Energy Flow Polynomials for Jet Substructure

Energy Flow Polynomials for Jet SubstructureMIT Jet Workshop

Cambridge, MA – January 11, 2018

Patrick T. Komiske IIICenter for Theoretical Physics, Massachusetts Institute of Technology

PTK, E.M. Metodiev, J. Thaler – 1712.07124

Patrick T. Komiske III (MIT) EFPs for Jet Substructure 1 / 28

https://indico.cern.ch/event/690007/

https://arxiv.org/abs/1712.07124

Energy Flow Polynomials

Part I - Introduction to Energy Flow Polynomials

Energy Flow Polynomials (EFPs)

Energy Flow Basis from IRC safety

Taming the (IRC-safe) Substructure Zoo

Spanning Substructure with Linear Regression


*Part I based on atalk by Eric Metodiev.

https://indico.physics.lbl.gov/indico/event/546/session/2/contribution/2


EFP Essentials

EFPG =M∑i1=1· · ·

M∑iN=1︸︷︷︸

Correlator

zi1 · · · ziN︸︷︷︸Energies

∏(k,`)∈G

θiki`︸︷︷︸Angles

e+e− measure: zi = Ei∑kEk

θij =(

2pµipjµ

EiEj

)β/2

Hadronic measure: zi = pT,i∑kpT,k

θij = (∆y2ij + ∆φ2

ij)β/2

e.g.

=M∑i1=1

M∑i2=1

M∑i3=1

M∑i4=1

zi1zi2zi3zi4θi1i2θi2i3θ2i2i4θi3i4

=M∑i1=1

M∑i2=1

M∑i3=1

M∑i4=1

M∑i5=1

zi1zi2zi3zi4zi5θi1i2θi2i3θi1i3θi1i4θi1i5θ2i4i5


of and

Figure by E.M. Metodiev

“fly-swatter”

“bowtie”



Multigraph/EFP Correspondence

Multigraph ←→ EFP

j ←→ zijk ` ←→ θiki`

=M∑i1=1

M∑i2=1

M∑i3=1

M∑i4=1

zi1zi2zi3zi4θi1i2θi1i3θi1i4

N Number of vertices ←→ N -particle correlatord Number of edges ←→ Degree of angular monomialχ Treewidth + 1 ←→ Optimal VE Complexity

Connected ←→ PrimeDisconnected ←→ Composite


EF Basis from IRC safety










EFPs Linearly Span IRC-safe Observables

Start with an arbitrary IRC-safe observable S(pµ1 , . . . , pµM )

Energy expansion*: Approx. S with polynomials of zij

IR safety: S unchanged by addition of infinitesimally soft particlesC safety: S unchanged by collinear splittings of particlesRelabeling symmetry: Particle indexing is arbitrary

See also F. Tkachov hep-ph/9601308, N. Sveshnikov and F. Tkachov hep-ph/9512370

=⇒ Energy correlators linearly span IRC-safe observables

Angular expansion*: Approx. angular part of S with polynomials of θijSimplify: Identify unique analytic structures that emerge as EFPsSimilar emergent multigraphs in M. Hogervorst et al. 1409.1581 and B. Henning et al. 1706.08520

=⇒ EFPs linearly span IRC-safe observables

*These expansions generically make use of the Stone-Weierstrass Theorem


S '∑G∈G

sGEFPG

https://arxiv.org/abs/hep-ph/9601308







Energy expansion*: Approx. S with polynomials of zijIR safety: S unchanged by addition of infinitesimally soft particlesC safety: S unchanged by collinear splittings of particlesRelabeling symmetry: Particle indexing is arbitrary


=⇒ Energy correlators linearly span IRC-safe observables

Angular expansion*: Approx. angular part of S with polynomials of θijSimplify: Identify unique analytic structures that emerge as EFPsSimilar emergent multigraphs in M. Hogervorst et al. 1409.1581 and B. Henning et al. 1706.08520




S '∑G∈G

sGEFPG








Energy expansion*: Approx. S with polynomials of zijIR safety: S unchanged by addition of infinitesimally soft particlesC safety: S unchanged by collinear splittings of particlesRelabeling symmetry: Particle indexing is arbitrary


=⇒ Energy correlators linearly span IRC-safe observablesAngular expansion*: Approx. angular part of S with polynomials of θijSimplify: Identify unique analytic structures that emerge as EFPsSimilar emergent multigraphs in M. Hogervorst et al. 1409.1581 and B. Henning et al. 1706.08520




S '∑G∈G

sGEFPG






Organization of the Energy Flow Basis

Need to select EFP subset GDetermine G by truncating in dFinite # of EFPs at each d

OEIS A050535:# of multigraphs with d edges# of EFPs of degree d

OEIS A076864:# of connect. multigraphs with d edges# of prime EFPs with degree d


https://oeis.org/A050535

https://oeis.org/A076864

Substructure Zoo









Substructure Zoo

Substructure Observables in the Energy Flow Context

Jet Mass: m2J

p2TJ

=M∑i1=1

M∑i2=1

zi1zi2(cosh(∆yi1i2)− cos(∆φi1i2)) = 12 × + · · ·

Angularities: λ(α) =M∑i=1

ziθαi

λ(4) = − 34 ×

λ(6) = − 32 × + 5

8 ×


C.F. Berger, T. Kucs, and G. Sterman, hep-ph/0303051S.D. Ellis, et al., 1001.0014A.J. Larkoski, J. Thaler, and W. Waalewijn, 1408.3122

using pT -centroid axis




Substructure Zoo

Substructure Observables in the Energy Flow Context

Energy Correlation Functions: e(β)N =

M∑i1

· · ·M∑

iN=1zi1 · · · ziN

∏k<`∈{1,...,N}

θβiki`

e(β)2 = , e

(β)3 = , e

(β)4 =

Geometric Moments: C =M∑i=1

zi

(∆y2

i ∆yi∆φi∆yi∆φi ∆φ2

i

), Pf = 4 detC

(TrC)2

Tr C = 12 × , 4 det C = − 1

2 ×


A.J. Larkoski, G.P. Salam, J. Thaler, 1305.0007with measure choice β

L.G. Almeida, et al., 0807.0234 J. Thaler and Lian-Tao Wang, 0806.0023 J. Gallicchio and M. Schwartz, 1211.7038

using pT -centroid axis





Linear Regression






-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

Lea

rned

Coeffi

cien

t

W Jets: Ang. α = 6

Pythia 8.226,√s = 13 TeV

R = 0.8, pT ∈ [500, 550] GeV

EFP β = 1, d ≤ 7





Linear Regression

Linear Models

S '∑G∈G

sGEFPG, S : IRC-safe observable, G : set of EFPs

Machine learn {sG} with a linear modelLinear models:

Convex with few/no hyperparameters to tuneAchieve global optimum via closed form solution or convergent iterationCannot have a simpler model (1 parm./input) sensitive to all inputsMany potential methods to analyze learned modelSee Ch. 3 and 4 of C. Bishop Pattern Recognition and Machine Learning


Linear Regression

Confirming Analytic Relationships

λ(2) = 12 × , λ(4) = − 3

4 ×

λ(6) = − 32 × + 5

8 ×


-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

Lea

rned

Coeffi

cien

t

W Jets: Ang. α = 2


R = 0.8, pT ∈ [500, 550] GeV

EFP β = 1, d ≤ 7


-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

Lea

rned

Coeffi

cien

t

W Jets: Ang. α = 4


R = 0.8, pT ∈ [500, 550] GeV

EFP β = 1, d ≤ 7


-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

Lea

rned

Coeffi

cien

t

W Jets: Ang. α = 6


R = 0.8, pT ∈ [500, 550] GeV

EFP β = 1, d ≤ 7


Linear Regression

Linear Regression and IRC-safetymJ/pT,J : IRC safe - no Taylor expansion due to square rootλ(α=1/2): IRC safe - no simple analytic relationshipτ

(β=1)2 : IRC safe - algorithmically definedτ

(β=1)21 : Sudakov safe - safe for 2-prong jets and moreτ

(β=1)32 : Sudakov safe - safe for 3-prong jets and more

Multiplicity: IRC unsafe

2 3 4 5 6 7

Max Degree of EFPs

0.0

0.2

0.4

0.6

0.8

1.0

Cor

r.C

oef

.5

th−

95th

Per

centi

le

QCD Jets


R = 0.8, pT ∈ [500, 550] GeV

EFP β = 1

mJ/pTJ

λ(α=1/2)

τ(β=1)2

τ(β=1)21

τ(β=1)32

Mult.

2 3 4 5 6 7

Max Degree of EFPs

0.0

0.2

0.4

0.6

0.8

1.0

Cor

r.C

oef

.5

th−

95th

Per

centi

le

W Jets


R = 0.8, pT ∈ [500, 550] GeV

EFP β = 1

mJ/pTJ

λ(α=1/2)

τ(β=1)2

τ(β=1)21

τ(β=1)32

Mult.

2 3 4 5 6 7

Max Degree of EFPs

0.0

0.2

0.4

0.6

0.8

1.0

Cor

r.C

oef

.5

th−

95th

Per

centi

le

Top Jets


R = 0.8, pT ∈ [500, 550] GeV

EFP β = 1

mJ/pTJ

λ(α=1/2)

τ(β=1)2

τ(β=1)21

τ(β=1)32

Mult.


Conclusions

Part I Conclusions

EFPs:Energy correlators with angular structures indexed by multigraphsLinearly span the space of IRC-safe observablesEncompass many existing classes of substructure observables

Linear regression:Linear models are the easiest and most tractable kind of model

Convex with few/no hyperparametersGlobal optimum via closed form solution or convergent iterationMany potential tools to analyze what’s learned

Works with EFPs to match onto many IRC-safe observables


Part II - Linear Jet Tagging with EFPs

Linear Classification with EFPs

Comparison with Modern Machine Learning

Fast Computation of EFPs


Linear Classification

Linear Classification Overview

Fit a decision plane, determined by a vector w

Fisher’s linear discriminant (LDA): closed-form solutionLogistic regression: Convex, iterative solution

Decision threshold t is determined by distance from the planeG is finite set of graphs corresponding to the inputs

Organization by d is natural (equivalent to the order of the expansion)Organization by N or χ also possible, (where is the information?)

Classifier =

t+

∑G∈G

wGEFPG

≥ 0, signal< 0, background




W vs. QCD jet classification (quark/gluon and top tagging in backup)

0.0 0.2 0.4 0.6 0.8 1.0

W Jet Efficiency

10−1

100

101

102

103

Inve

rse

QC

DJet

Mis

tag

Rat

e

EFPs: W vs. QCD


R = 0.8, pT ∈ [500, 550] GeV

EFP β = 0.5

d ≤ 3

d ≤ 6

d ≤ 7

Linear

DNN

better

300k training samplesLinear: Fisher’s linear discriminant

num. params. = num. EFPs < 1000100k test samples

DNN: Dense neural net(100 node fully-connected layer)× 3∼ 120k parameters50k validation, 50k test samples


https://azure.microsoft.com/en-us/


Which EFPs are Important?

0.0 0.2 0.4 0.6 0.8 1.0

W Jet Efficiency

10−1

100

101

102

103

Inve

rse

QC

DJet

Mis

tag

Rat

e

EFPs: W vs. QCD


R = 0.8, pT ∈ [500, 550] GeV

EFP β = 0.5, d ≤ 7

N ≤ 3

N ≤ 5

N ≤ 7

N ≤ 9

0.0 0.2 0.4 0.6 0.8 1.0

W Jet Efficiency

10−1

100

101

102

103

Inve

rse

QC

DJet

Mis

tag

Rat

e

EFPs: W vs. QCD


R = 0.8, pT ∈ [500, 550] GeV

EFP β = 0.5, d ≤ 7

χ ≤ 2

χ ≤ 3

χ ≤ 4

High-N EFPs are important for classification performance

Great classification performance with just χ = 2 EFPs!


Modern ML Comparison







Modern Machine Learning Comparison

0.0 0.2 0.4 0.6 0.8 1.0

W Jet Efficiency

10−1

100

101

102

103

Inve

rse

QC

DJet

Mis

tag

Rat

e

W vs. QCD


R = 0.8, pT ∈ [500, 550] GeV

EFP β = 0.5, d ≤ 7

EFPs, Lin.

EFPs, DNN

gray CNN

Nsubs, Lin.

Nsubs, DNN

color CNN

Linear and DNN same as beforeCNN: Convolutional neural net

33× 33 jet images(48 filters)× 3gray: pT channel onlycolor: pT and mult. channels∼ 80k parameters

(Linear classification with EFPs) ∼ (MML) for εs & 0.5!

N -subjetiness: 1011.2268, N -subjetiness basis: 1704.08249, NN Review: 1709.04464






Modern Machine Learning Comparison

0.0 0.2 0.4 0.6 0.8 1.0

Quark Jet Efficiency

10−1

100

101

102

103

Inve

rse

Glu

on

Jet

Mis

tag

Rat

e

Quark vs. Gluon


R = 0.4, pT ∈ [500, 550] GeV

EFP β = 0.5, d ≤ 7

EFPs, Lin.

EFPs, DNN

gray CNN

Nsubs, Lin.

Nsubs, DNN

color CNN

0.0 0.2 0.4 0.6 0.8 1.0

Top Jet Efficiency

10−1

100

101

102

103

Inve

rse

QC

DJet

Mis

tag

Rate

Top vs. QCD


R = 0.8, pT ∈ [500, 550] GeV

EFP β = 0.5, d ≤ 7

EFPs, Lin.

EFPs, DNN

gray CNN

Nsubs, Lin.

Nsubs, DNN

color CNN

(Linear classification with EFPs) & (MML) for εs & 0.5


Computation






Computation

Computational Complexity of ECF(G)s

M∑i1=1· · ·

M∑iN=1

Ei1 · · ·EiN

∏i<j∈{i1,...,iN}

θβij , ECFβN

v∏m=1

min(m){θβij}i<j∈{i1,...,iN}, vECFGβN

Implementation of ECF(G) formula runs in time O(MN )

With M ∼ 100, ECF(G)N=4 ∼ one hundred million operations

N = 4 is barely tractable, N ≥ 5 is essentially inaccessible

fjcontrib – EnergyCorrelator 1.2.0


1305.0007

1609.07483

http://fastjet.hepforge.org/svn/contrib/contribs/EnergyCorrelator/tags/1.2.0/



Computation

Computational Complexity of EFPs

Like other energy correlators, EFPs are naively O(MN )

Factorability of summand in EFP formula can speed up computation

=

M∑i1=1

M∑i1=1

M∑i3=1

zi1zi2zi3θ2i1i2θi2i3

M∑i4=1

M∑i5=1

zi4zi5θ4i4i5

=M∑i1=1

M∑i2=1

M∑i3=1

M∑i4=1

M∑i5=1

M∑i6=1

M∑i7=1

M∑i8=1

zi1zi2zi3zi4zi5zi6zi7zi8θi1i2θi1i3θi1i4θi1i5θi1i6θi1i7θi1i8︸︷︷︸O(M8)

=M∑i1=1

zi1

M∑i2=1

zi2θi1i2

7

︸︷︷︸O(M2)


1

2

3

4

5Composite EFPs are products of prime EFPs

Computation

Variable Elimination (VE)

Algorithm for finding optimal parentheses placement in EFP formulaReduces EFP computational complexity to O(Mχ):

Best case (NP-hard): χ = treewidth(G) + 1Heuristics can be used which work excellently for our small graphsχ = 2 for all tree graphs, χ = 3 for single-cycle graphs, χ = N for KN

χ 2 3 4

e.g.


Computation

EnergyFlow Python Package

A convenient and simple package for efficient implementation of EFPsCurrently written in pure Python using the NumPy library

Need a fast, arbitrary dimension multi-arrayWe’re working on a C++ implementation (not simple)


https://pkomiske.github.io/EnergyFlow

Conclusions

Conclusions

Linear classification with EFPs very comparable to MML methodsLinear methods =⇒ very nice both theoretically and experimentally

EFP linear structure potentially allows for theoretical calculationFully differentiable model, uncertainty/error propagation simpleConvex, global minimum is guaranteedNo/few hyperparametersInteresting methods made possible by linearity

Lasso regression for automatic feature selectionPCA, orthogonal subspaces, etc.

Efficient computation of EFPs has been achievedEnergyFlow Python package here, stay tuned for more

EFPs potentially bridge MML performance & theory understanding


https://pkomiske.github.io/EnergyFlow

Backup

Additional Slides


Backup

Quark/Gluon, Top Linear Classification with EFPs

0.0 0.2 0.4 0.6 0.8 1.0


10−1

100

101

102

103

Inve

rse

Glu

onJet

Mis

tag

Rat

e

EFPs: Quark vs. Gluon


R = 0.4, pT ∈ [500, 550] GeV

EFP β = 0.5

d ≤ 3

d ≤ 6

d ≤ 7

Linear

DNN

0.0 0.2 0.4 0.6 0.8 1.0

Top Jet Efficiency

10−1

100

101

102

103

Inve

rse

QC

DJet

Mis

tag

Rat

e

EFPs: Top vs. QCD


R = 0.8, pT ∈ [500, 550] GeV

EFP β = 0.5

d ≤ 3

d ≤ 6

d ≤ 7

Linear

DNN


Backup

Quark/Gluon and Top Tagging N Sweep

0.0 0.2 0.4 0.6 0.8 1.0


10−1

100

101

102

103

Inve

rse

Glu

onJet

Mis

tag

Rat

e



R = 0.4, pT ∈ [500, 550] GeV

EFP β = 0.5, d ≤ 7

N ≤ 3

N ≤ 5

N ≤ 7

N ≤ 9

0.0 0.2 0.4 0.6 0.8 1.0

Top Jet Efficiency

10−1

100

101

102

103

Inve

rse

QC

DJet

Mis

tag

Rat

e

EFPs: Top vs. QCD


R = 0.8, pT ∈ [500, 550] GeV

EFP β = 0.5, d ≤ 7

N ≤ 3

N ≤ 5

N ≤ 7

N ≤ 9


Backup

Quark/Gluon and Top Tagging χ Sweep

0.0 0.2 0.4 0.6 0.8 1.0


10−1

100

101

102

103

Inve

rse

Glu

onJet

Mis

tag

Rat

e



R = 0.4, pT ∈ [500, 550] GeV

EFP β = 0.5, d ≤ 7

χ ≤ 2

χ ≤ 3

χ ≤ 4

0.0 0.2 0.4 0.6 0.8 1.0

Top Jet Efficiency

10−1

100

101

102

103

Inve

rse

QC

DJet

Mis

tag

Rat

e

EFPs: Top vs. QCD


R = 0.8, pT ∈ [500, 550] GeV

EFP β = 0.5, d ≤ 7

χ ≤ 2

χ ≤ 3

χ ≤ 4


Backup

N -Subjettiness Linear/DNN Comparison

0.0 0.2 0.4 0.6 0.8 1.0


10−1

100

101

102

103

Inve

rse

Glu

onJet

Mis

tag

Rat

e

Nsubs: Quark vs. Gluon


R = 0.4, pT ∈ [500, 550] GeV

kT axes

2-body

6-body

10-body

Linear

DNN

0.0 0.2 0.4 0.6 0.8 1.0

W Jet Efficiency

10−1

100

101

102

103

Inve

rse

QC

DJet

Mis

tag

Rat

e

Nsubs: W vs. QCD


R = 0.8, pT ∈ [500, 550] GeV

kT axes

2-body

6-body

10-body

Linear

DNN

0.0 0.2 0.4 0.6 0.8 1.0

Top Jet Efficiency

10−1

100

101

102

103

Inve

rse

QC

DJet

Mis

tag

Rat

e

Nsubs: Top vs. QCD


R = 0.8, pT ∈ [500, 550] GeV

kT axes

2-body

6-body

10-body

Linear

DNN


Backup

VE Timing

Test our implementation of VE averaged over all EFPs with d ≤ 7

This includes prime EFPs up to N = 8 ! (Imagine N = 8 ECF, OMG)

24 25 26 27 28 29 210 211 212

M

10−4

10−3

10−2

10−1

100

101

102

Tim

e(s

)/

EF

P

χ ≤ 2

χ ≤ 3

χ ≤ 4

N 2 3 4 5 6 7 82 7 12 33 50 65 48 23

χ 3 11 42 82 80 334 2 1

Prime EFPs with d ≤ 7


Energy Flow Polynomials for Jet Substructure

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