Measurementofthesensitivityoftwoparticle p correlationsin pp … · TLAS-CONF-2020-018 2020 ATLAS...

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ATLAS CONF NoteATLAS-CONF-2020-018

29th May 2020

Measurement of the sensitivity of two particlecorrelations in pp collisions at

√s = 13 TeV to the

presence of jets with the ATLAS detector

The ATLAS Collaboration

Measurements of two-particle correlations in pp collisions show the presence of long-rangecorrelations along ∆η that are strikingly similar to those seen in heavy-ion collisions. Inheavy-ion collisions, the long-range correlations are known to arise from the collectivedynamics of the produced quark-gluon plasma (QGP). The similarity between the pp andheavy-ion measurements raises the possibility that a tiny droplet of the QGP is producedeven in pp collisions. However, models that attribute the correlation in pp collisions tosemi-hard processes can qualitatively reproduce the measurements. Thus performing thepp measurements with an active rejection of particles associated with semi-hard processes,such as low-pT jets, can further elucidate the origin of the long-range correlations. This notepresents measurements of two-particle correlations in pp collisions at

√s = 13 TeV, when

removing tracks associated with jets from the event. The jets are reconstructed from tracksusing the anti-kt algorithm, and all tracks within one unit of pseudorapidity of the jet areremoved from the correlation analysis. It is demonstrated that such removal of particles in thevicinity of jets affects the magnitude of long-range correlations only by a few percent.

© 2020 CERN for the benefit of the ATLAS Collaboration.Reproduction of this article or parts of it is allowed as specified in the CC-BY-4.0 license.

1 Introduction

In heavy-ion collisions, two-particle correlations (2PC) in relative azimuthal angle1 ∆φ = φa − φb andwith pseudorapidity separation ∆η = ηa − ηb show distinct long-range correlations along ∆η [1–4]. Theselong-range correlations are a simple manifestation of the single-particle anisotropies vn, which originatedue to the hydrodynamic expansion of the medium (see Refs. [3, 4] and references therein). The vn aredefined by parameterizing the azimuthal distribution of particles produced in collisions as:

dNdφ∝

(1 + 2

∞∑n=1

vn cos(n(φ − Φn))

), (1)

where the vn andΦn denote the magnitude and orientation of the single-particle anisotropies respectively.

Because of their hydrodynamic origin, such long-range correlations were not expected to be seen in smallercolliding systems, such as proton-nucleus (p+A) or proton-proton (pp) collisions, where hydrodynamicor collective phenomena were not commonly expected to develop. However, measurements by the CMSCollaboration showed the presence of such long-range correlations along ∆η at ∆φ ∼ 0, commonly calledthe “ridge”, in high-multiplicity pp collisions [5]. Further investigations of the full ∆φ structure ofthe long-range correlations in pp collisions by the ATLAS Collaboration [6, 7] demonstrated that thelong-range correlations are in fact produced from single-particle anisotropies similar to those in heavy-ioncollisions. Starting from the 2PC, the single-particle anisotropies vn were extracted. The measurementsshowed that the vn in pp collisions were independent of

√s at LHC energies, similar to what is observed in

heavy-ion collisions [4]. Further, the pT dependence of the v2 in pp collisions was qualitatively similar tothat measured in p+A and A+A collisions.

These measurements supported the interpretation in which the long-range correlations arise from collectiveeffects, like in heavy-ion collisions. However, it has been argued that the ridge may arise from hard orsemi-hard processes as well. In these processes multi-particle correlations between outgoing partons mayarise due to saturation of the parton configurations in the incident hadrons (see e.g. [8–12] and referencestherein). A first attempt to explicitly test for correlations between hard-scattering processes and the ridgeusing Z-tagged pp collisions [13] showed no difference between the v2 values in inclusive pp collisionsand Z-tagged collisions – and, thus, no apparent correlation. However, the pT or Q2 scales associatedwith the semi-hard processes contributing to the ridge may be too low to be probed using the Z-taggedmeasurements. Thus, additional insight may be provided by studying hard-scattering processes at parton(jet) transverse momentum scales pT . 10 GeV, which may be more relevant for saturation models.

Independent of any specific theoretical guidance, given that the origin of the ridge in pp collisions isnot understood, an empirical study of potential impact between hard processes at lower Q2 and the ridgemay provide new insight. This note presents vn measurements in pp collisions while actively rejectingparticles associated with low-pT jets. If the long-range correlations arise due to hard or semi-hard processes,then removing particles associated with jets from the analysis would weaken the long-range correlation.Alternatively, selecting events with jets may enhance or alter the two-particle correlations, even if fragmentsof the jets themselves are excluded from the analysis.

1 ATLAS uses a right-handed coordinate system with its origin at the nominal interaction point (IP) in the center of the detectorand the z-axis along the beam pipe. The x-axis points from the IP to the center of the LHC ring, and the y-axis pointsupward. Cylindrical coordinates (r, φ) are used in the transverse plane, φ being the azimuthal angle around the z-axis. Thepseudorapidity is defined in terms of the polar angle θ as η = − ln tan(θ/2).

2

Table 1: The list of L1 and NHLTtrk requirements for the HMT triggers used in this analysis. Also listed are the

luminosities sampled by the triggers.

L1 HLT Luminosity (nb−1)MBTS_1_1 NHLT

trk ≥40 5.3MBTS_1_1 NHLT

trk ≥50 8.7MBTS_1_1 NHLT

trk ≥60 9.7EL1

T >10 GeV NHLTtrk ≥90 64

The outline of the note is as follows: Section 2 describes the ATLAS detector subsystems used in thismeasurement. Section 3 describes the data set, event-selection cuts and tracking cuts used in the analysis.Section 4 describes the two-particle correlation procedure and the template-fit method, which is used tomeasure the flow harmonics vn in this analysis. It also describes the criteria for selecting the jets andthe procedure by which tracks associated with the jets are removed from the analysis. The systematicuncertainties associated with the measurement are discussed in Section 5. Section 6 studies the dependenceof the measurements on the pT thresholds used for the jets in the analysis. In Section 7 the final results ofthe measurements are described. Section 8 summarizes the final results of the analysis.

2 The ATLAS detector and trigger

The measurements presented in this note were performed using the ATLAS [14] inner detector (ID),minimum-bias trigger scintillators (MBTS), calorimeters and the trigger and data acquisition systems.The ID detects charged particles within the pseudorapidity range |η |<2.5 using a combination of siliconpixel detectors including the “insertable B-layer” (IBL) [15, 16], silicon microstrip detectors (SCT), anda straw-tube transition radiation tracker, all immersed in a 2 T axial magnetic field [17]. The MBTSsystem detects charged particles over 2.07 < |η | < 3.86 using two hodoscopes on each side of the detector,positioned at z = ±3.6 m. The ATLAS calorimeter system consists of a liquid argon (LAr) electromagneticcalorimeter covering |η | < 3.2, a steel–scintillator sampling hadronic calorimeter covering |η | < 1.7, aLAr hadronic calorimeter covering 1.5 < |η | < 3.2, and two LAr electromagnetic and hadronic forwardcalorimeters (FCal) covering 3.2 < |η | < 4.9.

The ATLAS trigger system [18] consists of a Level-1 (L1) trigger implemented using a combination ofdedicated electronics and programmable logic, and a software-based high-level trigger (HLT). Due tothe large interaction rates, only a small fraction of minimum-bias (MB) events could be recorded. TheMB events were selected by a L1 trigger that required a signal in at least one MBTS counter (MBTS_1),and a second trigger that required at least one reconstructed track at the HLT. In order to increase thenumber of events having high charged-particle multiplicity, several high-multiplicity triggers (HMT) wereimplemented. These apply a L1 requirement on either the transverse energy (ET) in the calorimeters or ahit in at least one MBTS counter on each side (MBTS_1_1), and an HLT requirement on the multiplicityof HLT-reconstructed charged-particle tracks. That multiplicity, NHLT

trk , is evaluated for tracks havingpT > 0.4 GeV that are associated with the reconstructed vertex with the highest multiplicity in the event.This last requirement suppresses the selection of events with multiple collisions (pileup), as long as thecollision vertices are not so close as to be indistinguishable. The HMT trigger configurations used in thisanalysis and the luminosities sampled by the triggers are summarized in Table 1.

3

3 Dataset, event and track selections

The√

s = 13 TeV pp data were collected over two periods: a set of low-luminosity runs in June 2015for which the number of collisions per bunch crossing, µ, varied between 0.002 and 0.04, and a set ofintermediate-luminosity runs in August 2015 where µ varied between 0.05 and 0.6. The different triggerslisted in Table 1 sampled different luminosities. The largest luminosity was sampled by the highestthreshold HMT trigger (last item in Table 1) and corresponds to an integrated value of 64 nb−1 .

3.1 Event and track selection

Additional requirements are imposed on the events selected by the MB and HMT triggers. The events arerequired to have a reconstructed vertex with the z-position of the vertex restricted to ±150 mm. Pileupinteractions – events with more than one pp collision – are suppressed by rejecting events containing morethan one good reconstructed vertex.

The reconstruction and performance of tracks and primary vertices in the ID are described in Refs. [19–21].The reconstructed tracks are required to satisfy the following selection criteria: pT > 0.4 GeV and |η | < 2.5;at least one pixel hit, with the additional requirement of a hit in the IBL if one is expected (if a hit is notexpected in the IBL, a hit in the next pixel layer is required if such a hit is expected); a minimum of sixhits in the SCT. In addition, the transverse impact parameter of the track with respect to the average beamposition, and the longitudinal impact parameter of the track relative to the vertex are required to be less than1.5 mm. Finally, in order to remove tracks with mismeasured pT due to interactions with the material orother effects, the track-fit χ2 probability is required to be larger than 0.01 for tracks having pT > 10 GeV.

The efficiencies ε(pT, η) of track reconstruction for the above track selection cuts are obtained using MonteCarlo (MC) generated events that are passed through a GEANT4 [22] simulation [23] of the ATLASdetector and reconstructed using the procedures applied to the data. For determining the efficiencies,non-diffractive 13 TeV pp events obtained from the Pythia 8 [24] event generator (with the A2 set oftuned parameters [25] and the MSTW2008LO PDFs [26]) are used. The efficiency increases by ∼3% frompT = 0.4 GeV to pT = 0.6 GeV and varies weakly with pT for pT > 0.6 GeV increasing by about ∼6% overthe 0.6–20 GeV pT range. The efficiency varies considerably with η; at pT ∼ 0.6 GeV it ranges from 87%at η = 0 to 76% at |η | = 1.5 and 69% for |η | > 2.0. The rate of tracks that are produced from randomcombinations of hits in the ID (“fakes”) is expected to be quite small and is not corrected for.

3.2 Event-activity classes

As in previous ATLAS analyses of long-range correlations in p+Pb [27, 28] and pp [6, 7, 13] collisions, theevent activity is quantified by N rec

ch : the total number of reconstructed tracks with pT > 0.4 GeV, passingthe track selections discussed in Section 3.1. From the simulated events (Section 3.1), it is estimatedthat the tracking inefficiency reduces the measured N rec

ch relative to the event generator multiplicity forpT > 0.4 GeV primary charged particles by an approximately multiplicity-independent factor of 1.18±0.05.The pp event activity can also be quantified using the total transverse energy deposited in the FCal (ΣEFCal

T ).Using the ΣEFCal

T to characterize the event activity has the advantage that independent sets of particles areused to determine the event activity and to measure the long-range correlations. Results presented in thisnote use both N rec

ch and the ΣEFCalT to quantify the event activity.

4

4 Methodology

4.1 Two-particle correlations and template-fits

This analysis uses the two-particle correlation method for measuring long-range correlations. The two-particle correlation method has been used extensively by ATLAS for flow measurements in Pb+Pb [3, 4,29], Xe+Xe [30], p+Pb [27, 28, 31], and pp [6, 7, 13] collisions. In two-particle correlation measurements,the distribution of particle pairs in relative azimuthal angle ∆φ are measured. To suppress short-rangecorrelations, the particles are required to be well separated in ∆η. In this analysis the separation is chosento be |∆η | > 2.

The strength of the long-range correlation can be quantified by extracting Fourier moments of the 2PCdistribution which are denoted vn,n and defined by:

C(∆φ) ∝ (1 + 2∑n

vn,n cos(n∆φ)). (2)

The vn,n are directly related to the single-particle anisotropies vn described in Eq. (1). In the case wherethe vn,n entirely result from the single-particle anisotropies, the vn,n(pa

T, pbT) is the product of the vn(p

aT)

and vn(pbT) [3], i.e.:

vn,n(paT, pb

T) = vn(paT)vn(p

bT), (3)

where, paT and pb

T are the pT of the two particles used in the correlation. Thus, the vn(pbT) can be obtained

as:

vn(pbT) =

vn,n(paT, pb

T)

vn(paT)

=vn,n(pa

T, pbT)√

vn,n(paT, pa

T), (4)

where vn,n(paT, pa

T) is the Fourier coefficient of the 2PC when both reference and associated particles arefrom the same pT range. This technique has been used extensively in heavy-ion collisions to obtain the flowharmonics [3]. However, in pp collisions a significant contribution to the 2PC arises from back-to-backdijets. These correlations must be removed before Eq. (3) or Eq. (4) can be used. To address these issues, theATLAS Collaboration developed a template-fit method [6, 7, 13] in which the measured 2PC is describedby a fit, Ctempl(∆φ), having two components: 1) the correlation that accounts for the dijet contribution,Cperiph(∆φ), which is measured using low-multiplicity events (called the “peripheral reference”) and 2) abulk contribution with a relative harmonic modulation, Cridge(∆φ):

Ctempl(∆φ) = FCperiph(∆φ) + G

(1 + 2

∑n=2

vn,n cos(n∆φ)

)(5)

≡ FCperiph(∆φ) + Cridge(∆φ),

where the coefficient F and the vn,n are fit parameters adjusted to reproduce the C(∆φ). The coefficient Gis not a free parameter, but is fixed by the requirement that the integrals of the Ctempl(∆φ) and C(∆φ) overthe full ∆φ range are equal. It was demonstrated in Refs. [6, 7] that the vn,n obtained from Eq. (5) obeyedthe factorization relationship of Eq. (3), suggesting that the long-range correlations in pp collisions arisefrom single-particle anisotropies.

5

4.2 Jet reconstruction and corrections

The analysis uses jets reconstructed from tracks (“track-jets”) using the anti-kt algorithm [32] with a radiusparameter of R = 0.4. The jet reconstruction is performed using the FastJet [33] package with the tracksthat pass the selection requirements listed in Section 3.1 as the input pseudo-jets. The track-jets are used tostudy the impact of the presence of jets on the flow signal. The default analysis is performed using jets thathave pT larger than 10 GeV. The analysis is also repeated using variations on the minimum jet pT over arange of values covering 6–12 GeV.

The transverse momentum of the jets and the number of constituents are corrected to account for theaverage combinatoric contribution of underlying event (UE) tracks. These corrections are evaluated in fineintervals of the multiplicity. The correction for the number of constituents is evaluated as:

Nconstituentscorrected = Nconstituents

original − πR2ρ(η, φ, N recch ), (6)

where, R = 0.4 (corresponding to the radius parameter of the anti-kt jets), and ρ is the average number-density of tracks in events with similar multiplicity, as the event containing the jet, evaluated as a functionof η and φ:

ρ(η, φ, N recch ) =

⟨dN trk

dηdφ

⟩Events

(η, φ, N recch ). (7)

The correction to the jet pT is evaluated using similar equations as Eqs. (6,7), but weighting each track byits pT, which accounts for the 〈pT〉 contribution from the UE. Equations (6,7) are a property of the UEand the jet radius only, and are not dependent on the jet pT. The average correction for the number of jetconstituents (jet pT) from the UE increases linearly with the event multiplicity, and is ∼ 2.1 (∼ 2.4 GeV) inevents with 120–130 reconstructed tracks. The requirement of pT > 10 GeV on the jet is imposed after theabove corrections.

The tracks in an event can be classified into two groups: the tracks associated with jets, and the UE tracks.To study the long-range correlations in the UE, it is more appropriate to assign events with similar numberof UE tracks, instead of total number of tracks, into the same event-activity class. To estimate the numberof UE tracks, the number of constituent tracks in jets (Eq. (6)), summed over all pT > 10 GeV jets in anevent, are removed from the measured multiplicity. The corrected multiplicity is given by:

N rec,corrch = N rec

ch −∑jets

Nconstituentscorrected . (8)

All subsequent results are presented using the corrected multiplicity given in Eq. (8). Additionally, for thetemplate-fits, the peripheral reference Cperiph(∆φ) (Eq. (5)) is constructed using events with N rec,corr

ch lessthan twenty.

4.2.1 Removal of jet-particles from 2PCs

It is not straightforward to remove tracks associated with jets from the 2PC analysis by simply rejecting alltracks within a R = 0.4 cone of the jet axis. Such a conical rejection would introduce artificial structuresalong the ∆φ direction in the 2PC distributions. Instead, tracks within ∆η = ±1 from the jet axis of anyjets with pjetT >10 GeV are dropped from the 2PC analysis. This rejection procedure removes slices of thedetector acceptance for all φ values, and thus does not introduce any acceptance effects along ∆φ. Such

6

a rejection of jet-associated particles has been previously considered in studies of p+Pb collisions [31].Figure 1 illustrates this procedure.

Figure 1: Figures illustrating removal of tracks associated with jets. The circles represent jets that have pT above10 GeV. Tracks within ∆η = ±1 from the jet axis of any jet are removed from the correlation. The rejection region isindicated by the shaded bands. The unshaded (clear or white) region indicates the η region from which tracks areused in the correlations analysis.

4.3 Event Sets

The events are categorized into four classes for which the vn measurements are performed:

• Inclusive : Analysis with no rejection of tracks based on a presence of jet applied.

• AllEvents : Analysis with tracks within one unit in η from any jet above the chosen threshold – defaultthreshold being 10 GeV– removed from the 2PC analysis. This removal procedure was discussed inSection 4.2.1.

• NoJet : Analysis performed using events that do not have even a single jet with pT greater than thechosen threshold. This sample is composed of events dominated by soft processes.

• WithJet : Analysis performed on events that have at least one jet with pT greater than the chosen pTthreshold. The tracks within one unit in η of any such jets are removed from the 2PC analysis. Thissample is enriched by events having a hard process.

The labels "Inclusive", "AllEvents", "NoJet" and "WithJet" will be used hereafter, corresponding to theabove samples. Note that the NoJet andWithJet samples add up to the AllEvents sample. Figure 2 showsthe multiplicity distributions for the above event categories, for several different selections on the minimumjet pT. The AllEvents and Inclusive samples have the same multiplicity distributions and so the distributionsfor the Inclusive sample are not shown. It is seen that in theWithJet sample the number of events at lowmultiplicities is significantly smaller than in the AllEvents sample. This is because requiring a jet in theID strongly reduces the probability of the event being a low-multiplicity event. The discrete jumps in thedistributions in Figure 2 correspond to the HMT triggers, which sampled different luminosities. Given theweak dependence of the vn on multiplicity (see Ref. [7] and the results in Section 7) the events selected bythe different triggers are combined without weights that account for the different luminosities sampled bythe triggers.

7

0 50 100 150 rec,corrchN

1

10

210

310

410

510

610

710

Eve

nts/

1 C

harg

ed T

rack

AllEventsNoJetWithJet

ATLAS Preliminary-1=13 TeV, 64 nbs pp

> 6 GeVT

Jet p


1

10

210

310

410

510

610

710

Eve

nts/

1 C

harg

ed T

rack



> 8 GeVT

Jet p


1

10

210

310

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610

710

Eve

nts/

1 C

harg

ed T

rack



> 10 GeVT

Jet p


1

10

210

310

410

510

610

710

Eve

nts/

1 C

harg

ed T

rack



> 12 GeVT

Jet p

Figure 2: The N rec,corrch distribution for AllEvents, NoJet, and WithJet selections. The individual panels show the

distributions for different jet pT thresholds. The discrete jumps in the distributions correspond to the HMT triggers.

5 Systematic uncertainties

The systematic uncertainties in the vn measurements arise from choosing the peripheral bin used inthe template fits, pileup, tracking efficiency, and pair acceptance. Each source is discussed separatelybelow. The variations are performed for both the ΣEFCal

T and multiplicity dependence studies and typicallygive similar uncertainties. The ΣEFCal

T dependence of the uncertainties are discussed below only for thevariations where they are significantly different than the multiplicity dependence. The total uncertainty isobtained by adding the individual uncertainties in quadrature.

• Choice of peripheral reference: The template-fit procedure uses events with fewer than 20reconstructed tracks to build Cperiph. The choice of 20 tracks is partially motivated by the fact thatthe mean reconstructed multiplicity of minimum-bias pp events is close to 20 tracks. To test thestability of the vn with respect to our choice of the peripheral reference, the analysis is repeatedwith an alternative Cperiph constructed from pp events with 10–20 and 10–30 reconstructed tracksand the change in the vn values is included as a systematic uncertainty. For the ΣEFCal

T dependencestudy, the default peripheral reference is constructed using the 5 < ΣEFCal

T < 15 GeV interval, withthe 10–15 GeV and 10–20 GeV intervals used as variations. For the multiplicity dependence study,this uncertainty for the v2 varies between 4% at low multiplicities to 1% at high multiplicities forthe AllEvents and NoJet samples. While for the higher order harmonics it is significantly larger atthe low multiplicities and up to ∼10% at the highest multiplicity. For the ΣEFCal

T dependence, the

8

uncertainties for v2 are between 4% at low ΣEFCalT to 1% at large ΣEFCal

T . While for v3 and v4 theyare within ∼4% and ∼6%, respectively and independent of ΣEFCal

T .

Track reconstruction efficiency: In evaluating the correlation functions, the tracks are weighted bythe inverse of their reconstruction efficiency, 1/ε(pT, η). Uncertainties in the efficiency, resultingfrom, e.g. uncertainty in the detector material budget, are propagated into the measured vn. Thisuncertainty is evaluated by varying the efficiency up and down within its uncertainties (∼ ±3%)in a pT-dependent manner and re-evaluating the vn. The corresponding uncertainty in the vn isestimated by repeating the analysis while varying the efficiency to its upper and lower extremes. Thisuncertainty is estimated to be within 0.5% for the v2 and 1% for v3 and v4.

Pair acceptance: This analysis uses the event-mixing [3, 4] to estimate and correct the 2PCs forthe pair acceptance of the detector. The mixed-event corrections are quite small, compared to thestatistical uncertainties in the measurements, and the entire correction is conservatively taken as thesystematic uncertainty. This uncertainty is ∼1% for the v2, ∼3% for v3 and ∼5% for v4.

Pileup: Pileup events, when included in the two-particle correlation measurement, dilute the vnsignal since they produce pairs where the two particles in the pair are from different pp collisions andthus have no physical correlations. For events containing multiple vertices, only tracks associatedwith the vertex having the largest

∑p2T, where the sum is over all tracks associated with the vertex,

are used in the analysis. However, events with multiple unresolved vertices still affect the results byincreasing the combinatoric pedestal in the correlations and thus reducing the vn,n. The fraction ofevents with merged vertices is estimated and taken as the relative uncertainty for the vn,n associatedwith pileup. The merged-vertex rate is estimated to vary from 1–3% over the multiplicity rangestudied in this note.

6 Dependence on jet-pT selection

This analysis uses jets with pT greater than 10 GeV for rejecting tracks via the procedure described inSection 4.2.1. In this section, the minimum pT threshold for the jets is varied and the effects on themeasured vn are compared to ensure that there are no systematic effects arising from the particular choice ofthe jet-pT threshold. Figure 3 compares the multiplicity dependence of the v2 for the 0.5–5 GeV pT intervalfor different jet pT thresholds. The pT-integrated v2 is observed to be nearly independent of multiplicityand its magnitude decreases by ∼10% as the minimum jet-pT threshold is decreased from 12 to 6 GeV,indicating a relatively small but systematic dependence on the jet-pT. However, some of this variationarises from a softening of the charged-particle pT spectra when lowering the jet-pT threshold. The softeningof the pT spectra results in a decrease of the vn integrated over 0.5–5 GeV, as the contributions fromhigher-pT particles, which have larger vn, is reduced. This is demonstrated in Figure 4 which compares thepT dependence of the vn for the different jet-pT thresholds for the multiplicity interval of 60–150. Here amuch smaller variation with the different jet-pT thresholds is observed for the AllEvents sample, indicatingthat the differential vn(pT) is not strongly affected by the requirement on the jet pT. For theWithJet samplethe statistical uncertainties are too large to observe clear trends in the v2.

9

0 20 40 60 80 100 120 140rec,corrchN

0

0.05

0.1

2v

> 10 GeVjet

Tp > 6 GeVjet

Tp > 7 GeVjet

Tp

> 8 GeVjet

Tp > 9 GeVjet

Tp > 12 GeVjet

Tp


<5 GeVb,a

Tp0.5<

|<5η∆2<| <20periph

chN≤0

Template FitAllEvents

0 20 40 60 80 100 120 140rec,corrchN

0.8

0.9

1

1.1

Rat

io to

10

GeV

Cas

e

0 20 40 60 80 100 120 140rec,corrchN

0

0.05

0.1

0.15

2v

> 10 GeVjet

Tp > 6 GeVjet

Tp > 7 GeVjet

Tp

> 8 GeVjet

Tp > 9 GeVjet

Tp


<5 GeVb,a

Tp0.5<

|<5η∆2<| <20periph

chN≤0

Template FitWithJet

0 20 40 60 80 100 120 140rec,corrchN

0.4

0.6

0.8

1

1.2

Rat

io to

10

GeV

Cas

e

Figure 3: Comparison of the v2 obtained with different jet-pT thresholds. The left panel corresponds to the event setof AllEvents, and the right panel to theWithJet sample. The plots are for the 0.5–5 GeV pT interval. The error barscorrespond to statistical uncertainties only.

0 1 2 3 4 5 6 7 8 [GeV]b

Tp

0

0.05

0.1

0.15

0.2

)b T

p( 2v

> 10 GeVjet

Tp > 6 GeVjet

Tp > 7 GeVjet

Tp

> 8 GeVjet

Tp > 9 GeVjet

Tp > 12 GeVjet

Tp


|<5η∆2<| <5 GeVa

Tp0.5<

<150rec,corrchN≤60 <20

periph

chN≤0

Template FitAllEvents

0 1 2 3 4 5 6 7 8 [GeV]b

Tp

0.9

0.95

1

1.05

1.1R

atio

to 1

0 G

eV C

ase

0 0.5 1 1.5 2 2.5 3 [GeV]b

Tp

0

0.1

0.2

0.3)b T

p( 2v

> 10 GeVjet

Tp > 6 GeVjet

Tp > 7 GeVjet

Tp

> 8 GeVjet

Tp > 9 GeVjet

Tp


|<5η∆2<| <5 GeVa

Tp0.5<


periph

chN≤0

Template FitWithJet

0 0.5 1 1.5 2 2.5 3 [GeV]b

Tp

0.5

1

1.5

Rat

io to

10

GeV

Cas

e

Figure 4: Comparison of the v2 as a function of pbT obtained with different jet-pT thresholds for 0.5 GeV< pa

T <5 GeV.The left panel corresponds to the event set of AllEvents, and the right panel to the WithJet sample. The plots are forthe 60–150 multiplicity interval. The error bars correspond to statistical uncertainties only.

7 Results

Figure 5 shows the 2PCs along ∆φ corresponding to 2 < |∆η | < 5 for the AllEvents, NoJet and WithJetsamples. The template fits to the 2PCs, including the two components of the fits are also shown in thefigure. Similar plots for the pb

T dependence are shown in Figure 6.

10

1− 0 1 2 3 4φ∆

0.98

0.99

1

1.01

1.02

1.03

)φ∆C

(

) φ∆C() + Gφ∆(periph

FC)φ∆(templ

C(0)

periph) +FCφ∆(ridge

C(0)

periphG + FC


<5 GeVb,aT

p0.5<|<5η∆2<|

<80rec,corrchN≤70 Inclusive

1− 0 1 2 3 4φ∆

0.98

0.99

1

1.01

1.02

1.03

)φ∆C

(


FC)φ∆(templ

C(0)


C(0)

periphG + FC


<5 GeVb,aT

p0.5<|<5η∆2<|


1− 0 1 2 3 4φ∆

0.98

0.99

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1.02

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)φ∆C

(


FC)φ∆(templ

C(0)


C(0)

periphG + FC


<5 GeVb,aT

p0.5<|<5η∆2<|

<80rec,corrchN≤70 AllEvents

1− 0 1 2 3 4φ∆

0.98

0.99

1

1.01

1.02

1.03

)φ∆C

(


FC)φ∆(templ

C(0)


C(0)

periphG + FC


<5 GeVb,aT

p0.5<|<5η∆2<|


1− 0 1 2 3 4φ∆

0.98

0.99

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Figure 5: Template fits to the two-particle correlations in ∆φ for 0.5 GeV < pa,bT < 5 GeV and 2 < |∆η | < 5. The

open points correspond to the scaled peripheral reference and the blue dash lines correspond to the Cridge. Fromtop to bottom the rows correspond to the Inclusive, AllEvents, NoJet andWithJet samples. From left to right, eachpanel corresponds to a different multiplicity interval. The error bars correspond to statistical uncertainties. Theuncertainties on the fits themselves are not shown for clarity, and are at least as large as the uncertainties on theFCperiph(∆φ) term.

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Figure 6: Template fits to the two-particle correlations in ∆φ for 0.5 GeV < paT < 5 GeV and 2 < |∆η | < 5. From

left to right, each panel corresponds to a different pbT interval. The open points correspond to the scaled peripheral

reference and the blue dash lines correspond to the Cridge. From top to bottom the rows correspond to the Inclusive,AllEvents, NoJet andWithJet samples. The error bars correspond to statistical uncertainties. The uncertainties on thefits themselves are not shown for clarity, and are at least as large as the uncertainties on the FCperiph(∆φ) term.

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Figure 7 compares the multiplicity dependence of the vn for n=2–4 from the AllEvents and NoJet tothe Inclusive pp results. The vn values are observed to vary only weakly with multiplicity as observedpreviously in Refs [6, 7]. It is observed that the v2 values in the AllEvents and NoJet samples, where tracksassociated with jets are removed or not present, are only marginally smaller (within 2–5%) than in theInclusive sample where no jet rejections are applied. This difference can partially arise from the softeningof the pT-spectra when removing tracks associated with jets, which affects the pT integrated v2 over the0.5–5 GeV pT range. Another contribution to the change in the v2 can be due to residual changes in theshape of the dijet correlations, that are not accounted for in the template fits, which are mitigated whenexplicitly rejecting jets as in the AllEvents and NoJet samples. The results for the v2 in the WithJet sampleare shown in Figure 8, and within uncertainties are consistent with the v2 in the Inclusive sample. Forthe harmonic v3 (Figure 7), it is interesting to note that the trend of the change is opposite to that for v2for the AllEvents and NoJet samples, where the v3 values are larger compared to the Inclusive sample.Additionally, the v3 values for the AllEvents and NoJet samples are observed to vary less with multiplicityat low-multiplicity compared to the Inclusive sample. The correlations arising from dijets typically affectthe even and odd-harmonics oppositely (see Ref. [30]), thus if there are residual dijet related biases in theInclusive sample, the improved jet rejection used in the AllEvents and NoJet samples would affect the v3and v2 oppositely. The relative effect of such biases on the v3 can be larger, as the magnitude of the v3 ismuch smaller than that of the v2. For the harmonic v4, within the statistical uncertainties the results for theAllEvents and WithJet samples are consistent with the Inclusive sample. The results for the WithJet sampleare statistically significant only for the v2 and are not shown for the v3 and v4 measurements.

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Figure 7: The left panels show the vn as a function of the (efficiency corrected) multiplicity. From top to bottomeach row shows the results for a different harmonic order. The data-points for the Inclusive sample are drawn at thenominal values while the data-points for the AllEvents and NoJet samples are shifted slightly for clarity. The rightpanels show the ratio of the vn for the different samples to the Inclusive sample. The error bars and bands correspondto statistical and systematic uncertainties, respectively. For the ratio plots, the correlated uncertainties between thenumerator and denominator are removed.

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Figure 8: Same as the top panels of Figure 7, but including the results for the WithJet sample. The data-points for theWithJet sample are evaluated over coarser multiplicity intervals to reduce statistical uncertainties.

Figure 9 compares the pbT-dependence of the vn between the AllEvents and NoJet samples to the Inclusive

sample. The plots are made for paT in 0.5–5 GeV. The differential v2(pb

T) and v3(pbT) values for the three

samples are quite similar over the pbT = 0.5–3 GeV range. At higher pT the v2 (v3) values are larger (smaller)

for the AllEvents and NoJet samples compared to the Inclusive sample. For the Inclusive sample the v2values become negative over the 6–8 GeV pb

T interval, but remain positive for the AllEvents and NoJetsamples. For v3 considerably large differences are seen between the AllEvents/NoJet samples and theInclusive sample for pT > 3 GeV. These features are possibly related to breakdown in the assumptions inthe template-fit which are improved when rejecting particles associated with jets. For v4 the uncertaintiesare too large to infer any clear trends between the samples. The results for the WithJet sample are shown inFigure 10 for the v2 and have too large statistical uncertainties to clearly observe any systematic trends.

Apart from the multiplicity dependence of the vn presented before, it is also useful to study the dependenceof the vn on the ΣEFCal

T . The ΣEFCalT acts as an independent measure of the event activity which is less

biased than the event multiplicity, as an independent set of particles are used to make the event-activityclasses and to produce the 2PCs. Figure 11 shows the comparison of the ΣEFCal

T dependence of the vnfor the different samples. The observed differences between the Inclusive and AllEvents samples for v2and v3 are similar to that observed in the multiplicity dependence measurements: namely, that the v2values are slightly smaller while the v3 values are larger in the AllEvents sample compared to the Inclusivesample. For v4 the uncertainties are too large to see any trends. Similar trends are observed for the NoJetsample. The results for the WithJet sample are shown in Figure 12 for the v2, and have too large statisticaluncertainties to clearly observe any systematic trend.

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Figure 9: The left panels show the pbT dependence of the vn obtained for the 60–150 multiplicity interval for

0.5 GeV< paT <5 GeV. From top to bottom each row shows the results for a different harmonic order. In the lower two

rows, the data-points for the Inclusive sample are drawn at the nominal values while the data-points for the AllEventsand NoJet samples are shifted slightly for clarity. The right panels show the ratio of the vn for the different samplesto the Inclusive sample. For the top right plot, the ratio is not shown for the 6–8 GeV pT interval where the v2 valuesfor the Inclusive sample are negative. The error bars and bands correspond to statistical and systematic uncertainties,respectively. For the ratio plots, the correlated uncertainties between the numerator and denominator are removed.

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Figure 10: Same as the top panels of Figure 9, but including the results for the WithJet sample. The data-points forthe WithJet sample are evaluated over coarser pT intervals to reduce statistical uncertainties.

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Figure 11: The left panels show the ΣEFCalT dependence of the vn. From top to bottom each panel shows the results

for a different harmonic order. The data-points for the Inclusive sample are drawn at the nominal values while thedata-points for the AllEvents and NoJet samples are shifted slightly for clarity. The right panels show the ratio of thevn for the different samples to the Inclusive sample. The error bars and bands correspond to statistical and systematicuncertainties, respectively. For the ratio plots, the correlated uncertainties between the numerator and denominatorare removed.

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Figure 12: Same as the top panels of Figure 11, but including the results for the WithJet sample. The data-points forthe WithJet sample are evaluated over coarser ΣEFCal

T intervals to reduce statistical uncertainties.

8 Summary

This analysis studied the long-range correlations in pp collisions when actively rejecting tracks in thevicinity of jets. This rejection is done by removing all tracks within ∆η = ±1 from the jet axis of track-jetsreconstructed using the anti-kt algorithm with a radius parameter of 0.4, that have pT > 10 GeV. Afterthis rejection the two-particle correlations are analyzed using the template-fit procedure to extract theFourier coefficients, vn for n =2–4, of the anisotropy. The measurements demonstrate that the magnitudeof the v2 integrated over the 0.5–5 GeV pT range decreases only marginally (2–5%) when applying thisrejection. Part of this decrease, especially at high multiplicity, can be attributed to the softening of thepT spectra when removing tracks associated with jets, while part of the reduction, especially at the lowmultiplicities, might be due to an improvement in the template fits when explicitly removing jets from themeasurements. On the other hand, the v3 values as a function of multiplicity increase by a few percent athigh multiplicities and show a somewhat larger increase at low multiplicities compared to the Inclusivesample, albeit with considerably larger statistical uncertainties compared to the v2. Measurements of thepT-differential v2 and v3 for events with multiplicities ≥ 60 show that the vn(pT) is only slightly affected bythis rejection, over the 0.5–3 GeV pT range. At higher pT, larger deviations are observed, especially for thev3. For the v4, given the statistical uncertainties in the measurement, no clear differences are observedwhen rejecting or not rejecting particles associated with jets. The measurements are also performed usingthe ΣEFCal

T as a measure of the event activity instead of the multiplicity. The trends seen in the ΣEFCalT

dependence are similar to those seen in the multiplicity dependence. These measurements indicate that thelong-range correlations seen in pp collisions are only slightly affected when particles associated with hardor semi-hard processes in the event are removed.

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Measurementofthesensitivityoftwoparticle p correlationsin pp … · TLAS-CONF-2020-018 2020 ATLAS...

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