Stochastic Identification of Jet particles in pPb and PbPb Martin - - PowerPoint PPT Presentation

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stochastic identification of jet particles in ppb and pbpb
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Stochastic Identification of Jet particles in pPb and PbPb Martin - - PowerPoint PPT Presentation

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BMBF Forschungsschwerpunkt BMBF Forschungsschwerpunkt 202 ALICE Experiment ALICE Experiment Stochastic Identification of Jet particles in pPb and PbPb Martin Schmidt Physikalisches Institut, University of Tbingen Jet Meeting

slide-1
SLIDE 1

BMBF Forschungsschwerpunkt ALICE Experiment BMBF Forschungsschwerpunkt ALICE Experiment

202

Stochastic Identification of Jet particles in pPb and PbPb

Martin Schmidt

Physikalisches Institut, University of Tübingen

Jet Meeting Tübingen - July 14th, 2017

  • M. Schmidt (PI Tübingen)

Jet PID in pPb/PbPb 14.07.2017 1 / 19

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SLIDE 2

Outline

Motivation The Experiment: ALICE@LHC Method: The Multi-Template Fit Challenges Current status Outlook

  • M. Schmidt (PI Tübingen)

Jet PID in pPb/PbPb 14.07.2017 2 / 19

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SLIDE 3

Why Jet Analysis?

What are jets? Why study jets?

  • M. Schmidt (PI Tübingen)

Jet PID in pPb/PbPb 14.07.2017 3 / 19

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SLIDE 4

Why Jet Analysis?

What are jets? Why study jets?

◮ pp collisions: QCD,

reference

  • M. Schmidt (PI Tübingen)

Jet PID in pPb/PbPb 14.07.2017 3 / 19

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SLIDE 5

Why Jet Analysis?

What are jets? Why study jets?

◮ pp collisions: QCD,

reference

◮ p–Pb collisions: Cold

nuclear matter effects

  • M. Schmidt (PI Tübingen)

Jet PID in pPb/PbPb 14.07.2017 3 / 19

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SLIDE 6

Why Jet Analysis?

What are jets? Why study jets?

◮ pp collisions: QCD,

reference

◮ p–Pb collisions: Cold

nuclear matter effects

◮ Pb–Pb collisions:

Medium properties

  • M. Schmidt (PI Tübingen)

Jet PID in pPb/PbPb 14.07.2017 3 / 19

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SLIDE 7

Use of (identified) fragmentation functions

pp collisions

◮ Extract information about the non-perturbative fragmentation process ◮ Reference for more complex systems

p–Pb collisions

◮ No medium formed ◮ Study influence of a nucleus (= cold nuclear matter)

Pb–Pb collisions

◮ Presence of a medium changes the jet ◮ Mechanism: e.g. jet quenching ◮ Theory predicts higher baryon and strangeness production in the

presence of a medium

  • M. Schmidt (PI Tübingen)

Jet PID in pPb/PbPb 14.07.2017 4 / 19

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SLIDE 8

Jet Reconstruction

This analysis: charged jets Input: charged tracks

◮ pT > 0.15 GeV/c ◮ |η| < 0.9

Jet reconstruction done with anti-kT-algorithm with resolution parameter R = 0.4 →gives jets with circular cones of radius R Only jets fully contained in acceptance: |ηjet| <0.9 - R

  • M. Schmidt (PI Tübingen)

Jet PID in pPb/PbPb 14.07.2017 5 / 19

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SLIDE 9

The Large Hadron Collider

Most powerful hadron collider at present: pp: √s = 13 TeV p–Pb: √sNN = 8 TeV Pb–Pb: √sNN = 5 TeV This talk: p–Pb at √sNN = 5.023 TeV (75M MB events), recorded in 2013

  • M. Schmidt (PI Tübingen)

Jet PID in pPb/PbPb 14.07.2017 6 / 19

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SLIDE 10

The Experiment: ALICE@LHC - The detector

VO ITS TPC TOF Central barrel: Solenoid with B = 0.5 T ITS

◮ tracking ◮ vertexing ◮ (PID)

TPC

◮ tracking ◮ PID

TOF

◮ (PID)

Trigger: V0 Multiplicity Estimator: VOA

  • M. Schmidt (PI Tübingen)

Jet PID in pPb/PbPb 14.07.2017 7 / 19

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SLIDE 11

The Multi-Template-Fit*

ALI-PERF-60751

2013/10/13

Track-by-Track PID for low pT Stochastic Particle Identification for high pT

*PhD-Thesis of B.Hess

  • M. Schmidt (PI Tübingen)

Jet PID in pPb/PbPb 14.07.2017 8 / 19

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SLIDE 12

The Multi-Template-Fit*

ALI-PERF-60751

2013/10/13

Track-by-Track PID for low pT Stochastic Particle Identification for high pT

1

Parametrize the TPC response

*PhD-Thesis of B.Hess

  • M. Schmidt (PI Tübingen)

Jet PID in pPb/PbPb 14.07.2017 8 / 19

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SLIDE 13

The Multi-Template-Fit*

ALI-PERF-60751

2013/10/13

Track-by-Track PID for low pT Stochastic Particle Identification for high pT

1

Parametrize the TPC response

2

Produce templates for species using data

*PhD-Thesis of B.Hess

  • M. Schmidt (PI Tübingen)

Jet PID in pPb/PbPb 14.07.2017 8 / 19

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SLIDE 14

The Multi-Template-Fit*

ALI-PERF-60751

2013/10/13

Track-by-Track PID for low pT Stochastic Particle Identification for high pT

1

Parametrize the TPC response

2

Produce templates for species using data

3

Fit templates to data in pT/z-slices

*PhD-Thesis of B.Hess

  • M. Schmidt (PI Tübingen)

Jet PID in pPb/PbPb 14.07.2017 8 / 19

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SLIDE 15

The Multi-Template-Fit

Entries 1 10

2

10

3

10

|

=0.4 R

T

k anti- 0.6-0.65

ch

z , c 10-15 GeV/

ch T, jet

p =7 TeV, s pp Measured Multi-template fit , template

  • π

+

+

π , template

  • +K

+

K , template p p+ , template

  • +e

+

e ALICE Preliminary

π

= dE/dx / <dE/dx>

π

’ ∆ 0.6 0.7 0.8 0.9 1 1.1 1.2 1.3 (Data - Fit) / Data

  • 0.4
  • 0.2

0.2 0.4

ALI−PREL−70018

  • M. Schmidt (PI Tübingen)

Jet PID in pPb/PbPb 14.07.2017 9 / 19

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SLIDE 16

Goal of Analysis

Create parametrizations of the TPC response (as spin-off) Stochastic particle identification (inclusive/jets)

◮ pp → done by Benjamin ◮ pPb → in progress ◮ PbPb → ToDo (High multiplicity environment challenging)

Extension to full jets included in framework Extension to jet structure parameters (distance to axis, momentum transverse to jet axis) included in framework

  • M. Schmidt (PI Tübingen)

Jet PID in pPb/PbPb 14.07.2017 10 / 19

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SLIDE 17

Occupancy effects in the TPC

Mean dE/dx changes due to occupancy effects in the TPC with the multiplicity

◮ Parametrized with linear fit ◮ Slope of parametrization depends on η and dE/dx - dependency is

parametrized

Challenges:

◮ The fit of the mean dE/dx is challenging ◮ Shape of detector response changes → not parametrized

  • M. Schmidt (PI Tübingen)

Jet PID in pPb/PbPb 14.07.2017 11 / 19

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SLIDE 18

Underlying Event

Underlying Event: Particles in the reconstructed jet not coming from the original parton Important in pPb and especially in PbPb pjet

T must be corrected

◮ area of jet is measured with ghost particles ◮ UE momentum density measured using kT jet-algorithm -> median

without the two leading jets gives the momentum density

◮ resulting UE momentum is subtracted jet-by-jet

particle yield of the UE must be subtracted from jet particle yield → shown in the next slides

  • M. Schmidt (PI Tübingen)

Jet PID in pPb/PbPb 14.07.2017 12 / 19

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SLIDE 19

Underlying Event Estimation

Assume: Equally distributed in φ Event display of Pb-Pb collision

  • M. Schmidt (PI Tübingen)

Jet PID in pPb/PbPb 14.07.2017 13 / 19

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SLIDE 20

Underlying Event Estimation

Assume: Equally distributed in φ Clone each jet cone and place it in the event Event display of Pb-Pb collision

  • M. Schmidt (PI Tübingen)

Jet PID in pPb/PbPb 14.07.2017 13 / 19

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SLIDE 21

Underlying Event Estimation

Assume: Equally distributed in φ Clone each jet cone and place it in the event

◮ Random Cones (RC,

systematics)

ηUEcone = ηjet ϕUEcone drawn randomly from [0, 2π], outside other jet cones

Event display of Pb-Pb collision

  • M. Schmidt (PI Tübingen)

Jet PID in pPb/PbPb 14.07.2017 13 / 19

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SLIDE 22

Underlying Event Estimation

Assume: Equally distributed in φ Clone each jet cone and place it in the event

◮ Random Cones (RC,

systematics)

ηUEcone = ηjet ϕUEcone drawn randomly from [0, 2π], outside other jet cones

◮ Perpendicular Cones (PC,

standard)

ηUEcone = ηjet ϕ±

UEcone = ϕjet ± π/2.0

90◦ 90◦

Event display of Pb-Pb collision

  • M. Schmidt (PI Tübingen)

Jet PID in pPb/PbPb 14.07.2017 13 / 19

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SLIDE 23

Underlying Event Estimation

Assume: Equally distributed in φ Clone each jet cone and place it in the event

◮ Random Cones (RC,

systematics)

ηUEcone = ηjet ϕUEcone drawn randomly from [0, 2π], outside other jet cones

◮ Perpendicular Cones (PC,

standard)

ηUEcone = ηjet ϕ±

UEcone = ϕjet ± π/2.0

Estimate yield of particles in UE cones → UE yield

90◦ 90◦

Event display of Pb-Pb collision

  • M. Schmidt (PI Tübingen)

Jet PID in pPb/PbPb 14.07.2017 13 / 19

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SLIDE 24

Underlying Event Subtraction

Subtracting the Underlying Event:

  • M. Schmidt (PI Tübingen)

Jet PID in pPb/PbPb 14.07.2017 14 / 19

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SLIDE 25

Underlying Event Subtraction

Subtracting the Underlying Event:

1

Estimate jet cone particle yield

(GeV/c)

T

p 0.2 1 2 3 4 5 6 10 20

  • 1

(GeV/c)

T

dN/dp

Jets

1/N

π

Jet cone particles π

  • M. Schmidt (PI Tübingen)

Jet PID in pPb/PbPb 14.07.2017 14 / 19

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SLIDE 26

Underlying Event Subtraction

Subtracting the Underlying Event:

1

Estimate jet cone particle yield

2

Estimate UE particle yield

(GeV/c)

T

p 0.2 1 2 3 4 5 6 10 20

  • 1

(GeV/c)

T

dN/dp

Jets

1/N

π

Jet cone particles π UE particles π

  • M. Schmidt (PI Tübingen)

Jet PID in pPb/PbPb 14.07.2017 14 / 19

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SLIDE 27

Underlying Event Subtraction

Subtracting the Underlying Event:

1

Estimate jet cone particle yield

2

Estimate UE particle yield

3

Jet particle yield = Jet cones particle yield - UE particle yield

(GeV/c)

T

p 0.2 1 2 3 4 5 6 10 20

  • 1

(GeV/c)

T

dN/dp

Jets

1/N

π

Jet cone particles π UE particles π Jet particles π

  • M. Schmidt (PI Tübingen)

Jet PID in pPb/PbPb 14.07.2017 14 / 19

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SLIDE 28

Other issues in pPb/PbPb

Jet reconstruction influenced by fluctuations in underlying event → examine using embedded jets? Hot to separate low pT-jets from background?

◮ Increase pjet

T -cut

◮ Use further methods to eliminate combinatorial jets

Corrections (for example energy resolution) done with a bin-by-bin correction → unfolding needed in PbPb?

  • M. Schmidt (PI Tübingen)

Jet PID in pPb/PbPb 14.07.2017 15 / 19

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SLIDE 29

Comparison to pp 7 TeV/c results

π Uncorrected K/

0.1 0.2 0.3 0.4 0.5 0.6 0.7

|

c 15-20 GeV/

ch T, jet

p =7 TeV, s in pp π K/ ALICE Preliminary

  • uncorr. (ref.)

ALICE TPC Coherent, uncorr. ALICE TPC Multi-Template, PYTHIA Perugia 0 + GEANT3

) c (GeV/

T, track

p

0.2 0.3 1 2 3 4 5 6 7 8 10 20

Scaled to ref.

0.6 0.8 1 1.2 1.4 | < 0.9

track

η | c > 0.15 GeV/

T, track

p | < 0.5

jet

η =0.4, | R ,

T

k anti-

ALI−PREL−68926

(GeV/c)

T

p 1 10 π Uncorrected K/ 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8

Figure: p–Pb 5 ATeV/c (WiP)

pp 7 TeV/c and p–Pb 5 ATeV/c look rather similar

  • M. Schmidt (PI Tübingen)

Jet PID in pPb/PbPb 14.07.2017 16 / 19

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SLIDE 30

Jet particles for 0-10% centrality (V0A)

(GeV/c)

T

p 0.2 0.3 0.4 1 2 3 4 5 6 7 8 9 10 20 30 40 Corrected Fractions of Jet particles

π K p

Figure: pjet

T = 20 − 30 GeV/c

Only PID uncertainties of jet cone particle yields included

  • M. Schmidt (PI Tübingen)

Jet PID in pPb/PbPb 14.07.2017 17 / 19

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SLIDE 31

Jet particles for 60-100% centrality (V0A)

(GeV/c)

T

p 0.2 0.3 0.4 1 2 3 4 5 6 7 8 9 10 20 30 40 Corrected Fractions of Jet particles

π K p

Figure: pjet

T = 20 − 30 GeV/c

Only PID uncertainties of jet cone particle yields included

  • M. Schmidt (PI Tübingen)

Jet PID in pPb/PbPb 14.07.2017 18 / 19

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SLIDE 32

Summary and Outlook

Summary: Charged hadrons in charged jet can be identified with the MTF for

◮ different centrality classes ◮ high jet-pT

The Underlying Event can be identified and subtracted Outlook: Understand influence of occupancy Understanding the Underlying Event and its influence on jet reconstruction Test analysis in high multiplicity proton-lead runs (end of 2016) Extend analysis to lead-lead collisions Full jets and jet structure: Possible tasks for new students

  • M. Schmidt (PI Tübingen)

Jet PID in pPb/PbPb 14.07.2017 19 / 19

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SLIDE 33

Backup

Backup

  • M. Schmidt (PI Tübingen)

Jet PID in pPb/PbPb 14.07.2017 20 / 19

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SLIDE 34

Results

Data: p–Pb at √sNN = 5.023 TeV/c Trigger: minimum bias (≈ 75M events) Centrality estimator: V0A η ≤ 0.9 Jet parameter: 0.4 Systematic uncertainties: expected dE/dx, σ, η-correction, TPC response shape, multiplicity correction, use of priors for template generation Bin-by-Bin correction of: Efficiency, acceptance, pT-resolution, secondary contamination, muon contamination

  • M. Schmidt (PI Tübingen)

Jet PID in pPb/PbPb 14.07.2017 21 / 19