were obtained by fitting a function, Gaussian or a Crystal Ball function [54] in the PHOS case or a Gaussian combined with an exponential low mass tail to account for bremsstrahlung [55] in the PCM case, to the background-subtracted invariant mass distribution, see Fig. 1. The Crystal Ball function was used in the PHOS analysis of pp data. A Gaussian was used alternatively to determine systematic uncertainties of the peak parameters. In the Pb–Pb case with worse resolution and smaller signal/background ratios, the difference between Crystal Ball and Gaussian fits disappeared and only the latter were used in the PHOS analysis. In the case of PHOS the number of reconstructed \(\pi ^0\)’s was obtained in each \(p_\mathrm{T}\) bin by integrating the background subtracted peak within 3 standard deviations around the mean value of the \(\pi ^0\) peak position. In the PCM analysis, the integration window was chosen to be asymmetric (\(m_{\pi ^0}-0.035\) GeV/\(\mathrm{c}^2\), \(m_{\pi ^0}+0.010\) GeV/\(\mathrm{c}^2\)) to take into account the left side tail of the \(\pi ^0\) peak due to bremsstrahlung energy loss of electrons and positrons from photon conversions. In both analyses the normalization and integration windows were varied to estimate the related systematic uncertainties. The peak positions and widths from the two analyses are compared to GEANT3 Monte Carlo simulations in Fig. 2 as a function of \(p_\mathrm{T}\). The input for the GEANT3 simulation came from the event generators PYTHIA 8 [56] and PHOJET [57] in the case of pp collisions (with roughly equal number of events) and from HIJING [58] in the case of Pb–Pb collisions. For the PCM analysis the full width at half maximum (FWHM) divided by \(2\sqrt{2\ln 2} \approx 2.35\) is shown. Note the decrease of the measured peak position with \(p_\mathrm{T}\) in Pb–Pb collisions for PHOS. This is due to the use of the core energy instead of the full cluster energy. At low \(p_\mathrm{T}\) in central Pb–Pb collisions, shower overlaps can increase the cluster energy thereby resulting in peak positions above the nominal \(\pi ^0\) mass. A good agreement in peak position and width between data and simulation is observed in both analyses. The remaining small deviations in the case of PHOS were taken into account as a systematic uncertainty related to the global energy scale.
Fig. 2

(Color online) Reconstructed \(\pi ^0\) peak width (upper row) and position (lower row) as a function of \(p_\mathrm{T}\) in pp collisions at \(\sqrt{s}=2.76\,{\mathrm {TeV}}\) (a, d), peripheral (b, e) and central (c, f) Pb–Pb collisions at \(\sqrt{s_{{\mathrm {NN}}}}= 2.76\,{\mathrm {TeV}}\) in PHOS and in the photon conversion method (PCM) compared to Monte Carlo (MC) simulations. The horizontal line in (df) indicates the nominal \(\pi ^0\) mass

The correction factor \(\varepsilon (p_\mathrm{T})\) for the PHOS detector response and the acceptance \(A(p_\mathrm{T})\) were calculated with GEANT3 Monte Carlo simulations tuned to reproduce the detector response. The factor \(\varepsilon (p_\mathrm{T})\) takes the loss of neutral pions due to analysis cuts, effects of the finite energy resolution and, in case of Pb–Pb collisions, effects of shower overlaps into account. The shape of the \(\pi ^0\) input spectrum needed for the calculation of \(\varepsilon (p_\mathrm{T})\) was determined iteratively by using a fit of the corrected spectrum of a given pass as input to the next. In the case of Pb–Pb collisions the embedding technique was used in the PHOS analysis: the PHOS response to single \(\pi ^0\)’s was simulated, the simulated \(\pi ^0\) event was added to a real Pb–Pb event on the cell signal level, after which the standard reconstruction procedure was performed. The correction factor \(\varepsilon (p_\mathrm{T}) = (N_{{\mathrm {rec}}}^{\mathrm {after}}(p_\mathrm{T})-N_{{\mathrm {rec}}}^{\mathrm {before}}(p_\mathrm{T}))/N_{{\mathrm {sim}}}(p_\mathrm{T})\) was defined as the ratio of the difference of the number of reconstructed \(\pi ^0\)’s after and before the embedding to the number of simulated \(\pi ^0\)’s. In the pp case, the PHOS occupancy was so low that embedding was not needed and \(\varepsilon (p_\mathrm{T})\) was obtained from the \(\pi ^0\) simulations alone. Both in the Pb–Pb and the pp analysis, an additional 2 % channel-by-channel decalibration was introduced to the Monte Carlo simulations, as well as an energy non-linearity observed in real data at low energies which is not reproduced by the GEANT simulations. This non-linearity is equal to \(2.2\,\%\) at \(p_\mathrm{T}=1\) GeV/\(c\) and decreases rapidly with \(p_\mathrm{T}\) (less than \(0.5\,\%\) at \(p_\mathrm{T}>3\) GeV/\(c\)). For PHOS, the \(\pi ^0\) acceptance \(A\) is zero for \(p_\mathrm{T}<0.4\) GeV/\(c\). The product \(\varepsilon \cdot A\) increases with \(p_\mathrm{T}\) and saturates at about \(1.4\times 10^{-2}\) for a neutral pion with \(p_\mathrm{T}>15\) GeV/\(c\). At high transverse momenta (\(p_\mathrm{T}>25\) GeV/\(c\)) \(\varepsilon \) decreases because of merging of clusters of \(\pi ^0\) decay photons due to the decreasing average opening angle of the \(\pi ^0\) decay photons. The correction factor \(\varepsilon \) does not show a centrality dependence for events in the \(20{-}80\) % class, but in the most central bin it increases by \(\sim \!10\) % due to an increase in cluster energies caused by cluster overlap.
Table 1

Summary of the relative systematic uncertainties in percent for selected \(p_\mathrm{T}\) bins for the PHOS and the PCM analyses

 

PHOS

 

pp

Pb–Pb, \(60{-}80\,\%\)

Pb–Pb, \(0{-}5\,\%\)

 

1.1 GeV/c

7.5 GeV/c

3 GeV/c

10 GeV/c

3 GeV/c

10 GeV/c

Yield extraction

\( 8\)

\( 2.3\)

0.8

6.8

\( 3.7\)

\( 5.7\)

Photon identification

1.7

1.7

\( 4.4\)

\( 4.4\)

Global \(E\) scale

\( 4\)

\( 6.2 \)

4.1

5.3

\( 6.1\)

\( 7.8\)

Non-linearity

\( 9\)

\( 1.5 \)

1.5

1.5

\( 1.5\)

\( 1.5\)

Conversion

\( 3.5\)

\( 3.5 \)

3.5

3.5

\( 3.5\)

\( 3.5\)

Module alignment

\( 4.1\)

\( 4.1 \)

4.1

4.1

\( 4.1\)

\( 4.1\)

Other

\( 2\)

\( 1.4 \)

2.4

2.4

\( 3.1\)

\( 3.4\)

Total

\( 13.9\)

\( 8.8\)

7.6

10.7

\(10.7\)

\(12.7\)

 

PCM

 

pp

Pb–Pb, \(60{-}80\,\%\)

Pb–Pb, \(0{-}5\,\%\)

 

1.1 GeV/c

5.0 GeV/c

1.1 GeV/c

5.0 GeV/c

1.1 GeV/c

5.0 GeV/c

Material budget

\(9.0\)

\( 9.0\)

9.0

9.0

\( 9.0\)

\( 9.0\)

Yield extraction

\( 0.6\)

\( 2.6\)

3.3

5.9

\( 10.6\)

\( 5.0 \)

\(e^{+}/e^{-}\) identification

\( 0.7\)

\( 1.4\)

2.9

5.3

\( 9.0\)

\( 10.5\)

Photon identification (\(\chi ^2 (\gamma )\))

\( 2.4\)

\( 0.9\)

3.7

4.6

\( 4.0\)

\( 6.7\)

\(\pi ^0\) reconstruction efficiency

\( 0.5\)

\( 3.6\)

3.5

4.1

\( 6.7\)

\( 8.4\)

Pile-up correction

\( 1.8\)

\( 1.8\)

2.0

2.0

Total

\( 9.5\)

\( 10.3\)

11.4

13.6

\( 18.3\)

\( 18.2\)

In the PCM, the photon conversion probability of about 8.6 % is compensated by the large TPC acceptance. Neutral pions were reconstructed in the rapidity interval \(|y|<0.6\) and the decay photons were required to satisfy \(|\eta | < 0.65\). The \(\pi ^0\) efficiency increases with \(p_\mathrm{T}\) below \(p_\mathrm{T}\approx 4\,{\mathrm {GeV}}/\)c and remains approximately constant for higher \(p_\mathrm{T}\) at values between \(1.0 \times 10^{-3}\) in central collisions (\(0{-}5\,\%\), energy asymmetry cut \(\alpha < 0.65\)) and \(1.5 \times 10^{-3}\) in peripheral collisions (\(60{-}80\,\%\), \(\alpha < 0.8\)). For the centrality classes \(0{-}5\), \(5{-}10\), \(10{-}20\), \(20{-}40\,\%\), for which \(\alpha < 0.65\) was used, the \(\pi ^0\) efficiency varies between \(1.0 \times 10^{-3}\) and \(1.2 \times 10^{-3}\). This small centrality dependence is dominated by the centrality dependence of the V0 finding efficiency. Further information on the PHOS and PCM efficiency corrections can be found in [49].

The invariant differential neutral pion yield was calculated as
$$\begin{aligned} E \frac{\mathrm{d}^3 N }{\mathrm{d}^3p} = \frac{1}{2\pi } \frac{1}{N_\mathrm{events}} \frac{1}{p_\mathrm{T}} \frac{1}{\varepsilon \,A}\frac{1}{ Br }\frac{N^{\pi ^0}}{\Delta y \Delta p_\mathrm{T}}, \end{aligned}$$
(2)
where \(N_\mathrm{events}\) is the number of events; \(p_\mathrm{T}\) is the transverse momentum within the bin to which the cross section has been assigned after the correction for the finite bin width \(\Delta p_\mathrm{T}\), \( Br \) is the branching ratio of the decay \(\pi ^0\rightarrow \gamma \gamma \), and \(N^{\pi ^0}\) is the number of reconstructed \(\pi ^0\)’s in a given \(\Delta y\) and \(\Delta p_\mathrm{T}\) bin. Finally, the invariant yields were corrected for the finite \(p_\mathrm{T}\) bin width following the prescription in [59], i.e., by plotting the measured average yield at a \(p_\mathrm{T}\) position for which the differential invariant yield coincides with the bin average. Secondary \(\pi ^0\)’s from weak decays or hadronic interactions in the detector material were subtracted using Monte Carlo simulations. The contribution of \(\pi ^0\)’s from K\(^0_{{\mathrm {s}}}\) as obtained from the used event generators was scaled in order to reproduce the measured K\(^0_{{\mathrm {s}}}\) yields [60]. The correction for secondary \(\pi ^0\)’s was smaller than \(2\,\%\) (\(5\,\%\)) for \(p_\mathrm{T}\gtrsim 2\,{\mathrm {GeV}}/\)c in the pp as well as in the Pb–Pb analysis for PCM (PHOS).

A summary of the systematic uncertainties for two representative \(p_\mathrm{T}\) values in pp, peripheral and central Pb–Pb collisions is shown in Table 1. In PHOS, one of the largest sources of the systematic uncertainty both at low and high \(p_\mathrm{T}\) is the raw yield extraction. It was estimated by varying the fitting range and the assumption about the shape of the background under the peak. In central collisions, major contributions to the systematic uncertainty are due to the efficiency of photon identification and the global energy scale. The former was evaluated by comparing efficiency-corrected \(\pi ^0\) yields, calculated with different identification criteria. The latter was estimated by varying the global energy scale within the tolerance which would still allow to reproduce the peak position in central and peripheral collisions. The uncertainty related to the non-linearity of the PHOS energy response was estimated by introducing different non-linearities into the Monte Carlo simulations under the condition that the simulated \(p_\mathrm{T}\) dependence of the \(\pi ^0\) peak position and peak width was still consistent with the data. The uncertainty of the PHOS measurement coming from the uncertainty of the fraction of photons lost due to conversion was estimated by comparing measurements without magnetic field to the measurements with magnetic field.

In the PCM measurement, the main sources of systematic uncertainties include the knowledge of the material budget, raw yield extraction, electron identification (PID), the additional photon identification cuts, and \(\pi ^0\) reconstruction efficiency. The uncertainty related to the pile-up correction is only relevant in pp and peripheral Pb–Pb collisions. The contribution from the raw \(\pi ^0\) yield extraction was estimated by changing the normalization range, the integration window, and the combinatorial background evaluation. Uncertainties related to the electron and photon identification cuts, and to the photon reconstruction efficiency were estimated by evaluating the stability of the results for different cuts. The total systematic uncertainties of the PCM and the PHOS results were calculated by adding the individual contributions in quadrature.
Fig. 3

(Color online) Ratio of the fully corrected \(\pi ^0\) spectra in pp collisions at \(\sqrt{s}=2.76\,{\mathrm {TeV}}\) measured with PHOS and PCM methods to the fit of the combined spectrum. Vertical lines represent statistical uncertainties, the boxes systematic uncertainties

Fig. 4

(Color online) Ratio of the fully corrected \(\pi ^0\) spectra in Pb–Pb collisions at \(\sqrt{s_{{\mathrm {NN}}}}=2.76\,{\mathrm {TeV}}\) in six centrality bins measured with PHOS and PCM to the fits to the combined result in each bin. Vertical lines represent statistical uncertainties, the boxes the systematic uncertainties

The comparisons of the fully corrected \(\pi ^0\) spectra measured by PHOS and PCM in pp and Pb–Pb collisions are presented in Figs. 3 and 4, respectively. For a better comparison the PCM and PHOS data points were divided by a function which was fitted to the combined spectrum. In all cases, agreement between the two measurements is found. The PHOS and PCM spectra were combined by calculating the average yields together with their statistical and systematic uncertainties by using the inverse squares of the total uncertainties of the PHOS and PCM measurements for a given \(p_\mathrm{T}\) bin as respective weights [40].

4 Results

The invariant neutral pion spectra measured in pp and Pb–Pb collisions are shown in Fig. 5. The \(p_\mathrm{T}\) range \(0.6{-}12\,{\mathrm {GeV}}/\)c covered by the measurements includes the region \(p_\mathrm{T}\approx 7\,{\mathrm {GeV}}/\)c where the charged hadron \(R_\mathrm{{AA}}\) exhibits the strongest suppression [35, 36, 37]. The invariant neutral pion yield in inelastic pp collisions shown in Fig. 5 is related to the invariant cross section as \(E\,{\mathrm {d}}^3\sigma /{\mathrm {d}}^3p = E\,{\mathrm {d}}^3N/{\mathrm {d}}^3p \times \sigma _{{\mathrm {inel}}}\). Above \(p_\mathrm{T}\approx 3\,{\mathrm {GeV}}/\)c the pp spectrum is well described by a power law \(E\,{\mathrm {d}}^3N/{\mathrm {d}}^3p \propto 1/p_\mathrm{T}^n\). A fit to \(p_\mathrm{T}> 3\,{\mathrm {GeV}}/\)c yields an exponent \(n = 6.0 \pm 0.1\) with \(\chi ^2/{\mathrm {ndf}} = 3.8/4\), which is significantly smaller than the value \(n = 8.22 \pm 0.09\) observed in pp collisions at \(\sqrt{s} = 200\,{\mathrm {GeV}}\) [31].
Fig. 5

(Color online) Invariant differential yields of neutral pions produced in Pb–Pb and inelastic pp collisions at \(\sqrt{s_{{\mathrm {NN}}}}=2.76\,{\mathrm {TeV}}\). The spectra are the weighted average of the PHOS and the PCM results. The vertical lines show the statistical uncertainties, systematic uncertainties are shown as boxes. Horizontal lines indicate the bin width. The horizontal position of the data points within a bin was determined by the procedure described in [59]. For the pp spectrum a fit with a power law function \(1/p_\mathrm{T}^n\) for \(p_\mathrm{T}> 3\,{\mathrm {GeV}}/\)c and a Tsallis function (also used in [48]) are shown. The extrapolation of the pp spectrum provided by the Tsallis fit is used in the \(R_\mathrm{{AA}}\) calculation for \(p_\mathrm{T}\gtrsim 8\,{\mathrm {GeV}}/\)c

Neutral pion production from hard scattering is dominated by the fragmentation of gluon jets in the \(p_\mathrm{T}\) range of the measurement. The presented \(\pi ^0\) spectrum in pp collisions can therefore help constrain the gluon-to-pion fragmentation function [61]. A next-to-leading-order (NLO) perturbative QCD calculation employing the DSS fragmentation function [62] agrees reasonably well with the measured neutral pion spectrum at \(\sqrt{s} = 0.9\,{\mathrm {TeV}}\). At \(\sqrt{s} =7\,{\mathrm {TeV}}\), however, the predicted invariant cross sections are larger than the measured ones [48]. The comparison to a NLO perturbative QCD calculation using the CTEQ6M5 parton distributions [63] and the DSS fragmentation functions in Fig. 6 shows that the calculation overpredicts the data already at \(\sqrt{s}=2.76\,{\mathrm {TeV}}\) by a similar factor as in pp collisions at \(\sqrt{s}=7\,{\mathrm {TeV}}\). The data are furthermore compared to a PYTHIA 8.176 (tune 4C) [56, 64] calculation which reproduces the shape of the spectrum with an overall offset of about 20 %. It will be interesting to see whether calculations in the framework of the color glass condensate [65], which describe the neutral pion spectrum in pp collisions at \(\sqrt{s} = 7\,{\mathrm {TeV}}\), will also provide a good description of the data at \(\sqrt{s} = 2.76\,{\mathrm {TeV}}\).
Fig. 6

(Color online) Ratio of data or theory calculations to a fit of the neutral pion spectrum in pp collisions at \(\sqrt{s_{{\mathrm {NN}}}}= 2.76\,{\mathrm {TeV}}\). The renormalization, factorization, and fragmentation scale of the next-to-leading order QCD calculation were varied simultaneously (\(\mu = 0.5 p_\mathrm{T}\), \(p_\mathrm{T}\), \(2 p_\mathrm{T}\)). The calculation employed the CTEQ6M5 [63] parton distribution functions and the DSS fragmentation function [62]. The solid red line is a comparison to the PYTHIA 8.176 (tune 4C) event generator [56, 64]

The nuclear modification factor, \(R_\mathrm{{AA}}\), was calculated according to Eq. 1. For \(p_\mathrm{T}> 8\,{\mathrm {GeV}}/\)c the extrapolation of the pp spectrum provided by the power law fit shown in Fig. 5 was used as a reference. The systematic uncertainty of the extrapolation was estimated based on the variation of the fit range (\(p_\mathrm{T}>2, 3, 4\,{\mathrm {GeV}}/\)c) and the systematic uncertainty in the bin from \(p_\mathrm{T}= 6{-}8\,{\mathrm {GeV}}/\)c. The average values of the nuclear overlap function \(T_{\mathrm{AA}}\) for each centrality class were taken from [46] and are given in Table 2. They were determined with a Glauber Monte Carlo calculation [66, 67] by defining percentiles with respect to the simulated impact parameter \(b\) and therefore represent purely geometric quantities.
Table 2

Values for the overlap function \(\langle T_{AA} \rangle \) for the centrality bins used in this analysis

Centrality class (%)

\(\langle T_{AA} \rangle \) (1/mb)

Rel. syst. uncert. (%)

\(0{-}5\)

\(26.32\)

\(3.2\)

\(5{-}10\)

\(20.56\)

\(3.3\)

\(10{-}20\)

\(14.39\)

\(3.1\)

\(20{-}40\)

\(6.85\)

\(3.3\)

\(40{-}60\)

\(1.996\)

\(4.9\)

\(60{-}80\)

\(0.4174\)

\(6.2\)

The combined \(R_\mathrm{{AA}}\) was calculated as a weighted average of the individual \(R_\mathrm{{AA}}\) measured with PHOS and PCM. This has the advantage of reduced systematic uncertainties of the combined result. In particular, the dominant uncertainty in the PCM, related to the material budget, cancels this way. The results for the combined \(R_\mathrm{{AA}}\) are shown in Fig. 7. In all centrality classes the measured \(R_\mathrm{{AA}}\) exhibits a maximum around \(p_\mathrm{T}\approx 1{-}2\,{\mathrm {GeV}}/\)c, a decrease in the range \(2 \lesssim p_\mathrm{T}\lesssim 3{-}6\,{\mathrm {GeV}}/\)c, and an approximately constant value in the measured \(p_\mathrm{T}\) range for higher \(p_\mathrm{T}\). For \(p_\mathrm{T}\gtrsim 6\,{\mathrm {GeV}}/\)c, where particle production is expected to be dominated by fragmentation of hard-scattered partons, \(R_\mathrm{{AA}}\) decreases with centrality from about \(0.5-0.7\) in the \(60{-}80\) % class to about 0.1 in the 0-5 % class. The \(R_\mathrm{{AA}}\) measurements for neutral pions and charged pions [68] agree with each other over the entire \(p_\mathrm{T}\) range for all centrality classes. Agreement between the neutral pion and charged particle \(R_\mathrm{{AA}}\) [37] is observed for \(p_\mathrm{T}\gtrsim 6\,{\mathrm {GeV}}/\)c.
Fig. 7

(Color online) Neutral pion nuclear modification factor \(R_\mathrm{{AA}}\) for three different centralities (\(0{-}5\), \(20{-}40\), \(60{-}80\,\%\)) in Pb–Pb collisions at \(\sqrt{s_{{\mathrm {NN}}}}= 2.76\,{\mathrm {TeV}}\). Vertical error bars reflect statistical uncertainties, boxes systematic uncertainties. Horizontal bars reflect the bin width. The boxes around unity reflect the uncertainty of the average nuclear overlap function (\(T_{AA}\)) and the normalization uncertainty of the pp spectrum added in quadrature

It is instructive to study the \(\sqrt{s_{{\mathrm {NN}}}}\) dependence of the neutral pion \(R_\mathrm{{AA}}\). Figure 8 shows that for central collisions the \(R_\mathrm{{AA}}\) at the LHC for \(p_\mathrm{T}\gtrsim 2\,{\mathrm {GeV}}/\)c lies below the data points at lower \(\sqrt{s_{{\mathrm {NN}}}}\). This indicates that the decrease of \(R_\mathrm{{AA}}\) resulting from the higher initial energy densities created at larger \(\sqrt{s_{{\mathrm {NN}}}}\) dominates over the increase of \(R_\mathrm{{AA}}\) expected from the harder initial parton \(p_\mathrm{T}\) spectra. To illustrated this point, one can consider a somewhat oversimplified model with a \(p_\mathrm{T}\) independent fractional energy loss \(\varepsilon \) in conjunction with \(p_\mathrm{T}\) spectra described by a power law [70]. In this model \(\varepsilon = 0.2\) corresponds to \(R_\mathrm{{AA}}^{\mathrm {RHIC}} \approx 0.25\) at \(\sqrt{s_{{\mathrm {NN}}}}= 0.2\,{\mathrm {TeV}}\). The same fractional energy loss in conjunction with the flatter spectra at \(\sqrt{s_{{\mathrm {NN}}}}= 2.76\,{\mathrm {TeV}}\), however, yield \(R_\mathrm{{AA}}^{\mathrm {LHC}} \approx 0.4\). The shape of \(R_\mathrm{{AA}}(p_\mathrm{T})\) in central collisions at \(\sqrt{s_{{\mathrm {NN}}}}= 200\,{\mathrm {GeV}}\) and \(\sqrt{s_{{\mathrm {NN}}}}= 2.76\,{\mathrm {TeV}}\) appears to be similar. Considering the data for all shown energies one observes that the value of \(p_\mathrm{T}\) with the maximum \(R_\mathrm{{AA}}\) value appears to shift towards lower \(p_\mathrm{T}\) with increasing \(\sqrt{s_{{\mathrm {NN}}}}\). The centrality dependence of \(R_\mathrm{{AA}}\) at \(p_\mathrm{T}= 7\,{\mathrm {GeV}}/\)c is shown in Fig. 9 for nuclear collisions at \(\sqrt{s_{{\mathrm {NN}}}}= 39\), \(62.4\), \(200\) [22, 34], and \(2{,}760\,{\mathrm {GeV}}\). At this transverse momentum soft particle production from the bulk should be negligible and parton energy loss is expected to be the dominant effect. It can be seen that the suppression in Pb–Pb collisions at the LHC is stronger than in Au–Au collisions at \(\sqrt{s_{{\mathrm {NN}}}}= 200\,{\mathrm {GeV}}\) for all centralities. In particular, the most peripheral class of the LHC data already shows a sizable suppression whereas at the lower energies the suppression appears to develop less abruptly as a function of the number of participating nucleons (\(N_\mathrm{{part}}\)).
Fig. 8

(Color online) Neutral pion nuclear modification factor, \(R_\mathrm{{AA}}\), in Pb–Pb collisions at \(\sqrt{s_{{\mathrm {NN}}}}= 2.76\,{\mathrm {TeV}}\) for the \(0{-}10\,\%\) class in comparison to results at lower energies. The box around unity reflects the uncertainty of the average nuclear overlap function (\(T_{AA}\)) and the normalization uncertainty of the pp spectrum added in quadrature. Horizontal bars reflect the bin width. The center-of-mass energy dependence of the neutral pion \(R_\mathrm{{AA}}\) is shown with results from Au–Au collisions at \(\sqrt{s_{{\mathrm {NN}}}}= 39\), \(62.4\) [34], and \(200\,{\mathrm {GeV}}\) [31] as well as the result from the CERN SPS [69] (using scaled p-C data as reference) along with the results for Pb–Pb at \(\sqrt{s_{{\mathrm {NN}}}}= 2.76\,{\mathrm {TeV}}\). The scale uncertainties of the measurements at lower energies of the order of \(10{-}15\,\%\) are not shown

Fig. 9

(Color online) Centrality dependence of the \(\pi ^0\) nuclear modification factor \(R_\mathrm{{AA}}\) at \(p_\mathrm{T}= 7\,{\mathrm {GeV}}/\)c in Au–Au and Pb–Pb collisions at \(\sqrt{s_{{\mathrm {NN}}}}= 39\), \(62.4\), \(200\) [22, 34], and \(2{,}760\,{\mathrm {GeV}}\)

Fig. 10

(Color online) Comparison of the measured nuclear modification factor \(R_\mathrm{{AA}}\) with a GLV calculation [38, 39] and with a WHDG [71] parton energy loss calculations. Vertical lines show the statistical uncertainties, systematic uncertainties are shown as boxes. Horizontal lines indicate the bin width. The boxes around unity reflect the scale uncertainties of data related to \(T_{AA}\) and the normalization of the pp spectrum

In Fig. 10 the measured \(R_\mathrm{{AA}}\) is compared with a GLV model calculation [38, 39] and with theoretical predictions from the WHDG model [71]. These models describe the interaction of a hard-scattered parton with the medium of high color charge density within perturbative QCD [11]. Both calculations assume that the hadronization of the hard-scattered parton occurs in the vacuum and is not affected by the medium. They model the energy loss of the parton but not the corresponding response of the medium. Their applicability is limited to transverse momenta above \(2{-}4\,{\mathrm {GeV}}/\)c as soft particle production from the bulk is not taken into account. The Pb–Pb \(\pi ^0\) spectra are therefore also compared to two models which aim at a description of the full \(p_\mathrm{T}\) range: an EPOS calculation [72] and a calculation by Nemchik et al. based on the combination of a hydrodynamic description at low \(p_\mathrm{T}\) and the absorption of color dipoles at higher \(p_\mathrm{T}\) [73, 74]. These comparisons are presented in Fig. 11.

The GLV calculation takes final-state radiative energy loss into account. It includes the broadening of the transverse momenta of the incoming partons in cold nuclear matter (“nuclear broadening” or “Cronin effect”). The main parameter of this model, the initial gluon density, was tuned to describe the neutral pion suppression observed in Au–Au collisions at RHIC. For the calculation of the parton energy loss in Pb–Pb collisions at the LHC the initial gluon density was constrained by the measured charged-particle multiplicities. The model can approximately reproduce the centrality and \(p_\mathrm{T}\) dependence of the \(\pi ^0\)\(R_\mathrm{{AA}}\).

The WHDG model takes into account collisional and radiative parton energy loss and geometrical path length fluctuations. The color charge density of the medium is assumed to be proportional to the number of participating nucleons from a Glauber model, and hard parton-parton scatterings are proportional to the number of binary nucleon-nucleon collisions. Parameters of the model were constrained by the neutral pion \(R_\mathrm{{AA}}\) measured at RHIC. Like in the case of the GLV calculation, the neutral pion \(R_\mathrm{{AA}}\) at the LHC is then predicted by translating the measured charged-particle multiplicity \({\mathrm {d}}N_{ch}/{\mathrm {d}}\eta \) in Pb–Pb collisions into an initial gluon density which is the free parameter of the model. For central collisions this yielded an increase in the gluon density from \({\mathrm {d}}N_g/{\mathrm {d}}y \approx 1400\) at RHIC to \({\mathrm {d}}N_g/{\mathrm {d}}y \approx 3{,}000\) at the LHC. The WHDG model reproduces the \(\pi ^0\)\(R_\mathrm{{AA}}\) in central collisions reasonably well, but predicts too strong suppression for more peripheral classes.
Fig. 11

(Color online) Comparison of the measured \(\pi ^0\) spectra for three centrality classes (\(0{-}5\), \(20{-}40\), \(60{-}80\,\%\)) with two calculations which make predictions for the full \(p_\mathrm{T}\) range of the measurement. The calculated spectra and the data points were divided by a fit of the measured \(\pi ^0\) spectra. For the data points the error bars represent the statistical uncertainties and the boxes the systematic uncertainties. Calculations with the EPOS event generator [72] are shown by the solid line. The fluctuations of the EPOS lines at high \(p_\mathrm{T}\) are due to limited statistics in the number of generated events. The calculations by Nemchik et al. [73, 74] combine a hydrodynamical model at low \(p_\mathrm{T}\) with a color dipole absorption model for \(p_\mathrm{T}\gtrsim 3\,{\mathrm {GeV}}/\)c. The two components and the sum (for \(p_\mathrm{T}\gtrsim 3\,{\mathrm {GeV}}/\)c) are shown separately

The two model predictions for the full \(p_\mathrm{T}\) range are compared to the measured spectra in Fig. 11. EPOS is based on the hadronization of flux tubes produced early in the collision. Hard scattering in this model produces strings with transversely moving parts. String segments with low energies are assumed to be part of the bulk whose space-time evolution is modeled within hydrodynamics. String segments with sufficiently large energy fragment in the vacuum. A third class of string segments with intermediate energies is considered to have enough energy to leave the medium accompanied by quark pick-up from the bulk during the fragmentation process. In EPOS particle production is determined by hydrodynamic flow at low \({p_\mathrm{T}\ ({\lesssim } 4\,{\mathrm {GeV}}/}\)c), followed at higher \({p_\mathrm{T}}\) by energy loss of high-\({p_\mathrm{T}}\) string segments. In central collisions the EPOS calculation describes the measured \(\pi ^0\) spectrum rather well. Towards more peripheral collisions a discrepancy develops for \({1 \lesssim p_\mathrm{T}\lesssim 5\,{\mathrm {GeV}}/}\)c which may possibly be attributed to underestimating the contribution of hydrodynamic flow in peripheral collisions.

The calculation by Nemchik et al. also combines a model for hadron suppression at high \(p_\mathrm{T}\) with a hydrodynamic description of bulk particle production at low \(p_\mathrm{T}\). Hadron suppression in this model results from the absorption of pre-hadrons, i.e., of color dipoles which are already formed in the medium by hard-scattered partons during the production of hadrons with large \(z = p_{{\mathrm {hadron}}}/p_{{\mathrm {parton}}}\). As the model, at high \(p_\mathrm{T}\), predicts only \(R_\mathrm{{AA}}\), the calculated \(R_\mathrm{{AA}}\) values were scaled by \(\langle T_{{\mathrm {AA}}} \rangle \times E\,{\mathrm {d}}^3\sigma _{{\mathrm {meas}}}^{\pi ^0}/{\mathrm {d}}^3p\) and then added to the calculated \(\pi ^0\) invariant yields from the hydrodynamic model in order to compare to the measured \(\pi ^0\) spectra. The hydrodynamic calculation dominates the total \(\pi ^0\) yield up to \(p_\mathrm{T}= 2\,{\mathrm {GeV}}/\)c and remains a significant contribution up to \(5\,{\mathrm {GeV}}/\)c. From about \(3\,{\mathrm {GeV}}/\)c the contribution from hard scattering becomes larger than the one from the hydrodynamic calculation. The spectrum in central Pb–Pb collisions (\(0{-}5\,\%\)) is approximately described except for the transition region between the hydrodynamic and the hard contribution. In the \(20{-}40\,\%\) class the hydrodynamic calculation overpredicts the data up to \(p_\mathrm{T}= 2\,{\mathrm {GeV}}/\)c.

5 Conclusions

Measurements of neutral pion production at midrapidity in pp and Pb–Pb collisions at \(\sqrt{s_{{\mathrm {NN}}}}=2.76\,{\mathrm {TeV}}\) were presented. The measurements were performed with two independent techniques, by measuring the photons with the electromagnetic calorimeter PHOS, and by measuring converted photons with the ALICE tracking system. The two independent measurements were found to give consistent results, and were combined for the final results.

The neutral pion spectrum in pp collisions was compared to a NLO perturbative QCD calculation using the DSS fragmentation functions. This calculation, which describes the pion spectrum in pp collisions at \(\sqrt{s} = 0.9\,{\mathrm {TeV}}\) rather well, tends to overpredict the \(\pi ^0\) cross section already at \(\sqrt{s} = 2.76\,{\mathrm {TeV}}\). Along with a similar observation in pp collision at \(\sqrt{s} = 7\,{\mathrm {TeV}}\) this indicates the likely need for improvements in the gluon-to-pion fragmentation function. A similar observation was made for transverse momentum spectra of charged particles in proton-proton and proton-antiproton collisons at \(1.96 \lesssim \sqrt{s} \lesssim 7\,{\mathrm {TeV}}\) [61, 75].

The neutral pion nuclear suppression factor \(R_\mathrm{{AA}}\) was calculated from the measured neutral pion spectra, and was compared to measurements at lower energies and to theoretical predictions. The \(\pi ^0\) suppression in the most central class (\(0{-}5\,\%\)) reaches values of up to \(8{-}10\) for \(5 \lesssim p_\mathrm{T}\lesssim 7\,{\mathrm {GeV}}/\)c. The suppression in Pb–Pb collisions at \(\sqrt{s_{{\mathrm {NN}}}}= 2.76\,{\mathrm {TeV}}\) is stronger than in Au–Au collisions at \(\sqrt{s_{{\mathrm {NN}}}}= 200\,{\mathrm {GeV}}\) (and lower energies) at RHIC for all centralities.

The general features of the centrality and \(p_\mathrm{T}\) dependence of the \(R_\mathrm{{AA}}\) for \(p_\mathrm{T}\gtrsim 2\,{\mathrm {GeV}}/\)c are approximately reproduced by GLV and WHDG parton energy loss calculations, although the WHDG calculation performs less well in peripheral collisions. For both calculations the main free parameter, the initial gluon density, was chosen to describe the neutral pion suppression at RHIC and then scaled to LHC energies based on the measured charged-particle multiplicities. The measured \(\pi ^0\) spectra were also compared to calculations with the EPOS event generator and a calculation by Nemchik et al. By combining soft particle production from a hydrodynamically evolving medium with a model for hadron suppression these models are capable of making predictions for the entire \(p_\mathrm{T}\) range. An important task on the theoretical side will be to establish whether the observed deviations from the data simply indicate a suboptimal adjustment of parameters or hint at important physical phenomena missing in the models. Future analyses based on runs with higher integrated luminosities, e.g. the 2011 LHC Pb–Pb run, will also include the ALICE lead-scintillator electromagnetic calorimeter (EMCal) and will allow us to extend the neutral pion measurement to higher transverse momenta. The role of initial-state effects on the particle production in Pb–Pb collisions will be investigated by measurements of particle production in p-Pb collisions.

Acknowledgments

We would like to thank Jan Nemchik, William A. Horowitz, Ivan Vitev, and Klaus Werner for providing the model calculations shown in this paper. This work was supported by the grants RFBR 10-02-91052 and RFBR 12-02-91527. The ALICE Collaboration would like to thank all its engineers and technicians for their invaluable contributions to the construction of the experiment and the CERN accelerator teams for the outstanding performance of the LHC complex. The ALICE Collaboration gratefully acknowledges the resources and support provided by all Grid centres and the Worldwide LHC Computing Grid (WLCG) collaboration. The ALICE Collaboration acknowledges the following funding agencies for their support in building and running the ALICE detector: State Committee of Science, World Federation of Scientists (WFS) and Swiss Fonds Kidagan, Armenia, Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), Financiadora de Estudos e Projetos (FINEP), Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP); National Natural Science Foundation of China (NSFC), the Chinese Ministry of Education (CMOE) and the Ministry of Science and Technology of China (MSTC); Ministry of Education and Youth of the Czech Republic; Danish Natural Science Research Council, the Carlsberg Foundation and the Danish National Research Foundation; The European Research Council under the European Community’s Seventh Framework Programme; Helsinki Institute of Physics and the Academy of Finland; French CNRS-IN2P3, the ‘Region Pays de Loire’, ‘Region Alsace’, ‘Region Auvergne’ and CEA, France; German BMBF and the Helmholtz Association; General Secretariat for Research and Technology, Ministry of Development, Greece; Hungarian OTKA and National Office for Research and Technology (NKTH); Department of Atomic Energy and Department of Science and Technology of the Government of India; Istituto Nazionale di Fisica Nucleare (INFN) and Centro Fermi - Museo Storico della Fisica e Centro Studi e Ricerche “Enrico Fermi”, Italy; MEXT Grant-in-Aid for Specially Promoted Research, Japan;

Joint Institute for Nuclear Research, Dubna; National Research Foundation of Korea (NRF); CONACYT, DGAPA, México, ALFA-EC and the EPLANET Program (European Particle Physics Latin American Network) Stichting voor Fundamenteel Onderzoek der Materie (FOM) and the Nederlandse Organisatie voor Wetenschappelijk Onderzoek (NWO), The Netherlands; Research Council of Norway (NFR); Polish Ministry of Science and Higher Education; National Science Centre, Poland; Ministry of National Education/Institute for Atomic Physics and CNCS-UEFISCDI - Romania; Ministry of Education and Science of Russian Federation, Russian Academy of Sciences, Russian Federal Agency of Atomic Energy, Russian Federal Agency for Science and Innovations and The Russian Foundation for Basic Research; Ministry of Education of Slovakia; Department of Science and Technology, South Africa; CIEMAT, EELA, Ministerio de Economía y Competitividad (MINECO) of Spain, Xunta de Galicia (Consellería de Educación), CEADEN, Cubaenergía, Cuba, and IAEA (International Atomic Energy Agency); Swedish Research Council (VR) and Knut & Alice Wallenberg Foundation (KAW); Ukraine Ministry of Education and Science; United Kingdom Science and Technology Facilities Council (STFC); The United States Department of Energy, the United States National Science Foundation, the State of Texas, and the State of Ohio.

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  • M. Gheata
    • 34
    • 57
  • B. Ghidini
    • 31
  • P. Ghosh
    • 124
  • S. K. Ghosh
    • 4
  • P. Gianotti
    • 66
  • P. Giubellino
    • 34
  • E. Gladysz-Dziadus
    • 110
  • P. Glässel
    • 87
  • A. Gomez Ramirez
    • 47
  • P. González-Zamora
    • 10
  • S. Gorbunov
    • 39
  • L. Görlich
    • 110
  • S. Gotovac
    • 109
  • L. K. Graczykowski
    • 126
  • A. Grelli
    • 52
  • A. Grigoras
    • 34
  • C. Grigoras
    • 34
  • V. Grigoriev
    • 70
  • A. Grigoryan
    • 1
  • S. Grigoryan
    • 61
  • B. Grinyov
    • 3
  • N. Grion
    • 104
  • J. F. Grosse-Oetringhaus
    • 34
  • J.-Y. Grossiord
    • 122
  • R. Grosso
    • 34
  • F. Guber
    • 51
  • R. Guernane
    • 65
  • B. Guerzoni
    • 26
  • M. Guilbaud
    • 122
  • K. Gulbrandsen
    • 74
  • H. Gulkanyan
    • 1
  • M. Gumbo
    • 83
  • T. Gunji
    • 119
  • A. Gupta
    • 84
  • R. Gupta
    • 84
  • K. H. Khan
    • 15
  • R. Haake
    • 49
  • Ø. Haaland
    • 17
  • C. Hadjidakis
    • 46
  • M. Haiduc
    • 57
  • H. Hamagaki
    • 119
  • G. Hamar
    • 128
  • L. D. Hanratty
    • 96
  • A. Hansen
    • 74
  • J. W. Harris
    • 129
  • H. Hartmann
    • 39
  • A. Harton
    • 13
  • D. Hatzifotiadou
    • 99
  • S. Hayashi
    • 119
  • S. T. Heckel
    • 48
  • M. Heide
    • 49
  • H. Helstrup
    • 35
  • A. Herghelegiu
    • 72
  • G. Herrera Corral
    • 11
  • B. A. Hess
    • 33
  • K. F. Hetland
    • 35
  • B. Hippolyte
    • 50
  • J. Hladky
    • 55
  • P. Hristov
    • 34
  • M. Huang
    • 17
  • T. J. Humanic
    • 19
  • N. Hussain
    • 41
  • D. Hutter
    • 39
  • D. S. Hwang
    • 20
  • R. Ilkaev
    • 93
  • I. Ilkiv
    • 71
  • M. Inaba
    • 120
  • G. M. Innocenti
    • 25
  • C. Ionita
    • 34
  • M. Ippolitov
    • 94
  • M. Irfan
    • 18
  • M. Ivanov
    • 91
  • V. Ivanov
    • 79
  • A. Jachołkowski
    • 27
  • P. M. Jacobs
    • 68
  • C. Jahnke
    • 113
  • H. J. Jang
    • 62
  • M. A. Janik
    • 126
  • P. H. S. Y. Jayarathna
    • 115
  • C. Jena
    • 28
  • S. Jena
    • 115
  • R. T. Jimenez Bustamante
    • 58
  • P. G. Jones
    • 96
  • H. Jung
    • 40
  • A. Jusko
    • 96
  • V. Kadyshevskiy
    • 61
  • S. Kalcher
    • 39
  • P. Kalinak
    • 54
  • A. Kalweit
    • 34
  • J. Kamin
    • 48
  • J. H. Kang
    • 130
  • V. Kaplin
    • 70
  • S. Kar
    • 124
  • A. Karasu Uysal
    • 63
  • O. Karavichev
    • 51
  • T. Karavicheva
    • 51
  • E. Karpechev
    • 51
  • U. Kebschull
    • 47
  • R. Keidel
    • 131
  • D. L. D. Keijdener
    • 52
  • M. M. Khan
    • 18
  • P. Khan
    • 95
  • S. A. Khan
    • 124
  • A. Khanzadeev
    • 79
  • Y. Kharlov
    • 106
  • B. Kileng
    • 35
  • B. Kim
    • 130
  • D. W. Kim
    • 40
    • 62
  • D. J. Kim
    • 116
  • J. S. Kim
    • 40
  • M. Kim
    • 40
  • M. Kim
    • 130
  • S. Kim
    • 20
  • T. Kim
    • 130
  • S. Kirsch
    • 39
  • I. Kisel
    • 39
  • S. Kiselev
    • 53
  • A. Kisiel
    • 126
  • G. Kiss
    • 128
  • J. L. Klay
    • 6
  • J. Klein
    • 87
  • C. Klein-Bösing
    • 49
  • A. Kluge
    • 34
  • M. L. Knichel
    • 91
  • A. G. Knospe
    • 111
  • C. Kobdaj
    • 34
    • 108
  • M. Kofarago
    • 34
  • M. K. Köhler
    • 91
  • T. Kollegger
    • 39
  • A. Kolojvari
    • 123
  • V. Kondratiev
    • 123
  • N. Kondratyeva
    • 70
  • A. Konevskikh
    • 51
  • V. Kovalenko
    • 123
  • M. Kowalski
    • 110
  • S. Kox
    • 65
  • G. Koyithatta Meethaleveedu
    • 44
  • J. Kral
    • 116
  • I. Králik
    • 54
  • F. Kramer
    • 48
  • A. Kravčáková
    • 38
  • M. Krelina
    • 37
  • M. Kretz
    • 39
  • M. Krivda
    • 54
    • 96
  • F. Krizek
    • 77
  • E. Kryshen
    • 34
  • M. Krzewicki
    • 91
  • V. Kučera
    • 77
  • Y. Kucheriaev
    • 94
  • T. Kugathasan
    • 34
  • C. Kuhn
    • 50
  • P. G. Kuijer
    • 75
  • I. Kulakov
    • 48
  • J. Kumar
    • 44
  • P. Kurashvili
    • 71
  • A. Kurepin
    • 51
  • A. B. Kurepin
    • 51
  • A. Kuryakin
    • 93
  • S. Kushpil
    • 77
  • M. J. Kweon
    • 87
  • Y. Kwon
    • 130
  • P. Ladron de Guevara
    • 58
  • C. Lagana Fernandes
    • 113
  • I. Lakomov
    • 46
  • R. Langoy
    • 125
  • C. Lara
    • 47
  • A. Lardeux
    • 107
  • A. Lattuca
    • 25
  • S. L. La Pointe
    • 52
  • P. La Rocca
    • 27
  • R. Lea
    • 24
  • L. Leardini
    • 87
  • G. R. Lee
    • 96
  • I. Legrand
    • 34
  • J. Lehnert
    • 48
  • R. C. Lemmon
    • 76
  • V. Lenti
    • 98
  • E. Leogrande
    • 52
  • M. Leoncino
    • 25
  • I. León Monzón
    • 112
  • P. Lévai
    • 128
  • S. Li
    • 7
    • 64
  • J. Lien
    • 125
  • R. Lietava
    • 96
  • S. Lindal
    • 21
  • V. Lindenstruth
    • 39
  • C. Lippmann
    • 91
  • M. A. Lisa
    • 19
  • H. M. Ljunggren
    • 32
  • D. F. Lodato
    • 52
  • P. I. Loenne
    • 17
  • V. R. Loggins
    • 127
  • V. Loginov
    • 70
  • D. Lohner
    • 87
  • C. Loizides
    • 68
  • X. Lopez
    • 64
  • E. López Torres
    • 9
  • X.-G. Lu
    • 87
  • P. Luettig
    • 48
  • M. Lunardon
    • 28
  • G. Luparello
    • 52
  • C. Luzzi
    • 34
  • R. Ma
    • 129
  • A. Maevskaya
    • 51
  • M. Mager
    • 34
  • D. P. Mahapatra
    • 56
  • S. M. Mahmood
    • 21
  • A. Maire
    • 87
  • R. D. Majka
    • 129
  • M. Malaev
    • 79
  • I. Maldonado Cervantes
    • 58
  • L. Malinina
    • 61
  • D. Mal’Kevich
    • 53
  • P. Malzacher
    • 91
  • A. Mamonov
    • 93
  • L. Manceau
    • 105
  • V. Manko
    • 94
  • F. Manso
    • 64
  • V. Manzari
    • 98
  • M. Marchisone
    • 25
    • 64
  • J. Mareš
    • 55
  • G. V. Margagliotti
    • 24
  • A. Margotti
    • 99
  • A. Marín
    • 91
  • C. Markert
    • 111
  • M. Marquard
    • 48
  • I. Martashvili
    • 118
  • N. A. Martin
    • 91
  • P. Martinengo
    • 34
  • M. I. Martínez
    • 2
  • G. Martínez García
    • 107
  • J. Martin Blanco
    • 107
  • Y. Martynov
    • 3
  • A. Mas
    • 107
  • S. Masciocchi
    • 91
  • M. Masera
    • 25
  • A. Masoni
    • 100
  • L. Massacrier
    • 107
  • A. Mastroserio
    • 31
  • A. Matyja
    • 110
  • C. Mayer
    • 110
  • J. Mazer
    • 118
  • M. A. Mazzoni
    • 103
  • F. Meddi
    • 22
  • A. Menchaca-Rocha
    • 59
  • J. Mercado Pérez
    • 87
  • M. Meres
    • 36
  • Y. Miake
    • 120
  • K. Mikhaylov
    • 53
    • 61
  • L. Milano
    • 34
  • J. Milosevic
    • 21
  • A. Mischke
    • 52
  • A. N. Mishra
    • 45
  • D. Miśkowiec
    • 91
  • J. Mitra
    • 124
  • C. M. Mitu
    • 57
  • J. Mlynarz
    • 127
  • N. Mohammadi
    • 52
  • B. Mohanty
    • 73
    • 124
  • L. Molnar
    • 50
  • L. Montaño Zetina
    • 11
  • E. Montes
    • 10
  • M. Morando
    • 28
  • D. A. Moreira De Godoy
    • 113
  • S. Moretto
    • 28
  • A. Morsch
    • 34
  • V. Muccifora
    • 66
  • E. Mudnic
    • 109
  • D. Mühlheim
    • 49
  • S. Muhuri
    • 124
  • M. Mukherjee
    • 124
  • H. Müller
    • 34
  • M. G. Munhoz
    • 113
  • S. Murray
    • 83
  • L. Musa
    • 34
  • J. Musinsky
    • 54
  • B. K. Nandi
    • 44
  • R. Nania
    • 99
  • E. Nappi
    • 98
  • C. Nattrass
    • 118
  • K. Nayak
    • 73
  • T. K. Nayak
    • 124
  • S. Nazarenko
    • 93
  • A. Nedosekin
    • 54
  • M. Nicassio
    • 91
  • M. Niculescu
    • 34
    • 57
  • B. S. Nielsen
    • 74
  • S. Nikolaev
    • 94
  • S. Nikulin
    • 94
  • V. Nikulin
    • 79
  • B. S. Nilsen
    • 80
  • F. Noferini
    • 12
    • 99
  • P. Nomokonov
    • 61
  • G. Nooren
    • 52
  • J. Norman
    • 117
  • A. Nyanin
    • 94
  • J. Nystrand
    • 17
  • H. Oeschler
    • 87
  • S. Oh
    • 129
  • S. K. Oh
    • 40
  • A. Okatan
    • 63
  • L. Olah
    • 128
  • J. Oleniacz
    • 126
  • A. C. Oliveira Da Silva
    • 113
  • J. Onderwaater
    • 91
  • C. Oppedisano
    • 105
  • A. Ortiz Velasquez
    • 32
  • A. Oskarsson
    • 32
  • J. Otwinowski
    • 91
  • K. Oyama
    • 87
  • P. Sahoo
    • 45
  • Y. Pachmayer
    • 87
  • M. Pachr
    • 37
  • P. Pagano
    • 29
  • G. Paić
    • 58
  • F. Painke
    • 39
  • C. Pajares
    • 16
  • S. K. Pal
    • 124
  • A. Palmeri
    • 101
  • D. Pant
    • 44
  • V. Papikyan
    • 1
  • G. S. Pappalardo
    • 101
  • P. Pareek
    • 45
  • W. J. Park
    • 91
  • S. Parmar
    • 81
  • A. Passfeld
    • 49
  • D. I. Patalakha
    • 106
  • V. Paticchio
    • 106
  • B. Paul
    • 95
  • T. Pawlak
    • 126
  • T. Peitzmann
    • 52
  • H. Pereira Da Costa
    • 14
  • E. Pereira De Oliveira Filho
    • 113
  • D. Peresunko
    • 94
  • C. E. Pérez Lara
    • 75
  • A. Pesci
    • 99
  • V. Peskov
    • 48
  • Y. Pestov
    • 5
  • V. Petráček
    • 37
  • M. Petran
    • 37
  • M. Petris
    • 72
  • M. Petrovici
    • 72
  • C. Petta
    • 27
  • S. Piano
    • 104
  • M. Pikna
    • 36
  • P. Pillot
    • 107
  • O. Pinazza
    • 34
    • 99
  • L. Pinsky
    • 115
  • D. B. Piyarathna
    • 115
  • M. Płoskoń
    • 68
  • M. Planinic
    • 92
    • 121
  • J. Pluta
    • 126
  • S. Pochybova
    • 128
  • P. L. M. Podesta-Lerma
    • 112
  • M. G. Poghosyan
    • 34
  • E. H. O. Pohjoisaho
    • 42
  • B. Polichtchouk
    • 106
  • N. Poljak
    • 92
  • A. Pop
    • 72
  • S. Porteboeuf-Houssais
    • 64
  • J. Porter
    • 68
  • B. Potukuchi
    • 84
  • S. K. Prasad
    • 127
  • R. Preghenella
    • 12
    • 99
  • F. Prino
    • 105
  • C. A. Pruneau
    • 127
  • I. Pshenichnov
    • 51
  • G. Puddu
    • 23
  • P. Pujahari
    • 127
  • V. Punin
    • 93
  • J. Putschke
    • 127
  • H. Qvigstad
    • 21
  • A. Rachevski
    • 106
  • S. Raha
    • 4
  • J. Rak
    • 116
  • A. Rakotozafindrabe
    • 14
  • L. Ramello
    • 30
  • R. Raniwala
    • 85
  • S. Raniwala
    • 85
  • S. S. Räsänen
    • 42
  • B. T. Rascanu
    • 48
  • D. Rathee
    • 81
  • A. W. Rauf
    • 15
  • V. Razazi
    • 23
  • K. F. Read
    • 118
  • J. S. Real
    • 65
  • K. Redlich
    • 71
  • R. J. Reed
    • 129
  • A. Rehman
    • 17
  • P. Reichelt
    • 48
  • M. Reicher
    • 52
  • F. Reidt
    • 34
  • R. Renfordt
    • 48
  • A. R. Reolon
    • 66
  • A. Reshetin
    • 51
  • F. Rettig
    • 39
  • J.-P. Revol
    • 34
  • K. Reygers
    • 87
  • V. Riabov
    • 79
  • R. A. Ricci
    • 67
  • T. Richert
    • 32
  • M. Richter
    • 21
  • P. Riedler
    • 34
  • W. Riegler
    • 34
  • F. Riggi
    • 27
  • A. Rivetti
    • 105
  • E. Rocco
    • 52
  • M. Rodríguez Cahuantzi
    • 2
  • A. Rodriguez Manso
    • 75
  • K. Røed
    • 21
  • E. Rogochaya
    • 61
  • S. Rohni
    • 84
  • D. Rohr
    • 39
  • D. Röhrich
    • 17
  • R. Romita
    • 76
  • F. Ronchetti
    • 66
  • L. Ronflette
    • 107
  • P. Rosnet
    • 64
  • A. Rossi
    • 34
  • F. Roukoutakis
    • 82
  • A. Roy
    • 45
  • C. Roy
    • 50
  • P. Roy
    • 95
  • A. J. Rubio Montero
    • 10
  • R. Rui
    • 24
  • R. Russo
    • 25
  • E. Ryabinkin
    • 94
  • Y. Ryabov
    • 79
  • A. Rybicki
    • 110
  • S. Sadovsky
    • 106
  • K. Šafařík
    • 34
  • B. Sahlmuller
    • 48
  • R. Sahoo
    • 45
  • P. K. Sahu
    • 56
  • J. Saini
    • 124
  • S. Sakai
    • 68
  • C. A. Salgado
    • 16
  • J. Salzwedel
    • 19
  • S. Sambyal
    • 84
  • V. Samsonov
    • 79
  • X. Sanchez Castro
    • 50
  • F. J. Sánchez Rodríguez
    • 112
  • L. Šándor
    • 54
  • A. Sandoval
    • 59
  • M. Sano
    • 120
  • G. Santagati
    • 27
  • D. Sarkar
    • 124
  • E. Scapparone
    • 99
  • F. Scarlassara
    • 28
  • R. P. Scharenberg
    • 89
  • C. Schiaua
    • 72
  • R. Schicker
    • 87
  • C. Schmidt
    • 91
  • H. R. Schmidt
    • 33
  • S. Schuchmann
    • 48
  • J. Schukraft
    • 34
  • M. Schulc
    • 37
  • T. Schuster
    • 129
  • Y. Schutz
    • 34
    • 107
  • K. Schwarz
    • 91
  • K. Schweda
    • 91
  • G. Scioli
    • 26
  • E. Scomparin
    • 105
  • R. Scott
    • 118
  • G. Segato
    • 28
  • J. E. Seger
    • 80
  • Y. Sekiguchi
    • 119
  • I. Selyuzhenkov
    • 91
  • J. Seo
    • 90
  • E. Serradilla
    • 10
    • 59
  • A. Sevcenco
    • 57
  • A. Shabetai
    • 107
  • G. Shabratova
    • 61
  • R. Shahoyan
    • 34
  • A. Shangaraev
    • 106
  • N. Sharma
    • 118
  • S. Sharma
    • 84
  • K. Shigaki
    • 43
  • K. Shtejer
    • 25
  • Y. Sibiriak
    • 94
  • S. Siddhanta
    • 100
  • T. Siemiarczuk
    • 71
  • D. Silvermyr
    • 78
  • C. Silvestre
    • 65
  • G. Simatovic
    • 121
  • R. Singaraju
    • 124
  • R. Singh
    • 84
  • S. Singha
    • 73
    • 124
  • V. Singhal
    • 124
  • B. C. Sinha
    • 124
  • T. Sinha
    • 95
  • B. Sitar
    • 36
  • M. Sitta
    • 30
  • T. B. Skaali
    • 21
  • K. Skjerdal
    • 17
  • M. Slupecki
    • 116
  • N. Smirnov
    • 129
  • R. J. M. Snellings
    • 52
  • C. Søgaard
    • 32
  • R. Soltz
    • 69
  • J. Song
    • 90
  • M. Song
    • 130
  • F. Soramel
    • 28
  • S. Sorensen
    • 118
  • M. Spacek
    • 37
  • E. Spiriti
    • 66
  • I. Sputowska
    • 110
  • M. Spyropoulou-Stassinaki
    • 82
  • B. K. Srivastava
    • 89
  • J. Stachel
    • 87
  • I. Stan
    • 57
  • G. Stefanek
    • 71
  • M. Steinpreis
    • 19
  • E. Stenlund
    • 32
  • G. Steyn
    • 60
  • J. H. Stiller
    • 87
  • D. Stocco
    • 107
  • M. Stolpovskiy
    • 106
  • P. Strmen
    • 36
  • A. A. P. Suaide
    • 113
  • T. Sugitate
    • 43
  • C. Suire
    • 46
  • M. Suleymanov
    • 15
  • R. Sultanov
    • 53
  • M. Šumbera
    • 77
  • T. Susa
    • 92
  • T. J. M. Symons
    • 68
  • A. Szabo
    • 36
  • A. Szanto de Toledo
    • 113
  • I. Szarka
    • 36
  • A. Szczepankiewicz
    • 34
  • M. Szymanski
    • 126
  • J. Takahashi
    • 114
  • M. A. Tangaro
    • 31
  • J. D. Tapia Takaki
    • 46
  • A. Tarantola Peloni
    • 48
  • A. Tarazona Martinez
    • 34
  • M. G. Tarzila
    • 72
  • A. Tauro
    • 34
  • G. Tejeda Muñoz
    • 2
  • A. Telesca
    • 34
  • J. Thäder
    • 91
  • D. Thomas
    • 52
  • R. Tieulent
    • 122
  • A. R. Timmins
    • 115
  • A. Toia
    • 102
  • V. Trubnikov
    • 3
  • W. H. Trzaska
    • 116
  • T. Tsuji
    • 119
  • A. Tumkin
    • 93
  • R. Turrisi
    • 102
  • T. S. Tveter
    • 21
  • K. Ullaland
    • 17
  • A. Uras
    • 122
  • G. L. Usai
    • 23
  • M. Vajzer
    • 77
  • M. Vala
    • 54
    • 61
  • L. Valencia Palomo
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  • S. Vallero
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  • P. Vande Vyvre
    • 34
  • J. Van Der Maarel
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  • J. W. Van Hoorne
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  • M. van Leeuwen
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  • A. Vargas
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  • M. Vargyas
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  • R. Varma
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  • A. Vasiliev
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  • V. Vechernin
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  • M. Veldhoen
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  • A. Velure
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  • M. Venaruzzo
    • 24
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  • E. Vercellin
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  • S. Vergara Limón
    • 2
  • R. Vernet
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  • M. Verweij
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  • L. Vickovic
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  • Z. Vilakazi
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  • O. Villalobos Baillie
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  • A. Vinogradov
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  • L. Vinogradov
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  • Y. Vinogradov
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  • S. A. Voloshin
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  • G. Volpe
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  1. 1.A.I. Alikhanyan National Science Laboratory (Yerevan Physics Institute) FoundationYerevanArmenia
  2. 2.Benemérita Universidad Autónoma de PueblaPueblaMexico
  3. 3.Bogolyubov Institute for Theoretical PhysicsKievUkraine
  4. 4.Department of Physics and Centre for Astroparticle Physics and Space Science (CAPSS)Bose InstituteKolkataIndia
  5. 5.Budker Institute for Nuclear PhysicsNovosibirskRussia
  6. 6.California Polytechnic State UniversitySan Luis ObispoUSA
  7. 7.Central China Normal UniversityWuhanChina
  8. 8.Centre de Calcul de l’IN2P3VilleurbanneFrance
  9. 9.Centro de Aplicaciones Tecnológicas y Desarrollo Nuclear (CEADEN)HavanaCuba
  10. 10.Centro de Investigaciones Energéticas Medioambientales y Tecnológicas (CIEMAT)MadridSpain
  11. 11.Centro de Investigación y de Estudios Avanzados (CINVESTAV)Mexico City and MéridaMexico
  12. 12.Centro Fermi-Museo Storico della Fisica e Centro Studi e Ricerche “Enrico Fermi”RomeItaly
  13. 13.Chicago State UniversityChicagoUSA
  14. 14.Commissariat à l’Energie Atomique, IRFUSaclayFrance
  15. 15.COMSATS Institute of Information Technology (CIIT)IslamabadPakistan
  16. 16.Departamento de Física de Partículas and IGFAEUniversidad de Santiago de CompostelaSantiago de CompostelaSpain
  17. 17.Department of Physics and TechnologyUniversity of BergenBergenNorway
  18. 18.Department of PhysicsAligarh Muslim UniversityAligarhIndia
  19. 19.Department of PhysicsOhio State UniversityColumbusUSA
  20. 20.Department of PhysicsSejong UniversitySeoulSouth Korea
  21. 21.Department of PhysicsUniversity of OsloOsloNorway
  22. 22.Dipartimento di Fisica dell’Università ‘La Sapienza’ and Sezione INFNRomeItaly
  23. 23.Dipartimento di Fisica dell’Università and Sezione INFNCagliariItaly
  24. 24.Dipartimento di Fisica dell’Università and Sezione INFNTriesteItaly
  25. 25.Dipartimento di Fisica dell’Università and Sezione INFNTurinItaly
  26. 26.Dipartimento di Fisica e Astronomia dell’Università and Sezione INFNBolognaItaly
  27. 27.Dipartimento di Fisica e Astronomia dell’Università and Sezione INFNCataniaItaly
  28. 28.Dipartimento di Fisica e Astronomia dell’Università and Sezione INFNPaduaItaly
  29. 29.Dipartimento di Fisica ‘E.R. Caianiello’ dell’Università and Gruppo Collegato INFNSalernoItaly
  30. 30.Dipartimento di Scienze e Innovazione Tecnologica dell’Università del Piemonte Orientale and Gruppo Collegato INFNAlessandriaItaly
  31. 31.Dipartimento Interateneo di Fisica ‘M. Merlin’ and Sezione INFNBariItaly
  32. 32.Division of Experimental High Energy PhysicsUniversity of LundLundSweden
  33. 33.Eberhard Karls Universität TübingenTübingenGermany
  34. 34.European Organization for Nuclear Research (CERN)GenevaSwitzerland
  35. 35.Faculty of EngineeringBergen University CollegeBergenNorway
  36. 36.Faculty of Mathematics, Physics and InformaticsComenius UniversityBratislavaSlovakia
  37. 37.Faculty of Nuclear Sciences and Physical EngineeringCzech Technical University in PraguePragueCzech Republic
  38. 38.Faculty of ScienceP.J. Šafárik UniversityKosiceSlovakia
  39. 39.Frankfurt Institute for Advanced StudiesJohann Wolfgang Goethe-Universität FrankfurtFrankfurtGermany
  40. 40.Gangneung-Wonju National UniversityGangneungSouth Korea
  41. 41.Department of PhysicsGauhati UniversityGuwahatiIndia
  42. 42.Helsinki Institute of Physics (HIP)HelsinkiFinland
  43. 43.Hiroshima UniversityHiroshimaJapan
  44. 44.Indian Institute of Technology Bombay (IIT)MumbaiIndia
  45. 45.Indian Institute of Technology Indore (IITI)IndoreIndia
  46. 46.Institut de Physique Nucléaire d’Orsay (IPNO)Université Paris-Sud, CNRS-IN2P3OrsayFrance
  47. 47.Institut für InformatikJohann Wolfgang Goethe-Universität FrankfurtFrankfurtGermany
  48. 48.Institut für KernphysikJohann Wolfgang Goethe-Universität FrankfurtFrankfurtGermany
  49. 49.Institut für KernphysikWestfälische Wilhelms-Universität MünsterMünsterGermany
  50. 50.Institut Pluridisciplinaire Hubert Curien (IPHC)Université de Strasbourg, CNRS-IN2P3StrasbourgFrance
  51. 51.Institute for Nuclear ResearchAcademy of SciencesMoscowRussia
  52. 52.Institute for Subatomic Physics of Utrecht UniversityUtrechtThe Netherlands
  53. 53.Institute for Theoretical and Experimental PhysicsMoscowRussia
  54. 54.Institute of Experimental PhysicsSlovak Academy of SciencesKosiceSlovakia
  55. 55.Institute of PhysicsAcademy of Sciences of the Czech RepublicPragueCzech Republic
  56. 56.Institute of PhysicsBhubaneswarIndia
  57. 57.Institute of Space Science (ISS)BucharestRomania
  58. 58.Instituto de Ciencias NuclearesUniversidad Nacional Autónoma de MéxicoMexico CityMexico
  59. 59.Instituto de FísicaUniversidad Nacional Autónoma de MéxicoMexico CityMexico
  60. 60.iThemba LABS, National Research FoundationSomerset WestSouth Africa
  61. 61.Joint Institute for Nuclear Research (JINR)DubnaRussia
  62. 62.Korea Institute of Science and Technology InformationTaejeonSouth Korea
  63. 63.KTO Karatay UniversityKonyaTurkey
  64. 64.Laboratoire de Physique Corpusculaire (LPC)Clermont Université, Université Blaise Pascal, CNRS-IN2P3Clermont-FerrandFrance
  65. 65.Laboratoire de Physique Subatomique et de CosmologieUniversité Grenoble-Alpes, CNRS-IN2P3GrenobleFrance
  66. 66.Laboratori Nazionali di Frascati, INFNFrascatiItaly
  67. 67.Laboratori Nazionali di Legnaro, INFNLegnaroItaly
  68. 68.Lawrence Berkeley National LaboratoryBerkeleyUSA
  69. 69.Lawrence Livermore National LaboratoryLivermoreUSA
  70. 70.Moscow Engineering Physics InstituteMoscowRussia
  71. 71.National Centre for Nuclear StudiesWarsawPoland
  72. 72.National Institute for Physics and Nuclear EngineeringBucharestRomania
  73. 73.National Institute of Science Education and ResearchBhubaneswarIndia
  74. 74.Niels Bohr InstituteUniversity of CopenhagenCopenhagenDenmark
  75. 75.Nikhef, National Institute for Subatomic PhysicsAmsterdamThe Netherlands
  76. 76.Nuclear Physics Group, STFC Daresbury LaboratoryDaresburyUK
  77. 77.Nuclear Physics InstituteAcademy of Sciences of the Czech RepublicŘežu PrahyCzech Republic
  78. 78.Oak Ridge National LaboratoryOak RidgeUSA
  79. 79.Petersburg Nuclear Physics InstituteGatchinaRussia
  80. 80.Physics DepartmentCreighton UniversityOmahaUSA
  81. 81.Physics DepartmentPanjab UniversityChandigarhIndia
  82. 82.Physics DepartmentUniversity of AthensAthensGreece
  83. 83.Physics DepartmentUniversity of Cape TownCape TownSouth Africa
  84. 84.Physics DepartmentUniversity of JammuJammuIndia
  85. 85.Physics DepartmentUniversity of RajasthanJaipurIndia
  86. 86.Physik DepartmentTechnische Universität MünchenMunichGermany
  87. 87.Physikalisches InstitutRuprecht-Karls-Universität HeidelbergHeidelbergGermany
  88. 88.Politecnico di TorinoTurinItaly
  89. 89.Purdue UniversityWest LafayetteUSA
  90. 90.Pusan National UniversityPusanSouth Korea
  91. 91.Research Division and ExtreMe Matter Institute EMMIGSI Helmholtzzentrum für SchwerionenforschungDarmstadtGermany
  92. 92.Rudjer Bošković InstituteZagrebCroatia
  93. 93.Russian Federal Nuclear Center (VNIIEF)SarovRussia
  94. 94.Russian Research Centre Kurchatov InstituteMoscowRussia
  95. 95.Saha Institute of Nuclear PhysicsKolkataIndia
  96. 96.School of Physics and AstronomyUniversity of BirminghamBirminghamUK
  97. 97.Sección Física, Departamento de CienciasPontificia Universidad Católica del PerúLimaPeru
  98. 98.Sezione INFNBariItaly
  99. 99.Sezione INFNBolognaItaly
  100. 100.Sezione INFNCagliariItaly
  101. 101.Sezione INFNCataniaItaly
  102. 102.Sezione INFNPaduaItaly
  103. 103.Sezione INFNRomeItaly
  104. 104.Sezione INFNTriesteItaly
  105. 105.Sezione INFNTurinItaly
  106. 106.SSC IHEP of NRC Kurchatov instituteProtvinoRussia
  107. 107.SUBATECH, Ecole des Mines de NantesUniversité de Nantes, CNRS-IN2P3NantesFrance
  108. 108.Suranaree University of TechnologyNakhon RatchasimaThailand
  109. 109.Technical University of Split FESBSplitCroatia
  110. 110.The Henryk Niewodniczanski Institute of Nuclear PhysicsPolish Academy of SciencesKracòwPoland
  111. 111.Physics DepartmentThe University of Texas at AustinAustinUSA
  112. 112.Universidad Autónoma de SinaloaCuliacánMexico
  113. 113.Universidade de São Paulo (USP)São PauloBrazil
  114. 114.Universidade Estadual de Campinas (UNICAMP)CampinasBrazil
  115. 115.University of HoustonHoustonUSA
  116. 116.University of JyväskyläJyväskyläFinland
  117. 117.University of LiverpoolLiverpoolUK
  118. 118.University of TennesseeKnoxvilleUSA
  119. 119.University of TokyoTokyoJapan
  120. 120.University of TsukubaTsukubaJapan
  121. 121.University of ZagrebZagrebCroatia
  122. 122.Université de Lyon, Université Lyon 1, CNRS/IN2P3, IPN-LyonVilleurbanneFrance
  123. 123.V. Fock Institute for PhysicsSt. Petersburg State UniversitySt. PetersburgRussia
  124. 124.Variable Energy Cyclotron CentreKolkataIndia
  125. 125.Vestfold University CollegeTonsbergNorway
  126. 126.Warsaw University of TechnologyWarsawPoland
  127. 127.Wayne State UniversityDetroitUSA
  128. 128.Wigner Research Centre for PhysicsHungarian Academy of SciencesBudapestHungary
  129. 129.Yale UniversityNew HavenUSA
  130. 130.Yonsei UniversitySeoulSouth Korea
  131. 131.Zentrum für Technologietransfer und Telekommunikation (ZTT), Fachhochschule WormsWormsGermany
  132. 132.CERNGeneva 23Switzerland

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