Deep learning jet modiﬁcations in heavy-ion collisions

June 3, 2021, Jets and their substructure from LHC data, CERN

Deep learning jet modifications in heavy-ioncollisions

JHEP03(2021)206 & arXiv:2106.11271

with Daniel Pablos and Konrad Tywoniuk

Yi-Lun Du

Outline

1 Motivation

2 Deep learning jet energy loss

3 ApplicationSensitivity of jet observables to in-medium modificationJet tomography

4 Conclusion and outlook

Yi-Lun Du Deep learning jet modifications in heavy-ion collisions June 3, 2021 1 / 15

Jets in the medium

14

How can jet modification be quantified?

modified jetjetIdeally…

How do jets from an identical hard process differ in vacuum and in medium?

A-Ap-p

Jasmine Brewer (MIT)

J. Brewer, HP’20

Quark-gluon plasma (QGP) created in heavy ion collision:deconfined phase, hot dense medium

Jets serve as hard probe to the medium properties

Jets are quenched in the medium via parton energy loss

Jasmine Brewer (MIT) 8

Key question: compare A-A jets to which p-p jets?

• Standard answer: match final (reconstructed) !"

RAA =Spectrum in AASpectrum in pp

� E. Epple 7

What we currently know about jet modification

ATLAS collaboration Phys. Lett. B 790 (2019)

ATLAS collaboration Phys. Rev. C 98 (2018)

Jets are: • quenched in central AA collisions• not quenched in MB pp collisions

Jets in the medium appear softerATLAS collaboration PLB 790 (2019) 108


Jet modifications: ambiguous interpretations

Yaxian MAO Central China Normal University

ALICE Overview (HP2020)

Jet substructure in central Pb-Pb collisions• Soft drop grooming allows to study medium modified parton shower by removing large angle soft radiation

11

• Results are fully corrected for background and detector effects

- No significant modification in zg distribution

- Modification of θg → narrowing jets

Ratio of jet observables distr.between medium and vacuum,BOTH with pjet

T > pcutT

Interplay: jet substructures, e.g., Rg , could– be modified during the passage through the

medium and/or– affect the amount of jet energy loss and then this

jet doesn’t pass the pT cut in the selection, i.e.,selection bias.

Jets produce emissions with smaller Rg inmedium than in vacuum: presumes medium scaledominatesJets with larger Rg in vacuum are moresuppressed in medium: presumes vacuum scaledominatesCan we disentangle these two effects withknowledge of the degree of quenching for eachindividual measured jets?


Energy loss ratio & Jet selectionsDefining the energy loss ratio

9Daniel Pablos University of Bergen

Vac

Med

Vacuum-like emission

Hypotheticalvacuum-like

emission

Medium inducedemission

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Ef

same early, high energy pattern,

up to matching/stopping scale

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R

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R

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Ei

The amount of energy and momentum lost by the energetic parton, as described by

Eq. (2.1), exactly corresponds to the amount of energy and momentum flowing into the

QGP hydrodynamic modes [70]. This will generate a wake that is correlated with the direc-

tion of the jet [78], whose contribution to the experimentally observable jet properties has

to be taken into account. The hybrid model provides an estimate of the wake contribution

to the final hadron spectrum by performing an expansion of the Cooper-Frye formula at

the perturbed freeze-out hypersurface, which yields [79]

Ed�N

d3p=

1

32⇡

mT

T 5cosh(y � yj) exp

h�mT

Tcosh(y � yj)

i

⇥(

pT�PT cos(�� j) +1

3mT �MT cosh(y � yj)

),

(2.5)

where pT , mT , � and y are the transverse momentum, transverse mass, azimuthal angle

and rapidity of the emitted thermal particles and where �PT and �MT = �E/ cosh yj are

the transverse momentum and transverse mass transferred from the jet, with azimuthal

angle and rapidity �j and yj , respectively. The distribution in Eq. (2.5) has been obtained

by considering that the background behaves as a Bjorken flow, which only has longitudinal

expansion. Generalizing it to the case in which there is transverse expansion can modify

such distribution, depending on the orientation of the jet with respect to the background

radial flow components [80–82]. The consequences of these observations will be explored

in the near future.

The partons that do not completely hydrodynamize are hadronized using the Lund

string model included in PYTHIA8. The contributions from the hadrons of the wake,

together with the fragmented hadrons, ensure event-by-event energy-momentum conserva-

tion.3

2.2 Jet energy loss ratio �jh and traversed path-length L

The main goal of this work is to determine, on a jet-by-jet basis, the amount of energy

loss, quantified through the variable

�jh ⌘Eh

f

Ehi

, (2.6)

su↵ered by jets due to the propagation through a hot and dense QCD medium. Here,

the subscript “jh” refers to the energy of the jet measured at hadronic level. These jets

3The distribution in Eq. (2.5) can become negative, most notably in the direction opposite to the jet in

the transverse plane. This reflects the absence of soft particles in such region of phase space compared to

an unperturbed QGP background as a result to the boost experienced by the fluid cell due to the injection

of momentum from the jet. In the present work we will ignore such negative contributions, since they

would show up as negative energy pixels in the jet images used in Section 3.1 (one would need to devise a

procedure to cancel out such negative contributions using particles from a real background which are close

in momentum and configuration space, such as in [79], which we leave for future work). It has been shown

that their contribution to jet observables with relatively small jet radius, such as the one used in the present

work, R = 0.4, is almost negligible [83], which guarantees that none of our conclusions will be a↵ected by

the omission of such contribution. A study of jets with a larger radius will be done in future publications.

– 6 –

FESIES

Study jet observables for jets that belong to 2 differentquenching classes:

– Unquenched class: χjh > 0.9.– Quenched class: χjh < 0.9.

pp jets: pT > 200 GeVPbPb jets:

– Final Energy Selection (FES): impose pT cut onfinal energy pT > 200 GeV→ Steeply falling energyloss dist. Biased by little quenched samples!

– Initial Energy Selection (IES): impose pT cut oninitial energy via χjh, pT/χjh > 200 GeV & pT > 100GeV→ More support of fairly quenched jets in thequenched class. More distinguishable!


Hybrid model

Hybrid Model

11Daniel Pablos University of Bergen

free parameterO(1)

Strongly coupled energy loss(hydrodynamization rate)

Hadrons from the hydro.wake (medium response)

PYTHIA8 down to hadro. scale(formation time argumentfor spacetime picture)

Can also studyfinite resolution effects(not done in present work)

Casalderrey-Solana, Gulhan, Milhano, DP, Rajagopal JHEP ‘15,‘16,‘17

Hulcher, DP, Rajagopal JHEP ‘18

PYTHIA8 down to hadronization scale

Strongly coupled energy loss at every stage

Hadrons from the hydro. wake (medium response)Casalderrey-Solana, Gulhan, Milhano, Daniel Pablos, Rajagopal JHEP ’15,’16,’17

Vacuum jets using p̂T ,min = 50GeV, with oversampling powerp4

T .

PbPb collisions in 0-5%centrality at

√s = 5.02 ATeV.

Reconstructed jets with anti-kT ,R = 0.4, required to be |η| < 2and pjet

T > 100 GeV.

∼ 250,000 jets. 80% for trainingand 20% for validation.


CNN Prediction & Interpretability

χjh

Jet image33x33

16features17x17

16features17x17

32features

9x9flattened

2592fc

128outputlayer

8x8 conv, 16bn, PReLu

dropout(0.2)avgpool(2x2)

7x7x16 conv, 16bn, PReLu

dropout(0.2)

6x6x16 conv, 32bn, PReLu

dropout(0.2)avgpool(2x2)

bn, PReLudropout(0.5)

0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0True jh

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

Pred

icted

jh

True jh versus predicted jh

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

0.4

0.3

0.2

0.1

0.0

0.1

0.2

0.3

0.4

Tran

slate

d Az

imut

hal A

ngle

Average of normalized jet image, 0.25< jh < 0.50



0.4 0.2 0.0 0.2 0.4Translated Pseudorapidity

0.4

0.3

0.2

0.1

0.0

0.1

0.2

0.3

0.4

Tran

slate

d Az

imut

hal A

ngle



Average of normalized jet image, 0.85< jh < 1


Correlation of jh with per-pixel of jet image

10 6

10 5

10 4

10 3

10 2

10 1

0.3

0.2

0.1

0.0

0.1

0.2

0.3

Jet quenching increases the numberof soft particles at large anglesJet shape can capture the main feature


Jet radius, RgRg ratio between PbPb and pp jets

FES: Selection bias towards jets withsmaller Rg , originated by pT cut.

IES:

– Unquenched class: still biased due toχjh cut: to belong to this class, a jet hadbetter to be with smaller Rg , comparedwith all pp jets.

– Quenched class presents featuresrelated to energy loss, compared withunquenched class: jet quenching leadsto enhancement of large Rg .

FES IES


Soft Drop multiplicity, nSDnSD ratio between PbPb and pp jets

FES: Selection bias towards jets withfewer nSD, originated by pT cut.

IES:

– Unquenched class: still biased due toχjh cut: to belong to this class, a jet hadbetter to be with fewer nSD, comparedwith all pp jets.

– Quenched class presents featuresrelated to energy loss, compared withunquenched class: jet quenching leadsto enhancement of large nSD.

FES IES


Groomed momentum sharing fraction, zgzg ratio between PbPb and pp jets

FES: No selection bias observed. Scaleof emission isn’t strongly dependent onsplitting fraction zg .

IES:

– Quenched class presents featuresrelated to energy loss, compared withunquenched class: jet quenching leadsto enhancement of smaller zg subjets.

FES IES

Y.-L. Du, D. Pablos, K. Tywoniuk, JHEP03(2021)206


Applications: Jet tomography, length VS χjh

0 2 4 6 8 10 12 14 16L (fm)

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

0.45

Norm

alize

d to

Uni

ty

Histogram for L w/weights0.95< p

jh<10.85< p

jh < 0.950.75< p

jh < 0.850.60< p

jh < 0.750.25< p

jh<0.60

Due to the strong correlationbetween L and χjh, selecting

jets with different χjh willnaturally select jets that

traversed different L.→ Great potential to maketomographic application!

5

0

5

10

y (f

m)

0.25< pjh < 0.60 0.60< p

jh < 0.75 0.75< pjh < 0.85

10 5 0 5x (fm)

5

0

5

10

y (f

m)

0.85< pjh < 0.95

10 5 0 5x (fm)

0.95< pjh < 1

10 5 0 5x (fm)

Inclusive in pjh

0.005

0.010

0.015

0.020

0.025

0.030


Applications: creation points & orientation

5

0

5

10

y (f

m)

FES

Creation points for centrality 0-5%FES-Glauber

5

0

5

10

y (f

m)

IES IES-Glauber

10 5 0 5x (fm)

5

0

5

10

y (f

m)

IES, Predicted

10 5 0 5x (fm)

IES, Predicted-Glauber

0.0025

0.0050

0.0075

0.0100

0.0125

0.0150

0.0175

0.0200

0.006

0.004

0.002

0.000

0.002

0.004

0.006

v2 =

⟨p2

x−p2y

p2x+p2

y

⟩

0 10 20 30 40 50 60 70Centrality (%)

0.01

0.00

0.01

0.02

0.03

0.04

0.05

v 2

v2 VS Centrality for R = 0.4 @ sNN = 5.02 TeVFESIESIES, Predicted

IES “removes” final state interactions (selectionbias), since we record “all” jets.IES provides access to the genuine jet creationpoint distribution and initial orientation.

Y.-L. Du, D. Pablos, K. Tywoniuk, arXiv: 2106.11271


Conclusion and outlookCNN can extract energy loss jet-by-jet from jet image with good performanceProcedure generalisable to many jet quenching modelsJet shape contains significant predictive power: angular distribution of soft particlesMitigate selection bias and reveal medium effects on various jet observablesOpen opportunity to make tomographic study

– Generalizability to other MC quenching models?– Applicability to more realistic environment: fluctuating background?– Better performance from other state-of-the-art neural networks?– Extract traversed length with better precision?– Unfold jet initial properties apart from jet energy?


Backup: Groomed jet image VS χjh

0.4

0.3

0.2

0.1

0.0

0.1

0.2

0.3

0.4

Tran

slate

d Az

imut

hal A

ngle

Average of normalized groomed jet image, 0.25< jh < 0.50




0.4

0.3

0.2

0.1

0.0

0.1

0.2

0.3

0.4

Tran

slate

d Az

imut

hal A

ngle



Average of normalized groomed jet image, 0.85< jh < 1


Correlation of jh with per-pixel of groomed jet image

10 6

10 5

10 4

10 3

10 2

10 1

0.3

0.2

0.1

0.0

0.1

0.2

0.3

Input Output Network LossGroomed jet image χjh CNN 0.0065

Jet image above 1 GeV χjh CNN 0.0042Jet image above 2 GeV χjh CNN 0.0066

Removing soft particles (atlarge angles) reducesperformance

Generalizability challenges:

– Large fluctuatingbackground

– Hardonization modelling– Other quenching models


Backup: Prediction performance with FCNNInput (size) Output Network Loss

FF (10) χjh FCNN 0.0058Jet shape (8) χjh FCNN 0.0033

FF, jet shape (18) χjh FCNN 0.0032FF, jet shape, features (25) χjh FCNN 0.0028

Jet image & FF, jet shape, features (25) χjh API: CNN&FCNN 0.0028

Jet shape outperforms jet FF.

Motivates construction from jetshape by 17-parameter fitting:

– Still a bit worse than CNN

Jet observables recover theperformance by jet image withequivalent predictive power:interpretability!

0.05 0.10 0.15 0.20 0.25 0.30 0.35r

10 2

10 1

(r)(P

bPb)

Jet shape0.95< jh<10.85< jh<0.950.75< jh<0.850.60< jh<0.750.25< jh<0.60Medium

10 1 100

z

10 2

10 1

100

101

D(z)

(PbP

b)

FF

0.95< jh<10.85< jh<0.950.75< jh<0.850.60< jh<0.750.25< jh<0.60Medium

0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0True jh

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

Fitte

d js jh

True jh versus fitted jsjh by jet shape

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

χjsjh =

∑i

(pTipT

)αirβii + γ


Backup: Jet tomography with χjh & v2

v2 =p2

x−p2y

p2x+p2

y

Top row: In-plane jets(v2 > 0) going left (px < 0)and right (px > 0)Bottom row: Out-of-planejets (v2 < 0) going up(py > 0) and down (py < 0)To get very quenched, jetshave to travel longer inmedium. So v2 & px ,y arehelpful for jet tomography.

x (fm)

10.07.55.0

2.50.0

2.55.0

7.510.0

y (fm)

10.07.5

5.02.5

0.02.5

5.07.510.0

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0.00

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0.10

0.25< p<0.75

x (fm)

10.07.55.0

2.50.0

2.55.0

7.510.0

y (fm)

10.07.5

5.02.5

0.02.5

5.07.510.0

0.02

0.00

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0.04

0.06

0.08

0.10

Creation points density for centrality 30-40%, R = 0.4 @ sNN = 5.02 TeV, FES, pT > 100 GeV

In-plane

0.75< p<0.95

x (fm)

10.07.55.0

2.50.0

2.55.0

7.510.0

y (fm)

10.07.5

5.02.5

0.02.5

5.07.510.0

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0.95< p<1.00

x (fm)

10.07.55.0

2.50.0

2.55.0

7.510.0

y (fm)

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5.02.5

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Inclusive in p

x (fm)

10.07.55.0

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7.510.0

y (fm)

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0.25< p<0.75

x (fm)

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7.510.0

y (fm)

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5.02.5

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5.07.510.0

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0.10

Out-of-plane

0.75< p<0.95

x (fm)

10.07.55.0

2.50.0

2.55.0

7.510.0

y (fm)

10.07.5

5.02.5

0.02.5

5.07.510.0

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0.02

0.04

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0.10

0.95< p<1.00

x (fm)

10.07.55.0

2.50.0

2.55.0

7.510.0

y (fm)

10.07.5

5.02.5

0.02.5

5.07.510.0

0.02

0.00

0.02

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Inclusive in p

0.000

0.006

0.012

0.018

0.024

0.030

0.036

0.042


Deep learning jet modiﬁcations in heavy-ion collisions

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