measurements of the underlying event

Measuring a Known Unknown of QCD

The Underlying Eventin Proton-Proton Collisions

at 900 GeV & 7 TeV

Gabriel HareUniversity of California, Santa Cruz

30th June 20111

• Introduction to the Underlying Event- The LHC, Parton Distributions, Color Rules, Event Topology

• Analysis Summary:- ATLAS Inner Detector

- Track Selection & Weights

- Event Selection & Weights

• Analysis Details

• Underlying Event Measurements:- Particle Number

- Transverse Momentum Density

- Mean Particle Transverse Momentum

★Conclusions

• Analysis Minutiae

➡References

Outline

2

Introduction to the Underlying Event

3

The Large Hadron Collider

Map overlay from : http://upload.wikimedia.org/wikipedia/commons/0/06/Location_Large_Hadron_Collider.PNGAccelerator layout from : http://public.web.cern.ch/public/en/research/AccelComplex-en.html

• Protons are produced by ionizing hydrogen.

• Accelerated sequentially in LinAc2 (50 MeV) ⇒ Booster (1.4 GeV) ⇒ PS (26 GeV) ⇒ SPS (450 GeV) ⇒ LHC (7 TeV)

• Protons are grouped in “bunches” in beams circulating in both directions that intersect at the center of ATLAS.

• “Events” are bunch crossings in which there is at least one collision of protons.

• “Pile-up” describes the situation in which there is more than one proton collision in an event.

4

up

up

down

up

up

down

anti-charm

charm

gluon

gluon

Proton Contents

proton

proton

proton

• “Parton” = any particle found in a proton.

• Mostly “Quarks” & “Gluons”.

• Quarks radiate gluons.

• Gluons split into a pairs of gluons or a quark & anti-quark.

• At high energies a proton is described by a “Parton Distribution”

5

quark

quarkquark

Color Charge Mnemonic• The “strong force” (which

communicates an SU(3) orientation) is quantized as gluons.

• The charge carried by quarks that interacts with the strong force can be in one of 3 states of quark charges referred to (by analogy) as “Colors”.

• Charges are represented by displacements in a plane.

• “Confinement” of the strong force requires that only color-neutral particles can break free from protons.

• Baryons are color neutral combinations of 3 quarks, such as a proton.

6

anti-quark

• Anti-quarks carry negative charges, described as the same color in the opposite direction.

• “Confinement” of the strong force requires that only color-neutral particles can break free from protons.

• Mesons are color neutral combinations of a quark and an anti-quark. Pions are the most common instance.

• Hadrons are either color-neutral combinations of three quarks or three anti-quarks.

anti-quark

anti-quark

Color Charge Mnemonic

7

• Colors are conserved!

• Gluons bind quarks & anti-quarks together by exchanging units color.

• There are 8 charge combinations for gluons:

• 6 gluon charges describe displacements between quark or anti-quark color states.

• 2 gluon non-colored states change the relative wave-front phase of quarks of different colors.

• The wave-front phase of quarks determines whether a pair of quarks can combine to a color neutral hadron or one of the 2 non-colored gluon states.

Color Charge Mnemonic

radiatedgreen + anti-red

gluon

radiatedgreen + anti-red

gluon

8

Underlying EventHard Scatter

OutgoingPartonFSR

OutgoingParton

ISR

IncomingParton

IncomingParton

Beam Remnants

IncomingProton

Beam Remnants

IncomingProton

pp Collision

MPI

IncommingParton

IncommingParton

FSROutgoingParton

OutgoingPartonISR

IncomingProton

IncomingProton

Transverse

Transverse

Toward

Away

In the context of event simulation the “Underlying Event” refers to everything that does not originate from the Hard Scatter outgoing partons.Model dependent contributions include:

• Multiple Parton Interactions (MPI):

- Associated with higher multiplicity events.

- Angular distribution that is independent of the Hard Scatter.

• Initial State Radiation (ISR):

- Angular distribution that is nearly independent of the Hard Scatter.

• Final State Radiation (FSR):

- Yields jets of particles in the Toward and Away regions.

9

1. Radiated particles also radiate (or split) so FSR results in a “shower” of quarks and gluons.

• Particles produced in a shower are generally close together in angle.

2. Quarks and gluons are joined into color-neutral “strings”.

• Energy is distributed along string.

3. Strings “fragment” into pieces with the masses of hadrons.

• Pions are the most frequently produced hadron.

4. The string fragment hadrons form clusters of higher transverse momentum particles that are described as “jets”.

• The are many possible definitions of jets...

• This analysis avoids the problem of choosing a jet definition.

Particle Jets

Event Display from : http://www.atlas.ch/photos/atlas_photos/selected-photos/events/Atlantis-dijet-highpt-159224_3533152.png

10

http://www.atlas.ch/photos/atlas_photos/selected-photos/events/Atlantis-dijet-highpt-159224_3533152.png

http://www.atlas.ch/photos/atlas_photos/selected-photos/events/Atlantis-dijet-highpt-159224_3533152.png

• Goal: Isolate the low-energy QCD contribution to events (in a Minimum Bias sample) that is independent of the Hard Scatter energy.

• Assume a Di-Jet structure for events.

- The ϕ intervals that are nearly transverse to the Di-Jets is assumed to be principally filled by the Underlying Event.

- The energy of each of the jets is correlated to the hard scatter energy.

➡ At low energies it is sufficient to use the highest pT (leading) track T1, rather than the highest ET (leading) jet.

• Define ϕ with respect to the leading track.

➡ π/3 < |ϕ| < 2π/3 defines the “Transverse” region.

• In the context of measurements, the content of the Transverse region of events will be identified as the “Underlying Event”.

- Correspondence between the measured Underlying Event and MPI or ISR is determined when generators are tuned.

Underlying Event

Transverse

Away

Toward

Transverse

Toward

Away

TransverseTransverse

T1

T2

+ϕ

11

Underlying Event:Analysis Summary

12

• Transverse Momentum:

- Partons with equal and opposite momentum generally yield jets with equal and opposite momentum.

- Generally parton momenta are not balanced resulting in a “boosted” collision.

‣ Jet momenta along the incoming parton axis are not equal.

‣ Jet “Transverse Momentum” (pT) with respect to the incoming parton axis remains equal.

• Particles:

• Ionize detector material yielding currents in a cluster of responsive detector elements which are individually recorded as “hits”.

• Tracks:

• Estimated trajectories of particles “reconstructed” from hits.

➡ Some particles might not be successfully reconstructed.

Tracks & ParticlesParton

Center of MassRest Frame

Jet 2

IncomingParton

IncomingParton

Jet 1

Jet 2TransverseMomentum


DetectorRest Frame

Jet 2

IncomingParton

IncomingParton

Jet 1



13

The ATLAS Inner Detector

• Tracker: |η| < 2.5 iRad- Pixel Detectors: 3 barrel cylinders, 3

disks in each end-cap.

- Inner-most pixel layer is “B-Layer”.

- Stereo-Strip Tracker (SCT): 4 barrel cylinders, 9 disks in each end-cap.

- Transition Radiation Tracker (TRT): Axial straws in the barrel, radial straws in the end-caps. (Coverage for |η| < 2.1 only)

- 2 Tesla Solenoid encloses the inner detector. Central charged particles require ~500 MeV pT to pass through entire Tracker.

MBTS

TRT

MBTS

• Trigger: 2.1 < |η| < 3.8- Minimum Bias Trigger Scintillator (MBTS):

16 cell disks in each end-cap.

- Event Trigger: Hit in any cell of the MBTS.

• Reconstruction:- Space-points are defined by Pixel hits, and

by hits on crossing strips in the SCT.

- Tracks are seeded using space-points from Pixel and SCT, and are extrapolated to include hits from Pixel, SCT, and TRT.

14

dtrack -2.5 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5

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ATLASPreliminary

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ATLASPreliminary

= 7 TeVs500 MeV ≤ pT

10.5

• Primary Vertex Tracks:

➡ Used to fill profiles.

- “Inside-Out” or “Low-pT” reconstruction methods.

- pT ≥ 100 MeV, |η| < 2.5 iRad,

- |d0Vtx| < 1.5 mm, |z0Vtx · Sin(θ)| < 1.5 mm.

- The track is not required to have been used when constructing the primary vertex.

- B-Layer hit if expected.

- ≥ 1 Pixel Hit, including B-Layer.

- SCT hit requirement depends on pT

‣ pT ≥ 100 MeV : ≥ 2 SCT Hits



- Fit requirement to suppress high pT fakes:

‣ pT ≥ 10 GeV : Prob(χ2, NDofF) ≥ 0.01

Orange plots from: https://atlas.web.cern.ch/Atlas/GROUPS/PHYSICS/CONFNOTES/ATLAS-CONF-2010-024/Yellow plots from : https://atlas.web.cern.ch/Atlas/GROUPS/PHYSICS/CONFNOTES/ATLAS-CONF-2010-046/

Plots compare average hit counts in Measured & Simulated events.

Track Selection

15

https://atlas.web.cern.ch/Atlas/GROUPS/PHYSICS/CONFNOTES/ATLAS-CONF-2010-024/

Track Selection• Preliminary Tracks:

➡ Used to reconstruct primary vertices & identify pile-up vertices.

- All reconstruction methods, (+ “Outside-In”, + “Very-Low-pT”)

- ≥ 1 Pixel Hit, ≥ 4 SCT Hits, ≥ 6 Pixel+SCT Hits,

- pT > 100 MeV, |η| < 2.5 iRad,

- |d0BS| < 4 mm, |σd0BS| < 0.9 mm, |σz0BS| < 10 mm.

• Beam-Spot Tracks:

➡ Used to characterize the trigger and vertex reconstruction efficiencies.

- Intended to be similar to Preliminary Tracks.

- Dependency on the vertex reconstruction is avoided by selecting with respect to the beam spot perigee.

- “Inside-Out”, “Low-pT” reconstruction methods.

- pT > 100 MeV, |η| < 2.5 iRad,

- |d0BS| < 1.8 mm,

- Same hit & fit requirements as Primary Vertex Tracks

Plots from : https://atlas.web.cern.ch/Atlas/GROUPS/PHYSICS/CONFNOTES/ATLAS-CONF-2010-046/

[mm]0d-10 -8 -6 -4 -2 0 2 4 6 8 10

Trac

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electronsnon-electronsprimariesall

MC ND: Data

< 150 MeV T

| < 2.5, 100 < pd 2, |* chn

= 900 GeVs

ATLAS Preliminary

[mm]0d-10 -8 -6 -4 -2 0 2 4 6 8 10

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electronsnon-electronsprimariesall

MC ND: Data

< 250 MeV T

| < 2.5, 200 < pd 2, |* chn

= 7 TeVsATLAS Preliminary

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[GeV]T

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Simulation (non-diffractive)ATLAS Preliminary

• Track reconstruction concludes with a χ2 fit to all hits associated with a track.

• Top: there is a clear difference in the mean fit probabilities Prob(χ2, NDofF) for correct & incorrect pT.

• Middle: pT migration from low pT tracks yields the majority of the tracks above 40 GeV.

• Bottom: When a particle scatters off of detector material (cryostat) the fit can yield a very high track pT.

Migration plots from : https://atlas.web.cern.ch/Atlas/GROUPS/PHYSICS/CONFNOTES/ATLAS-CONF-2010-046/Explanation from : http://indico.cern.ch/getFile.py/access?contribId=4&resId=0&materialId=slides&confId=102382

Track pT Migration

Hadronic interaction can fake high pT tracks

- topology observed in MC / data

- preferred at high |#|

June 14, 2010 – 13 : 45 DRAFT 31

z [mm]-3000 -2000 -1000 0 1000 2000 3000

R [

mm

]

0

100

200

300

400

500

600

Figure 31: MC distribution of badly-reconstructed tracks in the detector zR-plane. See text for definition

of badly measured tracks. The black boxes indicate the end vertex position of the matched generated par-

ticles, and the red (blue) boxes show the position of the SCT (Pixel) hits associated to the reconstructed

tracks. The gray dashed lines highlight ! = ±2.35, and ! = ±2.55 .

reconstructed track

detector materialcharged particle

interaction with material

Figure 32: Illustration of a low momentum charged particle (blue line) that is reconstructed with high

momentum (red line). The black filled dots with the vertical lines represent the silicon measurements.

[GeV]T

reco-track p

20 40 60 80 100 120 140 160 180 200

[G

eV

]T

mc-t

ruth

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En

trie

s

1

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310

Figure 33: Left plot: MC distribution of the mean track-fit "2 probability, for reconstructed and selected

tracks versus generated pT (y-axis) and reconstructed track pT (x-axis). Right plot: MC distribution of

the track-fit "2 probability for the badly measured tracks (in log-scale).

June 14, 2010 – 13 : 45 DRAFT 31

z [mm]-3000 -2000 -1000 0 1000 2000 3000

R [

mm

]

0

100

200

300

400

500

600

Figure 31: MC distribution of badly-reconstructed tracks in the detector zR-plane. See text for definition

of badly measured tracks. The black boxes indicate the end vertex position of the matched generated par-

ticles, and the red (blue) boxes show the position of the SCT (Pixel) hits associated to the reconstructed

tracks. The gray dashed lines highlight ! = ±2.35, and ! = ±2.55 .

reconstructed track

detector materialcharged particle

interaction with material

Figure 32: Illustration of a low momentum charged particle (blue line) that is reconstructed with high

momentum (red line). The black filled dots with the vertical lines represent the silicon measurements.

[GeV]T

reco-track p

20 40 60 80 100 120 140 160 180 200

[G

eV

]T

mc-t

ruth

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trie

s

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310

Figure 33: Left plot: MC distribution of the mean track-fit "2 probability, for reconstructed and selected

tracks versus generated pT (y-axis) and reconstructed track pT (x-axis). Right plot: MC distribution of

the track-fit "2 probability for the badly measured tracks (in log-scale).

!-2.5 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5

Entries

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- current track reconstruction setup seems not to have much discriminative power in this region (long extrapolation distances between constraining hits) 5

Issue: Mis-measured high-pT tracks (II)

Simulation Simulation

in MC: O(1%) are decays in flight

Wednesday, August 4, 2010

SCT HitsTrue EndPixel Hits

Alternative Requirements:• Require d0Vtx < 0.2✓ Used for systematics• Require TRT hits- TRT covers |η| < 2.1 only• Wald-Wolfowitz (check for

residual runs)- Correlated to fit probability

17



http://indico.cern.ch/getFile.py/access?contribId=4&resId=0&materialId=slides&confId=102382

http://indico.cern.ch/getFile.py/access?contribId=4&resId=0&materialId=slides&confId=102382

1. Fake tracks are defined to be tracks that cannot be matched to some true charged particle according to the following matching criteria.

- Cone matched if pT > 500 MeV & ΔR < 0.05

- Cone matched if pT ≤ 500 MeV & ΔR < 0.15 & one common hit in the pixel detector.

2. Secondary tracks resulting from decays (or material interactions) that are identified instead as primary tracks. (Secondary fraction ~ 0.02.)

3. Out of Kinematic Range tracks whose matched true particles are outside of the kinematic range, because either their pT is too low or their η is too high. (OKR fraction ~0.2, but only at pT & η edges.)

- A charged primary stable particle with ηtrue > 2.5 can be reconstructed if the vertex is displaced towards -z, and will pass the selection criteria if ηrec < 2.5.

4. Pile-up yields additional vertices that can merge with the primary vertex in the reconstruction.

Matching plot (top) from : ATL-COM-PHYS-2010-682Secondaries plot (middle) from : ATL-COM-INDET-2010-011Pile-Up plot (bottom) from : ATL-CONF-2010-046

False TracksMarch 15, 2010 – 11 : 25 DRAFT 2

d0 [mm]

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cks

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-110ATLAS Preliminary

Had. Interactions

Strange decays

Primaries

[mm]0

d-10 -8 -6 -4 -2 0 2 4 6 8 10

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Figure 1: Shape (left) and distribution (right) for the different sources of secondaries in the Monte Carlo.

In black the primaries, in green (light shaded) long lived particles and in blue particles from hadronic

interactions are shown.

for the primaries (0 < barcode < 200000) and the secondaries (barcode > 200000 and barcode = 01).48

These templates are used to fit the distribution in the data leaving the normalisation B for the secondaries49

free50

f (d0) = A! ( fp(d0) + B/A! fs(d0))

As default the fraction of all particles A in the Monte Carlo is normalized to the data for a sec-51

ondary scaling factor of 1 Whereas this is the default procedure, several alternative normalisations will52

be considered later for the studies of systematics 4.53

The distribution is fit to the data in the range 2mm <| d0 |< 10mm. It is therefore outside the selected54

range for the analysis and only marginally dependent on the resolution modelling. The secondaries in55

this range are dominated by hadronic interactions in the material. Because of their fairly similar shapes,56

the two classes of secondary particles are combined to one sample in the fit. Alternative fit conditions57

will be treated in the section on systematic uncertainties.58

From the fit a scaling factor B of 1.00±0.02 is obtain. Using this scaling factor we measure a fraction59

of secondaries in the data of 2.20±0.07%. These errors are purely statistical.60

The distribution of the data (error bars) and the full Monte Carlo without (left) is shown in Figure 2.61

The secondaries are displayed in green. The ratio of simulation distribution and data is shown in in the62

right plot. The !2 of this ratio distribution for d0 < 2mm is 169 with 158 d. o. f.. As can be seen the63

ratio is constant for the whole range of large | d0 | and no bias is observed. The remaining difference for64

small | d0 |, where the primaries dominate, is due to effects from the detector resolution.65

4 Estimation of Systematics66

The fit procedure has been varied to study systematic dependences.67

• Fixing the primary normalisation and fitting the secondary leads to a variation of the scaling factor68

by 0.03.69

• To test the normalization the fit was redone fixing the total numbers of tracks to the data. This70

leads to a change of the scaling factor of 0.02.71

1For a small fraction of secondaries the MC truth information is lost. These do not affect the analysis significantly

# tracks @ vertex5 10 15 20 25 30 35 40

/bin

evts

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Generated 2nd pile-up vertex

Observed 2nd vertex

= 7 TeVsATLASPreliminary

Pile-up

September 1, 2010 – 20 : 32 DRAFT 19

R(track,particle)!Minimal 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Arb

itra

ry u

nits

310

410

510

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all

with common hit

Figure 20: Minimal !R between a truth particle

and a reconstructed track. In red, one common

hit in the pixel detector is required.

R cut!0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4

Fra

ctio

n o

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ack

s [%

]

0

1

2

3

4

5

6R!match using hits but not using

R but not using hits!match using

R+common hit but not using hits!match using

Figure 21: Fraction of tracks that have a truth

match using one matching method and not an-

other as a function of the !R cut used for the

cone matching methods. The hit matching cut

is fixed to Pmatch >0.55.

closest match is kept. The cone based method will in some cases match a track to a truth particle that383

is nearby but did not generate the track. In these cases the track and the truth particle will not have384

any hit in common and will have very di"erent transverse momenta. This e"ect will be called fake385

matching in the following. In the previous analysis [8], which uses tracks with pT > 500 MeV a cone386

of !R < 0.05 was used. The e"ect of fake matching can be neglected for such a small cone in a low387

multiplicity environment like for minimum bias events. However, for low-pT tracks (pT < 500 MeV) the388

track direction resolution dramatically degrades and a larger cone (up to 0.15) is needed (Fig. 20).389

4.2 The Cone Plus Hit Based Matching390

If a large cone is used, the fraction of fake matches becomes significant. One solution is to reduce the391

fake matching by requiring a common hit between the track and the truth particle. The common hit is392

required to be in the pixel detector. After requiring the common hit, the tails at large !R are removed as393

shown in Fig. 20.394

The fraction of tracks that match to a truth particle using one matching method and not another is395

shown in Fig. 21 . The fraction of tracks with a truth match using the cone method and not the hit method396

increases linearly with the !R cut. This is due to the increase of the fake matching. However, with the397

additional hit requirement this fraction is under control. Note that most of the tracks with a truth match398

using the cone plus hit method and not the hit method are pions decaying to muons. In this case, most399

of the hits are generated by the muon but the track momentum reproduces the pion momentum. It is also400

clear that a small !R cut can not be used otherwise a large fraction of tracks with a hit match will be401

missed by the cone matching. A cut at 0.15 is chosen.402

The fraction of tracks matching to di"erent truth particles depending on whether the hit matching or the403

cone matching is used is small. This fraction becomes totally negligible after adding the hit requirement404

to the cone matching method.405

4.3 Conclusion From Truth Matching406

The cone plus hit matching allows a good description of tracks and their corresponding primary parti-407

cles. The problem of decaying primary particles is solved using the cone matching. The additional hit408

requirement suppresses the fake matching. The cut on !R removes most badly reconstructed tracks far409

pT ≤ 500 MeV

18

• The aggregation weight wtrk includes corrections for the reconstruction Efficiency, and for the fractions of Fakes, Secondaries, and tracks from Outside the Kinematic Range.

• Charged Primary Stable (CPS) Particles:‣ Stable Particles: τ > 3*10-2 ns

‣ Primary Particles: no Stable predecessor

‣ pT > 100 MeV & |η| < 2.5 iRad

• The reconstruction efficiency has an uncertainty σtrk due principally to material and interaction or decay uncertainties.

• This uncertainty’s effect is estimated by making three versions of the corrected profiles, one corrected using εtrk, and two others using εtrk±σtrk.

Track Weighting

wtrk pT,η( ) = 1

ε trk

* 1- fFake( ) * 1- fSec( ) * 1- fOKR( )

Table from: https://atlas.web.cern.ch/Atlas/GROUPS/PHYSICS/CONFNOTES/ATLAS-CONF-2010-046/

Systematic Uncertainty Size RegionTrack Selection ±1% flat in pT and �

Material ±2 � 15% decreases with pT, increases with |�|Resolution ±5% 100 < pT < 150 MeV only, flat in �⇥2 prob. cut 10% flat, only for pT > 10 GeV

Alignment and other high pT10-100% Only for pT > 10 GeV,

strong � dependence, larger for the negative � end-cap

Table 1: The systematic uncertainties on the tracking e�ciency. All uncertainties are quoted relative tothe track reconstruction e�ciency.

)+π (η-2.5 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5

fitte

d m

ass

ratio

0 SK

0.997

0.998

0.999

1

1.001

1.002

1.003

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= 7 TeVs

ATLAS Preliminary

(a)

)-π (η-2.5 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5

fitte

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ATLAS Preliminary

(b)

Figure 8: Fitted K0s mass ratios as a function of � for data and various MC simulated material descriptions

over to the nominal MC sample. The � values are obtained from the positive (a) and negative (b) track.The K0

s candidates considered for these plots are required to have a reconstructed decay radius smallerthan 25 mm, i.e. before the beam pipe. Furthermore, the two pion tracks of all K0

s candidates are requiredto have at least four silicon hits. The vertical error bars show the statistical uncertainty only (data andMC), while the horizontal orange bands indicate the uncertainty due to the magnetic field strength.

Detector has been increased by 10%, both in terms of radiation length and interaction length. The massversus � is shown in figure 8. From this study, one can see that the material description in the nominalMC sample models the observed masses in the barrel (|�| . 1.3) well; one can conclude that in the regionprobed by this study, 10% is a good estimate for the possible amount of extra material present in thedetector relative to the MC.

The track length method is also similar to that used in [2]; tracks are reconstructed using the Pixeldetector only and are matched to our good tracks that have the full track selection cuts applied. Thefraction of Pixel only tracks with a successful match to a full track defines the SCT extension rate.This rate is compared between data and Monte Carlo simulation; some example regions are shown inFigure 9. We compare to the nominal simulation, the additional 10% ID material sample and anothersample where only the external Pixel services are scaled by 20%. For this study the pT spectrum ofthe Monte Carlo is re-weighted to reproduce the observed spectrum. The barrel region (|�| < 1.3) againshows good agreement with the nominal Monte Carlo with small deviations observed in the lowest pTslice (100-200 MeV). The data in the region below |�| of 2.0 agrees better with the nominal sample thanwith the 10% enhanced material sample. The end-cap regions (|�| > 2.0) show deviations, in all pTslices, that are not covered by the 10% enhanced material sample. In this di�cult detector region the

10

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19

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Data 2010

ATLAS Preliminary = 7 TeVs

2* BSsel

| < 2.5, nd > 100 MeV, |T

p• Selected Events require:

- Data Quality: Stable colliding bunches, solenoid ON, and nominal inner detector performance.

- Trigger: At least one hit in the MBTS. → εtrig(nBS)

- Single Primary Vertex. → εvert(nBS, pTMin, Δz)

- The Primary Vertex is identified as the candidate with the highest Σ(pT2) of its preliminary tracks.

- No “Pile-Up”: At most one Primary Vertex Candidate with 4 or more associated preliminary tracks.

- When there are only 2 beam-spot tracks εvert depends on the lowest track pTMin and the Δz distance between the tracks.

- At least two selected tracks. → εevent

- Two selected tracks guarantees two beam-spot tracks.

- Variation of the track reconstruction efficiency by σtrk also varies εevent.

Candidate Events:∫L

Selected Events/Candidate Events

= 900 GeV 9.12 μb-1 357511/449666

= 7 TeV 190 μb-1 10033043/12805094

Event Selection & Weighting

Plots from: https://atlas.web.cern.ch/Atlas/GROUPS/PHYSICS/CONFNOTES/ATLAS-CONF-2010-046/

sLuminosity at 900 GeV from Liquid Argon Forward Calorimeter: ATL-COM-LUM-2010-002Luminosity at 7 TeV from LUCID: ATLAS-CONF-2010-046

wev = 1

ε trig nBS( ) * 1εvert nBS,pTMin,Δz( ) * 1

εlead

s

20

• There are two migration effects, both due to the possibility that the reconstruction will fail to identify the true highest pT Primary Stable Charged (PSC) particle.

1. If the highest pT PSC particle is incorrectly reconstructed. Or, the highest pT PSC particle is missed, the second highest pT PSC particle may be identified as the leading track instead.

- The effect is a reduction in the pT scale that characterizes the event.

- In the rise preceding the plateau the migration yields densities that are too high.

2. If the highest pT PSC particle is missed, the orientation of the reconstructed event will not be consistent with the orientation of the true event.

- The effect in this case is that the Transverse region may receive contributions from the Toward & Away regions where there is jet-like activity.

- This is most significant in the plateau region of the profile, and yields an increase in the track number & summed pT densities.

• These effects are corrected by a final bin-by bin unfolding.

- This unfolding assumes that migration in data and simulated events is similar.

- An associated systematic uncertainty is estimated by comparing MC09 Pythia and PhoJet unfolding factors.

Migration Effects

21

Underlying Event:Analysis Details

22

• Define P(T,pT) to be the distribution for track momentum pT, and track number T, with P(T) the pT distribution normalized to T.

• A sample for the n event drawn from this distribution yields tracks, where the t track has momentum .

• Suppose that we are interested in the total pT of tracks y in (a region of) an event. Using the distribution P(T,pT) this is simply:

• Suppose that there is pT dependent track finding efficiency and a dependent vertex finding efficiency .

• A sample or drawn from an efficiency is ∈{0,1}.

• If no corrections are applied the measured distribution converges to E(T)*V(pT)*P(T,pT).

• Applying the weight to each track, and to each event, the measured distribution converges to a function of the measured track number and the measured momentum normalized to the corrected number of tracks:

pT n,t[ ]

E t[ ]

MMeas

1 y( ) = pT *P(T,pT)T,pT

∫ ≈ pT n,t[ ]t

T

∑n

N

∑

V n[ ]

P T,pT( ) T pT

V T( )-1

E pT( )-1

T n[ ]

Track Correction

T V T( )

E pT( )

23

• Making a measurement of y, which is the total track pT corrected for the efficiency, is simply a matter of including the correction weights.

• The event-to-event variation of M1(y) is used in the definition of the statistical error of a measurement of M1(y). In this case, simply square the result of the weighted sum over t, and weight by .

• In the case of the event-mean track pT, the weighted sum of pT is divided by the weighted track count.

• CONCLUSION: Weighting by 1/ε is correct!• In the case of the mean track pT versus track number, the track

number migration is not corrected, so the corrected mean track pT refers to the (non-integer) average of the weighted track count, but the x axis bins will still refer to the integer count of measured tracks which receives contributions from higher true track number events.

M1 y( ) = V T n[ ]( )-1 E pT n,t[ ]( )-1 * pT n,t[ ]

t

T

∑n

N

∑

V T( )-1

M2 y( ) ≈ V T n[ ]( )-1 E pT n,t[ ]( )-1 * pT n,t[ ]

t

T

∑⎛⎝⎜⎞⎠⎟

2

n

N

∑

Track Correction

24

• In general we are working with 2-dimensional distributions S(x,y) defined by a counting a events from a finite sample.

- x : the event scale. (e.g. lead track pT)

- y : a region characterization. (e.g. scalar-summed track pT)

• Define the additive 1-dimensional moment curves [MN(x)](y) by filling each bin weighted by zN.

• S is the distribution of sampled events, including track and event correction weights (wev ≥ 1) and sample weights (ωev ≤ 1).

• We need M1, M2, M3, M4.

• For the statistical errors we also need X0 : a count of events without correction weights, and X1 a count of events with correction weights.

- Simulated events can have X0 sample weights ωev < 1 when a region of phase space is over-produced.

- The sample weight ensures a proportionate estimated uncertainty despite having a large sample.

MN x( )⎡⎣ ⎤⎦ y( ) = S x,y( ) * yN

y

∀

∫

Stat. Errors for Std. Dev.

25

• After combining all of the weighted samples, the normalized moments can be defined (combining histogram bins if desired).

• To begin with, we are interested in the mean v1 value of a probability distribution P(y), and it’s standard deviation v2 with respect to event-to-event variations.

• The statistical uncertainty for a measure of v1 is:

• In order to define a statistical for v2 a new sampled value y2 can be defined whose mean value is c2:

• A sample of y2 is reduced by one, since m was used in the definition.

c2 y2( ) = m4 y( ) - 4 * m3 y( ) * m1 y( ) - m2 y( )( )2+8 * m2 y( ) m1 y( )( )2

- 4 * m1 y( )( )4


mN y( ) = MN y( ) / X1 y( )

v1 y( ) = c1 y( ) = m1 y( )

v2 y( ) = c2 y( ) = m1 y( ) - m2 y( )

U v1 y( )( ) = U m1 y( )( ) = c2 y( ) X0 -1( )

y2 x( ) = y - m1 x( )( )2

U v2 y( )( ) = c2 y2( ) X0 - 2( ) 2 * v2 y( )( )

26

• All of the profiles considered here can be considered to be derived by a finite sample from a 2 dimensional probability density P(x,y).

• In the absence of migration with respect to X-axis bins, and in the absence of event selection bias, individual track weights wtrk are sufficient to correct only the mean values of the Y-axis distributions.

- For the mean transverse pT density the relevant distribution has the sum of the track pT (y1) in the transverse region as the Y-axis.

- For the standard deviation the relevant distribution has the square of the sum of the track pT less the mean squared (y2) as the Y-axis.

• In the entire event the individual CPS particle pT probability, and the number and pT densities as functions of eta are entirely corrected by track and event weights.

• The CPS particle number probability must be corrected for migration.

• The mean individual CPS particle pT as a function of the CPS particle number also must be corrected for migration, and in this case there is a correlation with the mean pT that must also be accounted for.


27

• All corrections are derived from a sample of events generated using the ATLAS MC09 tune of Pythia 6.4 and simulated in GEANT 4.

• Similar detector conditions (disabled modules) to those of the runs during which the data would be collected.

• A comparable misalignment is included in the simulation.

• However, the simulated events have a wider distribution of the primary vertex z position so it is necessary to assign a sample weight ωev(z0Vtx) to the simulated events.

• These simulated events were used to derive the reconstruction efficiency and false track fractions.

• These events are also used to derive the final correction factors, expressed as bin multipliers, to account for migration effects.

• The bin multiplier is simply defined to be the ratio of the values in the true profiles over the reconstructed & corrected values.

• An alternative set of correction factors derived using PhoJet was found to yield a difference of at most 2%.

Migration Correction

mmult x( ) =

vtrue x( )vcorr

reco x( )

28

• If there are insufficient statistics the correction factor will have a significant associated uncertainty.

• Assuming that the bin-by-bin unfolding only corrects migration, the extent of the migration is:

• Assuming that v and are uncorrelated the uncertainty on the value of is:

• The sample yielding is principally a subset of determined by the events lost due to inefficiencies.

• Thus, there is actually a correlation between and so the uncertainty is overestimated for .

• The uncertainty for the bin multiplier expressed in terms of the statistical uncertainties for and is:

Migration Uncertainty

vmigr x( ) = vtrue x( ) − vcorrreco x( )

mmult x( )

vtrue x( ) vcorrreco x( )

vmigr x( )

U vmigr x( )( ) = U vtrue x( )( )2 +U vcorr

reco x( )( )2



vmigr x( )

mmult x( ) vtrue x( ) vmigr x( )

U mmult x( )( ) =vtrue x( )2 *U vmigr x( )( )2

+U vtrue x( )( )2 * vmigr x( )2

vtrue x( ) - vmigr x( )( )2

29

• The false high pT fraction estimated in data (first %) is significantly higher than that fraction estimated in simulated events (second % in parenthesis).

- Estimated by comparing total track pT with SCT only track component pT.

- Estimated by looking at the d0Vtx distribution, since badly reconstructed tracks yield large tails.

• The first (1 GeV) bin could lose events:

- The bin value v(x1) systematic uncertainty has 0.04*vbin added in quadrature.

- The unfolding multiplier m(x1) systematic uncertainty has 0.04 added in quadrature.

• Events in the first (1 GeV) bin are used as a typical event added to higher pT bins. Based on the pT interval the absolute difference between data and simulated event fractions defines a combined uncertainty Ucomb that introduces and additional systematic uncertainty:

- Ufhpt is added in quadrature to U(v(x)).

- Ufhpt / v(x) is added in quadrature to U(mmult(x).

False high pT Uncertainty

Reference for mismeasured track fraction (bottom): ATL-COM-PHYS-2010-682

September 1, 2010 – 20 : 32 DRAFT 40

!-2.5 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5

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=7 TeVsData ) cut2"with Prob(

!-2.5 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5

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=7 TeVsMC ndiff ) cut2"with Prob(

Figure 44: Track ! distribution for Data (left) and simulation (right). The distributions are shown for

various pT slices. All distributions are normalised to unity over |!| < 1.

Table 5: Estimated fraction of mis-measured tracks in Data in bins of pT and !. The numbers in brackets

are for MC.

pT bin [GeV] ! < !2.25 |!| < 2.25 ! > 2.25 combined: |!| < 2.5

10 < pT " 15 20% (0.9%) 0.3% (0.1%) 1.2% (1.2%) 3% (0.2%)

15 < pT " 20 30% (5.5%) 2% (0.4%) 25% (3.5%) 6% (0.7%)

20 < pT " 30 50% (3.8%) 4% (0.7%) 25% (2.9%) 10% (0.9%)

30 < pT " 50 80% (29%) 10% (2.8%) 45% (49%) 30% (5%)

shape of MC or low-pT Data). The two ! distributions are normalised in the central ! region ([-1;+1]),766

after properly adding the estimated fraction of mis-measured tracks in the central ! region (from the first767

step, using the results of the SCT tracklet or the d0 method).768

Figure 44 shows the normalised ! distributions for various pT ranges. A clear excess is visible in769

Data in particular in endcap C (negative !). The full excess in the forward regions is attributed to mis-770

measured tracks.771

The resulting estimated fractions of mis-measured tracks in Data for the various pT and ! bins are772

summarised in Table 5, together with the corresponding fractions from MC. The two discussed estimation773

methods yield mostly similar results within statistics (cf. Fig. 45), in case of di!erent numbers the more774

conservative (higher estimated fraction) has been used in Table 5.775

It is important to note that both methods su!er from small statistics at high-pT. The statistical errors776

are one of the reasons to use the estimated fraction of mis-measured tracks as a systematic error and not to777

attempt to correct the MC. Furthermore, the definition of mis-measured tracks (50% relative di!erence)778

is arbitrary to some extent and does not take into account the actual di!erence between the true pT and779

the reconstructed pT.780

7 Track momentum resolution at high pT781

Studies using Z-bosons found that the track pT (core) resolution in data is about 10% w.r.t. nominal reso-782

lution worse than in MC for pT # 10 GeV. The impact of a 10% Gaussian smearing of the reconstructed783

track pT in MC is performed (cf. Fig. 46) and found to have a $ 7% e!ect for the binning used in this784

analysis. This e!ect is taken as a systematic uncertainty for tracks with pT > 10 GeV.785

Ufhpt = Ucomb * v x( ) - v x1( )

30

• The systematic uncertainty associated with the bin-by-bin unfolding for ϕ re-orientation is mostly due to the uncertainty in the modeling of the angular correlation between leading & sub-leading tracks.

• The migration in the pT of the leading track is a more contributor to migration, but the difference between Pythia and PhoJet is negligible.

• Comparing profiles, the uncertainty is estimated to be 2% uniformly.

• The difference between Pythia and PhoJet in the transverse region is equivalent to the difference between Pythia and Data. Consequently, the uncertainty associated with the unfolding is sufficient to account for this model dependency, since it allows for variation in the opposite direction.

between Leading and Subleading Track [rad]q6

0 0.5 1 1.5 2 2.5 3

Freq

uenc

y

0.1

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= 900 GeVs

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PYTHIA MC09PHOJETUncorrected Data

between Leading and Subleading Track [rad]q6

0 0.5 1 1.5 2 2.5 3Fr

eque

ncy

00.10.20.30.40.50.60.70.80.9

1 PreliminaryATLAS

= 7 TeVs

Transverse

PYTHIA MC09PHOJETUncorrected Data

Probability of Reorientationinto Transverse Region

Probability of Reorientationinto Transverse Region

7 TeV Data 0.288

Pythia MC09 Tune 0.253

PhoJet 0.219

Plots from: ATLAS-CONF-2010-029

Model Uncertainty

31

Total Uncertainty1. The measured and corrected profiles have a statistical uncertainty due

to the finite sample size X0, which receives no sample weight ωev.

2. The uncertainties in the efficiency weights wtrk are varied to estimate a systematic uncertainty. (The uncertainty in wev is negligible.)

3. The half width resulting from the efficiency variation is summed in quadrature with the statistical uncertainty in the data.

4. The unfolding factor has an uncertainty due to the finite sample size X0 which includes a sample weight ωev.

5. The statistical and systematic uncertainties in the corrected data (including the efficiency uncertainty) are combined with the uncertainty in the unfolding to yield a total uncertainty:

6. This systematic uncertainty is increased (symmetrically) to envelope the differences resulting from multiplicative unfolding using a multiplier derived from a PhoJet sample, rather than from the MC09 tune of Pythia.

mmult x( )

U vdata x( )( ) = vdata x( )2 *U mmult x( )( )2 +U vdata x( )( )2 * mmult x( )2

32

• Numbers in parenthesis refer to 900 GeV profiles.

• These uncertainties are estimated from the transverse pT density profiles, but are equal or less for all other profiles.

• 900 GeV: the lowest pT bin refers to 1.0 − 1.5 GeV, the intermediate pT bin refers to 4 − 5 GeV, and the highest pT bin refers to 9 − 10 GeV.

• 7 TeV: the lowest pT bin refers to 1.0 − 1.5 GeV, the intermediate pT bin refers to 9 − 10 GeV, and the highest pT bin refers to 18 − 20 GeV.

Total Uncertainty

References: ATLAS-CONF-2010-029

an event is reoriented such that the true towards and away regions lie in the transverse region

identified by the reconstruction. Comparing the |!!| distribution in uncorrected data to the

same distributions (uncorrected and reconstructed) predicted by PYTHIA and PHOJET, it

is seen that both generator models predict fewer event reorientations of this type. The final

correction to the data uses bin-by-bin unfolding factors that are derived from the PYTHIA

sample, so the relative magnitude of the systematic uncertainty associated with this e"ect

can again be estimated by the di"erence of the PYTHIA and PHOJET probabilities. This

di"erence is comparable with the di"erence between the data and PYTHIA predictions.

The uncertainty is applied in both directions, reasonably assuming a symmetric e"ect, so

the di"erence in PYTHIA and PHOJET corrections provides the systematic uncertainty in

the unfolding factor even though the PHOJET deviation from PYTHIA is in the opposite

direction from the data.

Table II summarizes the various contributions to the systematic uncertainties.

TABLE II. Summary of systematic uncertainties, shown for the lowest-, intermediate- and highest-

pT bins. For the analysis with 7 TeV (900 GeV) center-of-mass energy data, the lowest-pT bin refersto pleadT = 1.0! 1.5 GeV, the intermediate pT bin refers to pleadT = 9 ! 10 GeV (4 ! 5 GeV), andthe highest pT bin refers to pleadT = 18 ! 20 GeV (9! 10 GeV). The uncertainties shown are from

the transverse region charged!

pT distribution, and all the other profiles are estimated to havecomparable or less systematic uncertainty. Each uncertainty is given relative to the profile value atthat stage in the correction sequence and they are an average over all of the phase-space values. In

the cases where the uncertainties are di!erent for 900 GeV and 7 TeV analysis, the 900 GeV valueis shown in parentheses.

Leading charged particle bin Lowest-pT Intermediate-pT Highest-pT

Systematic uncertainty on unfoldingPYTHIA/PHOJET di"erence 4% 2% 2%PYTHIA unfolding stat. uncertainty < 0.1% 1% (2%) 4% (5%)

Systematic uncertainties from e!ciency correctionsTrack reconstruction 3% 4% 4%Leading track requirement 1% < 0.1% < 0.1%Trigger and vertex e#ciency —— < 0.1% (everywhere) ——Total from e#ciency corrections 2.5% 4% 4%

Systematic uncertainty for bin migrationBin migration due to mismeasured pT - 2.5% (0%) 5% (0%)

Total systematic uncertainty 4.5% 4.5% (5%) 8% (6.5%)

16

33

Underlying Event:Measurements

34

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Data 2009PYTHIA ATLAS MC09PYTHIA DWPYTHIA Perugia0PHOJETHERWIG+JIMMY ATLAS MC09

| < 2.5d> 0.5 GeV and |T

p ATLAS

> 1.5 GeVleadT

p


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900 GeV pT Density➡ Leading Track is

excluded. Plots are reflected.

• The emergence of jets is evident at the center and sides of the plots.

• Profiles begin at points along the “rise” of the Underlying Event.

• Shape in the transverse region is not well described by any M.C. tune.

Transverse

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35

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p


900 GeV Number Density➡ Leading Track is


• The emergence of jets is evident at the center and sides of the plots.

• Profiles begin at points along the “rise” of the Underlying Event.


Transverse

Away

Toward

Transverse

36

[GeV]leadT

p1 2 3 4 5 6 7 8 9 10

> [G

eV]

qdd/d Tp

-2<d

00.20.40.60.8

11.21.41.61.8

2All 3 Regions

= 900 GeVs| < 2.5d> 0.5 GeV and |T

pATLAS

Data 2009, Transverse RegionPYTHIA ATLAS MC09Data 2009, Toward RegionPYTHIA ATLAS MC09Data 2009, Away RegionPYTHIA ATLAS MC09

[GeV]leadT

p2 4 6 8 10 12 14 16 18 20

> [G

eV]

qdd/d Tp

-2<d

00.5

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5All 3 Regions

= 7 TeVs| < 2.5d> 0.5 GeV and |T

pATLAS


Transverse

Away

Toward

Transverse

pT Density

• The good match at low leading track pT in the Toward & Away region profiles indicates that the low energy parton shower is well described.

• The proportionality of the pT densities in the Toward & Away regions indicates the extent of the variation in the charged fraction of the total energy in each region.

• At low leading pT the Away summed pT ~ 2/3 Toward.


37

[GeV]lead

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1 2 3 4 5 6 7 8 9 10

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2 4 6 8 10 12 14 16 18 20

MC

/Da

ta

0.6

0.8

1

1.2

1.42 4 6 8 10 12 14 16 18 20

[G

eV

]!

d"

/dT

p#2

Std

. D

ev.

of d

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

Transverse Region

= 7 TeVs

| < 2.5"> 0.5 GeV and |T

p

ATLAS

Data 2010

PYTHIA ATLAS MC09

HERWIG+JIMMY ATLAS MC09

PYTHIA DW

PYTHIA Perugia0

PHOJET

• The Underlying Event is incorporated into jets originating from the Hard Scatter, yielding an incorrect increase in their energy.

• The standard deviation of the summed transverse momentum indicates the additional uncertainty in jet energy due to the underlying event.

Transverse

Away

Toward

Transverse


pT Density Std. Dev.

38

Transverse

Away

Toward

Transverse

q6

dd/d

chN2 d

0.20.25

0.3

0.350.4

0.450.5

0.550.6

= 900 GeVs

> 1.0 GeVleadT

p



| < 2.5d> 0.5 GeV and |T

p ATLAS

> 1.5 GeVleadT

p


[rad]wrt lead

q6-3 -2 -1 0 1 2 3

q6

dd/d

chN2 d

0.20.25

0.30.35

0.40.45

0.50.55

0.6

> 2.0 GeVleadT

p


[rad]wrt lead

q6-3 -2 -1 0 1 2 3

> 2.5 GeVleadT

p


900 GeV Number Density➡ Leading Track is


• Much lower ratio of number densities in towards / away jets than predicted by any M. C. tune.


39

q6

dd/d

chN2 d

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2 = 7 TeVs

> 1.0 GeVleadT

p



| < 2.5d> 0.5 GeV and |T

p ATLAS

> 2.0 GeVleadT

p


[rad]wrt lead

q6-3 -2 -1 0 1 2 3

q6

dd/d

chN2 d

0.2

0.4

0.6

0.81

1.2

1.4

1.6

1.8

> 3.0 GeVleadT

p


[rad]wrt lead

q6-3 -2 -1 0 1 2 3

> 5.0 GeVleadT

p


7 TeV Number Density➡ Leading Track is


• Unlike the 900 GeV data, the number density is higher near the Leading Track.


Transverse

Away

Toward

Transverse

40

Transverse

Away

Toward

Transverse

[GeV]leadT

p1 2 3 4 5 6 7 8 9 10

>qdd/d

chN2<d

0

0.2

0.4

0.6

0.8

1

1.2

1.4All 3 Regions

= 900 GeVs| < 2.5d> 0.5 GeV and |T

pATLAS


Number Density

[GeV]leadT

p2 4 6 8 10 12 14 16 18 20

>qdd/d

chN2<d

0

0.5

1

1.5

2

2.5All 3 Regions

= 7 TeVs| < 2.5d> 0.5 GeV and |T

pATLAS


• The Toward & Away regions are dominated by jet-like activity, yielding rising number densities.

• In contrast, the number density in the Transverse region appears to be independent of the energy scale defined by the pT of the leading track once it reaches the plateau.

• Leading track selection bias yields a cross-over of the Toward & Away profiles in both Data and Monte Carlo.


41

[GeV]leadT

p1 2 3 4 5 6 7 8 9 10

MC

/Dat

a

0.60.8

11.21.4

[GeV]leadT

p1 2 3 4 5 6 7 8 9 10

MC

/Dat

a

0.60.8

11.21.4

qdd/d

chN2St

d. D

ev. o

f d

0.1

0.2

0.3

0.4

0.5

0.6Transverse Region

= 900 GeVs| < 2.5d> 0.5 GeV and |

Tp

ATLAS

Data 2009PYTHIA ATLAS MC09HERWIG+JIMMY ATLAS MC09

PYTHIA DWPYTHIA Perugia0PHOJET

Lead Track pT > 1.0 GeV

[GeV]leadT

p2 4 6 8 10 12 14 16 18 20

MC

/Dat

a

0.60.8

11.21.4

[GeV]leadT

p2 4 6 8 10 12 14 16 18 20

MC

/Dat

a

0.60.8

11.21.4

qdd/d

chN2St

d. D

ev. o

f d

0.2

0.4

0.6

0.8

1


= 7 TeVs| < 2.5d> 0.5 GeV and |

Tp

ATLAS




• The ratio of the mean number of tracks to the standard deviation yields information about the production of the tracks.

• Uncorrelated Production (Geometric Distribution) ⇒ Mean ≈ Standard Deviation

• Sequential Production (Poisson Distribution) ⇒ Mean2 = Standard Deviation

• Particle production is between Uncorrelated (MPI) and Sequential (Shower).

Transverse

Away

Toward

Transverse


Number Density Std. Dev.

42

[GeV]lead

Tp

1 2 3 4 5 6 7 8 9 10

Ra

tio

0.6

0.8

1

1.2

1.4 DataMC09

1 2 3 4 5 6 7 8 9 10

> [G

eV

]T

 0.5 GeV and |

Tp

Data at 7 TeVData at 900 GeV

PYTHIA ATLAS MC09 at 7 TeVPYTHIA ATLAS MC09 at 900 GeV

[GeV]lead

Tp

1 2 3 4 5 6 7 8 9 10

Ra

tio

0.6

0.8

1

1.2

1.4 DataMC09

1 2 3 4 5 6 7 8 9 10

> [G

eV

]T

 0.5 GeV and |

Tp



[GeV]lead

Tp

1 2 3 4 5 6 7 8 9 10

Ra

tio

0.6

0.8

1

1.2

1.4 DataMC09

1 2 3 4 5 6 7 8 9 10

> [G

eV

]T

 0.5 GeV and |

Tp



Mean Particle pT• Top plot sections show the pT density at 900 GeV and 7

TeV.

• Bottom plot sections show the ratio of 7 TeV / 900 GeV results for Data and MC09 Pythia.

• Momentum spectrum of particles is well modeled.

• 900 GeV collisions have higher mean values in the towards regions because the leading particle is surrounded by fewer other particles.

Transverse

Away

Toward

Transverse

43

• There is a higher multiplicity of particles produced by additional parton interactions than was predicted.- The interaction multiplicity is estimated when tuning each model,

but would be meaningful only for that model.

• The transverse momentum spectrum of these particles was successfully predicted in all regions of the event.- This was surprising given the lower energy scale in the

transverse region.

• For the first time the standard deviation of the particle distributions was measured.- The standard deviation was not well described in higher energy

proton collisions.

- Regional fluctuations in the Underlying Event may be significant.

• MANY other measurements were also made in this analysis... and in subsequent analyses.

Conclusions

44

Underlying Event:Analysis Minutia

45

Pythia 6.4:MC09 Tune

Pythia uses pT ordered showers and a string hadronization model.

The ATLAS collaboration’s tune to CDF data at taken at 630 GeV and 1.8 TeV. Uses pT -ordered shower & color reconnection. The ISR and MPI cutoff scales are tuned separately. Uses the MRST LO* PDF.

Pythia 6.4:Perugia0 Tune

By Peter Skands. Mostly tuned using Tevatron and SppS Minimum-Bias data. Uses pT-ordered shower and CTEQ5L PDF.

Pythia 6.4:DW Tune

By Rick Field. Maximal ISR, virtuality-ordered shower. To fit data on the di-jet angular distribution measured by the D0 collaboration.

Tune is intended to provide a good description of the underlying event, but high ISR yields mismatches in other processes.

PhoJet

Dual Parton Model based, using pomeron exchange for soft QCD interactions. Incorporates a model for high-mass diffraction dissociation including multi-jet production.

The parameters & Pythia version used in the ATLAS tune are different from the standard tune by Ralph Engel.

Herwig+JimmyHerwig uses angular ordered showering and a cluster hadronization model.

Jimmy introduces multiple parton interactions by a poisson process.

Event Generation Tunes

Reference for MC09: ATL-PHYS-PUB-2010-002

Only Non-Diffractive samples are used for bin-by-bin unfolding.

46

• In order to combine the diffractive and non-diffractive components of events it is necessary to work with moments, and to ensure that the statistical uncertainty estimate is acceptable.

- A large sample of a phase space that contributes a small fraction of the events for some mean value should not substantially reduce the statistical uncertainty for that mean value.

• Given a diffractive sample (diff) and a non-diffractive sample (non) all additive quantities MN and X0 are rescaled by the fractional contributions to a given bin to yield total (tot) values.

• is the fractional contribution from a non-diffractive bin x.

• Normalizing the histograms enables addition weighted (as samples) by expected fractional contributions.

• If the X1 are proportional in the divided sample, then Vtot(x) is proportional to the single sample sum.

Including Diffraction

P x( ) =X x( )1

X1 x'( )x'∫

ωnon x( )∝ 1Xnon

1 x( ) *σnon *Pnon x( )

σnon *P x( )non +σdiff *Pdiff x( )

Vtot x( )∝ ωnon x( ) * Vnon x( )+ωdiff x( ) * Vdiff x( )( )

ωnon x( ) * Xnon1 x( )

47

• The alternative to the additive merging would be to define a rule for combining uncertainties associated with the profiles that result from sampling individual regions of phase space.

- One method is to recalculate the mean and variance for each bin using a sum of gaussian distributions defined by the non-diff and diff mean values and uncertainties, with normalization determined by their fractional contributions .

- Given separated and well measured mean values that contribute equally, the variance of the sum of their distributions would be half of the distance between them, so it is clearly not the statistical uncertainty on the combined mean.

• The remaining step is to determine the normalization for the additive result. (All other results are normalized in the derivations.)

• A sample weight must be ≤ 1. Sample weights < 1 yield statistical uncertainty estimates that are too large, which is acceptable.

• The normalization should be chosen for each bin so that is maximized subject to the constraint that .

• Consequently, largest partial will be rescaled to unit sample weight, so the same rescale makes all other .

Including Diffraction

Xtot0 x( )

ω x( )

Xtot0 x( )

ω x( ) ≤1

ω x( ) ω x( ) ≤1

48

• Track Reconstruction: Inside-Out

1. Space points are made from Pixel hit clusters and from intersection of SCT strip hit clusters.

2. Triplets of space points are used to define tracks, and adjacent hits are associated with the tracks.

3. Tracks are scored, and ambiguous hits are associated with the higher scored tracks.

4. Tracks are extended into the TRT, when possible.

5. Space-points that were not used in the primary inside-out reconstruction are used in a subsequently low-pT reconstruction.

- Low pT tracks are only used for the vertex reconstructions.

6. A final χ2 fit refines the tracks.

• Track Reconstruction: Outside-In

- After the Inside-Out track reconstruction sequence is finished...

- Principally recovers charged products of neutral decays, deflected electrons, and low pT tracks.

- Outside-In tracks are only used for the beam-spot reconstruction.

Reconstruction Sequence

49

• Beam Spot Estimation

1. For each event a single vertex (constructed via fast Billoir method) is constructed using all tracks.

2. The distribution of the event single vertices is used to characterize the beam spot on 5 lumi-block (~10 minute) intervals.

• Primary Vertex Reconstruction

1. “Vertex Tracks” compatible with the beam spot are selected.

2. Multiple primary vertices (constructed via Strandlie et al’s method) are identified.

- Tracks whose perigee is more than 7σ from a vertex are used to seed new vertices.

- A minimum of two tracks are required to construct a vertex.

3. Only vertices that are incompatible with the beam spot (by the same requirements as for “Vertex Tracks”) will be considered as Primary Vertex Candidates.


50

• Shared hits refer to hits that lie on multiple track trajectories.

• Holes refer to missing hits on track trajectories. Detector elements that have been found to be non-responsive are not counted as holes.

Initial Cut Standard Si-Seed Low-pT Si-Seed

Minimum pT 500 MeV 100 MeV

Maximum z0DC 250 mm 250 mm

Maximum η 2.7 2.7

Maximum d0DC 10 mm 100 mm

Pixel + SCT Hits ≥ 7 ≥ 5

Pixel Hits ≥ 0 ≥ 2

Shared Hits ≤ 3 ≤ 2

Pixel Holes ≤ 2 ≤ 1

SCT Holes ≤ 2 ≤ 2

Double Holes ≤ 1 ≤ 1


51

MBTS Trigger Validation• The efficiency of the L1_MBTS_1 trigger for minimum bias events was

determined by comparison to events selected by the mbSpTrk trigger, and to events selected by a random trigger.

• At Level 1 both the MBTS & mbSpTrk triggers require a BPTX (beam pick-up detector) indicating the presence of bunches in at least one beam.

- Colliding bunch events were subsequently selected for the analysis.

• At Level 1 the MBTS_1 trigger also requires at least one hit in any cell of the MBTS.

• At Level 2 the mbSpTrk trigger requires at least 4 pixel space points and 4 SCT space points.

- This requirement removes noise events.

• At the Event Filter the mbSpTrk trigger requires at least one track (by any author) with pT > 200 MeV and |z0BS| < 200 mm.

- This requirement removes beam gas & halo events.

52

MBTS Trigger Validation• It may be the case that events passing the mbSpTrk trigger do not

pass the offline event selection criteria.

- In order to avoid correlations with the vertex reconstruction, events without a reconstructed primary vertex are selected if there is at least one beam spot track.

• The estimated MBTS_1 efficiency εL1_MBTS_1 is defined by the ratio:

• The extent of any correlations between the MBTS trigger and the mbSpTrk trigger can be estimated using events selected by a random trigger instead of the mbSpTrk trigger.

• Any difference between εL1_MBTS_1 and ε’L1_MBTS_1 can be attributed to correlations between the MBTS and mbSpTrk triggers.

εL1_MBTS_1 =

N L1_MBTS_1∧mbSpTrk ∧OFFLINE( )N mbSpTrk ∧OFFLINE( )

εL1_MBTS_1 =

N L1_MBTS_1∧RNDM∧OFFLINE( )N RNDM∧OFFLINE( )

53

ATLASInner Detector Tracker

& MBTS

54

• Standing at the A end of ATLAS and looking along the beam line in the -z direction toward the C end, +x and ϕ = 0 is to horizontal and to the right toward the LHC ring center, y > 0 and ϕ > 0 is above, and ϕ < 0 is below.

• The perigee parameterization of a track with respect to a point describes the closest approach to the z-axial line through the point.

• d0 > 0 when ϕ increases along the track.

• θ > 0 and equals 0 in the +z direction. η ≥ 0 when z ≥ 0.

• The solenoid internal field is oriented in the +z direction, and the toroid field in in the +ϕ direction.

• |p| is used rather than pT since the same track description must pertain to tracks in the toroid field as well.

Perigee Parameters5

p

track

d0

ex

ey

ez

pT

x-y plane

z0

!

"

Figure 3: The perigee representation ex-pressed in the ATLAS track parameterisation.The local expression of the point of closest ap-proach is given by the signed transverse im-pact parameter d0 and the longitudinal im-pact parameter z0. The momentum directionis expressed in global coordinates using the az-imuthal angle ⇥ that is defined in the projectedx � y plane and the polar angle �, which ismeasured with respect to the global z axis.

Neutral Parameters Recently, the ATLAS tracking EDM has been extended to deploy a dedicatedschema for neutral particle representations [8]. The fifth parameter of the representation as given inEq. (1) is hereby modified to represent 1/q, omitting the charge definition. Charged and neutral trajec-tory representations are realised through the same templated class objects to avoid code duplication,while keeping the type diversity to prevent misinterpretations to happen during the reconstructionflow. The extrapolation package and propagation tools have been adapted to cope with both chargedand neutral types, but the ATLAS Track class remains restricted to charged trajectories2. Neutralparameters are only transported along a straight line to the provided target surface. Material e�ectsare not taken into account and thus the navigation process is not necessary in this context. This doc-uments concentrates therefore on the extrapolation process of charged track representations and willonly briefly mention the particularities for neutral parameterisation in the various di�erent modules.

2 Propagation

The mathematical propagation of track parameters to a destination surface is — when omittingenergy loss and multiple scattering e�ects — determined by the starting parameters and the traversedmagnetic field. A homogenous magnetic field setup (no field or constant field value and direction)allows to use an underlying parametric track model for the propagation. Many propagation processescan then be solved purely analytically to find the intersection of the track with the destination surfaceand even for the transported covariances. However, the highly inhomogeneous magnetic field of theATLAS detector setup requires tracking of particles by numerical methods. Figure 4 shows themagnetic field of the ATLAS detector in an r � z projection for both, the Inner Detector in detail,and the Muon Spectrometer.The variety of the di�erent propagation techniques is enhanced by di�erent implementations of acommon abstract AlgTool interface, the IPropagator. The interface for propagator AlgTool classesis kept very simple; it reflects the pure principle of the task: an input TrackParameters object, adestination surface, magnetic field properties and a boolean for the surface bound handling is passedthrough the method signature, while on the other hand the propagated parameters are returned asthe method value. Returning a pointer to a new object puts the responsibility of memory cleanuponto the client algorithm, but complies fully with the factory pattern design described in Sec. 1.2.The following main interface methods are defined for the IPropagator interface:

• The propagate() method shall be used in cases when the track parameters to be transportedare likely to carry a covariance matrix and the client algorithm relies on the transported errordescription as well. If the input parameters do not have associated errors, only the parametersare transported to the destination surface.

• To save CPU time, the propagateParameters() that only performs the transport of the pa-2This is because neutral particles are not subject of tracking in the classical terms of track finding and track fitting.

Diagram from: http://cdsweb.cern.ch/record/1038100/files/soft-pub-2007-005.pdf

55

http://cdsweb.cern.ch/record/1038100/files/soft-pub-2007-005.pdf

http://cdsweb.cern.ch/record/1038100/files/soft-pub-2007-005.pdf

ATLAS Inner Detector ( R, Z, Eta )

56

Inner Detector: Pixel• Resolution:

– 50 x 400 micron^2 (φ x η)

– > 4e-4 x 3e-3 radian^2 (φ x η)

– Radial separation > 35 millimeters

• Performance:– > 0.80 hit efficiency

– Inner-most layer (referred to as the “B-layer”) is crucial for track ambiguity resolution.

• Time over Threshold:– Time over threshold can characterize energy loss of

particles.

– Can contribute to particle identification.

– Pions, kaons, protons, deuterons appear as bands below.

p (GeV)-2.5 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5

)2 c

m-1

dE/d

x (M

eV g

012

34

567

89

10

1

10

210

310

410

510ATLAS PreliminaryGood Pixels>=3

57

Inner Detector: SCT• Stereographic hit location by finding

intersecting lines. – Reduced track density at > 300 mm permits this method

of tracking.– > 0.99 hit efficiency.

• Skew: ±40, milli-Rad from the η-line, with other side axial (0 skew).– Hits on intersecting strips define a space point. The

requirement of multiple hits suppresses noise.– Small stereo angle suppresses spurious space points.

– Small skew with respect to η is preferred since the measurement of p⊥ relies on φ resolution.

• Resolution:– 17 x 580 micron^2 (φ x η)

– > 1e-4 x 3e-3 radian^2 (φ x η)

– Radial separation > 72 millimeters

• Dimensions:– 80 micron pitch– 12 centimeter chained length

58

Inner Detector: TRT• Tracking by Xe based gas ionization.• At high voltages, in-falling electrons yield an

avalanche, resulting in a current pulse.• Material between straws induces transition

radiation.– Peak frequency in x-ray.

– Power is proportional to Lorentz γ.

• Transition radiation (but not prompt photons) also ionize the gas, yielding a larger current.– High threshold output tags electrons.

– 7-10 high threshold hits for > 2 GeV electrons.

• Resolution:– ~ 0.003 x 0.8 (φ x η)

• Dimensions:– 2 millimeter straw radius

– 71.2 centimeter anode wire to midpoint

59

Inner Detector: MBTS• Minimum Bias Trigger Scintillator

(MBTS) shown as octagons at the front & back of the TRT layer of the inner detector.– Actually associated with Calorimeter...

• Four triggers are defined:– L1_MBTS_1 (at least one hit in any cell)

★ Used for this analysis

– L1_MBTS_2 (at least two cells with hits)

– L1_MBTS_1_1 (at least one hit in each disk)

• During the runs used for this analysis no pre-scale was applied to any of the MBTS triggers.

• Location:– ±3560 mm along the z axis from the center

of the detector. (2 cm thick)

– η coverage from 2.09 to 3.84 (overlaps the tracker η coverage)

to the beam direction. Each disk is separated into an inner and an outer disk respectively covering the

radial regions (153, 426) mm and (426, 890) mm. In ! these regions correspond to (3.84, 2.82) and

(2.82,2.09), respectively. Both the inner and the outer disks are organised into 8 independent " sectors

2#/8 radians wide. These sectors are placed such that the first sector has its edges at " = 0 and " = #/4

radians. Light emitted by each scintillator counter is collected by wavelength-shifting optical fibers and

guided to a photomultiplier tube (PMT). The PMT signals are readout by the Tile Calorimeter (TileCal)

electronics. The MBTS signals, after being shaped and amplified by the TileCal electronics, are further

amplified by a factor of two and fed into leading edge discriminators. The NIM pulses are stretched and

sent to the CTP. An MBTS hit is defined as a MBTS signal above a discriminator threshold of 60 mV,

At the CTP input these signals are stretched to 200 ns to negate the effect of time-walk and the MBTS

multiplicity is calculated. The MBTS multiplicity is calculated for each side independently. From such

inputs three relevant L1 trigger items are formed: L1 MBTS 1, L1 MBTS 2 and L1 MBTS 1 1. These

items require a BPTX signal, from either side, and respectively at least one MBTS hit, at least two MBTS

hits and at least one MBTS hit per side. All three triggers were running unprescaled with no additional

requirements in the High-Level Trigger HLT for the entire dataset used in this analysis.

Figure 1: MBTS disk configuration.

2.3 Inner Detector based Minimum Bias Trigger

The Inner Detector Minimum Bias Trigger, called in the following mbSpTrk 1, provides an alternative

method to the MBTS system to select inelastic interactions. mbSpTrk is seeded by the BPTX trigger at

L1. The event selection takes place at the HLT. The mbSpTrk trigger uses the ID silicon sub-detectors:

the pixel and the silicon central tracker (SCT) [5]. The trigger therefore covers the complete !-region up

to |! | < 2.5 where track based measurements are performed.

At the Level-2 trigger (L2) the algorithms detect central detector activity by forming spacepoints in

both the pixel and SCT detectors. The spacepoints are 3-dimensional hit representations formed from hit

clusters. The different detector technology of the pixel and SCT systems is reflected in the spacepoint

formation respectively. While the pixel spacepoints are made by a direct transformation of pixel clusters

into spacepoints, the SCT spacepoints are only created, if a pair of strip clusters originate from opposite

1Note that the actual trigger names used in this analysis are mbSpTrk BX0 and mbSpTrk BX1. These triggers are seeded

from L1 BPTX0 and L1 BPTX1 respectively. For simplicity we will refer to the OR of these triggers as mbSpTrk.

2

60

• Minimum Bias 1.0 (900 GeV first analysis)

- Summary: http://arxiv.org/abs/1003.3124v2

- Tracking (ATLAS only): ATL-COM-INDET-2010-010

- Vertexing (ATLAS only): ATL-COM-INDET-2010-009

- MBTS Trigger (ATLAS only): ATL-COM-DAQ-2010-003

- Luminosity (ATLAS only): ATL-COM-LUM-2010-002

• MinBias 1.5 (7 TeV & re-analyzed 900 GeV)

- CONF Results: https://atlas.web.cern.ch/Atlas/GROUPS/PHYSICS/CONFNOTES/ATLAS-CONF-2010-024/

• Minimum Bias 2.0 (re-analyzed 7 TeV & 900 GeV with Low-pT tracks)

- Summary (ATLAS only): ATL-COM-PHYS-2010-362

- Tracking (ATLAS only): ATL-PHYS-INT-2010-112

- Fake high pT Tracks: http://indico.cern.ch/getFile.py/access?contribId=4&resId=0&materialId=slides&confId=102382


- PAPER: http://link.aps.org/doi/10.1103/PhysRevD.83.112001

References

61

http://arxiv.org/abs/1003.3124v1

http://arxiv.org/abs/1003.3124v1









• Underlying Event 1.5 (based on Minimum Bias 1.5)


• Underlying Event 2.0 (based on Minimum Bias 2.0)


- PAPER: arXiv:1012.0791v2

• Tuning to MinBias & Underlying Event

- T. Sjostrand et al., PYTHIA 6.4 Physics and Manual, JHEP 05 (2006) 026

- A. Sherstnev et al., Eur. Phys. J. C 55, 553– 575 (2008)

- MC09 & MC09c: PHYS-PUB-2010-002

- AMBT1: https://atlas.web.cern.ch/Atlas/GROUPS/PHYSICS/CONFNOTES/ATLAS-CONF-2010-031/

References

62













• The ATLAS Collaboration, The ATLAS Experiment at the CERN Large Hadron Collider, JINST 3 (2008) S08003

• The ATLAS Collaboration, Expected Performance of the ATLAS Experiment - Detector, Trigger and Physics, CERN-OPEN-2008-020 & arXiv:0901.0512

• T. Cornelissen et al., Concepts, Design and Implementation of the ATLAS New Tracking (NEWT), ATL-SOFT-PUB-2007-007

• A. Salzburger, The ATLAS Extrapolation Package, ATL-SOFT-PUB-2007-005

• G. Piacquadio et al., Primary Vertex Reconstruction in the ATLAS Experiment at LHC, Journal of Physics: Conference Series 119 (2008) 032033

• R. Frühwirth et al., Adaptive MultiVertex Fitting, CMS-CR-2004-062

References

63

• When considering these results the phase spaces for Charged Primary Stable Particles are:

- Particle pT > 100 MeV & |η| < 2.5 & ≥ 2 Particles

- Particle pT > 500 MeV & |η| < 2.5 & ≥ 1 Particle with pT > 1 GeV

- Particle pT > 500 MeV & |η| < 0.8 & ≥ 1 Particle

- Particle pT > 1 GeV & |η| < 0.8 & ≥ 1 Particle

• The profile shapes are not smoothed. (The bin by bin correction does not require regularization.)

• The statistical uncertainties are uncorrelated. (The bin by bin correction does not mix bin contents.)

• The systematic uncertainties include correlated and uncorrelated contributions.

- Uncorrelated: statistical uncertainty.

- Correlated: Model dependency. (Pythia MC09 versus PhoJet.)

- Correlated: Track reconstruction efficiency uncertainty.

Summary

mmult x( )

65

• Results:- Standard (pT > 500 MeV) Results

- Low pT (pT > 100 MeV) Results

- Region Comparisons

- Energy Ratios

• ATLAS Inner Detector Tracker & MBTS:- Pixel Details

- SCT Details

- TRT Details

- MBTS Details

- MBTS Trigger Validation

• Analysis Minutia

➡References

Additional Information

66

Results at √s of 900 GeV & 7 TeV are shown for Charged Primary Stable Particles with pT > 500 MeV.In some cases results with pT > 100 MeV are also shown.Data is compared to: Pythia MC09, Pythia Perugia0, Pythia DW, PhoJet, Herwig+Jimmy.Data statistical errors are vertical bars. Data statistical+systematic uncertainties are filled boxes.

ResultsProfiles RegionsRegionsRegions Abscissa Sampled

OrdinateNumber Density Toward Transverse Away Leading Track pT

Region Track Number /Region Area

Standard Deviation ofNumber Density

Transverse Leading Track pTRegion Track Number /

Region Area

Eta Dependency ofNumber Density

Transverse Leading Track η Region Track Number /Region Area

pT Density Toward Transverse Away Leading Track pTRegion Summed Track pT /

Region Area

Standard Deviation ofpT Density

Transverse Leading Track pTRegion Summed Track pT /

Region Area

Mean Particle pT Transverse Leading Track pTRegion Summed Track pT /

Region Track Number

Mean pT Correlation Toward Transverse Away Region Track Number Region Summed Track pT /Region Track Number

Angular Number Density ϕ Intervals from Leading Trackϕ Intervals from Leading Trackϕ Intervals from Leading Track ϕ from Leading Track Region Track Number /Region Area

Angular pT Density ϕ Intervals from Leading Trackϕ Intervals from Leading Trackϕ Intervals from Leading Track ϕ from Leading Track Region Summed Track pT /Region Area

67

Trans. Number Density


[GeV]leadT

p1 2 3 4 5 6 7 8 9 10

MC

/Dat

a

0.60.8

11.21.4

[GeV]leadT

p1 2 3 4 5 6 7 8 9 10

MC

/Dat

a

0.60.8

11.21.4

>qdd/d

chN2<d

0.1

0.2

0.3

0.4

0.5

0.6

0.7


= 900 GeVs| < 2.5d> 0.5 GeV and |

Tp

ATLAS




[GeV]leadT

p2 4 6 8 10 12 14 16 18 20

MC

/Dat

a

0.60.8

11.21.4

[GeV]leadT

p2 4 6 8 10 12 14 16 18 20

MC

/Dat

a

0.60.8

11.21.4

>qdd/d

chN2<d

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6 Transverse Region = 7 TeVs

| < 2.5d> 0.5 GeV and |T

p

ATLAS




[GeV]leadT

p2 4 6 8 10 12 14 16 18 20

MC

/Dat

a

0.60.8

11.21.4

[GeV]leadT

p2 4 6 8 10 12 14 16 18 20

MC

/Dat

a

0.60.8

11.21.4

qdd/d

chN2St

d. D

ev. o

f d

0.2

0.4

0.6

0.8

1


= 7 TeVs| < 2.5d> 0.5 GeV and |

Tp

ATLAS




[GeV]leadT

p1 2 3 4 5 6 7 8 9 10

MC

/Dat

a

0.60.8

11.21.4

[GeV]leadT

p1 2 3 4 5 6 7 8 9 10

MC

/Dat

a

0.60.8

11.21.4

qdd/d

chN2St

d. D

ev. o

f d

0.1

0.2

0.3

0.4

0.5


= 900 GeVs| < 2.5d> 0.5 GeV and |

Tp

ATLAS




Transverse

Away

Toward

Transverse

• Mean charged particle number underestimated by all M.C.

• Std. Dev. provides an additional constraint for M.C. tuning.

• Herwig+Jimmy provides the best estimate at 7 TeV.

• PhoJet provides the best estimate at 900 GeV.

68

Transverse

Away

Toward

Transverse

[GeV]leadT

p1 2 3 4 5 6 7 8 9 10

>qdd/d

chN2<d

0

0.2

0.4

0.6

0.8

1

1.2

1.4All 3 Regions

= 900 GeVs| < 2.5d> 0.5 GeV and |T

pATLAS


Number Density

[GeV]leadT

p2 4 6 8 10 12 14 16 18 20

>qdd/d

chN2<d

0

0.5

1

1.5

2

2.5All 3 Regions

= 7 TeVs| < 2.5d> 0.5 GeV and |T

pATLAS


• The Toward & Away regions are dominated by jet-like activity, yielding rising number densities.

• In contrast, the number density in the Transverse region appears to be independent of the energy scale defined by the pT of the leading track once it reaches the plateau.

• Leading track selection bias yields a cross-over of the Toward & Away profiles in both Data and Monte Carlo.


69

[GeV]leadT

p1 2 3 4 5 6 7 8 9 10

Rat

io

1

1.5

2

2.5DataMC09

>qdd/d

chN2<d

0.2

0.4

0.6

0.8

1

1.21.4

1.6

1.8

2Toward Region PreliminaryATLAS

| <2.5d> 0.5 GeV and |Tp

Data at 7 TeVPYTHIA ATLAS MC09 Tune at 7 TeVData at 900 GeVPYTHIA ATLAS MC09 Tune at 900 GeV

Number Density

[GeV]leadT

p1 2 3 4 5 6 7 8 9 10

Rat

io

11.21.41.61.8

22.22.4 Data

MC09

>qdd/d

chN2<d

0.2

0.4

0.6

0.8

1

1.21.4

1.6

1.8

2Away Region PreliminaryATLAS

| <2.5d> 0.5 GeV and |Tp


[GeV]leadT

p1 2 3 4 5 6 7 8 9 10

Rat

io

1

1.5

2

2.5

3 DataMC09

>qdd/d

chN2<d

0.2

0.4

0.6

0.8

1

1.2Transverse Region PreliminaryATLAS

| <2.5d> 0.5 GeV and |Tp


• Top plots show the number density at 900 GeV and 7 TeV.

• Bottom plots show the ratio of 7 TeV / 900 GeV results for Data and MC09 Pythia.

Transverse

Away

Toward

Transverse

70

|leadd|0 0.5 1 1.5 2 2.5

MC

/Dat

a

0.6

0.8

1

1.2

1.4

>qdd/d

chN2<d

0.2

0.4

0.6

0.8

1

1.21.4

1.6

1.8

2Transverse Region

= 7 TeVs| <2.5d> 0.5 GeV and |Tp

Data 2010PYTHIA ATLAS MC09 TunePYTHIA DW Tune

PYTHIA Perugia0 Tune

PHOJET

with > 5 GeV Tleadp

Transverse

Away

Toward

Transverse

• For tracks with pT > 500 MeV there is a correlation between the number of tracks in the transverse region and the pseudorapidity of the highest pT track.

• When tracks with pT > 100 MeV are considered instead this correlation is nearly obscured.

• No such correlation is included in any of the M.C. models to which the data is compared. (The M.C. profiles are nearly flat.)


|leadd|0 0.5 1 1.5 2 2.5

MC

/Dat

a

0.6

0.8

1

1.2

1.4

|leadd|0 0.5 1 1.5 2 2.5

MC

/Dat

a

0.6

0.8

1

1.2

1.4

>qdd

/dchN2

<d

0.5

1

1.5

2

2.5

3

3.5

4 > 5 GeVlead

TTransverse Region, with p

= 7 TeVs| < 2.5d> 0.1 GeV and |Tp

ATLAS



Eta Dependency

71

• In these plots the minimum track pT is lowered to 100 MeV.

• At 7 TeV the total uncertainty is principally due to the uncertainty in track reconstruction efficiency.

• The re-ordering of the M.C. predictions is due to differences in the low pT spectra of tracks in the underlying event.

Transverse

Away

Toward

Transverse


[GeV]leadT

p1 2 3 4 5 6 7 8 9 10

MC

/Dat

a

0.6

0.8

1

1.2

1.4

[GeV]leadT

p1 2 3 4 5 6 7 8 9 10

MC

/Dat

a

0.6

0.8

1

1.2

1.4

>qdd

/dchN2

<d

0.2

0.4

0.6

0.8

1

1.21.4

1.6

1.8

2Transverse Region

= 900 GeVs| < 2.5d> 0.1 GeV and |Tp

ATLAS



Lead Track pT > 100 MeV

[GeV]leadT

p2 4 6 8 10 12 14 16 18 20

MC

/Dat

a

0.6

0.8

1

1.2

1.4

[GeV]leadT

p2 4 6 8 10 12 14 16 18 20

MC

/Dat

a

0.6

0.8

1

1.2

1.4

>qdd

/dchN2

<d

0.5

1

1.5

2

2.5

3 Transverse Region = 7 TeVs

| < 2.5d> 0.1 GeV and |Tp

ATLAS





72

[GeV]leadT

p2 4 6 8 10 12 14 16 18 20

MC

/Dat

a

0.60.8

11.21.4

[GeV]leadT

p2 4 6 8 10 12 14 16 18 20

MC

/Dat

a

0.60.8

11.21.4

[GeV

]qdd

/d Tp-2

Std.

Dev

. of d

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2Transverse Region

= 7 TeVs| < 2.5d> 0.5 GeV and |

Tp

ATLAS



[GeV]leadT

p1 2 3 4 5 6 7 8 9 10

MC

/Dat

a

0.60.8

11.21.4

[GeV]leadT

p1 2 3 4 5 6 7 8 9 10

MC

/Dat

a

0.60.8

11.21.4

[GeV

]qdd

/d Tp-2

Std.

Dev

. of d

0.1

0.2

0.3

0.4

0.5

0.6


= 900 GeVs| < 2.5d> 0.5 GeV and |

Tp

ATLAS



[GeV]leadT

p2 4 6 8 10 12 14 16 18 20

MC

/Dat

a

0.60.8

11.21.4

[GeV]leadT

p2 4 6 8 10 12 14 16 18 20

MC

/Dat

a

0.60.8

11.21.4

> [G

eV]

qdd/d Tp

-2<d

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6


= 7 TeVs| < 2.5d> 0.5 GeV and |

Tp

ATLAS



[GeV]leadT

p1 2 3 4 5 6 7 8 9 10

MC

/Dat

a

0.60.8

11.21.4

[GeV]leadT

p1 2 3 4 5 6 7 8 9 10

MC

/Dat

a

0.60.8

11.21.4

> [G

eV]

qdd/d Tp

-2<d

0.1

0.2

0.3

0.4

0.5

0.6


= 900 GeVs| < 2.5d> 0.5 GeV and |

Tp

ATLAS



Trans. pT DensityLead Track pT > 1.0 GeV Lead Track pT > 1.0 GeV

Lead Track pT > 1.0 GeVLead Track pT > 1.0 GeV

• Mean charged particle pT continues to rise.

• Pythia DW tune provides the best estimates.

• Std. Dev. overestimated by Pythia DW where the mean value matches.

• Jet Energy scale corrections for U.E. should be on an event by event basis.

Transverse

Away

Toward

Transverse

73

Transverse

Away

Toward

Transverse





[GeV]leadT

p1 2 3 4 5 6 7 8 9 10

MC

/Dat

a

0.6

0.8

1

1.2

1.4

[GeV]leadT

p1 2 3 4 5 6 7 8 9 10

MC

/Dat

a

0.6

0.8

1

1.2

1.4

> [G

eV]

qdd

/d Tp-2

<d

0.1

0.2

0.3

0.4

0.5

0.60.7

0.8

0.9

1Transverse Region

= 900 GeVs| < 2.5d> 0.1 GeV and |Tp

ATLAS




[GeV]leadT

p2 4 6 8 10 12 14 16 18 20

MC

/Dat

a

0.6

0.8

1

1.2

1.4

[GeV]leadT

p2 4 6 8 10 12 14 16 18 20

MC

/Dat

a

0.6

0.8

1

1.2

1.4

> [G

eV]

qdd

/d Tp-2

<d

0.5

1

1.5

2

2.5

3Transverse Region

= 7 TeVs| < 2.5d> 0.1 GeV and |Tp

ATLAS




Trans. pT Density

74

[GeV]leadT

p2 4 6 8 10 12 14 16 18 20

MC

/Dat

a

0.60.8

11.21.4

[GeV]leadT

p2 4 6 8 10 12 14 16 18 20

MC

/Dat

a

0.60.8

11.21.4

[GeV

]qdd

/d Tp-2

Std.

Dev

. of d

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2Transverse Region

= 7 TeVs| < 2.5d> 0.5 GeV and |

Tp

ATLAS



[GeV]leadT

p1 2 3 4 5 6 7 8 9 10

MC

/Dat

a

0.60.8

11.21.4

[GeV]leadT

p1 2 3 4 5 6 7 8 9 10

MC

/Dat

a

0.60.8

11.21.4

[GeV

]qdd

/d Tp-2

Std.

Dev

. of d

0.1

0.2

0.3

0.4

0.5

0.6


= 900 GeVs| < 2.5d> 0.5 GeV and |

Tp

ATLAS



[GeV]leadT

p2 4 6 8 10 12 14 16 18 20

MC

/Dat

a

0.60.8

11.21.4

[GeV]leadT

p2 4 6 8 10 12 14 16 18 20

MC

/Dat

a

0.60.8

11.21.4

> [G

eV]

qdd/d Tp

-2<d

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6


= 7 TeVs| < 2.5d> 0.5 GeV and |

Tp

ATLAS



[GeV]leadT

p1 2 3 4 5 6 7 8 9 10

MC

/Dat

a

0.60.8

11.21.4

[GeV]leadT

p1 2 3 4 5 6 7 8 9 10

MC

/Dat

a

0.60.8

11.21.4

> [G

eV]

qdd/d Tp

-2<d

0.1

0.2

0.3

0.4

0.5

0.6


= 900 GeVs| < 2.5d> 0.5 GeV and |

Tp

ATLAS



Trans. pT DensityLead Track pT > 1.0 GeV Lead Track pT > 1.0 GeV

Lead Track pT > 1.0 GeVLead Track pT > 1.0 GeV

• Mean charged particle pT continues to rise.

• Pythia DW tune provides the best estimates.

• Std. Dev. overestimated by Pythia DW where the mean value matches.

• Jet Energy scale corrections for U.E. should be on an event by event basis.

Transverse

Away

Toward

Transverse

75

[GeV]leadT

p1 2 3 4 5 6 7 8 9 10

Rat

io

11.21.41.61.8

22.22.4 Data

MC09

> [G

eV]

qdd/d Tp

-2<d

0.5

1

1.5

2

2.5


| <2.5d> 0.5 GeV and |Tp


[GeV]leadT

p1 2 3 4 5 6 7 8 9 10

Rat

io

11.5

22.5

33.5 Data

MC09

> [G

eV]

qdd/d Tp

-2<d

0

0.2

0.4

0.6

0.8

1

1.2 Transverse Region PreliminaryATLAS| <2.5d> 0.5 GeV and |Tp


[GeV]leadT

p1 2 3 4 5 6 7 8 9 10

Rat

io

11.21.41.61.8

22.22.4 Data

MC09

> [G

eV]

qdd/d Tp

-2<d

0.2

0.4

0.6

0.8

1

1.21.4

1.6

1.8


| <2.5d> 0.5 GeV and |Tp


pT Density• Top plots show the number density at 900 GeV and 7 TeV.


Transverse

Away

Toward

Transverse

76

Transverse

Away

Toward

Transverse


[GeV]leadT

p1 2 3 4 5 6 7 8 9 10

MC

/Dat

a

0.6

0.8

1

1.2

1.4

[GeV]leadT

p1 2 3 4 5 6 7 8 9 10

MC

/Dat

a

0.6

0.8

1

1.2

1.4

>qdd

/dchN2

<d

0.2

0.4

0.6

0.8

1

1.21.4

1.6

1.8

2Transverse Region

= 900 GeVs| < 2.5d> 0.1 GeV and |Tp

ATLAS




[GeV]leadT

p2 4 6 8 10 12 14 16 18 20

MC

/Dat

a

0.6

0.8

1

1.2

1.4

[GeV]leadT

p2 4 6 8 10 12 14 16 18 20

MC

/Dat

a

0.6

0.8

1

1.2

1.4

>qdd

/dchN2

<d

0.5

1

1.5

2

2.5

3 Transverse Region = 7 TeVs

| < 2.5d> 0.1 GeV and |Tp

ATLAS





77

[GeV]leadT

p1 2 3 4 5 6 7 8 9 10

MC

/Dat

a

0.60.8

11.21.4

[GeV]leadT

p1 2 3 4 5 6 7 8 9 10

MC

/Dat

a

0.60.8

11.21.4

>qdd/d

chN2<d

0.1

0.2

0.3

0.4

0.5

0.6

0.7


= 900 GeVs| < 2.5d> 0.5 GeV and |

Tp

ATLAS




[GeV]leadT

p2 4 6 8 10 12 14 16 18 20

MC

/Dat

a

0.60.8

11.21.4

[GeV]leadT

p2 4 6 8 10 12 14 16 18 20

MC

/Dat

a

0.60.8

11.21.4

>qdd/d

chN2<d

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6 Transverse Region = 7 TeVs

| < 2.5d> 0.5 GeV and |T

p

ATLAS




[GeV]leadT

p2 4 6 8 10 12 14 16 18 20

MC

/Dat

a

0.60.8

11.21.4

[GeV]leadT

p2 4 6 8 10 12 14 16 18 20

MC

/Dat

a

0.60.8

11.21.4

qdd/d

chN2St

d. D

ev. o

f d

0.2

0.4

0.6

0.8

1


= 7 TeVs| < 2.5d> 0.5 GeV and |

Tp

ATLAS




[GeV]leadT

p1 2 3 4 5 6 7 8 9 10

MC

/Dat

a

0.60.8

11.21.4

[GeV]leadT

p1 2 3 4 5 6 7 8 9 10

MC

/Dat

a

0.60.8

11.21.4

qdd/d

chN2St

d. D

ev. o

f d

0.1

0.2

0.3

0.4

0.5


= 900 GeVs| < 2.5d> 0.5 GeV and |

Tp

ATLAS




Transverse

Away

Toward

Transverse

• Mean charged particle number underestimated by all M.C.

• Std. Dev. provides an additional constraint for M.C. tuning.

• Herwig+Jimmy provides the best estimate at 7 TeV.

• PhoJet provides the best estimate at 900 GeV.

78

chN5 10 15 20 25 30

MC

/Dat

a

0.9

1

1.1

1.2

chN5 10 15 20 25 30

MC

/Dat

a

0.9

1

1.1

1.2

> [G

eV]

T 0.5 GeV and |

Tp

ATLAS




chN2 4 6 8 10 12 14 16 18 20

MC

/Dat

a

0.9

1

1.1

1.2

chN2 4 6 8 10 12 14 16 18 20

MC

/Dat

a

0.9

1

1.1

1.2

> [G

eV]

T 0.5 GeV and |

Tp

ATLAS




[GeV]leadT

p1 2 3 4 5 6 7 8 9 10

MC

/Dat

a

0.8

0.9

1

1.1

1.2

[GeV]leadT

p1 2 3 4 5 6 7 8 9 10

MC

/Dat

a

0.8

0.9

1

1.1

1.2

> [G

eV]

T 0.5 GeV and |T

p

ATLAS




[GeV]leadT

p2 4 6 8 10 12 14 16 18 20

MC

/Dat

a

0.8

0.9

1

1.1

1.2

[GeV]leadT

p2 4 6 8 10 12 14 16 18 20

MC

/Dat

a

0.8

0.9

1

1.1

1.2

> [G

eV]

T 0.5 GeV and |

Tp

ATLAS




• M.C. predictions for mean particle pT as a function of leading track pT are good.

• pT density mismatch is from the number density.

• The mean pT as a function of charged particle number is sensitive to fragmentation, and independent of √s, and region.

Transverse

Away

Toward

Transverse

Avg. Particle pT

79

[GeV]leadT

p1 2 3 4 5 6 7 8 9 10

Rat

io

0.6

0.8

1

1.2

1.4 DataMC09

> [G

eV]

T 0.5 GeV and |Tp


[GeV]leadT

p1 2 3 4 5 6 7 8 9 10

Rat

io

0.6

0.8

1

1.2

1.4 DataMC09

> [G

eV]

T 0.5 GeV and |Tp


Avg. Particle pT• Top plots show the number density at 900 GeV and 7 TeV.


Transverse

Away

Toward

Transverse

[GeV]leadT

p1 2 3 4 5 6 7 8 9 10

Rat

io

0.6

0.8

1

1.2

1.4 DataMC09

> [G

eV]

T 0.5 GeV and |Tp


80

chN2 4 6 8 10 12 14 16 18 20

Rat

io

0.6

0.8

1

1.2

1.4 DataMC09

> [G

eV]

T 0.5 GeV and |Tp


chN2 4 6 8 10 12 14 16 18 20

Rat

io

0.6

0.8

1

1.2

1.4 DataMC09

> [G

eV]

T 0.5 GeV and |Tp


chN2 4 6 8 10 12 14 16 18 20

Rat

io

0.6

0.8

1

1.2

1.4 DataMC09

> [G

eV]

T 0.5 GeV and |Tp


Avg. Particle pT• Top plots show the number density at 900 GeV and 7 TeV.


Transverse

Away

Toward

Transverse

81

• The track-based Underlying Event analysis has been repeated several times...

• At √s = 900 GeV & 7 TeV center of mass energies, with:

- (Shown) pT ≥ 100 MeV & |η| < 2.5 iRad

- (Show) pT ≥ 500 MeV & |η| < 2.5 iRad

- (CERN Common) pT ≥ 500 MeV & |η| < 0.8 iRad

- (CERN Common) pT ≥ 1 GeV & |η| < 0.8 iRad

• The LHC will run at 2.76 TeV, yielding an additional set of results for each phase space!

Additional Results

82

Toward

Away


T1

T2

+ϕ

Transverse

Away

Toward

Transverse

Away

Transverse

Toward


Away

Toward

Transverse

Toward

Away

BackgroundBackground

T1

T2

+ϕ

Transverse

Away

Toward

Transverse

Transverse

Away

Toward

Transverse

86

Hard Scatter

OutgoingParton

FSR

OutgoingParton

ISR

IncomingParton

IncomingParton

Beam RemnantsBeam Remnants

IncomingProton

IncomingProton

IncomingProton

IncomingProton

Transverse

TransverseAway

Toward

MPI

IncommingParton

IncommingParton

FSR

OutgoingParton

OutgoingParton

ISR

87

Hard Scatter

OutgoingPartonFSR

OutgoingParton

ISR

IncomingParton

IncomingParton

Beam Remnants

IncomingProton

Beam Remnants

IncomingProton

MPI

IncommingParton

IncommingParton

FSROutgoingParton

OutgoingPartonISR

Beam Remnants

IncomingProton

Beam Remnants

IncomingProton

pp Collision

IncomingProton

IncomingProton

Transverse

Transverse

Toward

Away

88

up

up

down

up

up

down

anti-charm

charm

gluon

gluonEnergy ~ Magnification

90

PartonCenter of Mass

Rest Frame

Jet

IncomingParton

IncomingParton

Jet

DetectorRest Frame

Jet

IncomingParton

IncomingParton

Jet

91

Jet 1

Jet 3

Jet 2

92

measurements of the underlying event

Documents