Measurement of jet spectra reconstructed with charged
particles in Pb-Pb collisions at √sNN = 5.02 TeV
with the ALICE detector at the LHC(LHC-ALICE実験 √sNN=5.02 TeV 鉛鉛衝突実験における荷電粒子ジェットの測定)

Hiroki Yokoyama
LPSC, Université Grenoble-Alpes, CNRS/IN2P3
University of Tsukuba, Japan
29/03/2018 PhD defence1Outline
Introduction
Quark-Gluon Plasma
Jet Quenching
Motivation
LHC-ALICE experiment
EMCAL L1 trigger development
Jet measurement with √sNN = 5.02 TeV Pb-Pb collisions
Results
Nuclear Modification Factor
parton path length dependence
centre-of-mass energy dependence
Summary2

Hiroki Yokoyama : PhD defence ( 29/03/2018 )Introduction
LHC-ALICE experiment
EMCAL L1 trigger development
Jet measurement with √sNN = 5.02 TeV Pb-Pb collisions
Results3

Quark Gluon Plasma (QGP)
Hot & dense colour thermalised QCD matter
Deconfined state of quarks and gluonsprevailing at the early Universe ~1μs after big bang
Theoretically inferred through lattice gauge simulations of QCD
Tc~154(9) MeV , εc~0.18-0.5 GeV/fm3

LHC HI programme

Pramana. 84. 773-786
4

Hiroki Yokoyama : PhD defence ( 29/03/2018 )How to Create QGP?
Ultra-Relativistic Heavy Ion Collisionhigh-energy nucleus-nucleus collisions at particle accelerators (RHIC, LHC)
create QGP for a finite time to quantitatively map out the QCD phase diagram
Hard Probes

Soft Probes

produced by high-pT QCD processbulk of created particles

initial hard parton scattering

dominant at low-pT“Jets”
Hard parton
production

before Collision

QGP Phase
5

Hadron Phase
Hiroki Yokoyama : PhD defence ( 29/03/2018 )Hard Probes of the QGP : Jets
Jet Quenching
partons' energy loss in the QGP
Energy attenuation/disappearance or shapemodification of observed Jets
evaluation of these modifications allows to assess
QGP properties
1. Self-produced probes
short QGP lifetime ( ~10-23 s )2. Probe the small system
high-Q2 process
3. Probe the entire medium evolution
occur at early stage : τ ~ 1/Q
LO pQCD

4. Well calibrated probes
production rate calculable within pQCD
5. Copious production at the LHC
6. Access to initial parton kinematics
via jet reconstruction

hep-ph/0212109
6

Hiroki Yokoyama : PhD defence ( 29/03/2018 )Parton Energy Loss in QGP
Jet-medium interaction
Collisional energy loss
Radiative energy loss
Which is dominant for energy loss in QGP ?
parton energy loss depends onparton specie
parton momentum
path length
collision geometry
beam energy

ΔE measurement provides information on QGP properties(medium temperature, gluon density, parton-medium coupling strength…)
7

Hiroki Yokoyama : PhD defence ( 29/03/2018 )Experimental Results of Jet Quenching
Nuclear modification factor : RAA

Angular correlation of high-pT hadrons
disappearance of away side peakin central Heavy-Ion collisions(Au-Au , √sNN = 200 GeV)

high-pT hadrons = leading constituent of jet

fragmentation of quenched partonsstrong suppression : RAA ~ 0.1 to 0.4

4 < pTtrig [GeV/c] < 6
2 < pTasso [GeV/c] < pTtrig

PLB 720 (2013) 52

PRL 91, 072304 (2003)
8

Hiroki Yokoyama : PhD defence ( 29/03/2018 )Motivation
Jet Quenching
Parton energy loss through parton-medium interaction
Particle-medium coupling strength, medium temperature, medium density
Jet Measurement
Reconstructed jet is powerful probe for QGP study
Smaller surface bias than single particle measurement
Direct comparison to theory due to recovering initial parton kinematics
Jet-by-Jet measurement is enabled in LHC energy
Development of jet reconstruction technique
Copious production of high-momentum jets
Jet measurement at √sNN = 5.02 TeV in LHC-ALICEFirst fully reconstructed jet measurement at highest centre of mass energy
Extreme condition of the medium in the world
Complementary measurement at low jet pT region lower than 100 GeV/c.
Centre-of-mass energy dependence, path length dependence of jet energy loss
9

Hiroki Yokoyama : PhD defence ( 29/03/2018 )Introduction
LHC-ALICE experiment
EMCAL L1 trigger development
Jet measurement with √sNN = 5.02 TeV Pb-Pb collisions
Results10

Large Hadron Collider
Largest and most powerful particle collider built by CERN
Run1 ( 2009 ~ 2013 )pp: √s = 2.76, 7, 8 TeV

Pb-Pb : √sNN = 2.76 TeV
p-Pb: √sNN = 5.02 TeVLong Shutdown 1
upgrade on beam energy
and luminosity
Run2 ( 2015 ~ 2018 )pp: √s = 5.02 TeV, 13 TeV

Pb-Pb : √sNN = 5.02 TeV
p-Pb : √sNN = 8 TeV

11Hiroki Yokoyama : PhD defence ( 29/03/2018 )Jet Measurement in LHC-ALICE
Charged Particles : |η| < 0.9, 0<φ<2π

Neutral particles : |η| < 0.7

ITS : silicon tracking detector

EMCAL, ( DCAL : Run2 from 2015- )TPC : time projection chamber

Pb-Scintillator sampling calorimeter
PHOS

Charged Jet

lead-tungsten crystal (PWO) based calorimeter

Charged+Neutral = Full Jet
EMCAL

ITS

TPC

PHOS

DCAL
12

Hiroki Yokoyama : PhD defence ( 29/03/2018 )My PhD Activity
2014 Apr.

2015 May

LHC LS12015 Nov. 2016 Feb.

5TeV pp, PbPb collisions

ALICE Calorimeter Trigger Upgrade

NOW

LHC Run2

Charged Jet Measurement“Development of EMCALs' Trigger System in LHC-ALICE”(日本物理学会第70 回年次大会, 2015 Mar.)“A Summary Trigger Unit for the ALICE Electromagnetic Calorimeters”(Quark Matter 2015, 2015 Sep. )“Measurement of Inclusive Charged Jet Production in pp and Pb-Pb collisions at √sNN = 5.02 TeV with
ALICE”(Hard Probes 2016, 2016 Sep.)“The nuclear modification of charged jets in √sNN = 5.02 TeV Pb-Pb collisions at LHC-ALICE”(日本物理学会第72 回年次大会, 2017 Mar.)“Measurement of Jet Quenching in the ALICE Experiment at the LHC”
(2017 Joint Workshop of the FKPPL/TYL-FJPPL, TYL-FJPPL Young Investigator Award 2017 招待講演, 2017 May)other international workshop ( 7 times )13

Hiroki Yokoyama : PhD defence ( 29/03/2018 )hep-ph/0212109

Introduction
LHC-ALICE experiment
EMCAL L1 trigger development
Jet measurement with √sNN = 5.02 TeV Pb-Pb collisions
Results

14

ALICE electromagnetic calorimeters
EMCAL and DCAL
large acceptance coverage
to improve jet energy resolution by adding neutral constituents
DCAL is installed at the opposite side of EMCAL in azimuth
for the correlation study of back-to-back jets
PHOShigh granularity and high energy resolution
focus on direct photon and π0 measurement with very high energy
As a trigger detector
Level-0 (fast but simple, low threshold) and Level-1
EMCAL and DCAL : high energy jets and photons
PHOS : high energy photon

15

Hiroki Yokoyama : PhD defence ( 29/03/2018 )ALICE Calorimeter Trigger Upgrade
Level-1 trigger upgrade during LS1
to select events with large energy deposit
within these calorimeters
produced by Summary Trigger Unit (STU)
Firmware development of STU
new firmware : DCAL and PHOS
NEW trigger algorithm• pp, p-Pb : constant threshold• Pb-Pb : soft background event-by-event

subtraction in EMCAL estimated from themedian of jet patch energies in DCAL
(and vice versa)

implementation of STU busy signal

EMCAL STUBKG energy
DCAL STU

fit for new data format
Commissioning & QA

Jet/Photon
Triggers

PHOS STU

DCS software upgradeOﬄine code development (mapping, decoder)16

Hiroki Yokoyama : PhD defence ( 29/03/2018 )L1-photon and L1-Jet Algorithm
Minimum unit for trigger calculation : “FastOR”
L1-photon

L1-Jet2x2 FastOR/patch

8x8 (or 16x16) FastOR/patch

FastOR basis sliding window

Jet primitive (=4x4 FastOR) basis sliding window
DCAL+PHOS combined
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jet-patch(2x2)jet-patch(4x4)X

jet primitive

Hiroki Yokoyama : PhD defence ( 29/03/2018 )Soft Background Subtraction

18

Hiroki Yokoyama : PhD defence ( 29/03/2018 )In operation since 2015 Pb-Pb runs
Correlation of estimated BKG densities
new BKG communication working

ρ (DCAL) (GeV)

Trigger Performance
300
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ALICE Pb-Pb sNN = 5.02 TeV
L1-Jet Trigger
LHC15o run245793

104

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Threshold is decided by trigger rejection

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Trigger turn-on efficiently with different threshold

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HIROKI YOKOYAMA

L1 Trigger Rejection

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L1-Jet Trigger, 16× 16 patch

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DCAL

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19

Hiroki Yokoyama : PhD defence ( 29/03/2018 )Introduction
LHC-ALICE experiment
EMCAL L1 trigger development
Jet measurement with √sNN = 5.02 TeV Pb-Pb collisions
Results

20

Analysis Flow

Event√sNN = 5.02 TeV, Pb-Pb
Min. Bias trigger( 3.36M events )| vZ | < 10 cm

1. Event/Track Selection

2. Jet Reconstruction

Charged Particle| η | < 0.9
pT > 0.15 GeV/chybrid track selection

anti-kT algorithm
R = 0.2| ηjet | < 0.7
pTlead > 5 GeV/c

Average Background Density
kT algorithm
median calculation

3. Soft BKG subtraction• subtract average BKG
event-by-event

4. Unfolding• correct detector eﬀects• correct BKG fluctuation

SVD unfolding method
input response of detector
eﬀects and BKG fluctuation

5. Inclusive Jet spectrum
21

Hiroki Yokoyama : PhD defence ( 29/03/2018 )Track Selection
Hybrid Tracks = Global + Complementary Tracks

Tracking Efficiency

Global Tracks

HĲING+GEANT simulation
smaller in the most central
collisions due to large multiplicity

ITS+TPC with track matchinglarge inefficient region in SPD (inner-most lTS layer)-> non-uniformity of track distribution in ηφComplementary Tracks
primary vertex constraint to improve tracking pT
resolution
this thesis

1/Nev dNtrack /dφ

200
ALICE Pb-Pb sNN = 5.02 TeV

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Hybrid Tracks
Global Tracks
Complementary Tracks

160
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Tracking Efficiency

this thesis

without SPD hits

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50 - 90 %

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track

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(rad)

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T,gen

22

Hiroki Yokoyama : PhD defence ( 29/03/2018 )Challenge in Heavy-Ion Collisions
Large background contribution to jet energy
dNch/dη ~ 1300 ( 0-10% centrality )Average Background Density : ρρ (GeV/c)kT clusters excluding two leading clusters

event-by-event calculation
ρ ~ 145 GeV/c for 0-10%

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Pb-Pb,
TeVTeVALICE
Pb-Pb,sNNs=NN5.02= 5.02105p p > 0.15
GeV/c
> 0.15
GeV/cT,track

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T,track( ~18 GeV/c for R=0.2 jets )Combinatorial Jets Removal
random combination of BKG particles
minimum leading constituent pTlead > 5 GeV/c isrequired

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charged trackcentrality
multiplicity(%)
23

Hiroki Yokoyama : PhD defence ( 29/03/2018 )Soft Background Subtraction
Average background is
subtracted from jet momentum

Leading track pT bias
up to 35 GeV/c for pTlead > 5 GeV/c ( 0-10% )smaller in peripheral• softer fragmentation in central collisions• and/or larger combinatorial Jet

35GeV/c

20GeV/c24

Hiroki Yokoyama : PhD defence ( 29/03/2018 )Underlying Event Fluctuation
Background fluctuation : δpT

δpT width ( magnitude of fluctuation )from region to region around averagebackground~ 5 GeV/c ( 0-10% )smaller in peripheral

Random Cone method(exclude leading jet neighbour : Δr > 1.0)

25

Hiroki Yokoyama : PhD defence ( 29/03/2018 )yield [a.u.]

Unfolding1

Generated−1

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10− 2Measured jet spectrum is distorted by−3

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Detector effect ( tracking efficiency and pT resolution )10− 4Background fluctuations

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Unfold measured spectrum to get true spectrum
Unfolded/Generated

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[GeV/c]

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Mm = Gm,d · Dd

Dd = Gd,t · Tt
spectrum corrected
for BKG fluctuation

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Detector Eﬀects

RM given by
δpT distribution

80p

Background Fluctuations

Measuredjet spectrum

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Detector Simulation

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[GeV/c]

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“unknown” true
jet spectrum

Inverted Response matrix gives “TRUE” jet spectrum via

Mm = Gm,d · Gd,t · Tt = Gm,t · Tt
26

Hiroki Yokoyama : PhD defence ( 29/03/2018 )Charged Jet Cross Section

Systematic Uncertainties
Unfolding Procedure (shape unc.)
δpT distribution(correlated unc.)
UE density due to elliptic flow
(correlated unc.)
Tracking efficiency and resolution(correlated unc.)

27

Hiroki Yokoyama : PhD defence ( 29/03/2018 )Introduction
LHC-ALICE experiment
EMCAL L1 trigger development
Jet measurement with √sNN = 5.02 TeV Pb-Pb collisions
Results

28

Jet Suppression
Nuclear modification factor : RAA
RAA > 1 : enhancement of jet yield
RAA < 1 : suppression
Strong jet suppression is observed in the most central collisions
smaller in peripheral (= closer to pp)( POWHEG : NLO pQCD calculation with MC )29

Hiroki Yokoyama : PhD defence ( 29/03/2018 )Simplified Energy Loss Model (1)Assumption : fractional jet disappearance :RAA : const.

RAA is constantsame shape with pp except yield reduction
spectrum fitting with one free parameter𝛘2 = 4.3E-1 (0-10%)30

Hiroki Yokoyama : PhD defence ( 29/03/2018 )Simplified Energy Loss Model (2)ΔpT/pT : const.

Assumption : fractional energy loss :ΔpT is proportional to pT
same shape with pp except pT shift
spectrum fitting with one free parameter𝛘2 = 5.8E-1 (0-10%)

31

Hiroki Yokoyama : PhD defence ( 29/03/2018 )Simplified Energy Loss Model (3)ΔpT : const.

Assumption : constant energy loss
ΔpT is constant ( NO pT dependence )same shape with pp except pT shift
spectrum fitting with one free parameter
RAA distribution well described ( up to ~90 GeV/c )𝛘2 = 4.1E-2 (0-10%)

32

Hiroki Yokoyama : PhD defence ( 29/03/2018 )Energy Loss v.s. Path Length
Parton path length is estimated by Toy Model
Path Length : L

average path length in transverse overlapped area
Energy loss increases with parton path length~10 GeV/c at most central collisions ( for R=0.2 charged jets )quadratic function fit gives better agreement than linear fit
not ruled out collisional energy loss due to large uncertainty

33

Hiroki Yokoyama : PhD defence ( 29/03/2018 )Centre-of-mass energy dependence
dNch/dη∝ dN /dηgcharged multiplicity density per unit transverse arealarger energy loss in LHC than RHIC
due to higher gluon density in QGP

34

Hiroki Yokoyama : PhD defence ( 29/03/2018 )Summary
Jet Quenching
parton energy loss in QGP
jet is great probe to assess QGP properties
ALICE EMCAL Trigger Upgrade during LS1
L1-photon/Jet triggers production by STU
New detector, New triggering algorithm
In operation since first Pb-Pb runs in RUN2
Inclusive Jet Measurement in √sNN = 5.02 TeV Pb-Pb collisions
strong jet suppression is observed in the most central collision
constant energy loss model well describes observed RAA
jet energy loss increases with parton's path length
larger in LHC energy than RHIC due to higher gluon density

35

Hiroki Yokoyama : PhD defence ( 29/03/2018 )Backup36

Q&A
18 GeV/c for R=0.2 の根拠、意味は？
Delta pT の分布がガウス分布ではない点について、pure な thermal 分布する粒子を
eta, phi で降った場合、ガウス分布になるか？
“excluding leading jet neighbor” とあるが、sub leading の寄与は無視できるのか？
Flow Bias +- 4 GeV/c は rho に対してか？
Delta_pT, jet vs. L について、radiative なのか、collisional なのか、議論して欲しい
Delta_pT, jet の sqrt(s) 依存性、特に RHIC との比較、横軸 dN/deta (~gluon deisty,
energy density)にした場合の議論も加えて欲しい。energy loss が radiative なのかどうかを議論。
single track efficiency (centrality, pT 依存性)を示して欲しい
論文について：プロットと本文での結論を結びつけて書いて欲しい(1), (2) の Simplify energy loss model に加えて、Yield が減る方向（↓）のモデルも入れて、それではデータを説明できないことを示して欲しい。
37

Hiroki Yokoyama : PhD defence ( 29/03/2018 )Assurance of tracking quality
single track efficiency
HĲING+GEANT simulation
tracking inefficiency due to high multiplicity is less than few % down to~0.3 GeV/c
bit larger at lower pT
Tracking Efficiency

this thesis

1.2
ALICE Pb-Pb sNN = 5.02 TeV
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HIJING (PbPb)
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10 - 30 %
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T,gen

38

Hiroki Yokoyama : PhD defence ( 29/03/2018 )Eﬀect from sub-leading jet to δpT evaluation
The nominal selection of RC is “RC excluding leading jet neighbor”
NOT usual to catch back-to-back jets in detector acceptancedue to the limitation of | ηtrack | < 0.9

this thesis

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RC w/o lead.Jet (∆ r>1.0)/d p“RC away from sub leading”-> variation of systematic uncertainty~5% at low jet pT

1/ N ev dN

jet(Nominal)

3rd leading jets are much rare

RC w/o lead.Jet (∆ r>0.5)

10− 3

RC w/o lead.Jet (∆ r>1.5)
RC w/o 2 lead.Jet (∆ r>1.0)

10− 4

NOT included as systematic variation
RC selection with too many jet excluded,
results in too much bias
ratio

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39

Hiroki Yokoyama : PhD defence ( 29/03/2018 )S/B energy ratio in reconstructed jetρ(0-10%) = 145 GeV/c for R=0.2 jets.
average underlying event energy for R=0.2 jet is ~18 GeV/c in 0-10%
ρ(0-10%) * πR2 = 18 GeV/c
S/B = ~1.7 for 30 GeV/c jet
S/B = ~2.7 for 50 GeV/c jet

40

Hiroki Yokoyama : PhD defence ( 29/03/2018 )How to determine Flow BiaskT cluster selection on median calculationaccording to direction of event leading track
event leading track pT > 5 GeV/c-> effect of particles fragment outside of jet-> effect of elliptic flow (leading track tends to be found in-plane)
The bias for ρ calculation : ±4 GeV/c±4 (GeV/c) * πR2 = ±0.5 GeV/c, for RC with R=0.2[GeV/c]δpT distribution shift of ±0.5 GeV/c as variation

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leading trackin event4

regionfull event

φkT clus. - φlead.

this thesis

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Perpendicular

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Hiroki Yokoyama : PhD defence ( 29/03/2018 )δpT distribution for thermal track pT distribution
asymmetry shape of δpT distribution even in ηφ-randomised track
when track pT distribution follows pure thermal distribution, does it
becomes gaussian?
Generate N=2350 (assuming 0-10%), ηΦ-randomised tracks according tocharged track spectrum fit at low-pT.
-> asymmetry δpT distribution

42

Hiroki Yokoyama : PhD defence ( 29/03/2018 )δpT distribution for thermal track pT distribution
asymmetry shape of δpT distribution even in ηφ-randomised track
when track pT distribution follows pure thermal distribution, does it
becomes gaussian?
Generate N=2350 (assuming 0-10%), ηΦ-randomised tracks according tocharged track spectrum fit at low-pT.
-> asymmetry δpT distribution

43

Hiroki Yokoyama : PhD defence ( 29/03/2018 )Average underlying event stability (1)number of excluded kT clusters for median calculation
0 (no exclusion) to 4, R=0.2< 1% for 0-10%, 10-30% and 30-50%
Δρ < 1.5 GeV/c ( Δ(ρπR2) < 180MeV/c )< 3% for 50-90%
Δρ < 110 MeV/c ( Δ(ρπR2) < 20MeV/c )44

Hiroki Yokoyama : PhD defence ( 29/03/2018 )Average underlying event stability (2)resolution parameter of kT algorithm
from R=0.2 to 0.5 (number of excluded clusters : 2)
< 2% for 0-10%, 10-30% and 30-50%
Δρ < 3 GeV/c ( Δ(ρπR2) < 370MeV/c )< 30% for 50-90%
Δρ < 110 MeV/c ( Δ(ρπR2) < 140MeV/c )45

Hiroki Yokoyama : PhD defence ( 29/03/2018 )46

Hiroki Yokoyama : PhD defence ( 29/03/2018 )47

Hiroki Yokoyama : PhD defence ( 29/03/2018 )48

Hiroki Yokoyama : PhD defence ( 29/03/2018 )Expected Trigger Performance
DCAL-BKG-density subtraction
from EMCAL jet patch

LHC11h data,
central , V0-dep threshold

Trigger efficiency
BKG density subtraction
turn on steeply

Rejection factor
BKG density subtraction
larger rejection power

central event
figures by Rosi, and Marta

49

Hiroki Yokoyama : PhD defence ( 29/03/2018 )Trigger System of ALICE Calorimeters
PHOS
PHOS
FEE TRU
PHOS
PHOSTRU
TRU

fastOR amp(analog)

PHOSPHOS
TRU TRU
PHOS
PHOSTRU
TRU

L0_localfastOR amp*28TRUs

DCAL
DCAL
FEE TRU
DCAL
DCALTRU
TRU

fastOR amp(analog)

DCAL
DCAL
TRUTRUDCAL
DCALTRU
TRU*14TRUsEMCALDCALFEETRUDCAL
DCALTRU
TRU

fastOR amp(analog)

EMCAL
DCAL
TRUTRUDCAL
DCALTRU
TRU

PHOS
STU

PHOS_L0
PHOS_L1_photon_low
PHOS_L1_photon

CTP

subregion data

L0_localfastOR amp

DCAL
STU

DCAL_L0
DCAL+PHOS_L1_Jet_low
DCAL+PHOS_L1_Jet
DCAL_L1_photon_low
DCAL_L1_photon

background density

L0_localfastOR amp

EMCAL
STU

EMCAL_L0
EMCAL_L1_Jet_low
EMCAL_L1_Jet
EMCAL_L1_photon_low
EMCAL_L1_photon*32TRUs

Analog sum
inside 1fastOR

digitise fastOR Amp
L0 trigger calculation

L1 photon/Jet
Trigger calculation
50

Hiroki Yokoyama : PhD defence ( 29/03/2018 )L1-Jet/photon trigger eﬃciency

51

Hiroki Yokoyama : PhD defence ( 29/03/2018 )Jet Reconstruction Algorithm
FastJet anti-kT algorithm ( p=-1, p=1 for kT algorithm)
calculate dĳ and diB by all particles combination
when minimum “d” among them is part of dĳ
merge particle “i” and “j”
when minimum “d” among them is part of diB
that cluster defined as jet
repeat until no particle are left

52

Hiroki Yokoyama : PhD defence ( 29/03/2018 )Jet Reconstruction
Combine/classify particles into clusters sequentiallybased on pT weighted distance( correspondence between parton level and detector level ).
Anti-kT algorithm
start clustering from high-pT particles

Signal Jet in Heavy Ion collisions

Circular and centred around harder energy deposit ( with radius ~R )kT algorithm
start clustering from low-pT particles

Estimation of Soft BKG

anti-kTkT

JHEP 04 (2008) 063
53

Hiroki Yokoyama : PhD defence ( 29/03/2018 )Unfolding
detector RM

background RM

given by PYTHIA

created by δpT distribution( w/o lead. jet )track reduction according to tracking
inefficiency in Pb-Pb collisions

this thesis

0.35

200

0.5

ALICE Pb-Pb sNN = 5.02 TeV
Anti-kT R = 0.2, Charged Jets|η | < 0.7, p lead > 5 GeV/cjet
TCentrality: 0-10 %

180
160
140

0.45
0.4
0.35

p

140

0.4

T,ch,jet

160

0.45

gen

ALICE Pb-Pb sNN = 5.02 TeV
Anti-kT R = 0.2, Charged Jets|η | < 0.7, p lead > 5 GeV/cjet
TCentrality: 0-10 %180(GeV/c)

0.5

120

0.3

120

0.3

100

0.25

100

0.25

80

0.2

80

0.2

60

0.15

−9

60

0.15

−10

40

0.1

−11

20

0.05

0

0.05

0

20

40

60

80 100 120 140 160 180 200

p rec

T,ch,jet0(GeV/c)(GeV/c)20

T,ch,jet

0.1

gen

40

this thesis
200

10

ALICE Pb-Pb sNN = 5.02 TeV
Anti-kT R = 0.2, Charged Jets|η jet | < 0.7, p lead
> 5 GeV/c
T
Centrality: 0-10 %

180
160
140

p

p gen

T,ch,jet(GeV/c)this thesis
200

10
10

−12

10

0
30

−13

10

120

10−14

100

10

80

10

40

50

60

70

80

90

100 110 120

p rec(GeV/c)T,ch,jet

0

−15
−16

10−17

60

−18

10

40

−19

10

20
0
30

−20

10

40

50

60

70

80

Jet finding eﬃciency

90

100 110 120

p rec(GeV/c)T,ch,jet10−21

unfold spectrum with combined RM

given by PYTHIA
same track reduction and pTlead selection with detector RM.
54

Hiroki Yokoyama : PhD defence ( 29/03/2018 )Jet Energy Resolution
Jet energy resolution is derived by the Response Matrixes
Effect from Underlying Event Fluctuation
dominant in lower jet pT
Detector Effectdominant at higher jet pT
T,jet

1

this thesisσ(p rec ) / p gen

ALICE Pb-Pb sNN = 5.02 TeV
Anti-kT R = 0.2, Charged Jets

0.9
0.8

|η | < 0.7, p lead > 5 GeV/cjet
TCentrality: 0-10 %

0.7
0.6

T,jet

T,jet

σ(p rec ) / p gen

T,jetthis thesis

Background fluctuation
Detector effectCombined

0.5
0.4

1
0.8
0.6
0.4

0.2

0.2

0.1

0.1
40

60

80
p

gen
T,ch jetBackground fluctuation
Detector effectCombined

0.5
0.3

20

|η | < 0.7, p lead > 5 GeV/cjet
TCentrality: 30-50 %

0.7

0.3

0

ALICE Pb-Pb sNN = 5.02 TeV
Anti-kT R = 0.2, Charged Jets

0.9

100 120(GeV/c)

55

0

20

40

60

80
p

gen
T,ch jet

100 120(GeV/c)Hiroki Yokoyama : PhD defence ( 29/03/2018 )Unfolding QA
particle level : PYTHIA8
detector level : PYTHIA8 + GEANT
measured spectrum
smeared detector level jet spectrum
yield [a.u.]

BKG fluctuation σ = 4 GeV/c assumed
Unfolded and Generated match within
1~2%accuracy

1

Generated−1

10

Unfolded

109

Particle Level
Detector Level
Smeared

108

107

−3

10

1

Measured

10− 1

10− 4

Unfolded
−2

10

−5

10

10− 3

−6

10

10− 4

106

Unfolded/Generated

Jet Yield [a.u.]

this thesis

yield [a.u.]

10− 2

10− 5

105
10− 6

104

10− 7
−8

0

20

40

60

80

100

p

jet
T

120

10

0

20

40

60

80

100 120 140 160 180 200

pjet [GeV/c][GeV/c]

T

56

−7
1.2
10
0
1.15
1.1

20

40

60

80

100

120

140

160

p

jet
T

180

200

[GeV/c]

1.05
1
0.95
0.9
0.85
0.80

20

40

60

80

100

120

140

p

160

180

gen

[GeV/c]

T,jet

200

Hiroki Yokoyama : PhD defence ( 29/03/2018 )Systematic Uncertainty
Unfolding Procedure (shape unc.)

Tracking eﬃciency and resolution(correlated unc.)

1. unfolded pT range

8. Tracking Eﬃciency

2. measured pT range± 4% from nominal value

3. Unfolding method (SVD)

9. Tracking Eﬃciency

Bayesian unfolding± 20% from nominal value

4. Regularisation parameter
5. Generator selection
δpT distribution(correlated unc.)
6. RC selection (without lead. jet Δr < 1.0)
Δr < 0.5 and 1.5
apart from 1st and 2nd lead. jets
UE density due to elliptic flow
(correlated unc.)
7. Flow Bias
± 4 GeV/c for background density

0-10 % centrality
57

Hiroki Yokoyama : PhD defence ( 29/03/2018 )<Ncoll> scaled Charged Jet Cross Section

58

Hiroki Yokoyama : PhD defence ( 29/03/2018 )pp Inclusive Jet Cross Section
Jet cross section at √s = 5.02 TeV pp collisions in 2015
POWHEG NLO calculations well describes measured spectrum within
systematic uncertainties
Ritsuya Hosokawa

Dataset√s = 5.02 TeV, pp collisions
MB triggered events ( 25.5M events )Charged track selection| η | < 0.9, pTtrack > 0.15 GeV/c
Jet reconstruction
anti-kt jet reconstruction algorithm
R = 0.2| η | < 0.7, ( pTlead > 5 GeV/c for RAA ref. )Unfoldingto correct for detector effects

59

Hiroki Yokoyama : PhD defence ( 29/03/2018 )Comparison with Charged Particle (√sNN = 2.76 TeV)
Energy scale is different
momentum fraction of leading track : ~0.5 ( in pp collisions )Roughly consistent with jet RAA
after energy scaling

60

Hiroki Yokoyama : PhD defence ( 29/03/2018 )Comparison with √sNN = 2.76 TeV
ALICE, Pb+Pb √sNN = 2.76 TeV, R=0.2 charged jet
pp reference : PYTHIA MC
Equivalent RAA
hotter/denser medium
harder collisionstronger jet suppression

smaller RAA

flatter jet spectrum

larger RAA

61

Hiroki Yokoyama : PhD defence ( 29/03/2018 )R AA

ATLAS jet RAA : 5.02 TeV vs. 2.76 TeV

1.5
ATLAS Preliminary
anti-k t R = 0.4 jets

0 - 10 %|y | < 2.1

ATLAS, s NN = 5.02 TeV, this analysis
ATLAS, s NN = 2.76 TeV, arXiv: 1411.2357

1

0.52015 Pb+Pb data, 0.49 nb-1
2015 pp data, 25 pb-1
0

100
0

200

300

400

500

600

900
p [GeV]
T

62

Hiroki Yokoyama : PhD defence ( 29/03/2018 )R AAComparison to ATLAS jet RAA

1.5
ATLAS Preliminary
anti-k t R = 0.4 jets

ATLAS, sNN = 5.02 TeV, this analysis

0 - 10 %|y | < 2.1

ATLAS, sNN = 2.76 TeV, arXiv: 1411.2357
1

0.52015 Pb+Pb data, 0.49 nb-1
2015 pp data, 25 pb-1
0

100
0

200

300

400

500 600900
p [GeV]
T

63

Hiroki Yokoyama : PhD defence ( 29/03/2018 )ALICE ch. particle RAA : 5.02 TeV vs. 2.76 TeV

ALI-PREL-107300

64

Hiroki Yokoyama : PhD defence ( 29/03/2018 )RAA : cone size dependence @ CMS

65

Hiroki Yokoyama : PhD defence ( 29/03/2018 )