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Optimal Jet Finder (OJF) in ATLASMichel Lefebvre, Rolf Seuster, Rob McPherson, Frank Michel Lefebvre, Rolf Seuster, Rob McPherson, Frank
Berghaus Damir LelasBerghaus Damir LelasBerghaus, Damir Lelas Berghaus, Damir Lelas
((University of Victoria, British Columbia, CanadaUniversity of Victoria, British Columbia, Canada))
ATLAS T i &Ph i W k J t/T /Et i T i dATLAS T i &Ph i W k J t/T /Et i T i dATLAS Trigger&Physics Week, Jet/Tau/Etmiss Trigger and ATLAS Trigger&Physics Week, Jet/Tau/Etmiss Trigger and Combined Performance MeetingCombined Performance Meeting
20 M h 200720 M h 2007
OUTLINE:OUTLINE:
20 March 200720 March 2007
•• Introduction to OJFIntroduction to OJF
•• OJF in ATLAS: experiences, findings and results OJF in ATLAS: experiences, findings and results
•• Some thoughts and remarksSome thoughts and remarksgg
•• Conclusions and OutlookConclusions and Outlook
Optimal Jet Finder (OJF)
Introduced and developed by:
F.V. Tkachov, D.Yu. Grigoriev and E. JankowskiF.V. Tkachov, D.Yu. Grigoriev and E. Jankowski
Short introduction: Phys. Rev. Lett. 91, 061801 (2003)Phys. Rev. Lett. 91, 061801 (2003)
Int. Journal of Mod. Phys. A, Vol 17, No 21 (2002) 2783Int. Journal of Mod. Phys. A, Vol 17, No 21 (2002) 2783--2884.2884.Int. Journal of Mod. Phys. A, Vol 17, No 21 (2002) 2783Int. Journal of Mod. Phys. A, Vol 17, No 21 (2002) 2783 2884.2884.
Authors webpage (with links to source code, etc.):
http://www inr ac ru/~ftkachov/projects/jets/welcome htmlhttp://www.inr.ac.ru/ ftkachov/projects/jets/welcome.html
Presented in JetRec phone meetings in January and March 2007
Results in this talk are from private OJF implementation in Athena
Official implementation can be expected soon (R. Seuster)
Discussions with experts has startedp
D.Lelas (UVic) Optimal Jet Finder (OJF) in ATLAS 2
Optimal Jet Finder (OJF)Tries to extract as much (jet) information from a HEP event as possible
(that is what authors call ‘optimal’…)
OJF starts from rather ambitious, global point of view (i.e. from event view):
HEP event: list of particles ( t h d l i t ll t l t )
parts,,2,1, napa K=(partons • hadrons • calorimeter cells • towers • preclusters)
}{recombination matrix jetsp arts}{ nnjaz ×
tn
∑=
=p arts
1
n
aaajj pzq the 4-momentum qj of the j-th jet
expressed by 4-momenta pa of the ti l
lt li t f j t 21j
particles
result: list of jets jets,,2,1, njq j K=
D.Lelas (UVic) Optimal Jet Finder (OJF) in ATLAS 3
Optimal Jet Finder (OJF)Any allowed value of the Recombination Matrix Recombination Matrix {{zzajaj }} from previous slidejj
describes some jet configuration
‘Whole trick’ is to find desired optimal jet configuration by minimizing ‘Whole trick’ is to find desired optimal jet configuration by minimizing
some function some function Ω({Ω({zzajaj})})Definition from Optimal Jet Finder gives (for cylindrical kinematics (pp coll.):p g ( y (pp )
{ }( ) ∑∑ ∑ ⊥⊥⊥ +⎟⎟⎠
⎞⎜⎜⎝
⎛ −+
−=Ω
p artsp art22
2 2sin
2sinh4 n
aa
n njaja
ajaTotaj EzzER
Ezjets ϕϕηη
== =⎟⎠
⎜⎝ 11 1 22 aa jR
∑ ⊥p artsn
aaajEz η ‘width’ of the j-th jet energy outside jets
p artsn ⊥q∑
=
⊥
=≡p arts
1
1n
aaaj
aj
Ezη ( ) ( )2y2x
aaa ppE +≡⊥ ∑=
−≡jets
11
n
jaja zz
( ) ∑=
≡≡p arts
1,
aaajjjj pzEq q ( )jj
j
j ϕϕ sin,cos≡⊥q
q
E is the energy of the a-th particle
0, ≥aja zzEa is the energy of the a th particle
RR>0 is a parameter with a similar meaning as the cone radius>0 is a parameter with a similar meaning as the cone radius
D.Lelas (UVic) Optimal Jet Finder (OJF) in ATLAS 4
OJF features
The authors claim it is based on an optimal jet definition that The authors claim it is based on an optimal jet definition that solves the problem of jet definition in generalsolves the problem of jet definition in general
OJF is infrared and collinear safeOJF is infrared and collinear safeOJF is infrared and collinear safeOJF is infrared and collinear safeno issues intrinsic with seedsno issues intrinsic with seedsno issues with overlapping cones: all jets are “ready to use”no issues with overlapping cones: all jets are “ready to use”
Particle energy can be shared among jetsParticle energy can be shared among jetsHadronization is always an effect of the interaction of at least two hard Hadronization is always an effect of the interaction of at least two hard
t l i i t t j t h d th t i thit l i i t t j t h d th t i thipartons evolving into two jets, so some hadrons that emerge in this partons evolving into two jets, so some hadrons that emerge in this process can belong partially to both jetsprocess can belong partially to both jets
The association between particles and jets is obtained through the The association between particles and jets is obtained through the minimization of a global functionminimization of a global function
global variables are a biglobal variables are a bi--product of this procedureproduct of this procedureglobal variables are a biglobal variables are a bi product of this procedureproduct of this procedure
Particles (or part of them) are allowed to be outside all jetsParticles (or part of them) are allowed to be outside all jetsbut with a penalty in the global function to minimizebut with a penalty in the global function to minimizebut with a penalty in the global function to minimizebut with a penalty in the global function to minimize
D.Lelas (UVic) Optimal Jet Finder (OJF) in ATLAS 5
OJF running modesThe algorithm can be used in two modes:The algorithm can be used in two modes:The algorithm can be used in two modes:The algorithm can be used in two modes:
1) Number of jets fixed1) Number of jets fixed-- Parameter Parameter RR (related to conventional (related to conventional RRconecone) and ) and number number
of initial random of initial random ZZajaj configurationsconfigurations to be specifiedto be specified
2) Number of jets not fixed, obtained by the algorithm2) Number of jets not fixed, obtained by the algorithm2) Number of jets not fixed, obtained by the algorithm2) Number of jets not fixed, obtained by the algorithm-- Parameter Parameter RR, number of initial random , number of initial random ZZaj aj configurations configurations andand
ParameterParameter ωω to be specified:to be specified:-- Parameter Parameter ωωcutcut to be specified:to be specified:
-- related to the jet resolution related to the jet resolution yycutcut of conventional of conventional
bi i l i hbi i l i hrecombination algorithmsrecombination algorithms
-- upper limit to the fractional transverse energy not upper limit to the fractional transverse energy not
associated to any jetsassociated to any jets
D.Lelas (UVic) Optimal Jet Finder (OJF) in ATLAS 6
Fully hadronic t-tbar sample
Sample 5204 (13000 events)Sample 5204 (13000 events)
MC@NLO fully hadronic tMC@NLO fully hadronic t--tbar, full simulation, CSC12 prod.tbar, full simulation, CSC12 prod.
AOD produced using Athena 12.0.6 and OJF private codeAOD produced using Athena 12.0.6 and OJF private code
-- final jet cutfinal jet cut EETT > 7 GeV> 7 GeVfinal jet cut final jet cut EET T > 7 GeV> 7 GeV
-- Jet collections using OJF:Jet collections using OJF:
H1 i ht il bl C H1 i htH1 i ht il bl C H1 i ht→-- no proper H1 weights available: use Cone H1 weightsno proper H1 weights available: use Cone H1 weights
-- number of jets fixed to 6, number of jets fixed to 6, RR parameter set to parameter set to 0.4 or 0.70.4 or 0.7
→
(labels for following plots: (labels for following plots: OJFN6R4_xxxOJFN6R4_xxx and and OJFN6R7_xxxOJFN6R7_xxx, respect.), respect.)
-- number of jets not fixed, number of jets not fixed, RR parameter set to parameter set to 0.70.7 and and ωωcutcutj ,j , pp cutcut
set to set to 0.40.4 (labels for following plots: (labels for following plots: OJFR7W4_xxxOJFR7W4_xxx))
jet collection from MC Truth CaloTowers and TopoClustersjet collection from MC Truth CaloTowers and TopoClusters-- jet collection from MC Truth, CaloTowers and TopoClustersjet collection from MC Truth, CaloTowers and TopoClusters
D.Lelas (UVic) Optimal Jet Finder (OJF) in ATLAS 7
Jet multiplicity: CaloTower jetsWith N_jets > 5 cut
even
ts
c t-t
bar e
hadr
onic
With N_jets > 5 cut
fully
h
OJF jets only OJF jets only
D.Lelas (UVic) Optimal Jet Finder (OJF) in ATLAS 8
Jet-Parton matching (ttbar events)
Matching criteriaMatching criteriaFor each parton, look for a matching jetFor each parton, look for a matching jet
-- restrict search in a region limited by restrict search in a region limited by ΔΔRRmax max < 0.2< 0.2-- keep the closest jet in this regionkeep the closest jet in this region
Demand that a jet be matched only onceDemand that a jet be matched only once-- matched 1 to 1matched 1 to 1-- one can then study matching efficiency one can then study matching efficiency
Study events with “true” jet hypothesis (for ttbar events)Study events with “true” jet hypothesis (for ttbar events)Require all 6 partons to be matched 1 to 1Require all 6 partons to be matched 1 to 1
-- this way we can study the top mass reconstruction this way we can study the top mass reconstruction f f diff t j t l ithf f diff t j t l ithperformance for different jet algorithmsperformance for different jet algorithms
For much more quantitative details on matching eff., For much more quantitative details on matching eff., please see extra slides!!please see extra slides!!
D.Lelas (UVic) Optimal Jet Finder (OJF) in ATLAS 9
Normalized distributions: CaloTower jetsro
nic
| < 3
Jet – Parton
All 1 to 1 matched jets in events with
ully
had
ets
in |η
R < 0.2
at least 6 jets in |eta| < 3
OJF with R = 0.7 is between Cone4 and 7
rton
in fu
east
6 je Similar results for topoJets and truthJets
(extra slides)
1 je
t-par
with
at l
ed 1
to
even
ts w
mat
cht-t
bar
D.Lelas (UVic) Optimal Jet Finder (OJF) in ATLAS 10
Normalized Normalized ηη distributions: OJF towerJetsdistributions: OJF towerJetsev
ents
|η
| < 3
c t-t
bar e
6 je
ts in
hadr
onic
at le
ast 6
fully
hw
ith a
D.Lelas (UVic) Optimal Jet Finder (OJF) in ATLAS 11
Normalized E distributions: towerJetsNormalized E distributions: towerJetsev
ents
|η
| < 3
c t-t
bar e
6 je
ts in
hadr
onic
at le
ast 6
fully
hw
ith a
D.Lelas (UVic) Optimal Jet Finder (OJF) in ATLAS 12
Normalized E distributions: topoJetsNormalized E distributions: topoJetsev
ents
|η
| < 3
c t-t
bar e
6 je
ts in
hadr
onic
at le
ast 6
fully
hw
ith a
D.Lelas (UVic) Optimal Jet Finder (OJF) in ATLAS 13
Normalized pNormalized pTT distributions: towerJetsdistributions: towerJetsev
ents
|η
| < 3
c t-t
bar e
6 je
ts in
hadr
onic
at le
ast 6
fully
hw
ith a
D.Lelas (UVic) Optimal Jet Finder (OJF) in ATLAS 14
Normalized pNormalized pTT distributions: OJF towerJetsdistributions: OJF towerJetsev
ents
|η
| < 3
c t-t
bar e
6 je
ts in
hadr
onic
at le
ast 6
fully
hw
ith a
D.Lelas (UVic) Optimal Jet Finder (OJF) in ATLAS 15
Normalized pNormalized pTT distributions: topoJetsdistributions: topoJetsev
ents
|η
| < 3
c t-t
bar e
6 je
ts in
hadr
onic
at le
ast 6
fully
hw
ith a
D.Lelas (UVic) Optimal Jet Finder (OJF) in ATLAS 16
Normalized pNormalized pTT distributions: OJF topoJetsdistributions: OJF topoJetsev
ents
|η
| < 3
c t-t
bar e
6 je
ts in
hadr
onic
at le
ast 6
fully
hw
ith a
D.Lelas (UVic) Optimal Jet Finder (OJF) in ATLAS 17
W mass plots: TowerJetsW mass plots: TowerJetst 6
jets
1
%6.15=⟩⟨M
RMS %6.16=⟩⟨M
RMS
at le
ast
hed
1 to
nt
s w
ith
ns m
atch
tbar
eve
ll pa
rton
RMS RMSroni
c t-t
3w
ith a
%0.18=⟩⟨M
RMS %1.17=⟩⟨M
RMS
fully
had
n |η
| < 3
D.Lelas (UVic) Optimal Jet Finder (OJF) in ATLAS 18
f in
W mass plots: OJF TowerJetsW mass plots: OJF TowerJetst 6
jets
1
%2.16=⟩⟨M
RMS %3.15=⟩⟨M
RMSat le
ast
hed
1 to
⟩⟨M ⟩⟨
nts
with
ns
mat
chtb
ar e
vell
parto
nro
nic
t-t3
with
afu
lly h
adn
|η| <
3 %5.15=⟩⟨M
RMS
f in
D.Lelas (UVic) Optimal Jet Finder (OJF) in ATLAS 19
top mass plots: TopoJetstop mass plots: TopoJetst 6
jets
1
%9.12=⟩⟨M
RMS %3.12=⟩⟨M
RMSat le
ast
hed
1 to
⟩⟨ ⟩⟨
nts
with
ns
mat
chtb
ar e
vell
parto
n
RMSroni
c t-t
3w
ith a
RMS%5.14=⟩⟨M
RMS
fully
had
n |η
| < 3 %2.13=
⟩⟨MRMS
f in
D.Lelas (UVic) Optimal Jet Finder (OJF) in ATLAS 20
top mass plots: OJF TopoJetstop mass plots: OJF TopoJetst 6
jets
1
%0.12=⟩⟨M
RMS %5.11=⟩⟨M
RMSat le
ast
hed
1 to
⟩⟨ ⟩⟨
nts
with
ns
mat
chtb
ar e
vell
parto
nro
nic
t-t3
with
afu
lly h
adn
|η| <
3 %5.13=⟩⟨M
RMS
f in
D.Lelas (UVic) Optimal Jet Finder (OJF) in ATLAS 21
Conclusions and OutlookThere is additional jet finder on the market, Optimal Jet Finder (OJF)j p ( )
The authors claim it is based on an optimal jet definition that solves the problem
of jet definition in general OJF is based on global event propertiesof jet definition in general. OJF is based on global event properties
It is infra-red and collinear safe and there are no seed-related problems nor
overlapping jets. It could provide more event info than standard jet algorithms
Official implementation in Athena is very much in progressp y p g
First tests with OJF in the ATLAS environment are quite encouraging.
A t ith Kt d C i d b t ld lik t d b tt ☺Agreement with Kt and Cone is good, but we would like to do better ☺
More systematic tests in progress:- fine tuning of OJF parameter spaceg p p- studies with different physical samples
Years of testing for Cone and Kt, OJF deserves a bit of attention as well
Hopefully, this is just a beginning of long and interesting journey…
D.Lelas (UVic) Optimal Jet Finder (OJF) in ATLAS 22
Extra Slides
Normalized distributions: Topo jetsni
c t-
3Jet – Parton
All 1 to 1 matched jets in events with
y ha
dron
n |η
| < 3
R < 0.2
at least 6 jets in |eta| < 3
OJF with R = 0.7 is between Cone4 and
on in
fully
t 6 je
ts in Cone7
jet-p
arto
at l
east
d 1
to 1
jnt
s w
ithm
atch
edtb
ar e
vem t
Normalized distributions: truthJetsNormalized distributions: truthJetson
ic
< 3
lly h
adro
ts in
|η| <
ton
in fu
ast 6
jet
jet-p
art
with
at l
eaed
1 to
1ev
ents
wm
atch
et-t
bar e
Event and Jet hypothesis selection
Try to compare jet matching efficiency for Try to compare jet matching efficiency for different jetdifferent jet algorithmsalgorithms
Fully hadronic tFully hadronic t--tbar, choice of sampletbar, choice of sampleRequire at least 6 jets in |Require at least 6 jets in |ηη| < 3| < 3Require at least 6 jets in |Require at least 6 jets in |ηη| 3| 3-- clearly, algorithms with more than 6 jets will have a betterclearly, algorithms with more than 6 jets will have a better
chance at 6 jetchance at 6 jet--parton matchingparton matchingonly consider the 6 highest pT jetsonly consider the 6 highest pT jets-- algos with more than 6 jets still have a better chance at algos with more than 6 jets still have a better chance at
6 jet6 jet--parton matchingparton matchingonly consider events with exactly 6 jetsonly consider events with exactly 6 jets
h f i t 6 j th f i t 6 j t t t hit t hi-- perhaps a more fair way to compare 6 jetperhaps a more fair way to compare 6 jet--parton matching parton matching for OJF with fixed number of jets (= 6)for OJF with fixed number of jets (= 6)
No jet ENo jet E cuts appliedcuts appliedNo jet ENo jet ETT cuts appliedcuts applied
Jet-Parton matching: CaloTower jets
It is very difficult to come up with totally fair matching-fair matchingefficiency comparisons. But, even without tuning, OJF ltOJF results are already comparable to Cone and Kt
Jet-Parton matching: topoJets
It is very difficult to ith t t llcome up with totally
fair matching-efficiency comparisons. But, p ,even without tuning, OJF results are already comparable to C d KtCone and Kt
Normalized pNormalized pTT distributions: truthJetsdistributions: truthJetsev
ents
|η
| < 3
c t-t
bar e
6 je
ts in
hadr
onic
at le
ast 6
fully
hw
ith a
Normalized pNormalized pTT distributions: OJF truthJetsdistributions: OJF truthJetsev
ents
|η
| < 3
c t-t
bar e
6 je
ts in
hadr
onic
at le
ast 6
fully
hw
ith a
Normalized E distributions: OJF towerJetsNormalized E distributions: OJF towerJetsev
ents
|η
| < 3
c t-t
bar e
6 je
ts in
hadr
onic
at le
ast 6
fully
hw
ith a
Normalized E distributions: OJF topoJetsNormalized E distributions: OJF topoJetsev
ents
n
|η| <
3c
t-tba
r 6
jets
i nha
dron
iat
leas
t fu
lly
with
Normalized E distributions: truthJetsNormalized E distributions: truthJetsev
ents
|η
| < 3
c t-t
bar e
6 je
ts in
hadr
onic
at le
ast 6
fully
hw
ith a
W mass plots: TopoJetsW mass plots: TopoJetst 6
jets
1
at le
ast
hed
1 to
nt
s w
ith
ns m
atch
tbar
eve
ll pa
rton
roni
c t-t
3w
ith a
fully
had
n |η
| < 3
f in
W mass plots: OJF TopoJetsW mass plots: OJF TopoJetst 6
jets
1
at le
ast
hed
1 to
nt
s w
ith
ns m
atch
tbar
eve
ll pa
rton
roni
c t-t
3w
ith a
fully
had
n |η
| < 3
f in
top mass plots: TowerJetstop mass plots: TowerJetst 6
jets
1
at le
ast
hed
1 to
nt
s w
ith
ns m
atch
tbar
eve
ll pa
rton
roni
c t-t
3w
ith a
fully
had
n |η
| < 3
f in
top mass plots: OJF TowerJetstop mass plots: OJF TowerJetst 6
jets
1
at le
ast
hed
1 to
nt
s w
ith
ns m
atch
tbar
eve
ll pa
rton
roni
c t-t
3w
ith a
fully
had
n |η
| < 3
f in