Recent Hard Probes
Results from ALICE

Rachid Guernane
LPSC GrenobleTGSW2018, Sept 21

Fundamental question:
How do collective phenomena and macroscropic properties of matter arise fromthe elementary interactions of a non-abelian quantum field theory?

Opportunities

Tools

StatusConstraining equilibrium properties of
QCD matter (eos,)

Flow and fluctuation
measurements in AA

advanced

Measuring medium properties with hardauto-generated probes ()Quarkonia, RAA’s ,photons

in progress

Accessing microscropic structure ofQCD matter in AA

Jet substructure, heavy
flavor transport

in reachControlling initial conditionspA (light AA) runs,
npdf global fits, small-x

in reach

Testing hydrodynamization andthermalization

Combined jet and flow
analysesstrategyt.b.d.

Understanding “heavy-ion like behavior”
in small systems (pp, pA)

Flow, hadrochemistry,jets

recentsurprises

Urs Wiedemann

List to be refined and extended

The QCD phase diagram• Understand the behaviour of QCD matter at thelimit of high density and/or temperature

The ALICE program• LHC Run 2 data analysis is in full swing!
• Significant increase in integrated luminosity in pp, p-Pb, and Pb-Pb collisions
allows more and more precise investigation of statistics hungry probes• LHC will have done 12 ~one month heavy-ion runs between 2010 and 2030
(LS4)
- 5/12 done already- LHC scheduled 3.5 weeks of Pb-Pb collision in November 2018

Jets physics in heavy-ion collisions• Jets are collimated sprays of hadrons- Proxies for short-distance quarks & gluons• Jets are a self-generated probes of the medium- High-pT partons produced in the very early stageof the collisions (1~ 1⁄3 ≪ 1 fm) w/ production
peratures reached in the most central Au+Au collisions
rate calculable
pQCD
at RHIC, and w/i
2.2±0.5
GeV2 /fm at temperatures reached11in the most central Pb+Pb collisions at LHC. Values of q̂- Medium-modified
partoncascadedue to toin the hadronic phase
are assumed
to be proportional
the hadron density in a hadron resonance gas model with(in)elastic
energy loss
the normalization in a cold nuclear matter determined by
DIS data [81]. Values of q̂ in the QGP phase are consid• Elastic
rescatterings of the hard partons off theered proportional to T 3 and the coefficient is determinedby fitting color
to the experimental
datamediumcharges (∝6)on RAA at RHIC and76LHC separately. In the HT-M model the procedure is• Medium-inducedgluon(∝67)
similar except that q̂ issoft
assumedto beradiation
proportional
to the22of q̂ extracted in Sec IV are for gluon jets and therefore- From hard
tothesoftscales (~8)
9/4 times
corresponding
values for quark jets). For

temperatures close to and below the QCD phase transition, q̂ is assumed to follow the entropy density, andq̂/T 3 shown in Fig. 10 is calculated according to the parameterized EOS [96] that is used in the hydrodynamicevolution of the bulk medium. In both HT approaches,.
no jet energy dependence of q̂ is considered.
Considering the variation of the q̂ ;values between thefive di↵erent models studied here as theoretical uncer-

McGill-AMY
GLV-CUJET

5ˆ 3q/T

local entropy density and its initial value is q̂ = 0.89±0.11

in the center ofcollisions
the most central• Jet physicsGeVin/fmheavy-ionisAu+Au
a collisions at RHIC, and q̂ = 1.29 ± 0.27 GeV /fm in the most
multiscalecentral
problem
Pb+Pb collisions at LHC (note that the values

MARTINI
HT-BW
HT-M

43213qˆ N/Teff(DIS)

Au+Au at RHIC
Pb+Pb at LHC

Qualitative extraction of medium properties
00
0.1
0.2
0.3
0.4
0.5
(temperature and jet energy dependence of theT (GeV)
jet transport coefficient "! = : ⁄<) throughFIG. 10. (Color90, ThePRConline)
014909(2014)
assumed temperature dependence of the "!scaledGeV . ⁄q̂/T
fm in di↵er~$.jet&transport± (. )parameter
phenomenological study of jet quenchingent jet quenching models for an initial quark jet with energy3Jets at ALICE
EMCal/DCal Pb-scintillator
sampling calorimeter which
covers:• |η| < 0.7,
• ΔφEMCAL~110°,ΔφDCAL~60°
tower
Δη~0.014 × Δφ~0.014+ dedicated L1-jet trigger

Remove contaminationfrom charged particles
Tracking: |η|< 0.9, 0<φ<2π• TPC: gas drift detector• ITS: silicon detector

Chargedconstituents

JetNeutral
constituentsCharged jet nuclear
modification factor RAA• RAA quantifies the magnitude of jet suppression,
which arise mainly from final-state interactions
with constituents of the medium
Nuclear modification factor RAA• Compares HI and pp collisions and removes thePartons lose energy
through interactions with the medium → jet quenchinggeometricalscaling●●Compares HI and pp collisions and removes the geometrical scaling.
Jet nuclear modification factor quantifies the magnitude of jet suppression,
which arise mainly from final-state interactions with constituents of the medium.
4Charged
cross section in pp vs POWHEG
Jet jetRAAd2σ/dp dη (mb c /GeV)

10− 110− 210− 310− 410− 110− 2

ALICE Preliminary

pp s = 5.02 TeV

10− 310− 4anti-k T , R=0.2|η |<0.7MC/Datapjettrack,chT

10− 5> 0.15 GeV/ c

1.20.80.8
405060p jet,ch (GeV/ c )0.610T

ALI−PREL−113801

jetp track,ch > 0.15 GeV/ c

1.2130|η |<0.51.4120

pp s = 5.02 TeVT

1.40.610

ALICE Preliminary

anti-k T , R=0.4

MC/Data

10− 5ALICEPOWHEG (UE subtracted)
PYTHIA6 (Perugia-2011)
PYTHIA8 (Monash-2013)

T

ALICE
POWHEG (UE subtracted)
PYTHIA6 (Perugia-2011)
PYTHIA8 (Monash-2013)

Td2σ/dp dη (mb c /GeV)

Inclusive yields in pp

2030405060

p jet,ch (GeV/ c )T

ALI−PREL−113806

ØMeasured
jet crosssections areare
welldescribed by• NNLO jetcharged
cross section
calculations
getting
POWHEG+Pythia8
prediction
mature, see arXiv:1801.06415(NLO pQCD + parton shower, hadronization)

Jet !""

Inclusive yields in Pb-Pb• Reconstructed jets are required to have apT > 5 GeV/c leading charged particle• The spectra are unfolded for detector effects andbackground fluctuations

Jet !""• Strong jet suppression (!## = 0.3 ÷ 0.5) observed in central
Pb-Pb collisions- Slowly rising with *+,-./ (flattening for *+,-./ ≳ 200 GeV/6)• Suppression stronger for more central collisions- Longer average path length, denser medium• Similar suppression observed in R=0.2 and R=0.3• Very complementary pT coverage w/ ATLAS & CMS

Inclusive ratio: R = 0.2 / R = 0.3

Jet cross-section ratio R=0.2/R=0.3• The ratio of jet cross-sections
at different R is an inclusive jet
shape observable, sensitive tothe R-dependence of jetenergy loss

• The ratio of jet cross-sections atdifferent R is an inclusive jet shapeobservable, sensitive to theR-dependence of •jetenergy loss
With the current precision, thePb-Pb jet cross-section ratio• Such ratios are infraredand collinear
R = 0.2 / R = 0.3 is consistentsafe and are sensitive
to thewith POWHEG+Pythia8transverse energy profile of the jets
• Extension to R = 0.4, lower pT,
and reduction ofconsistentuncertainties• With the current precision,in progresswith the NLO pQCD
prediction

ALI-PREL-159657

James Mulligan, Yale University

Energy loss of charged
particles and jets

• Charged jet RCP is similar tothat observed for single
hadrons over a broad
momentum range

RCP is the ratio of central toperipheral collisions yields- This is contrary to theexpectation of jet suppression tobe smaller than for hadrons(since jet reconstruction collects
multiple jet fragments into thejet cone, thus recovering some of
the medium-inducedfragmentation)- The momentum is redistributed
to angle larger than R=0.3 byinteraction with the medium

Jet structure• Modifications of perturbative parton showers induced byinteractions with the color charges in the dense partonic
mediumObservables

Full jetLarge structure

Constituents

RAA, AJ

Jet mass, zg

Jet shape+ jet flavor dependence!

Jet structure

Jet shape functions

Radial energy profile

2nd moment constituent pT distribution• The jet core is more collimated and fragments harderthan in vacuum!

Jet structure• Where in the shower evolution the mediummodifications occur?
• Jet grooming- Isolate the hard prongs of a jet and remove softwide-angle radiation- Momentum sharing zg between leading subjets
The splitting into two branches
becomes increasingly more
unbalanced as the Pb-Pb collisions
becomes more central• Partons in the medium act as
decoherent emitters?
PLB 725 (2013) 357

Heavy flavor jets
• Δ!g > Δ!q > Δ!Q- Quark vs. gluon energy loss
- Mass effect: radiation damping in deadcone• Small rate of thermal production inthe QGP (mc,b ≫ T )• Heavy quark jets vs. hadrons- Access the kinematics of hard
scattering in an unbiased way(potentially contain more information)- Easier to measure (detection efficiency,systematics, HQ FFs…)- Typically larger energies

arXiv:1306.0909

Impact parameter based b-tagging
in pp and p-Pb
• Track Counting algorithm- Discriminator defined as the signed
impact parameter (d.c.a. to theprimary vertex) significance of the Nthmost displaced track (N value drivenby efficiency & purity)+ other taggers (SV, electrons…)

displacement is measurable with high-precision vertex detectors and serves as main physics property to discriminate b-jets. The displacement can be measured via secondary
vertex reconstruction
2.2 Results
and track impact parameters. For each track, two impact parameters are defined as the track disALICE Simulation
flavourtheperformanceof the present b-taggingJet
algorithm,
the mistagging efficien
tance of closest approach to the primary vertex, either in the transverse orTotheevaluate
longitudinal
10 plan.
beauty (ML, cut)PYTHIA + HIJING, p-Pb s NN = 5.02 TeV
charm(ML)havebeen calculated.
The
mistagging
efficiency
fraction
The conventional approach is to cut on such parameters to extract a b-jet
enriched
sample. Instead,FastJet,
anti-k T, R = 0.4,
|η | < 0.5 represents theudsg(ML) of jets of a certain true
jetcharm (cut)1
the present approach applies deep-learning techniques to several low-levelparameters.
udsg
(cut)
that hasbeen wrongly tagged as b-jets. In this context, the b-jet
efficiency
is the fraction of ta
Efficiency

Deep-learning b-tagging−1b-jets out of the 10true
full sample. The mistagging efficiencies are compared to existing simularesults from ALICE using a cut-based approach [8].

2.1 Model design and input features

Multilayered CNNs

Jet constituents

Impact parameters D/Z, jT

η, φ, r(vx,vy,vz), σvtx, χ2, Lxy, σxy

Convolution 1D
Dropout per layer: 0.1
Convolution 1D
Dropout per layer: 0.1
16Conv. kernel: 2
16
Conv. kernel: 2Max pooling
Pool length: 2Max pooling
Pool length: 232
Conv. kernel: 232
Conv. kernel: 264Conv. kernel: 264
Conv. kernel: 296
Conv. kernel: 296
Conv. kernel: 2Max pooling
Pool length: 2Max pooling
Pool length: 264
Conv. kernel: 264
Conv. kernel: 2Convolution 1D
Dropout per layer: 0.1
128

Conv. kernel: 4Max pooling

Pool length: 264

Conv. kernel: 232

Conv. kernel: 2Max pooling

Pool length: 2Mistagging efficiency

Secondary vertices

10−2

arXiv:1709.08497

10−3
1
10−4
10−1

10−5
10−2
10−6−330 ≤ p20

genT, jet< 40 GeV/c30

4050

60

7080

10ALI−SIMUL−13108590100p gen (GeV/c )T, jetFigure 3: c- and10
udsg-jets (mis-)tagging efficiencies for given b-jet efficiency (20%) vs. generated jeand comparison to cut-based
results.
−5−410

Jet flavour
charm (ML)
ALICE Simulation
udsg (ML)10−6 the mistagging efficiency for charm and light-flavor
Figure 2 shows
jets (udsg) for given b
PYTHIA + HIJING, p-Pb s NN = 5.02 TeV
charm (cut)gen
FastJet,antik,
R=0.4,|
η|<0.5
udsg (cut)efficiencies andfor jets with 30  TpT,jet < 40jetGeV/c. For both jet types,
the mistagging efficien
Rüdiger
10−7 Haake

Merge (concatenation)
Fully-connected
Dropout per layer: 0.25
L2 regularization
128

Machine and deep learning techniques128in heavy-ion collisions with ALICEare much lower for all b-jet
than for 0.5
the standard
method.
0.2 efficiencies
0.3
0.4
0.6 cut-based
0.7
0.8

Sigmoid neuron
for binary classification

128

b-jet efficiency

ALI−SIMUL−131081

128

udsg (cut)Mistagging efficiency

Efficiency

Figurevertices,2: c- andFigure 1: Design of the used model: Three branches – one processing secondary
and udsg-jets
two for the(mis-)tagging efficiencies for several b-jet efficiencies for jets with 30  pT
Jet flavour c udsg
ALICE
Simulation results.
ALICE
Simulation
40
GeV/c
andcomparison
to cut-based
Jetflavour
jet constituents
merged and further processed by a four-layered
fully-connected network with higher
b-jet efficiency
10 – arebeauty (ML, cut)PYTHIA + HIJING, p-Pb s NN = 5.02 TeVPYTHIA + HIJING, p-Pb s NN = 5.02 TeV
1ε = 0.6charm(ML)dropout. The output
is eventuallymergedneuron with sigmoid
activation
functiontobe
suitablefor|<0.5
FastJet,antik,
R=0.4,|η
FastJet,anti-k T, R =0.4, |η to| < a0.5
udsg (ML)T
ε = 0.5jet
jetcharm(cut)classification 1tasks.
ε = 0.4
ML-based tagging method
10−1

10The classifierconsists of three branches (subnetworks) of multilayered convolutional neuralnetworks (CNNs).
On top, a multilayered fully-connected network is processing the mergeddata.
10−210−2
Keras [3] is used for creation and training of the model. The model is depicted in Fig. 1. It uses the−3
following 10
input
features:−110−310• Secondaryvertices: relative vertex position: (vx , vy , vz ), vertex dispersion svtx , vertex fit c 2 ,decay−5length in xy-plane Lxy , uncertainty sxy .
10−4−410• Jet constituent
properties I: coordinates of the tracks, relative to the jet (h, f , r).
10−62030

40

5060

70

80

90100
p gen (GeV/c )10−520ALI−SIMUL−131085
T, jet Z (longitudinal), track j• Jetconstituent properties II: impact parameters D (transverse),
T
ALI−SIMUL−131089

30405060

708090100
p gen (GeV/c )T, jetComparison with D0-tagged jets

What about charm jets?
• Charged jets containing a D
their constituents
Ø mesonD0-taggedamong
jets are measured
down to 3 GeV/c

The measurement
is robust against
combinato-Ø Invariant
mass analysis
to extract
backgroundD-jet
raw spectrum
Ø D mesons must come from a hard scattering- Background
fromthe selec
Ø Jetsspectrum
from charm quarks
are measured
side bands

ØStrong D-taggedcharged jet
suppression
is obs• Strong
suppression
ofthecentral
Pb-Pb collisions at low pT0D -tagged jets in central
Pb-Pb collisions 0

Ø RAA of charged jets and D -tagged charged jets
Hintof more
suppressionof collisions
lowto -theD meson
Rincentral
Pb-PbAA0pT D -jets than inclusive jets at
higher pT- Similar to D meson RAA

Conclusion• Inclusive jet suppression in 5.02 TeV Pb-Pb collisions• Exploring jet substructure• Heavy-flavor jet tagging• Measurement of hadron-jet/jet-hadron correlations• The rise of Machine Learning for jets
- Discriminating quark and gluon jets
- Heavy-flavor tagging…

• Even after 25 years, this is just the beginning- Large upgrade program in preparation!
• Continuous read-out of 50 kHz Pb-Pb collisions• Upgrade of ITS, TPC, MFT will be installed starting from next year

- ALICE will continue to take data at least until 2028 (LS4)