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Published for SISSA by Springer

Received: April 18, 2011

Accepted: August 4, 2011

Published: August 19, 2011

Charged particle transverse momentum spectra in pp

collisions at
√

s = 0.9 and 7 TeV

The CMS collaboration

Abstract: The charged particle transverse momentum (pT) spectra are presented for pp
collisions at

√
s = 0.9 and 7 TeV. The data samples were collected with the CMS detector at

the LHC and correspond to integrated luminosities of 231µb−1 and 2.96 pb−1, respectively.
Calorimeter-based high-transverse-energy triggers are employed to enhance the statistical
reach of the high-pT measurements. The results are compared with leading and next-to-
leading order QCD and with an empirical scaling of measurements at different collision
energies using the scaling variable xT ≡ 2pT/

√
s over the pT range up to 200 GeV/c. Using

a combination of xT scaling and direct interpolation at fixed pT, a reference transverse
momentum spectrum at

√
s = 2.76 TeV is constructed, which can be used for studying

high-pT particle suppression in the dense QCD medium produced in heavy-ion collisions
at that centre-of-mass energy.

Keywords: Hadron-Hadron Scattering

Open Access, Copyright CERN,

for the benefit of the CMS collaboration

doi:10.1007/JHEP08(2011)086

http://dx.doi.org/10.1007/JHEP08(2011)086


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Contents

1 Introduction 1

2 The CMS detector 2

3 Event selection 3

4 Primary vertex 5

5 Track selection 7

6 Event classification by leading-jet energy 7

7 Corrections and systematic uncertainties 10

8 Results 12

9 Interpolation to 2.76 TeV 16

10 Summary 17

The CMS collaboration 22

1 Introduction

The charged particle transverse momentum (pT) spectrum is an important observable for
understanding the fundamental quantum chromodynamic (QCD) interactions involved in
proton-proton collisions. While the energy dependence of the bulk of particle production
with pT below a few GeV/c is typically described either empirically or with phenomeno-
logical models, the rest of the spectrum can be well described by a convolution of parton
distribution functions, the hard-scattering cross section from perturbative calculations,
and fragmentation functions. Such a prescription has been generally successful over a
large range of lower energy pp and pp̄ collisions [1–7]. Along with measurements of the
jet production cross section and fragmentation functions, measurements of high-pT spec-
tra provide a test of factorised perturbative QCD (pQCD) [8] at the highest collision
energy to date.

In addition to its relevance to the understanding of pQCD, the charged particle spec-
trum in pp collisions will be an important reference for measurements of high-pT particle
suppression in the dense QCD medium produced in heavy-ion collisions. At the Rela-
tivistic Heavy Ion Collider (RHIC), the sizable suppression of high-pT particle production,
compared to the spectrum expected from a superposition of a corresponding number of

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pp collisions, was one of the first indications of strong final-state medium effects [9–12].
A similar measurement of nuclear modification to charged particle pT spectra has been
one of the first heavy-ion results at the Large Hadron Collider (LHC) [13]. The reference
spectrum for the PbPb collisions at √sNN = 2.76 TeV per nucleon can be constrained by
interpolating between the pp spectra measured at

√
s = 0.9 and 7 TeV.

In this paper, the phase-space-invariant differential yield E d3Nch/dp
3 is presented for

primary charged particles with energy (E) and momentum (p), averaged over the pseu-
dorapidity acceptance of the Compact Muon Solenoid (CMS) tracking system (|η| < 2.4).
The pseudorapidity is defined as –ln[tan(θ/2)], with θ being the polar angle of the charged
particle with respect to the counterclockwise beam direction. The number of primary
charged particles (Nch) is defined to include decay products of particles with proper life-
times less than 1 cm. Using the integrated luminosities calculated in refs. [14, 15] with an
estimated uncertainty of 11% and 4% at

√
s = 0.9 and 7 TeV, respectively, the differential

cross sections are constructed and compared to a scaling with the variable xT ≡ 2pT/
√
s.

Such a scaling has already been observed for pp̄ measurements at lower collision ener-
gies [4, 5, 16, 17]. For consistency with the CDF measurements at

√
s = 0.63, 1.8, and

1.96 TeV, the pseudorapidity range of the xT distributions has been restricted to |η| < 1.0.
Finally, using the new measurements presented in this paper, as well as previously

measured pp and pp̄ cross sections, an estimate of the differential transverse momentum
cross section is constructed at the interpolated energy of

√
s = 2.76 TeV, corresponding to

the nucleon-nucleon centre-of-mass energy of PbPb collisions recorded at the LHC.
The paper is organised as follows: section 2 contains a description of the CMS detector;

section 3 describes the trigger and event selection; sections 4 and 5 detail the reconstruction
and selection of primary vertices and tracks; section 6 explains the characterisation of
events based on the leading-jet transverse energy; section 7 describes the various applied
corrections and systematic uncertainties; section 8 presents the final invariant differential
yields and comparisons to data and simulation; and section 9 discusses the interpolation
procedures used to construct a reference spectrum at

√
s = 2.76 TeV.

2 The CMS detector

A detailed description of the CMS experiment can be found in ref. [18]. The central feature
of the CMS apparatus is a superconducting solenoid of 6 m internal diameter, providing
an axial magnetic field of 3.8 T. Immersed in the magnetic field are the pixel tracker, the
silicon strip tracker, the lead tungstate crystal electromagnetic calorimeter (ECAL), and
the brass/scintillator hadron calorimeter (HCAL). Muons are measured in gas ionisation
detectors embedded in the steel return yoke.

The CMS experiment uses a right-handed coordinate system, with the origin at the
nominal interaction point, the x axis pointing to the centre of the LHC ring, the y axis
pointing up perpendicular to the plane of the LHC, and the z axis along the counterclock-
wise beam direction. The azimuthal angle, φ, is measured in the (x, y) plane.

The tracker consists of 1440 silicon pixel and 15 148 silicon strip detector modules and
measures charged particle trajectories within the nominal pseudorapidity range |η| < 2.4.

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The pixel tracker consists of three 53.3 cm-long barrel layers and two endcap disks on each
side of the barrel section. The innermost barrel layer has a radius of 4.4 cm, while for
the second and third layers the radii are 7.3 cm and 10.2 cm, respectively. The tracker
is designed to provide an impact parameter resolution of about 100µm and a transverse
momentum resolution of about 0.7 % for 1 GeV/c charged particles at normal incidence
(η = 0) [19].

The tracker was aligned as described in ref. [20] using cosmic ray data prior to the
LHC commissioning. The precision achieved for the positions of the detector modules with
respect to particle trajectories is 3–4µm in the barrel for the coordinate in the bending
plane (φ).

Two elements of the CMS detector monitoring system, the beam scintillator counters
(BSC) [18, 21] and the beam pick-up timing for the experiments devices (BPTX) [18, 22],
were used to trigger the detector readout. The BSCs are located at a distance of 10.86 m
from the nominal interaction point (IP), one on each side, and are sensitive in the |η| range
from 3.23 to 4.65. Each BSC is a set of 16 scintillator tiles. The BSC elements have a time
resolution of 3 ns, an average minimum ionising particle detection efficiency of 95.7%, and
are designed to provide hit and coincidence rates. The two BPTX devices, located around
the beam pipe at a position of z = ±175 m from the IP, are designed to provide precise
information on the bunch structure and timing of the incoming beam, with better than
0.2 ns time resolution.

The two steel/quartz-fibre forward calorimeters (HF), which extend the calorimetric
coverage beyond the barrel and endcap detectors to the |η| region between 2.9 and 5.2,
were used for further offline selection of collision events.

The detailed Monte Carlo (MC) simulation of the CMS detector response is based on
geant4 [23]. Simulated events were processed and reconstructed in the same manner as
collision data.

3 Event selection

This analysis uses data samples collected from 0.9 and 7 TeV pp collisions in the first months
of the 2010 LHC running, corresponding to integrated luminosities of (231 ± 25)µb−1

and (2.96 ± 0.12) pb−1, respectively [14, 15]. This section gives a brief description of the
requirements imposed to select good events for this analysis. A more detailed description
of the CMS trigger selections can be found in ref. [24].

First, a minimum bias trigger was used to select events with a signal in any of the
BSC tiles, coincident with a signal from either of the two BPTX detectors, indicating the
presence of at least one proton bunch crossing the interaction point. From this sample,
collision events were selected offline by requiring a coincidence of BPTX signals, indicating
the presence of both beams.

To select preferentially non-single-diffractive (NSD) events, at least one forward
calorimeter (HF) tower with energy deposition E > 3 GeV in each of the forward and
backward hemispheres was required. Events with beam-halo muons crossing the detector
were identified and rejected based on the time difference between BSC hits on either side

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of the interaction point. Beam-induced background events, producing anomalous numbers
of low-quality tracks, were rejected by requiring that at least 25% of the charged particles
reconstructed in the pixel-silicon tracking system satisfied the highPurity criterion. This
criterion, described in ref. [25], consists of numerous selections on the properties of the
tracks, including the normalised χ2, the compatibility with the beamline and primary ver-
tices, the number of hit layers, the number of ‘3D’ layers, and the number of lost layers.
The selection on the fraction of highPurity tracks was only applied to events with more than
10 tracks, providing a clean separation between real pp collisions and beam backgrounds.
The remaining non-collision event fraction, determined by applying the same selections to
events where only a single beam was crossing the interaction point, is estimated to be less
than 2 x 10−5. Events were required to have at least one primary vertex, reconstructed
according to the description in the following section from triplets of pixel hits. A further
requirement, namely at least one vertex found from fully reconstructed tracks (see next
section for details) with number of degrees of freedom (Ndof) greater than four, was im-
posed to improve the robustness against triggered events containing multiple pp collisions,
i.e., “event pileup”. The loss in event selection efficiency from the fully-reconstructed-track
vertex compared to the pixel vertex alone was determined entirely from data, based on a
subset of early runs with negligible event pileup. The percentage of events remaining after
each selection step is presented in table 1.

For a large part of the 7 TeV data collection, the minimum bias trigger paths had
to be prescaled by large factors because of the increasing instantaneous luminosity of the
LHC. In order to maximise the pT reach of the charged particle transverse momentum
measurement at this centre-of-mass energy, two high-level trigger (HLT) paths were used
that selected events with minimum uncorrected transverse jet energies (ET) of 15 and
50 GeV, based only on information from the calorimeters. While the higher threshold path
was not prescaled during the 7 TeV data-taking period corresponding to the 2.96 pb−1 used
in this analysis, the lower threshold path had to be prescaled for a significant fraction
of this sample. The 0.9 TeV data sample consists of 6.8 million minimum bias triggered
events, while the 7 TeV sample is composed of 18.7 million minimum bias events, and 1.4
(5.6) million events selected with the HLT minimum-ET values of 15 (50) GeV.

The selection efficiency for NSD events was determined based on simulated events from
the pythia [26] event generator (version 6.420, tune D6T [27]) that were subsequently
passed through a Monte Carlo simulation of the CMS detector response. The resulting
event selection efficiency as a function of the multiplicity of reconstructed charged particles
is shown for 7 TeV collisions in figure 1(a). The corresponding event selection efficiency
is calculated by the same technique for the 0.9 TeV data (not shown). Based on events
simulated with phojet [28, 29] and pythia, the remaining fraction of single-diffractive
(SD) events in the selected sample was estimated to be (5± 1)% and (6± 1)% for the 0.9
and 7 TeV data, respectively.

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Collision energy 0.9 TeV 7 TeV
Selection Percentage passing each selection cut
One BSC + one BPTX 100.0 100.0
BPTX coincidence 94.49 90.05
Beam halo rejection 94.08 89.83
HF coincidence 73.27 83.32
Beam background rejection 73.26 83.32
Valid pixel-track vertex 70.14 82.48
Quality full-track vertex 64.04 77.35

Table 1. Summary of event selection steps applied to the 0.9 and 7 TeV collision data sets and the
percentage of events from the original minimum bias samples that remain after each step.

Charged particle multiplicity

0 10 20 30 40 50

se
le

ct
ed

S
D

  o
r 

 f
se

le
ct

ed
N

S
D

ε

0.0

0.2

0.4

0.6

0.8

1.0

1.2

CMS Simulation

PYTHIA 7 TeV

NSD selection efficiency

pixel vertex (NSD)

track vertex (NSD)

Selected event SD fraction

pixel vertex (SD)

track vertex (SD)

(a)

 [cm]0
PVz

-15 -10 -5 0 5 10 15

 [c
m

]
1 P

V
z

-15

-10

-5

0

5

10

15

1

10

210

-1
Ldt = 10.2 nb∫ = 7 TeVsCMS  (b)

Figure 1. (a) The efficiency (εselectedNSD in eq. (7.2)) for selecting non-single-diffractive (NSD)
events as a function of the multiplicity of reconstructed charged particles in the tracker acceptance
(|η| < 2.4) after applying the full event selection described in the text, including a single pixel-track
vertex (filled circles) and additionally requiring a fully-reconstructed-track vertex with Ndof > 4
(open circles) as described in section 4. Also, the remaining single-diffractive (SD) fraction (f selected

SD

in eq. (7.2)) as a function of charged particle multiplicity for the same selections (solid and dashed
lines). (b) Correlation between the z positions, z0

PV and z1
PV, of the two vertices with the most

associated tracks for measured events with more than one fully-reconstructed-track vertex satisfying
the quality selections.

4 Primary vertex

In this analysis, two separate algorithms are employed to determine the primary vertex
position. The first is a highly efficient algorithm based on pixel triplet tracks that requires
a minimum of just a single track consistent with the beam-spot position. The position of
the beam-spot, taken as the centre of the region where the LHC beams collide, is calculated
for each LHC fill based on the average over many events of the three-dimensional fitted

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vertex positions [25]. The second vertex-finding algorithm, based on fully reconstructed
tracks with hits also in the silicon strip tracker, is less efficient in selecting low-multiplicity
events, but more robust in discriminating against event pileup. Since pileup is significant
over the majority of the analysed data sample, only the fully-reconstructed-track vertex
is used to construct the raw charged particle momentum spectra. The raw spectra are
subsequently corrected for the fraction of events with fewer than four tracks (and the
fraction of tracks in such low-multiplicity events), based on a subset of the event sample
selected with the more efficient pixel-track vertex requirement during collision runs with
negligible event pileup.

To determine the z position of the pixel vertex in each event, tracks consisting of three
pixel hits are constructed with a minimum pT of 75 MeV/c from a region within a transverse
distance of 0.2 cm from the beam axis. The x and y positions of the pixel vertex are taken
from the transverse position of the beam axis. Fitted tracks are selected based on the
requirement that the transverse impact parameter is less than three times the quadratic
sum of the transverse errors on the track impact parameter and the beam axis position.
The selected tracks are then passed to an agglomerative algorithm [30], which iteratively
clusters the tracks into vertex-candidates. The procedure is halted when the distance
between nearest clusters, normalised by their respective position uncertainties, reaches 12.
Only vertices consisting of at least two tracks are kept, except when the event contains a
single reconstructed track, which occurs in 1.67% (0.99%) of the events at

√
s = 0.9 (7) TeV.

In the case of multiple vertex-candidates, only the vertex with the most associated tracks
is kept. While this occurs in as many as 20% of events, the rejected vertex typically has
very few associated tracks and is highly correlated in z position to the vertex with the
most associated tracks. These characteristics imply that the rejected vertices are not from
event pileup, but rather from tracks in the tails of the impact parameter distribution that
are not agglomerated into the primary vertex.

The fully-reconstructed-track vertex algorithm begins from a set of tracks selected
according to their transverse impact parameter to the beam-spot (< 2 cm), number of hits
(> 6), and normalised χ2 (< 20). These tracks are passed to an adaptive vertex fitter, in
which tracks are assigned a weight between 0 and 1 according to their compatibility with the
common vertex [25]. Quality vertices are further required to have more than four degrees of
freedom (Ndof), corresponding to at least four tracks with weights of approximately one.
For events with multiple reconstructed vertices passing the quality selection, the correlation
between the z positions of the two vertices with the most associated tracks is shown in
figure 1(b). Other than the diagonal region without multiple vertices, expected from the
algorithmic parameter of at least a 1 cm separation, the uncorrelated positions of the two
vertices are indicative of random event pileup.

The event pileup rate is estimated from the fraction of events with multiple recon-
structed vertices, after correcting for vertices that are not found because of their proximity.
The beam conditions varied over the analysed minimum bias data samples, such that the
corrected fraction of pileup events is in the range (0.4–7.5)%. The uncertainty on the event
pileup fraction, determined from the largest correction to the multiple-vertex fraction, is a
constant factor of 0.2% and 1.2% for the 0.9 and 7 TeV data, respectively.

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5 Track selection

This analysis uses tracks from the standard CMS reconstruction algorithm, which consists
of multiple iterations of a combinatorial track finder based on various seeding layer pat-
terns [31]. After each iteration, hits belonging unambiguously to tracks in the previous
step are removed from consideration for subsequent steps.

In order to minimise the contribution from misidentified tracks and tracks with poor
momentum resolution, a number of quality selections are applied. These include the high-
Purity selection mentioned in section 3, the requirement of at least five hits on the track,
the normalized χ2 per degree of freedom divided by the number of tracker layers used in the
fit less than a maximum value which varies from 0.48 and 0.07 depending on η and pT, and
a relative momentum uncertainty of less than 20%. Furthermore, to reject non-primary
tracks (i.e., the products of weak decays and secondary interactions with detector mate-
rial), only the pixel-seeded tracking iterations are used, and selections are placed on the
impact parameter of the tracks with respect to the primary vertex position. Specifically,
the transverse and longitudinal impact parameters are required to be less than 0.2 cm and
also less than 3 times the sum in quadrature of the uncertainties on the impact parameter
and the corresponding vertex position. In the case of multiple quality reconstructed ver-
tices in the minimum bias event samples, tracks that pass the impact parameter selections
with respect to any vertex are used in the analysis. The number of events, by which the
track pT distribution is normalised, is then scaled by a factor to account for the event
pileup fraction. In contrast, for the jet-triggered samples, tracks are selected based on the
impact parameter with respect to the single vertex responsible for the trigger. The primary
vertex of the hard-scattering process is identified as the vertex with the largest value of∑
p2
T for the associated fitted tracks.
With the above-mentioned selections applied to the reconstructed tracks, the algo-

rithmic efficiency determined from simulated pythia events is greater than 85% (80%)
for tracks with transverse momentum above 2.0 (0.4) GeV/c averaged over |η| < 2.4 (fig-
ure 2(a)). In the same kinematic region, misidentified and non-primary tracks are each
below 1%, while multiple reconstruction occurs for less than 0.01% of tracks.

6 Event classification by leading-jet energy

All events in this analysis are classified according to the transverse energy of the most ener-
getic reconstructed jet, defined as the leading jet. Jets are reconstructed from calorimeter
deposits alone using the anti-kT algorithm [32] with cone radius R =

√
(∆φ)2 + (∆η)2 =

0.5. The measured energy of the jet is adjusted according to corrections based on a MC
description of the CMS calorimeter response with a 3–6% uncertainty on the jet energy
scale [33].

The motivation for classifying events according to the leading-jet transverse energy
is twofold. First, the degrading effect of the local-track density on the high-pT tracking
performance (e.g., inside a jet) can be parametrised according to this variable. Based on
events simulated with pythia in minimum bias and QCD samples with various thresholds

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η
-2 -1 0 1 2

A
lg

or
ith

m
ic

 e
ffi

ci
en

cy

0.5

0.6

0.7

0.8

0.9

1

     PYTHIA 7 TeV

 > 0.4 GeV/c
T

p

 > 2.0 GeV/c
T

p

CMS Simulation(a)

 [GeV/c]
T

p
1 10 210

tr ε ×
A

 

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

  PYTHIA 7 TeV
<20 GeVT 0<E

<40 GeVT 20<E

<60 GeVT 40<E

<80 GeVT 60<E

<100 GeVT 80<E

<120 GeVT 100<E

<200 GeVT 180<E

<400 GeVT 380<E

CMS Simulation(b)

Fake rate

Figure 2. (a) The algorithmic tracking efficiency for two different momentum ranges as a function
of η. (b) The product of geometrical acceptance (A) with tracking efficiency (εtr) (upper points)
and the misidentification (‘fake’) rate (lower points) as a function of transverse momentum for
tracks with |η| < 1 in bins of corrected leading-jet transverse energy.

on the hard-scattering scale (p̂T), the efficiency and misidentification rates of the selected
tracks are estimated as a function of transverse momentum in bins of leading-jet transverse
energy (see figure 2(b)). Second, as discussed in section 3, calorimeter-based triggers with
leading-jet transverse energy thresholds of 15 GeV (Jet15U) and 50 GeV (Jet50U) were used
to extend the pT reach of the 7 TeV measurement.

To avoid potential biases from the jet-trigger selection, it is desirable to operate in
a region where the trigger is fully efficient. The region above which the jet trigger with
an uncorrected energy threshold of 15 GeV becomes fully efficient is determined by first
plotting the leading-jet ET distribution for a sample of events selected with the prescaled
minimum bias trigger and the offline selections described in section 3. This distribution
is then compared to the subset of those events which also fire the 15 GeV jet trigger as a
function of corrected transverse energy. The resulting ratio is the trigger efficiency curve
presented in the lower panel of figure 3(a). The 15 GeV jet trigger achieves more than 99%
efficiency at a corrected energy of ET = 45 GeV. The analogous procedure is repeated
on a sample of events selected by the 15 GeV jet trigger to determine that the 50 GeV jet
trigger becomes fully efficient above ET = 95 GeV. For the trigger efficiency study, an early
subset of the data (10.2 nb−1) was used, because the minimum bias and lower-threshold jet
triggers were highly prescaled in the later runs. In the upper panel of figure 3(a), the ET

distributions from the jet-triggered sample are normalised per equivalent minimum bias
event by matching their integrals in the regions where the triggers are fully efficient.

For the 7 TeV analysis, events are divided into three classes based on leading-jet ET:
below 60 GeV, between 60 and 120 GeV, and above 120 GeV. Since each event is uniquely
assigned to one such leading-jet ET range, the overall dNch/dpT distribution is simply

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 [GeV]
corr jet
TE

0 100 200 300

) 
  [

1/
G

eV
]

T
/d

E
L

ea
d

in
g

 J
et

) 
(d

N
E

vt

M
B

(1
/N

-910

-710

-510

-310

-110

)-1bµMinBias trigger (413 
  + Jet15U

)-1Jet15U trigger (10.2 nb
  + Jet50U

)-1Jet50U trigger (2.96 pb

 = 7 TeVs(a)   CMS  

 [GeV]
corr jet
TE

0 100 200 300

T
ri

g
g

er
  E

ff
ic

.

0

0.5

1

Jet15U / MinBias

Jet50U / Jet15U

T
p

1 10 210

]
-1

 [(
G

eV
/c

)
T

) 
dN

/d
p

ev
t

(1
/N

-1210

-1010

-810

-610

-410

-210

1

210

410

 < 60 GeV)
T

HLT MB (E

 < 120 GeV)T E≤HLT Jet15U (60 

 120 GeV)≥ 
T

HLT Jet50U (E

-1Ldt = 2.96 pb∫|<2.4  η = 7 TeV  |s CMS    

Combined samples

)
T

MB (all E

 < 60 GeV)
T

MB (E

 60 GeV)≥ 
T

 +  Jet15U (E

(b)

 [GeV/c]
T

p
1 10 210

R
at

io

0.8

1.0

1.2

Combined/MB

Combined/[MB + Jet15U]

Figure 3. (a) Upper panel: distributions of the corrected transverse energy of leading jets nor-
malised by the number of selected minimum bias events NEvt

MB . Lower panel: the efficiency turn-on
curves for the jet triggers with uncorrected energy thresholds of 15 and 50 GeV. (b) Upper panel:
the three contributions to the charged particle transverse momentum spectrum and their sum (solid
circles). Open squares show the minimum bias spectrum for all values of leading-jet ET; open tri-
angles show the spectrum with the addition of only the lower threshold jet trigger. Lower panel:
the ratio of the combined spectrum to minimum bias only (solid circles) and with the addition of
only the lower threshold jet trigger (open triangles).

the sum of the spectra from the three ranges, each corresponding to a fully-efficient HLT
selection (i.e., minimum bias, 15 GeV jet trigger, and 50 GeV jet trigger). The contributions
to the spectra from the jet-triggered events are normalised per selected minimum bias event;
the fraction of minimum bias events containing a leading jet with greater than either 60
or 120 GeV is calculated as shown in figure 3(a) by matching the fully-efficient regions of
the leading-jet ET distributions. The three contributions to the combined charged particle
transverse momentum spectrum are shown in figure 3(b). The lower panel of that figure
compares the combined spectrum first to the minimum bias spectrum alone and then to
a spectrum constructed with the addition of only the lower-threshold jet trigger. These
are all in good agreement within their respective statistical uncertainties. A pT-dependent
systematic uncertainty of 0–4% is attributed to the normalisation of the contributions from
the triggered samples. This value is determined by changing the leading-jet ET ranges that
separate the three samples (e.g., to ET = 40 and 100 GeV), by basing the normalisation
directly on the HLT prescale values, and by comparing the normalisations determined from
different subsets of the full data sample.

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7 Corrections and systematic uncertainties

To obtain the final phase-space-invariant charged particle differential momentum distribu-
tion, a number of corrections must be applied to the raw distributions of reconstructed
charged particles, according to the following equation:

E
d3Nch

dp3
(pT, η) =

∑
M,E

jet
T

N raw
track(M,Ejet

T , pT, η) · wtr(pT, η, E
jet
T ) · wev(M)

2πpT ·∆pT ·∆η ·
∑

M
N selected(M) · (1−f0

NSD)−1 · (1+fpileup) · wev(M)
,

(7.1)
where N raw

track is the raw number of tracks in a bin with transverse momentum width ∆pT

and pseudorapidity width ∆η, and N selected is the number of selected events. An event
weight wev (see eq. (7.2)) is applied as a function of the multiplicity of reconstructed
charged particles (M), while a track weight wtr (see eq. (7.3)) is applied for each M and
leading-jet transverse energy (Ejet

T ), as a function of pT; the final results are summed over
M and Ejet

T . The number of selected events is corrected for the fraction of NSD events
(f0

NSD) that have zero reconstructed tracks in the tracker acceptance of |η| < 2.4 (about
5%) and for the pileup event fraction (fpileup).

The multiplicity-dependent event weight wev accounts for the efficiency of the event
selection for accepting NSD events (εselectedNSD ) and for the fraction of SD events (f selected

SD )
that contaminate the selected sample (about 5% overall):

wev(M) =
1

εselectedNSD

(1− f selected
SD ). (7.2)

The correction factor wtr, by which each track is weighted, is calculated for each bin
in transverse momentum, pseudorapidity, and leading-jet transverse energy. This factor
accounts for the geometric detector acceptance (A) and algorithmic tracking efficiency (εtr),
as well as the fraction of tracks corresponding to the same, multiply reconstructed charged
particle (D), the fraction of tracks corresponding to a non-primary charged particle (S),
and the fraction of misidentified (‘fake’) tracks that do not correspond to any charged
particle (F ):

wtr(pT, η, E
jet
T ) =

(1− F ) · (1− S)
A · εtr · (1 +D)

. (7.3)

The common uncertainty related to the triggering and event selection efficiency is dis-
cussed in detail in ref. [34]. Contributions from uncertain diffractive-event fractions and
detector inefficiencies in the BSC and HF combine to contribute a scale error of ±3.5% to
the total systematic uncertainty at

√
s = 7 TeV (see table 2). At

√
s = 0.9 TeV, the diffrac-

tive fractions are slightly better constrained, hence an uncertainty of ±3.2% is assigned.
Using simulated events generated with pythia tune D6T, the various terms in eq. (7.3)

are estimated by matching selected reconstructed tracks to simulated tracks based on the
requirement that they share 75% of their hits. As an example, the algorithmic efficiency
(εtr) versus η is presented in figure 2(a). The slight asymmetry between the positive and
negative hemispheres is attributed to a slightly displaced beam-spot and the distribution

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Source Uncertainty [%]
Collision energy 0.9 TeV 7 TeV
Event selection 3.2 3.5
Pileup effect on vertexing 0.2 1.2
Acceptance 1.5 1.5
Reconstruction efficiency 2.2 2.2
Occupancy effect on efficiency 0.0–0.5 0.0–2.8
Misidentified track rate 0.3–1.0 0.3–3.0
Correction for secondary particles 1.0 1.0
Momentum resolution and binning 0.3–1.5 0.3–2.7
Normalisation of jet-triggered spectra — 0.0–4.0
Total 4.3–4.7 4.7–7.9
Total excluding event selection uncertainty 2.9–3.4 3.1–7.1
Total including luminosity uncertainty 11.4–11.6 5.1–8.1

Table 2. Summary of the various contributions to the estimated systematic uncertainty.

of dead channels in the tracker. The systematic uncertainties assigned to the various track-
ing corrections are discussed below and are summarised, along with the total systematic
uncertainty, in table 2.

The uncertainty on the geometrical acceptance of the tracker was estimated from
three sources. First, the efficiency of the pixel hit reconstruction was estimated from a
data-driven technique involving the projection of two-hit combinations (called tracklets)
onto the third layer in search of a compatible hit. The observed efficiency of (99.0± 0.5)%
leads to a 0.3% uncertainty on the acceptance of pixel-seeded tracks. Second, the variation
of the geometrical acceptance was estimated for a variety of generator tunes including
pythia8 [35] and the Perugia0 [36] tune of pythia. Third, the variation was estimated
after shifting the generated beam-spot and modifying the width of the generated z vertex
distribution. The latter two effects each contribute a 1% shift in the acceptance.

In a similar fashion, using the different generator tunes results in a 2% shift in the
reconstruction efficiency. An additional series of checks was performed by varying the cuts
imposed during the track selection and in the determination of the corresponding MC-
based corrections. The resulting variation in the corrected results contributes another 1%
to the reconstruction efficiency uncertainty.

Since the dependence of the reconstruction efficiency on local hit density has been
parametrised in terms of leading-jet transverse energy, both the uncertainty on the jet
energy scale and the accuracy of the jet-fragmentation description become relevant. The
former contribution is estimated by convolving the dependence of the tracking efficiency on
the leading-jet transverse energy (see figure 2(b)) with a 4% uncertainty in the jet energy
scale [33]. The latter contribution is estimated by comparing the pythia-based corrections
to herwig++ [37]. The resulting pT-dependent uncertainty on the occupancy is in the
range (0.0–2.8)%.

Based on studies of different generator tunes and MC samples with different hard-
scattering scales, the assigned uncertainty to the misidentified-track correction grows lin-

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early as a function of pT from 0.3 to 3.0%. An additional check was performed for tracks
with pT above 10 GeV/c to correlate the reconstructed track momentum with the deposited
energy in the projected ECAL and HCAL cells. For the selected tracks in this analysis,
there is no evidence of any excess of high-pT misidentified tracks characterised by atypically
little energy deposited in the calorimeters. The correction for secondaries and feed-down
from weak decays is assigned a 1% systematic uncertainty, which is large compared to the
scale of the contributions, but intended to account for the uncertainties in the K0

S and Λ
fractions [38].

The tendency for finite bin widths (up to 40 GeV/c) and a finite transverse momentum
resolution (rising from 1 to 5% in the range pT = 10–150 GeV/c) to deform a steeply falling
spectrum is corrected based on the shape of the pT spectrum and the MC-based pT response
matrix. The effect of momentum resolution alone is 0.5–2.5%, while the wide binning results
in an additional correction ranging from a fraction of a percent up to approximately 20%
in the widest high-pT bins. The correction for the two effects is determined by fitting
an empirical function to the differential yield, smearing it with the MC-based momentum
resolution, re-binning into the bins of the final invariant yield, and dividing by the original
fitted form. The quoted systematic uncertainty of 0.3–2.7% is estimated by varying the
fitted form of the spectrum and by performing multiple iterations of the unsmearing with
successively more accurate input spectra.

In addition to the uncertainties from the event selection efficiency weighting and the
tracking corrections described above, the total systematic uncertainty contains a contri-
bution from the uncertainty on the estimation of the event pileup fraction of 0.2 and
1.2% for the 0.9 and 7 TeV data, respectively. In the cases where the total integrated
luminosity is used to normalise the results, this contributes an additional 4% (11%) scale
uncertainty [14, 15] for

√
s = 7 (0.9) TeV. Assuming that the various pT-dependent con-

tributions are uncorrelated, the total systematic uncertainty is determined from their sum
in quadrature, as indicated in table 2.

8 Results

After applying the corrections described in the previous section, the resulting invariant
differential yields for charged particles within |η| < 2.4 are shown for a limited pT range in
figures 4(a) and 4(b) in order to quantify the agreement with previous CMS measurements
at
√
s = 0.9 and 7 TeV [24, 34]. At each energy, both CMS measurements are divided

by a Tsallis fit [39] to the earlier measurement and the ratios compared in the lower
panels. For the earlier measurements, the error bars indicate the statistical plus systematic
uncertainties added in quadrature. The bands around the new measurements represent all
contributions to the systematic uncertainty, except the contribution from the common
event selection. Statistical uncertainties are negligible on the new measurements in this
pT range. Below pT = 4 GeV/c for the 0.9 TeV sample and below pT = 6 GeV/c at

√
s =

7 TeV, which are the limits of the previously published CMS spectra, the new results are in
reasonable agreement with the earlier measurements. However, the measured spectra do
deviate from the Tsallis fits in the earlier papers by as much as 20% at low pT. The origin

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|<2.4η = 0.9 TeV,  |s     

-1bµLdt = 231 ∫CMS     

CMS (JHEP 02 (2010) 041)

Tsallis fit (JHEP 02 (2010) 041)

(a)

 [GeV/c]
T

p

0 0.5 1 1.5 2 2.5 3 3.5 4

D
at

a 
/ F

it

0.6

0.8

1.0

1.2
 [GeV/c]

T
p

0 1 2 3 4 5 6

]
3 c

-2
 [G

eV
3

N
/d

p
3

E
d

-510

-410

-310

-210

-110

1

10
|<2.4η = 7 TeV,  |s     

-1Ldt = 2.96 pb∫CMS     

CMS (PRL 105, 022002)

Tsallis fit (PRL 105, 022002)

(b)

 [GeV/c]
T

p

0 1 2 3 4 5 6
D

at
a 

/ F
it

0.8

1.0

1.2

Figure 4. (a) Upper panel: the invariant charged particle differential yield from the present analysis
(solid circles) and the previous CMS measurements at

√
s = 0.9 TeV (stars) over the limited pT

range of the earlier result. Lower panel: the ratio of the new (solid circles) and previous (stars)
CMS results to a Tsallis fit of the earlier measurement. Error bars on the earlier measurement are
the statistical plus systematic uncertainties added in quadrature. The systematic uncertainty band
around the new measurement consists of all contributions, except for the common event selection
uncertainty. (b) The same for

√
s = 7 TeV.

of the small difference between the two CMS measurements at
√
s = 7 TeV is attributed

to the different tracking algorithms used in the two measurements, as well as the different
pythia tunes used to determine the tracking corrections.

In the upper plots of figures 5(a) and 5(b), the charged particle differential transverse
momentum yields from this analysis are displayed for

√
s = 0.9 and 7 TeV, respectively.

The latter distribution covers the pT range up to 200 GeV/c, the largest range ever measured
in a colliding beam experiment. Also shown in the figures are various generator-level MC
predictions for the yields [27, 35, 36, 40]. The lower plots of figures 5(a) and 5(b) show
the ratios of the data to the various MC predictions. As already observed in ref. [34],
there is a deficit of pT < 1 GeV/c particles in the predicted 7 TeV spectra for several
of the popular pythia tunes. For the whole pT range above 1 GeV/c, pythia8 is the
most consistent with the new 7 TeV result (within 10%). This provides an important
constraint on the different generator parameters responsible for sizable variations among
the tunes. A similar but slightly larger spread is observed in figure 5(a) for different
generator parameters at

√
s = 0.9 TeV, where the CMS measurement is most consistently

described by the ProQ20 tune.

As discussed in ref. [41, 42], a robust prediction of pQCD hard processes is the power-
law scaling of the inclusive charged particle invariant differential cross section with the

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-1210

-1010

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|<2.4η = 0.9 TeV,   |s    
-1bµLdt = 231 ∫CMS    

PYTHIA D6T

PYTHIA Perugia0

PYTHIA ProQ20

PYTHIA 8

(a)

 [GeV/c]
T

p
1 10

D
A

TA
 / 

M
C

0.6
0.8
1.0
1.2
1.4
1.6
1.8

 [GeV/c]
T

p
1 10 210

]
3 c

-2
 [G

eV
3

N
/d

p
3

E
d

-1710

-1510

-1310

-1110

-910

-710

-510

-310

-110

10

|<2.4η = 7 TeV,   |s    
-1Ldt = 2.96 pb∫CMS    

PYTHIA D6T

PYTHIA Perugia0

PYTHIA ProQ20

PYTHIA 8

(b)

 [GeV/c]
T

p
1 10 210

D
A

TA
 / 

M
C

0.6

0.8

1.0

1.2

1.4

Figure 5. (a) Upper panel: the invariant charged particle differential yield at
√
s = 0.9 TeV

compared with the predictions of four tunes of the pythia MC generator. Lower panel: the ratio of
the new CMS measurement to the four pythia tunes. The grey band corresponds to the statistical
and systematic uncertainties added in quadrature. (b) The same for

√
s = 7 TeV.

variable xT:

E
d3σ

dp3
= F (xT)/pn(xT,

√
s)

T = F ′(xT)/
√
s
n(xT,

√
s)
, (8.1)

where F and F ′ are independent of
√
s, and the slow evolution of the power-law exponent

n with xT and
√
s (n ' 5–6) is due to the running of αs and changes in the parton

distribution and fragmentation functions. In the upper plot of figure 6(a), the 0.9 and
7 TeV pp measurements from this analysis are compared to the empirical scaling observed
from measurements over a range of lower pp̄ collision energies by plotting

√
s
n
E d3σ/dp3.

For the purpose of reporting the CMS results as differential cross sections, the integrated
luminosities for the analysed data samples were measured according to the descriptions
in ref. [14, 15]. Also, to compare with the published results from the CDF experiment at√
s = 0.63, 1.8, and 1.96 TeV, the pseudorapidity range has been restricted to |η| < 1.0.

Whereas an exponent n = 5.5 was found in ref. [42] from a global fit to only the previous
pp̄ measurements from

√
s = 0.2 to 1.96 TeV, the xT scaling presented in this paper is

optimised for use in an interpolation between the CDF and CMS measurements from
√
s =

0.9 to 7 TeV. Within this range, the best scaling is achieved with an exponent of n = 4.9±
0.1. This is consistent with the predictions of next-to-leading-order (NLO) calculations,
where the scaling is also found to be optimised for this value of the exponent [42]. From
the lower panel of figure 6(a), it is apparent that the NLO calculations over-predict the
measured cross sections by almost a factor of two at all collision energies. This is in spite

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Tx
-410 -310 -210 -110

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G

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3

/d
p

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3

E
d

4.
9

/G
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(

510

710

910

1110

1310

1510

1710

1910

2110

|<1.0)η) + X (|-+h+ 0.5(h→) p   pp(

)-1CMS 7 TeV (2.96 pb
)-1bµCMS 0.9 TeV (231 

CDF 1.96 TeV

CDF 1.8 TeV
CDF 0.63 TeV
Global power-law fit

(a)

Tx

-410 -310 -210 -110

D
at

a/
N

LO

0.5

1.0

1.5  = 0.9 TeVs   = 1.96 TeVs   = 7 TeVs  
Tx

0.01 0.02 0.03 0.04 0.05 0.06 0.07

 / 
F

it
3

/d
p

σ
3

E
d

4.
9

/G
eV

)
s

(

0.5

1

1.5

2

2.5

3

3.5

 = 2.76 TeVs (GeV/c) for 
T

p
10 20 30 40 50 60 70 80 90

(b) ) + fit-1CMS 7 TeV (2.96 pb

) + fit-1bµCMS 0.9 TeV (231 

CDF 1.96 TeV + fit
 interpolations

T
2.76 TeV x

(corrected by NLO ratios)

Tx

0.01 0.02 0.03 0.04 0.05 0.06 0.07
N

LO
 r

at
io

1.0

1.2

1.4

1.6

1.8  = 2.75 TeV)s = 0.9, 1.96, 7 TeV /  s (
3

/dpσ3
Ed

4.9
)s  Ratio of (

 = 0.9 TeVs   = 1.96 TeVs   = 7 TeVs  

Figure 6. (a) Upper panel: inclusive charged particle invariant differential cross sections, scaled
by
√
s
4.9, for |η| < 1.0 as a function of the scaling parameter xT. The result is the average of the

positive and negative charged particles. Lower panel: ratios of differential cross sections measured
at 0.9, 1.96, and 7 TeV to those predicted by NLO calculations for factorisation scales ranging from
0.5–2.0 pT. (b) Upper panel: ratios of the scaled differential cross sections to the global power-law
xT fit described in the text (coloured markers) and fits to these ratios (similarly coloured thin lines).
The expected ratio for

√
s = 2.76 TeV after applying NLO-based corrections to each of the three

measurements as described in the text (solid blue lines). The uncertainty from the NLO parameters
is represented by the shaded band. The upper axis translates xT to pT for

√
s = 2.76 TeV. Lower

panel: ratios of the NLO-calculated cross sections at three different energies, scaled by
√
s
4.9, to

the cross section calculated at
√
s = 2.75 TeV. The width of the bands represents the variation of

the factorisation scale by a factor of two.

of the relatively good agreement in the inclusive jet spectrum [43, 44], which suggests that
the fragmentation functions are not well tuned for LHC energies.

The CMS results are consistent over the accessible xT range with the empirical xT

scaling given by eq. (8.1) and established at lower energies. This quality of the scaling
is more easily seen in the upper panel of figure 6(b), where the points show the ratio of
the various differential cross sections, scaled by

√
s
4.9, to the result of a global power-

law fit to the CDF and CMS data from figure 6(a). The fitting function is of the form
F ′(xT) = p0 · [1 + (xT/p1)]p2 , where p0, p1, and p2 are free parameters, and the region
below pT = 3.5 GeV/c has been excluded to avoid complications from soft-particle produc-
tion. Considering the somewhat näıve power-law function and the expected non-scaling
effects [45], the new measurement is in reasonable agreement with the global power-law fit
result (within roughly 50%) over its full xT range.

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9 Interpolation to 2.76 TeV

In order to construct a predicted reference charged particle differential cross section at√
s = 2.76 TeV for comparison with the measured PbPb heavy-ion spectrum, two different

techniques are used in partially overlapping transverse momentum regimes. In the high-pT

range from 5.0–200 GeV/c, where approximate xT scaling is expected to hold, the estimated
2.76 TeV cross section is derived from a common xT-scaling curve, based on the CDF and
CMS measurements shown in figure 6(a). In the low-pT range from 1.0–20 GeV/c, it is
possible to interpolate directly between the several measured cross section values as a
function of

√
s at each fixed pT value.

As discussed in the previous section, the upper panel of figure 6(b) shows the residual
difference from perfect xT scaling with exponent n = 4.9 for the 0.9 and 7 TeV CMS
measurements and for the 1.96 TeV CDF measurement [4, 5] . The

√
s and xT dependence

of the residuals are not unexpected, since this behaviour is predicted by NLO calculations.
This can be seen in the lower panel of figure 6(b), which shows the predicted deviation from
perfect xT scaling for calculated NLO cross sections at several collision energies with respect
to a reference centre-of-mass energy of 2.75 TeV [42]. The calculations were performed using
the CTEQ66 parton distribution functions [46], DSS fragmentation [47], and a factorisation
scale µ = pT [42]. Taking the magnitude of the xT-scaling violation from NLO (ranging
from 0–20%), each of the three measurements in data (i.e., 0.9, 1.96, and 7 TeV) can be
corrected separately to arrive at an expectation for the 2.76 TeV cross section. The three
independent interpolations based on NLO-corrected xT scaling are shown as solid blue
lines in the upper panel of figure 6(b). The combined ‘best estimate’ (shown as a shaded
band) has an associated uncertainty that covers the deviations of up to 12% observed by
varying the factorisation scale from µ = 0.5 pT to µ = 2.0 pT for each of the three collision
energies. The error band is expanded below pT ≈ 8 GeV/c to include the full difference
between the 1.96 and 7 TeV results, since the evolution of the spectra below this value —
corresponding to xT = 0.0023 (7 TeV), 0.0082 (1.96 TeV), and 0.018 (0.9 TeV) — is no longer
consistently described by xT scaling and the NLO-based corrections. In addition to the
12% contribution from the uncertainty on the NLO-based correction, the final uncertainty
on the interpolated cross section has an additional component to account for possible
correlations in the luminosity uncertainty between the three measurements. This term,
taken as equal to the smallest individual uncertainty (4%), is added in quadrature.

The direct interpolation of cross sections at a fixed value of pT is done using CDF
measurements at

√
s = 0.63, 1.8 and 1.96 TeV [4, 5, 17], the new CMS measurements

at
√
s = 0.9 and 7 TeV, as well as an earlier result at

√
s = 2.36 TeV [24]. The latter

measurement is converted to a differential cross section assuming the total inelastic cross
section of 60.52 mb from pythia. At each energy, an empirical fit to the pT distribution
is first constructed to provide a continuous estimation independent of different binning.
Then, in arbitrarily small pT bins, these empirical fits are evaluated and the evolution of
the cross section with

√
s is parametrised by a second-order polynomial. Two examples of

these fits are shown in figure 7(a) for pT = 3 and 9 GeV/c. The uncertainty on the value
of the fit evaluated at

√
s = 2.76 TeV is taken from the covariance matrix of the fit terms,

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with an additional 4% added in quadrature to account conservatively for any correlation
in the luminosity uncertainty between the different measurements.

To arrive at a single interpolated spectrum over the full pT range, a linear combination
of the two techniques is used with weights that vary linearly across the overlap range from
pT = 5 GeV/c (only direct interpolation at fixed pT) to pT = 20 GeV/c (only xT scaling with
NLO-based residual correction). In the pT range where the two techniques overlap, the
different methods agree to within their respective systematic uncertainties. (The fixed-pT

interpolation value is typically around 8% lower than the xT interpolation.) The resulting
predicted 2.76 TeV differential cross section is shown in the upper panel of figure 7(b), and
its ratio with respect to various pythia tunes at that centre-of-mass energy in the lower
panel. The uncertainty on the predicted cross section, shown by the grey band in the lower
panel, is the weighted sum (where applicable) of the uncertainties derived from the two
methods described in the preceding paragraphs. Also shown in the lower panel of figure 7(b)
is the ratio of the predicted 2.76 TeV cross section to that found by simply scaling the CMS
measured 7 TeV result by the expected 2.75 TeV to 7 TeV ratio from NLO calculations [42].
The interpolation used in the recent ALICE publication [13] is a few percent lower than the
result quoted in this paper, but consistent within the respective systematic uncertainties.
The behavior of the various generators compared to the interpolated 2.76 TeV cross section
is broadly similar to the 0.9 TeV invariant yields presented in figure 7(b). The ProQ20 tune
agrees most closely (within 15%) with the interpolated cross section above 2 GeV/c. Future
analysis of a recently recorded 2.76 TeV pp collision sample will provide verification of this
result and a reduction in the systematic uncertainties.

10 Summary

In this paper, measurements of the phase-space-invariant differential yield E d3Nch/dp
3 at√

s = 0.9 and 7 TeV have been presented for primary charged particles, averaged over the
pseudorapidity acceptance of the CMS tracking system (|η| < 2.4). The results have been
shown to be in reasonable agreement with the previously published CMS measurements at√
s = 0.9 and 7 TeV [24, 34] and, except for the surplus of tracks at very low transverse

momentum, with pythia leading-order pQCD. The 7 TeV data are most consistent with
pythia8, which agrees at the 10% level over the full pT range of the measurement. In con-
trast, the 0.9 TeV data are considerably better described by the ProQ20 tune. Additionally,
the consistency of the 0.9 and 7 TeV spectra has been demonstrated with an empirical xT

scaling that unifies the differential cross sections from a wide range of collision energies
onto a common curve. Furthermore, within the theoretical uncertainties of the NLO cal-
culations, the residual breaking of xT scaling above pT ≈ 8 GeV/c is consistent between
the measured cross sections and the NLO calculations.

This result has removed a large uncertainty from an important ingredient of existing
and future PbPb measurements, namely the pp reference spectrum corresponding to the
energy of the 2010 PbPb run: 2.76 TeV per nucleon. By employing a combination of tech-
niques to interpolate between the results presented here at

√
s = 0.9 and 7 TeV, including

information from existing CDF measurements at
√
s = 0.63, 1.8, and 1.96 TeV, a pp refer-

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G
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/d

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σ3

E
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(a)

 = 3 GeV/c
T

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|<1.0)η) + X (|-+h+ 0.5(h→) p   pp(

)-1CMS 7 TeV (2.96 pb CDF 1.96 TeV

)-1bµCMS 2.36 TeV (0.2 CDF 1.8 TeV

)-1bµCMS 0.9 TeV (231 CDF 0.63 TeV

 [TeV]s
1 10

3
] x

 1
0

3 c
-2

 [m
b 

G
eV

3
/d

p
σ3

E
d

-510

-410

 = 9 GeV/c
T

p

 = 2.76 TeV interpolated values

 scaling interp.Tx

 [GeV/c]
T

p
1 10 210

]
3 c

-2
 [m

b 
G

eV
3

/d
p

σ
3

E
d

-1410

-1210

-1010

-810

-610

-410

-210

1

210

|<1.0η = 2.76 TeV,   |s    

CMS Interpolation

PYTHIA D6T
PYTHIA Perugia0

PYTHIA ProQ20

PYTHIA 8
NLO rescaled CMS 7 TeV (F. Arleo et al.)

 = 64 mb)mbσALICE (scaled by 

(b)

 [GeV/c]
T

p
1 10 210

In
te

rp
. /

 O
th

er
s

0.6
0.8
1.0
1.2
1.4
1.6

Figure 7. (a) Interpolations between measured charged particle differential cross sections at
different

√
s for the two example values of pT = 3 and 9 GeV/c. Second-order polynomial fits to the

measured data are shown by the solid lines. The open squares show the resulting interpolated cross
sections for

√
s = 2.76 TeV. The open circle on the lower panel represents the corresponding estimate

from the xT-scaling approach in the overlap region where both can be estimated. (b) Upper panel:
the predicted 2.76 TeV charged particle differential transverse momentum cross section, based on
the combined direct pT interpolation and NLO-corrected xT-scaling techniques described in the
text. Lower panel: ratios of combined interpolation to predictions from several pythia tunes, an
NLO-based rescaling approach [42], and the ALICE interpolation used in ref. [13].

ence at
√
s = 2.76 TeV has been constructed over a large range of transverse momentum

(pT = 1–100 GeV/c) with systematic uncertainties of less than 13%.

Acknowledgments

We wish to congratulate our colleagues in the CERN accelerator departments for the excel-
lent performance of the LHC machine. We thank the technical and administrative staff at
CERN and other CMS institutes, and acknowledge support from: FMSR (Austria); FNRS
and FWO (Belgium); CNPq, CAPES, FAPERJ, and FAPESP (Brazil); MES (Bulgaria);
CERN; CAS, MoST, and NSFC (China); COLCIENCIAS (Colombia); MSES (Croatia);
RPF (Cyprus); Academy of Sciences and NICPB (Estonia); Academy of Finland, MEC,
and HIP (Finland); CEA and CNRS/IN2P3 (France); BMBF, DFG, and HGF (Germany);
GSRT (Greece); OTKA and NKTH (Hungary); DAE and DST (India); IPM (Iran); SFI
(Ireland); INFN (Italy); NRF and WCU (Korea); LAS (Lithuania); CINVESTAV, CONA-
CYT, SEP, and UASLP-FAI (Mexico); PAEC (Pakistan); SCSR (Poland); FCT (Portu-
gal); JINR (Armenia, Belarus, Georgia, Ukraine, Uzbekistan); MST and MAE (Russia);

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MSTD (Serbia); MICINN and CPAN (Spain); Swiss Funding Agencies (Switzerland); NSC
(Taipei); TUBITAK and TAEK (Turkey); STFC (United Kingdom); DOE and NSF (USA).
Individuals have received support from the Marie-Curie programme and the European Re-
search Council (European Union); the Leventis Foundation; the A. P. Sloan Foundation;
the Alexander von Humboldt Foundation; the Associazione per lo Sviluppo Scientifico e
Tecnologico del Piemonte (Italy); the Belgian Federal Science Policy Office; the Fonds pour
la Formation à la Recherche dans l’Industrie et dans l’Agriculture (FRIA-Belgium); and
the Agentschap voor Innovatie door Wetenschap en Technologie (IWT-Belgium).

Open Access. This article is distributed under the terms of the Creative Commons
Attribution Noncommercial License which permits any noncommercial use, distribution,
and reproduction in any medium, provided the original author(s) and source are credited.

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The CMS collaboration

Yerevan Physics Institute, Yerevan, Armenia

S. Chatrchyan, V. Khachatryan, A.M. Sirunyan, A. Tumasyan

Institut für Hochenergiephysik der OeAW, Wien, Austria

W. Adam, T. Bergauer, M. Dragicevic, J. Erö, C. Fabjan, M. Friedl, R. Frühwirth,
V.M. Ghete, J. Hammer1, S. Hänsel, M. Hoch, N. Hörmann, J. Hrubec, M. Jeitler,
W. Kiesenhofer, M. Krammer, D. Liko, I. Mikulec, M. Pernicka, H. Rohringer,
R. Schöfbeck, J. Strauss, A. Taurok, F. Teischinger, P. Wagner, W. Waltenberger,
G. Walzel, E. Widl, C.-E. Wulz

National Centre for Particle and High Energy Physics, Minsk, Belarus

V. Mossolov, N. Shumeiko, J. Suarez Gonzalez

Universiteit Antwerpen, Antwerpen, Belgium

L. Benucci, E.A. De Wolf, X. Janssen, J. Maes, T. Maes, L. Mucibello, S. Ochesanu,
B. Roland, R. Rougny, M. Selvaggi, H. Van Haevermaet, P. Van Mechelen, N. Van Remortel

Vrije Universiteit Brussel, Brussel, Belgium

F. Blekman, S. Blyweert, J. D’Hondt, O. Devroede, R. Gonzalez Suarez, A. Kalogeropoulos,
M. Maes, W. Van Doninck, P. Van Mulders, G.P. Van Onsem, I. Villella

Université Libre de Bruxelles, Bruxelles, Belgium

O. Charaf, B. Clerbaux, G. De Lentdecker, V. Dero, A.P.R. Gay, G.H. Hammad, T. Hreus,
P.E. Marage, L. Thomas, C. Vander Velde, P. Vanlaer

Ghent University, Ghent, Belgium

V. Adler, A. Cimmino, S. Costantini, M. Grunewald, B. Klein, J. Lellouch, A. Marinov,
J. Mccartin, D. Ryckbosch, F. Thyssen, M. Tytgat, L. Vanelderen, P. Verwilligen, S. Walsh,
N. Zaganidis

Université Catholique de Louvain, Louvain-la-Neuve, Belgium

S. Basegmez, G. Bruno, J. Caudron, L. Ceard, E. Cortina Gil, J. De Favereau De Jeneret,
C. Delaere1, D. Favart, A. Giammanco, G. Grégoire, J. Hollar, V. Lemaitre, J. Liao,
O. Militaru, S. Ovyn, D. Pagano, A. Pin, K. Piotrzkowski, N. Schul

Université de Mons, Mons, Belgium

N. Beliy, T. Caebergs, E. Daubie

Centro Brasileiro de Pesquisas Fisicas, Rio de Janeiro, Brazil

G.A. Alves, D. De Jesus Damiao, M.E. Pol, M.H.G. Souza

Universidade do Estado do Rio de Janeiro, Rio de Janeiro, Brazil

W. Carvalho, E.M. Da Costa, C. De Oliveira Martins, S. Fonseca De Souza, L. Mundim,
H. Nogima, V. Oguri, W.L. Prado Da Silva, A. Santoro, S.M. Silva Do Amaral, A. Sznajder,
F. Torres Da Silva De Araujo

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Instituto de Fisica Teorica, Universidade Estadual Paulista, Sao Paulo, Brazil
F.A. Dias, T.R. Fernandez Perez Tomei, E. M. Gregores2, C. Lagana, F. Marinho,
P.G. Mercadante2, S.F. Novaes, Sandra S. Padula

Institute for Nuclear Research and Nuclear Energy, Sofia, Bulgaria
N. Darmenov1, L. Dimitrov, V. Genchev1, P. Iaydjiev1, S. Piperov, M. Rodozov,
S. Stoykova, G. Sultanov, V. Tcholakov, R. Trayanov, I. Vankov

University of Sofia, Sofia, Bulgaria
A. Dimitrov, R. Hadjiiska, A. Karadzhinova, V. Kozhuharov, L. Litov, M. Mateev,
B. Pavlov, P. Petkov

Institute of High Energy Physics, Beijing, China
J.G. Bian, G.M. Chen, H.S. Chen, C.H. Jiang, D. Liang, S. Liang, X. Meng, J. Tao,
J. Wang, J. Wang, X. Wang, Z. Wang, H. Xiao, M. Xu, J. Zang, Z. Zhang

State Key Lab. of Nucl. Phys. and Tech., Peking University, Beijing, China
Y. Ban, S. Guo, Y. Guo, W. Li, Y. Mao, S.J. Qian, H. Teng, L. Zhang, B. Zhu, W. Zou

Universidad de Los Andes, Bogota, Colombia
A. Cabrera, B. Gomez Moreno, A.A. Ocampo Rios, A.F. Osorio Oliveros, J.C. Sanabria

Technical University of Split, Split, Croatia
N. Godinovic, D. Lelas, K. Lelas, R. Plestina3, D. Polic, I. Puljak

University of Split, Split, Croatia
Z. Antunovic, M. Dzelalija

Institute Rudjer Boskovic, Zagreb, Croatia
V. Brigljevic, S. Duric, K. Kadija, S. Morovic

University of Cyprus, Nicosia, Cyprus
A. Attikis, M. Galanti, J. Mousa, C. Nicolaou, F. Ptochos, P.A. Razis

Charles University, Prague, Czech Republic
M. Finger, M. Finger Jr.

Academy of Scientific Research and Technology of the Arab Republic of Egypt,
Egyptian Network of High Energy Physics, Cairo, Egypt
Y. Assran4, S. Khalil5, M.A. Mahmoud6

National Institute of Chemical Physics and Biophysics, Tallinn, Estonia
A. Hektor, M. Kadastik, M. Müntel, M. Raidal, L. Rebane

Department of Physics, University of Helsinki, Helsinki, Finland
V. Azzolini, P. Eerola, G. Fedi

Helsinki Institute of Physics, Helsinki, Finland
S. Czellar, J. Härkönen, A. Heikkinen, V. Karimäki, R. Kinnunen, M.J. Kortelainen,
T. Lampén, K. Lassila-Perini, S. Lehti, T. Lindén, P. Luukka, T. Mäenpää, E. Tuominen,
J. Tuominiemi, E. Tuovinen, D. Ungaro, L. Wendland

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Lappeenranta University of Technology, Lappeenranta, Finland

K. Banzuzi, A. Korpela, T. Tuuva

Laboratoire d’Annecy-le-Vieux de Physique des Particules, IN2P3-CNRS,
Annecy-le-Vieux, France

D. Sillou

DSM/IRFU, CEA/Saclay, Gif-sur-Yvette, France

M. Besancon, S. Choudhury, M. Dejardin, D. Denegri, B. Fabbro, J.L. Faure, F. Ferri,
S. Ganjour, F.X. Gentit, A. Givernaud, P. Gras, G. Hamel de Monchenault, P. Jarry,
E. Locci, J. Malcles, M. Marionneau, L. Millischer, J. Rander, A. Rosowsky, I. Shreyber,
M. Titov, P. Verrecchia

Laboratoire Leprince-Ringuet, Ecole Polytechnique, IN2P3-CNRS, Palaiseau,
France

S. Baffioni, F. Beaudette, L. Benhabib, L. Bianchini, M. Bluj7, C. Broutin, P. Busson,
C. Charlot, T. Dahms, L. Dobrzynski, S. Elgammal, R. Granier de Cassagnac, M. Hague-
nauer, P. Miné, C. Mironov, C. Ochando, P. Paganini, D. Sabes, R. Salerno, Y. Sirois,
C. Thiebaux, B. Wyslouch8, A. Zabi

Institut Pluridisciplinaire Hubert Curien, Université de Strasbourg, Univer-
sité de Haute Alsace Mulhouse, CNRS/IN2P3, Strasbourg, France

J.-L. Agram9, J. Andrea, D. Bloch, D. Bodin, J.-M. Brom, M. Cardaci, E.C. Chabert,
C. Collard, E. Conte9, F. Drouhin9, C. Ferro, J.-C. Fontaine9, D. Gelé, U. Goerlach,
S. Greder, P. Juillot, M. Karim9, A.-C. Le Bihan, Y. Mikami, P. Van Hove

Centre de Calcul de l’Institut National de Physique Nucleaire et de Physique
des Particules (IN2P3), Villeurbanne, France

F. Fassi, D. Mercier

Université de Lyon, Université Claude Bernard Lyon 1, CNRS-IN2P3, Institut
de Physique Nucléaire de Lyon, Villeurbanne, France

C. Baty, S. Beauceron, N. Beaupere, M. Bedjidian, O. Bondu, G. Boudoul, D. Boumediene,
H. Brun, J. Chasserat, R. Chierici, D. Contardo, P. Depasse, H. El Mamouni, J. Fay,
S. Gascon, B. Ille, T. Kurca, T. Le Grand, M. Lethuillier, L. Mirabito, S. Perries, V. Sordini,
S. Tosi, Y. Tschudi, P. Verdier

Institute of High Energy Physics and Informatization, Tbilisi State University,
Tbilisi, Georgia

D. Lomidze

RWTH Aachen University, I. Physikalisches Institut, Aachen, Germany

G. Anagnostou, M. Edelhoff, L. Feld, N. Heracleous, O. Hindrichs, R. Jussen, K. Klein,
J. Merz, N. Mohr, A. Ostapchuk, A. Perieanu, F. Raupach, J. Sammet, S. Schael,
D. Sprenger, H. Weber, M. Weber, B. Wittmer

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RWTH Aachen University, III. Physikalisches Institut A, Aachen, Germany

M. Ata, W. Bender, E. Dietz-Laursonn, M. Erdmann, J. Frangenheim, T. Hebbeker,
A. Hinzmann, K. Hoepfner, T. Klimkovich, D. Klingebiel, P. Kreuzer, D. Lanske†,
C. Magass, M. Merschmeyer, A. Meyer, P. Papacz, H. Pieta, H. Reithler, S.A. Schmitz,
L. Sonnenschein, J. Steggemann, D. Teyssier

RWTH Aachen University, III. Physikalisches Institut B, Aachen, Germany

M. Bontenackels, M. Davids, M. Duda, G. Flügge, H. Geenen, M. Giffels, W. Haj Ahmad,
D. Heydhausen, T. Kress, Y. Kuessel, A. Linn, A. Nowack, L. Perchalla, O. Pooth,
J. Rennefeld, P. Sauerland, A. Stahl, M. Thomas, D. Tornier, M.H. Zoeller

Deutsches Elektronen-Synchrotron, Hamburg, Germany

M. Aldaya Martin, W. Behrenhoff, U. Behrens, M. Bergholz10, A. Bethani, K. Borras,
A. Cakir, A. Campbell, E. Castro, D. Dammann, G. Eckerlin, D. Eckstein, A. Floss-
dorf, G. Flucke, A. Geiser, J. Hauk, H. Jung1, M. Kasemann, I. Katkov11, P. Katsas,
C. Kleinwort, H. Kluge, A. Knutsson, M. Krämer, D. Krücker, E. Kuznetsova, W. Lange,
W. Lohmann10, R. Mankel, M. Marienfeld, I.-A. Melzer-Pellmann, A.B. Meyer, J. Mnich,
A. Mussgiller, J. Olzem, D. Pitzl, A. Raspereza, A. Raval, M. Rosin, R. Schmidt10,
T. Schoerner-Sadenius, N. Sen, A. Spiridonov, M. Stein, J. Tomaszewska, R. Walsh,
C. Wissing

University of Hamburg, Hamburg, Germany

C. Autermann, V. Blobel, S. Bobrovskyi, J. Draeger, H. Enderle, U. Gebbert, K. Kaschube,
G. Kaussen, R. Klanner, J. Lange, B. Mura, S. Naumann-Emme, F. Nowak, N. Pietsch,
C. Sander, H. Schettler, P. Schleper, M. Schröder, T. Schum, J. Schwandt, H. Stadie,
G. Steinbrück, J. Thomsen

Institut für Experimentelle Kernphysik, Karlsruhe, Germany

C. Barth, J. Bauer, V. Buege, T. Chwalek, W. De Boer, A. Dierlamm, G. Dirkes,
M. Feindt, J. Gruschke, C. Hackstein, F. Hartmann, M. Heinrich, H. Held, K.H. Hoffmann,
S. Honc, J.R. Komaragiri, T. Kuhr, D. Martschei, S. Mueller, Th. Müller, M. Niegel,
O. Oberst, A. Oehler, J. Ott, T. Peiffer, G. Quast, K. Rabbertz, F. Ratnikov, N. Ratnikova,
M. Renz, C. Saout, A. Scheurer, P. Schieferdecker, F.-P. Schilling, M. Schmanau, G. Schott,
H.J. Simonis, F.M. Stober, D. Troendle, J. Wagner-Kuhr, T. Weiler, M. Zeise, V. Zhukov11,
E.B. Ziebarth

Institute of Nuclear Physics ”Demokritos”, Aghia Paraskevi, Greece

G. Daskalakis, T. Geralis, S. Kesisoglou, A. Kyriakis, D. Loukas, I. Manolakos, A. Markou,
C. Markou, C. Mavrommatis, E. Ntomari, E. Petrakou

University of Athens, Athens, Greece

L. Gouskos, T.J. Mertzimekis, A. Panagiotou, E. Stiliaris

University of Ioánnina, Ioánnina, Greece

I. Evangelou, C. Foudas, P. Kokkas, N. Manthos, I. Papadopoulos, V. Patras, F.A. Triantis

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KFKI Research Institute for Particle and Nuclear Physics, Budapest, Hungary
A. Aranyi, G. Bencze, L. Boldizsar, C. Hajdu1, P. Hidas, D. Horvath12, A. Kapusi,
K. Krajczar13, F. Sikler1, G.I. Veres13, G. Vesztergombi13

Institute of Nuclear Research ATOMKI, Debrecen, Hungary
N. Beni, J. Molnar, J. Palinkas, Z. Szillasi, V. Veszpremi

University of Debrecen, Debrecen, Hungary
P. Raics, Z.L. Trocsanyi, B. Ujvari

Panjab University, Chandigarh, India
S. Bansal, S.B. Beri, V. Bhatnagar, N. Dhingra, R. Gupta, M. Jindal, M. Kaur, J.M. Kohli,
M.Z. Mehta, N. Nishu, L.K. Saini, A. Sharma, A.P. Singh, J.B. Singh, S.P. Singh

University of Delhi, Delhi, India
S. Ahuja, S. Bhattacharya, B.C. Choudhary, B. Gomber, P. Gupta, S. Jain, S. Jain,
R. Khurana, A. Kumar, K. Ranjan, R.K. Shivpuri

Bhabha Atomic Research Centre, Mumbai, India
R.K. Choudhury, D. Dutta, S. Kailas, V. Kumar, A.K. Mohanty1, L.M. Pant, P. Shukla

Tata Institute of Fundamental Research - EHEP, Mumbai, India
T. Aziz, M. Guchait14, A. Gurtu, M. Maity15, D. Majumder, G. Majumder, K. Mazumdar,
G.B. Mohanty, A. Saha, K. Sudhakar, N. Wickramage

Tata Institute of Fundamental Research - HECR, Mumbai, India
S. Banerjee, S. Dugad, N.K. Mondal

Institute for Research and Fundamental Sciences (IPM), Tehran, Iran
H. Arfaei, H. Bakhshiansohi16, S.M. Etesami, A. Fahim16, M. Hashemi, A. Jafari16,
M. Khakzad, A. Mohammadi17, M. Mohammadi Najafabadi, S. Paktinat Mehdiabadi,
B. Safarzadeh, M. Zeinali18

INFN Sezione di Bari a, Università di Bari b, Politecnico di Bari c, Bari, Italy
M. Abbresciaa,b, L. Barbonea,b, C. Calabriaa,b, A. Colaleoa, D. Creanzaa,c, N. De
Filippisa,c,1, M. De Palmaa,b, L. Fiorea, G. Iasellia,c, L. Lusitoa,b, G. Maggia,c, M. Maggia,
N. Mannaa,b, B. Marangellia,b, S. Mya,c, S. Nuzzoa,b, N. Pacificoa,b, G.A. Pierroa,
A. Pompilia,b, G. Pugliesea,c, F. Romanoa,c, G. Rosellia,b, G. Selvaggia,b, L. Silvestrisa,
R. Trentaduea, S. Tupputia,b, G. Zitoa

INFN Sezione di Bologna a, Università di Bologna b, Bologna, Italy
G. Abbiendia, A.C. Benvenutia, D. Bonacorsia, S. Braibant-Giacomellia,b, L. Brigliadoria,
P. Capiluppia,b, A. Castroa,b, F.R. Cavalloa, M. Cuffiania,b, G.M. Dallavallea, F. Fabbria,
A. Fanfania,b, D. Fasanellaa, P. Giacomellia, M. Giuntaa, C. Grandia, S. Marcellinia,
G. Masettib, M. Meneghellia,b, A. Montanaria, F.L. Navarriaa,b, F. Odoricia, A. Perrottaa,
F. Primaveraa, A.M. Rossia,b, T. Rovellia,b, G. Sirolia,b, R. Travaglinia,b

INFN Sezione di Catania a, Università di Catania b, Catania, Italy
S. Albergoa,b, G. Cappelloa,b, M. Chiorbolia,b,1, S. Costaa,b, A. Tricomia,b, C. Tuvea

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INFN Sezione di Firenze a, Università di Firenze b, Firenze, Italy
G. Barbaglia, V. Ciullia,b, C. Civininia, R. D’Alessandroa,b, E. Focardia,b, S. Frosalia,b,
E. Galloa, S. Gonzia,b, P. Lenzia,b, M. Meschinia, S. Paolettia, G. Sguazzonia,
A. Tropianoa,1

INFN Laboratori Nazionali di Frascati, Frascati, Italy
L. Benussi, S. Bianco, S. Colafranceschi19, F. Fabbri, D. Piccolo

INFN Sezione di Genova, Genova, Italy
P. Fabbricatore, R. Musenich

INFN Sezione di Milano-Biccoca a, Università di Milano-Bicocca b, Milano,
Italy
A. Benagliaa,b, F. De Guioa,b,1, L. Di Matteoa,b, S. Gennai1, A. Ghezzia,b, S. Malvezzia,
A. Martellia,b, A. Massironia,b, D. Menascea, L. Moronia, M. Paganonia,b, D. Pedrinia,
S. Ragazzia,b, N. Redaellia, S. Salaa, T. Tabarelli de Fatisa,b

INFN Sezione di Napoli a, Università di Napoli ”Federico II” b, Napoli, Italy
S. Buontempoa, C.A. Carrillo Montoyaa,1, N. Cavalloa,20, A. De Cosaa,b, F. Fabozzia,20,
A.O.M. Iorioa,1, L. Listaa, M. Merolaa,b, P. Paoluccia

INFN Sezione di Padova a, Università di Padova b, Università di
Trento (Trento) c, Padova, Italy
P. Azzia, N. Bacchettaa, P. Bellana,b, D. Biselloa,b, A. Brancaa, R. Carlina,b, P. Checchiaa,
M. De Mattiaa,b, T. Dorigoa, U. Dossellia, F. Fanzagoa, F. Gasparinia,b, U. Gasparinia,b,
A. Gozzelino, S. Lacapraraa,21, I. Lazzizzeraa,c, M. Margonia,b, M. Mazzucatoa,
A.T. Meneguzzoa,b, M. Nespoloa,1, L. Perrozzia,1, N. Pozzobona,b, P. Ronchesea,b,
F. Simonettoa,b, E. Torassaa, M. Tosia,b, S. Vaninia,b, P. Zottoa,b, G. Zumerlea,b

INFN Sezione di Pavia a, Università di Pavia b, Pavia, Italy
P. Baessoa,b, U. Berzanoa, S.P. Rattia,b, C. Riccardia,b, P. Torrea,b, P. Vituloa,b,
C. Viviania,b

INFN Sezione di Perugia a, Università di Perugia b, Perugia, Italy
M. Biasinia,b, G.M. Bileia, B. Caponeria,b, L. Fanòa,b, P. Laricciaa,b, A. Lucaronia,b,1,
G. Mantovania,b, M. Menichellia, A. Nappia,b, F. Romeoa,b, A. Santocchiaa,b, S. Taronia,b,1,
M. Valdataa,b

INFN Sezione di Pisa a, Università di Pisa b, Scuola Normale Superiore di
Pisa c, Pisa, Italy
P. Azzurria,c, G. Bagliesia, J. Bernardinia,b, T. Boccalia,1, G. Broccoloa,c, R. Castaldia,
R.T. D’Agnoloa,c, R. Dell’Orsoa, F. Fioria,b, L. Foàa,c, A. Giassia, A. Kraana,
F. Ligabuea,c, T. Lomtadzea, L. Martinia,22, A. Messineoa,b, F. Pallaa, G. Segneria,
A.T. Serbana, P. Spagnoloa, R. Tenchinia, G. Tonellia,b,1, A. Venturia,1, P.G. Verdinia

INFN Sezione di Roma a, Università di Roma ”La Sapienza” b, Roma, Italy
L. Baronea,b, F. Cavallaria, D. Del Rea,b, E. Di Marcoa,b, M. Diemoza, D. Francia,b,
M. Grassia,1, E. Longoa,b, S. Nourbakhsha, G. Organtinia,b, F. Pandolfia,b,1, R. Paramattia,
S. Rahatloua,b, C. Rovelli1

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INFN Sezione di Torino a, Università di Torino b, Università del Piemonte
Orientale (Novara) c, Torino, Italy
N. Amapanea,b, R. Arcidiaconoa,c, S. Argiroa,b, M. Arneodoa,c, C. Biinoa, C. Bottaa,b,1,
N. Cartigliaa, R. Castelloa,b, M. Costaa,b, N. Demariaa, A. Grazianoa,b,1, C. Mariottia,
M. Maronea,b, S. Masellia, E. Migliorea,b, G. Milaa,b, V. Monacoa,b, M. Musicha,b,
M.M. Obertinoa,c, N. Pastronea, M. Pelliccionia,b, A. Romeroa,b, M. Ruspaa,c, R. Sacchia,b,
V. Solaa,b, A. Solanoa,b, A. Staianoa, A. Vilela Pereiraa

INFN Sezione di Trieste a, Università di Trieste b, Trieste, Italy
S. Belfortea, F. Cossuttia, G. Della Riccaa,b, B. Gobboa, D. Montaninoa,b, A. Penzoa

Kangwon National University, Chunchon, Korea
S.G. Heo, S.K. Nam

Kyungpook National University, Daegu, Korea
S. Chang, J. Chung, D.H. Kim, G.N. Kim, J.E. Kim, D.J. Kong, H. Park, S.R. Ro, D. Son,
D.C. Son, T. Son

Chonnam National University, Institute for Universe and Elementary Particles,
Kwangju, Korea
Zero Kim, J.Y. Kim, S. Song

Korea University, Seoul, Korea
S. Choi, B. Hong, M.S. Jeong, M. Jo, H. Kim, J.H. Kim, T.J. Kim, K.S. Lee, D.H. Moon,
S.K. Park, H.B. Rhee, E. Seo, S. Shin, K.S. Sim

University of Seoul, Seoul, Korea
M. Choi, S. Kang, H. Kim, C. Park, I.C. Park, S. Park, G. Ryu

Sungkyunkwan University, Suwon, Korea
Y. Choi, Y.K. Choi, J. Goh, M.S. Kim, E. Kwon, J. Lee, S. Lee, H. Seo, I. Yu

Vilnius University, Vilnius, Lithuania
M.J. Bilinskas, I. Grigelionis, M. Janulis, D. Martisiute, P. Petrov, T. Sabonis

Centro de Investigacion y de Estudios Avanzados del IPN, Mexico City, Mexico
H. Castilla-Valdez, E. De La Cruz-Burelo, I. Heredia-de La Cruz, R. Lopez-Fernandez,
R. Magaña Villalba, A. Sánchez-Hernández, L.M. Villasenor-Cendejas

Universidad Iberoamericana, Mexico City, Mexico
S. Carrillo Moreno, F. Vazquez Valencia

Benemerita Universidad Autonoma de Puebla, Puebla, Mexico
H.A. Salazar Ibarguen

Universidad Autónoma de San Luis Potośı, San Luis Potośı, Mexico
E. Casimiro Linares, A. Morelos Pineda, M.A. Reyes-Santos

University of Auckland, Auckland, New Zealand
D. Krofcheck, J. Tam, C.H. Yiu

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University of Canterbury, Christchurch, New Zealand
P.H. Butler, R. Doesburg, H. Silverwood

National Centre for Physics, Quaid-I-Azam University, Islamabad, Pakistan
M. Ahmad, I. Ahmed, M.I. Asghar, H.R. Hoorani, W.A. Khan, T. Khurshid, S. Qazi

Institute of Experimental Physics, Faculty of Physics, University of Warsaw,
Warsaw, Poland
G. Brona, M. Cwiok, W. Dominik, K. Doroba, A. Kalinowski, M. Konecki, J. Krolikowski

Soltan Institute for Nuclear Studies, Warsaw, Poland
T. Frueboes, R. Gokieli, M. Górski, M. Kazana, K. Nawrocki, K. Romanowska-Rybinska,
M. Szleper, G. Wrochna, P. Zalewski

Laboratório de Instrumentação e F́ısica Experimental de Part́ıculas, Lisboa,
Portugal
N. Almeida, P. Bargassa, A. David, P. Faccioli, P.G. Ferreira Parracho, M. Gallinaro,
P. Musella, A. Nayak, P.Q. Ribeiro, J. Seixas, J. Varela

Joint Institute for Nuclear Research, Dubna, Russia
S. Afanasiev, I. Belotelov, P. Bunin, I. Golutvin, A. Kamenev, V. Karjavin, G. Kozlov,
A. Lanev, P. Moisenz, V. Palichik, V. Perelygin, S. Shmatov, V. Smirnov, A. Volodko,
A. Zarubin

Petersburg Nuclear Physics Institute, Gatchina (St Petersburg), Russia
V. Golovtsov, Y. Ivanov, V. Kim, P. Levchenko, V. Murzin, V. Oreshkin, I. Smirnov,
V. Sulimov, L. Uvarov, S. Vavilov, A. Vorobyev, A. Vorobyev

Institute for Nuclear Research, Moscow, Russia
Yu. Andreev, A. Dermenev, S. Gninenko, N. Golubev, M. Kirsanov, N. Krasnikov,
V. Matveev, A. Pashenkov, A. Toropin, S. Troitsky

Institute for Theoretical and Experimental Physics, Moscow, Russia
V. Epshteyn, V. Gavrilov, V. Kaftanov†, M. Kossov1, A. Krokhotin, N. Lychkovskaya,
V. Popov, G. Safronov, S. Semenov, V. Stolin, E. Vlasov, A. Zhokin

Moscow State University, Moscow, Russia
E. Boos, M. Dubinin23, L. Dudko, A. Ershov, O. Kodolova, V. Korotkikh, I. Lokhtin,
A. Markina, S. Obraztsov, M. Perfilov, S. Petrushanko, L. Sarycheva, V. Savrin, A. Snigirev

P.N. Lebedev Physical Institute, Moscow, Russia
V. Andreev, M. Azarkin, I. Dremin, M. Kirakosyan, A. Leonidov, S.V. Rusakov, A. Vino-
gradov

State Research Center of Russian Federation, Institute for High Energy
Physics, Protvino, Russia
I. Azhgirey, S. Bitioukov, V. Grishin1, V. Kachanov, D. Konstantinov, A. Korablev,
V. Krychkine, V. Petrov, R. Ryutin, S. Slabospitsky, A. Sobol, L. Tourtchanovitch,
S. Troshin, N. Tyurin, A. Uzunian, A. Volkov

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University of Belgrade, Faculty of Physics and Vinca Institute of Nuclear
Sciences, Belgrade, Serbia

P. Adzic24, M. Djordjevic, D. Krpic24, J. Milosevic

Centro de Investigaciones Energéticas Medioambientales y Tec-
nológicas (CIEMAT), Madrid, Spain

M. Aguilar-Benitez, J. Alcaraz Maestre, P. Arce, C. Battilana, E. Calvo, M. Cepeda,
M. Cerrada, M. Chamizo Llatas, N. Colino, B. De La Cruz, A. Delgado Peris, C. Diez
Pardos, D. Domı́nguez Vázquez, C. Fernandez Bedoya, J.P. Fernández Ramos, A. Ferrando,
J. Flix, M.C. Fouz, P. Garcia-Abia, O. Gonzalez Lopez, S. Goy Lopez, J.M. Hernandez,
M.I. Josa, G. Merino, J. Puerta Pelayo, I. Redondo, L. Romero, J. Santaolalla, M.S. Soares,
C. Willmott

Universidad Autónoma de Madrid, Madrid, Spain

C. Albajar, G. Codispoti, J.F. de Trocóniz

Universidad de Oviedo, Oviedo, Spain

J. Cuevas, J. Fernandez Menendez, S. Folgueras, I. Gonzalez Caballero, L. Lloret Iglesias,
J.M. Vizan Garcia

Instituto de F́ısica de Cantabria (IFCA), CSIC-Universidad de Cantabria,
Santander, Spain

J.A. Brochero Cifuentes, I.J. Cabrillo, A. Calderon, S.H. Chuang, J. Duarte Campderros,
M. Felcini25, M. Fernandez, G. Gomez, J. Gonzalez Sanchez, C. Jorda, P. Lobelle Pardo,
A. Lopez Virto, J. Marco, R. Marco, C. Martinez Rivero, F. Matorras, F.J. Munoz Sanchez,
J. Piedra Gomez26, T. Rodrigo, A.Y. Rodŕıguez-Marrero, A. Ruiz-Jimeno, L. Scodellaro,
M. Sobron Sanudo, I. Vila, R. Vilar Cortabitarte

CERN, European Organization for Nuclear Research, Geneva, Switzerland

D. Abbaneo, E. Auffray, G. Auzinger, P. Baillon, A.H. Ball, D. Barney, A.J. Bell27,
D. Benedetti, C. Bernet3, W. Bialas, P. Bloch, A. Bocci, S. Bolognesi, M. Bona, H. Breuker,
K. Bunkowski, T. Camporesi, G. Cerminara, J.A. Coarasa Perez, B. Curé, D. D’Enterria,
A. De Roeck, S. Di Guida, N. Dupont-Sagorin, A. Elliott-Peisert, B. Frisch, W. Funk,
A. Gaddi, G. Georgiou, H. Gerwig, D. Gigi, K. Gill, D. Giordano, F. Glege, R. Gomez-
Reino Garrido, M. Gouzevitch, P. Govoni, S. Gowdy, L. Guiducci, M. Hansen, C. Hartl,
J. Harvey, J. Hegeman, B. Hegner, H.F. Hoffmann, A. Honma, V. Innocente, P. Janot,
K. Kaadze, E. Karavakis, P. Lecoq, C. Lourenço, T. Mäki, M. Malberti, L. Malgeri,
M. Mannelli, L. Masetti, A. Maurisset, F. Meijers, S. Mersi, E. Meschi, R. Moser,
M.U. Mozer, M. Mulders, E. Nesvold1, M. Nguyen, T. Orimoto, L. Orsini, E. Perez,
A. Petrilli, A. Pfeiffer, M. Pierini, M. Pimiä, D. Piparo, G. Polese, A. Racz, J. Rodrigues
Antunes, G. Rolandi28, T. Rommerskirchen, M. Rovere, H. Sakulin, C. Schäfer, C. Schwick,
I. Segoni, A. Sharma, P. Siegrist, M. Simon, P. Sphicas29, M. Spiropulu23, M. Stoye,
M. Tadel, P. Tropea, A. Tsirou, P. Vichoudis, M. Voutilainen, W.D. Zeuner

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Paul Scherrer Institut, Villigen, Switzerland

W. Bertl, K. Deiters, W. Erdmann, K. Gabathuler, R. Horisberger, Q. Ingram,
H.C. Kaestli, S. König, D. Kotlinski, U. Langenegger, F. Meier, D. Renker, T. Rohe,
J. Sibille30, A. Starodumov31

Institute for Particle Physics, ETH Zurich, Zurich, Switzerland

P. Bortignon, L. Caminada32, N. Chanon, Z. Chen, S. Cittolin, G. Dissertori, M. Dittmar,
J. Eugster, K. Freudenreich, C. Grab, A. Hervé, W. Hintz, P. Lecomte, W. Lustermann,
C. Marchica32, P. Martinez Ruiz del Arbol, P. Meridiani, P. Milenovic33, F. Moortgat,
C. Nägeli32, P. Nef, F. Nessi-Tedaldi, L. Pape, F. Pauss, T. Punz, A. Rizzi, F.J. Ronga,
M. Rossini, L. Sala, A.K. Sanchez, M.-C. Sawley, B. Stieger, L. Tauscher†, A. Thea,
K. Theofilatos, D. Treille, C. Urscheler, R. Wallny, M. Weber, L. Wehrli, J. Weng

Universität Zürich, Zurich, Switzerland

E. Aguiló, C. Amsler, V. Chiochia, S. De Visscher, C. Favaro, M. Ivova Rikova, B. Millan
Mejias, P. Otiougova, C. Regenfus, P. Robmann, A. Schmidt, H. Snoek

National Central University, Chung-Li, Taiwan

Y.H. Chang, K.H. Chen, S. Dutta, C.M. Kuo, S.W. Li, W. Lin, Z.K. Liu, Y.J. Lu,
D. Mekterovic, R. Volpe, J.H. Wu, S.S. Yu

National Taiwan University (NTU), Taipei, Taiwan

P. Bartalini, P. Chang, Y.H. Chang, Y.W. Chang, Y. Chao, K.F. Chen, W.-S. Hou,
Y. Hsiung, K.Y. Kao, Y.J. Lei, R.-S. Lu, J.G. Shiu, Y.M. Tzeng, M. Wang

Cukurova University, Adana, Turkey

A. Adiguzel, M.N. Bakirci34, S. Cerci35, C. Dozen, I. Dumanoglu, E. Eskut, S. Girgis,
G. Gokbulut, I. Hos, E.E. Kangal, A. Kayis Topaksu, G. Onengut, K. Ozdemir, S. Ozturk,
A. Polatoz, K. Sogut36, D. Sunar Cerci35, B. Tali35, H. Topakli34, D. Uzun, L.N. Vergili,
M. Vergili

Middle East Technical University, Physics Department, Ankara, Turkey

I.V. Akin, T. Aliev, S. Bilmis, M. Deniz, H. Gamsizkan, A.M. Guler, K. Ocalan,
A. Ozpineci, M. Serin, R. Sever, U.E. Surat, E. Yildirim, M. Zeyrek

Bogazici University, Istanbul, Turkey

M. Deliomeroglu, D. Demir37, E. Gülmez, B. Isildak, M. Kaya38, O. Kaya38,
S. Ozkorucuklu39, N. Sonmez40

National Scientific Center, Kharkov Institute of Physics and Technology,
Kharkov, Ukraine

L. Levchuk

University of Bristol, Bristol, United Kingdom

F. Bostock, J.J. Brooke, T.L. Cheng, E. Clement, D. Cussans, R. Frazier, J. Goldstein,
M. Grimes, M. Hansen, D. Hartley, G.P. Heath, H.F. Heath, L. Kreczko, S. Metson,
D.M. Newbold41, K. Nirunpong, A. Poll, S. Senkin, V.J. Smith, S. Ward

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Rutherford Appleton Laboratory, Didcot, United Kingdom

L. Basso42, K.W. Bell, A. Belyaev42, C. Brew, R.M. Brown, B. Camanzi, D.J.A. Cockerill,
J.A. Coughlan, K. Harder, S. Harper, J. Jackson, B.W. Kennedy, E. Olaiya, D. Petyt,
B.C. Radburn-Smith, C.H. Shepherd-Themistocleous, I.R. Tomalin, W.J. Womersley,
S.D. Worm

Imperial College, London, United Kingdom

R. Bainbridge, G. Ball, J. Ballin, R. Beuselinck, O. Buchmuller, D. Colling, N. Cripps,
M. Cutajar, G. Davies, M. Della Negra, W. Ferguson, J. Fulcher, D. Futyan, A. Gilbert,
A. Guneratne Bryer, G. Hall, Z. Hatherell, J. Hays, G. Iles, M. Jarvis, G. Karapostoli,
L. Lyons, B.C. MacEvoy, A.-M. Magnan, J. Marrouche, B. Mathias, R. Nandi, J. Nash,
A. Nikitenko31, A. Papageorgiou, M. Pesaresi, K. Petridis, M. Pioppi43, D.M. Raymond,
S. Rogerson, N. Rompotis, A. Rose, M.J. Ryan, C. Seez, P. Sharp, A. Sparrow, A. Tapper,
S. Tourneur, M. Vazquez Acosta, T. Virdee, S. Wakefield, N. Wardle, D. Wardrope,
T. Whyntie

Brunel University, Uxbridge, United Kingdom

M. Barrett, M. Chadwick, J.E. Cole, P.R. Hobson, A. Khan, P. Kyberd, D. Leslie,
W. Martin, I.D. Reid, L. Teodorescu

Baylor University, Waco, USA

K. Hatakeyama, H. Liu

Boston University, Boston, USA

T. Bose, E. Carrera Jarrin, C. Fantasia, A. Heister, J. St. John, P. Lawson, D. Lazic,
J. Rohlf, D. Sperka, L. Sulak

Brown University, Providence, USA

A. Avetisyan, S. Bhattacharya, J.P. Chou, D. Cutts, A. Ferapontov, U. Heintz, S. Jabeen,
G. Kukartsev, G. Landsberg, M. Luk, M. Narain, D. Nguyen, M. Segala, T. Sinthuprasith,
T. Speer, K.V. Tsang

University of California, Davis, Davis, USA

R. Breedon, M. Calderon De La Barca Sanchez, S. Chauhan, M. Chertok, J. Conway,
P.T. Cox, J. Dolen, R. Erbacher, E. Friis, W. Ko, A. Kopecky, R. Lander, H. Liu,
S. Maruyama, T. Miceli, M. Nikolic, D. Pellett, J. Robles, S. Salur, T. Schwarz, M. Searle,
J. Smith, M. Squires, M. Tripathi, R. Vasquez Sierra, C. Veelken

University of California, Los Angeles, Los Angeles, USA

V. Andreev, K. Arisaka, D. Cline, R. Cousins, A. Deisher, J. Duris, S. Erhan, C. Farrell,
J. Hauser, M. Ignatenko, C. Jarvis, C. Plager, G. Rakness, P. Schlein†, J. Tucker, V. Valuev

University of California, Riverside, Riverside, USA

J. Babb, A. Chandra, R. Clare, J. Ellison, J.W. Gary, F. Giordano, G. Hanson, G.Y. Jeng,
S.C. Kao, F. Liu, H. Liu, O.R. Long, A. Luthra, H. Nguyen, B.C. Shen†, R. Stringer,
J. Sturdy, S. Sumowidagdo, R. Wilken, S. Wimpenny

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University of California, San Diego, La Jolla, USA
W. Andrews, J.G. Branson, G.B. Cerati, E. Dusinberre, D. Evans, F. Golf, A. Holzner,
R. Kelley, M. Lebourgeois, J. Letts, B. Mangano, S. Padhi, C. Palmer, G. Petruc-
ciani, H. Pi, M. Pieri, R. Ranieri, M. Sani, V. Sharma, S. Simon, Y. Tu, A. Vartak,
S. Wasserbaech44, F. Würthwein, A. Yagil, J. Yoo

University of California, Santa Barbara, Santa Barbara, USA
D. Barge, R. Bellan, C. Campagnari, M. D’Alfonso, T. Danielson, K. Flowers, P. Geffert,
J. Incandela, C. Justus, P. Kalavase, S.A. Koay, D. Kovalskyi, V. Krutelyov, S. Lowette,
N. Mccoll, V. Pavlunin, F. Rebassoo, J. Ribnik, J. Richman, R. Rossin, D. Stuart, W. To,
J.R. Vlimant

California Institute of Technology, Pasadena, USA
A. Apresyan, A. Bornheim, J. Bunn, Y. Chen, M. Gataullin, Y. Ma, A. Mott, H.B. New-
man, C. Rogan, K. Shin, V. Timciuc, P. Traczyk, J. Veverka, R. Wilkinson, Y. Yang,
R.Y. Zhu

Carnegie Mellon University, Pittsburgh, USA
B. Akgun, R. Carroll, T. Ferguson, Y. Iiyama, D.W. Jang, S.Y. Jun, Y.