matching of w+jets in madevent and pythia · madgraph / madevent conclusions jet matching – me...

MatchingME+Pythia

Johan Alwall

Jet matching –ME vs. PS

Jet matchingschemes

Matching inMadGraph /MadEvent

Conclusions

Matching of W +jets in

MadEvent and Pythia

Johan Alwall

SLAC

Berkeley workshop on Boson + jets productionMarch 27, 2008

1 / 19

MatchingME+Pythia

Johan Alwall


Jet matchingschemes


Conclusions

Outline

1 Jet matching – ME vs. PS

2 Jet matching schemes

3 Matching in MadGraph / MadEvent

4 Conclusions

2 / 19

MatchingME+Pythia

Johan Alwall


Jet matchingschemes


Conclusions

Jet matching – ME vs. PS

Matrix elements

1 Fixed order calculation

2 Computationally expensive

3 Limited number ofparticles

4 Valid when partons arehard and well separated

5 Quantum interferencecorrect

6 Needed for multi-jetdescription

Parton showers

1 Resums logs to all orders

2 Computationally cheap

3 No limit on particlemultiplicity

4 Valid when partons arecollinear and/or soft

5 Partial quantum interferencethrough angular ordering

6 Needed for hadronization/detector simulation

Matrix element and Parton showers complementary approaches

Both necessary in high-precision studies of multijet processes

Need to combine without double-counting

3 / 19

MatchingME+Pythia

Johan Alwall


Jet matchingschemes

CKKW matching

MLM matching

Differencesbetween CKKWand MLM


Conclusions

Jet matching schemesThe simple idea behind matching

Use matrix element description for well separated jets,and parton showers for collinear jets

Phase-space cutoff to separate regions

=⇒ No double-counting between jet multiplicities

Difficulties

Get smooth transition between regions

No/small dependence from precise cutoff

No/small dependence from largest multiplicity sample

How to accomplish this

CKKW matching (Catani, Krauss, Kuhn, Webber)

MLM matching (M.L. Mangano)

(Interesting newcomers: SCET Schwartz,GenEvA Bauer, Tackman, Thaler)

4 / 19

MatchingME+Pythia

Johan Alwall


Jet matchingschemes

CKKW matching

MLM matching



Conclusions

CKKW matchingCatani, Krauss, Kuhn, Webber [hep-ph/0109231], Krauss [hep-ph/0205283]

Imitate parton shower procedure for matrix elements

1 Choose a cutoff scale dini

2 Generate multiparton event with dmin = dini

3 Cluster event with kT algorithm to find “parton showerhistory”

4 Use di ' k2T in each vertex as scale for αs

5 Weight event with Sudakov factor ∆(di , dj) for each partonline between vertices i and j

6 Shower event, allowing only emissions with kT < dini (“vetoedshower”)

7 For highest multiplicity sample, use min(di ) of event as dini

Boost-invariant kT measure:{diB = p2

T ,i

dij = min(p2T ,i , p

2T ,j)R(i , j)

R(i , j) = cosh∆ηij − cos ∆φij

5 / 19

MatchingME+Pythia

Johan Alwall


Jet matchingschemes

CKKW matching

MLM matching



Conclusions

Final-state showers:Combination of NLL Sudakov factors and vetoed NLLshowers guarantees independence of qini to NLL order

Initial-state showers: Proof by example (Sherpa)

Problem in practice: No NLL shower implementation!(Sherpa uses Pythia-like showers and adapted Sudakovs)

/GeV)1log(Q-0.5 0 0.5 1 1.5 2 2.5 3

/GeV

)1

/dlo

g(Q

σ dσ

1/

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SHERPA

=100 GeVcutQ

/GeV)1log(Q-0.5 0 0.5 1 1.5 2 2.5 3

/GeV

)1

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σ dσ

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SHERPA

=30 GeVcutQ

/GeV)1log(Q-0.5 0 0.5 1 1.5 2 2.5 3

/GeV

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SHERPA

=15 GeVcutQ

Differential 0→ 1 jet rate by Sherpa in pp → Z + jets for three

different dini, compared to averaged reference curve

[hep-ph/0503280]

6 / 19

MatchingME+Pythia

Johan Alwall


Jet matchingschemes

CKKW matching

MLM matching



Conclusions

MLM matchingM.L. Mangano [2002, Alpgen home page, arXiv:0706.2569]

Use shower hardness to separate ME/PS

1 Generate multiparton event with cut on jet pT , η and ∆R2 Cluster event and use k2

T for αs scale (as in CKKW)3 Shower event (using Pythia or Herwig)4 Collect showered partons in cone jets with

pT ,min(jet) > pT ,min(parton)5 Keep event only if each jet corresponds to one parton

(“matched”)6 For highest multiplicity sample, allow extra jets with

pT < ppartonTmin

Keep Discard unless highest multiplicity7 / 19

MatchingME+Pythia

Johan Alwall


Jet matchingschemes

CKKW matching

MLM matching



Conclusions

Differences between CKKW and MLM

CKKW scheme: Assumes intimate knowledge of andmodifications to parton shower. Needs analytical form forparton shower Sudakovs.

MLM scheme: Effective Sudakov suppression directly fromparton shower

However: MLM not sensitive to parton types of internal lines(remedied by pseudoshower approach, see below)

Factorization scale: In CKKW jet resolution scale, in MLMcentral scale.

Highest multiplicity treatment – less obvious in MLM than inCKKW

MLM only for hadronic collisions (so far)

CKKW with pseudoshowers

Lonnblad [hep-ph/0112284] (ARIADNE)Mrenna, Richardsson [hep-ph/0312274]

Apply parton shower stepwize to clustered event, reject eventif too hard emission

Apply vetoed parton shower as in the CKKW approach

8 / 19

MatchingME+Pythia

Johan Alwall


Jet matchingschemes


Old and NewPythia showers

Impact of showeron matching

pT (W ) at theTevatron

Summary ofshower impact

“Shower kT ”scheme

Conclusions

Matching in MadGraph / MadEventJ.A. et al. [arXiv:0706.2569], [work in progress](cf. Mrenna, Richardsson [hep-ph/0312274])

CKKW scheme (for Sherpa showers) (with S. Hoche)

MLM scheme (Pythia showers, old & new)

MLM scheme with kT jets (Pythia showers, old & new)

“Shower kT scheme” (Pythia showers, new)

Details of MadEvent kT MLM scheme

1 Generate multiparton event with kT clustering cutoff dcut

2 Cluster event and use kT for αs scale3 Shower event with Pythia4 Perform jet clustering with kT algorithm, dmin(jet) > dcut

5 Match clustered jets to partons (d(jet, parton) < dmin(jet))6 Discard events where jets not matched7 For highest multiplicity sample, jets matched if

d(jet, parton) < dmin(parton, parton)

9 / 19

MatchingME+Pythia

Johan Alwall


Jet matchingschemes







Conclusions

Advantages with kT jets

Immediate comparison with CKKW scheme

One matching parameter (d jetmin vs. pT ,min,∆Rmin)

Easy to check smoothness by plotting jet rates djet

Allows to use “shower kT scheme”

Allows straightforward investigations of parton showers

Shower kT scheme

Keep/reject event based on kT of hardest shower emission (asreported by Pythia)

Highest multiplicity treatment as in CKKW, use min dparton ascutoff

No jet clustering

No need of “fiducial region”, can use kmatchT = dME

cut

Need similar kT definitions in ME and PS (only “new”,pT -ordered showers at present)

10 / 19

MatchingME+Pythia

Johan Alwall


Jet matchingschemes







Conclusions

Old and New Pythia showers

Parton showers

Sudakov form factors for non-branching probability betweenscales

∆(t1, t2) = exp

{−

∫ t1

t2

dt ′

t ′

∫ 1−ε(t)

ε(t)

dzαs(t)

2πP(z)

}

QCD splitting functions Pa→b(z) for z distribution

Different choice of evolution variable t=⇒ “Old” Pythia showers: Q2

=⇒ “New” Pythia showers: p2T (dipole-inspired)

Can give quite different distributions

11 / 19

MatchingME+Pythia

Johan Alwall


Jet matchingschemes







Conclusions

Comparisons between old and new Pythia showers

Differential jet rates in W production at the Tevatron

) (GeV)2

log(Qpar0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 2.2

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Unmatched "new" Pythia

Unmatched "old" Pythia

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1Unmatched "new" Pythia

Unmatched "old" Pythia

0→ 1 jet rate 1→ 2 jet rate

pT (W ) in W production at the Tevatron

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pb

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Unmatched "new" PythiaUnmatched "old" Pythia

D0 data

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pb

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Unmatched "new" PythiaUnmatched "old" Pythia

D0 data

12 / 19

MatchingME+Pythia

Johan Alwall


Jet matchingschemes







Conclusions

Impact of shower on matching

) (GeV)1

log(Qpar0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 2.2

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Matched sum, new showers0-jet sample1-jet2-jet3-jet4-jetMatched sum, old showers

) (GeV)1

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Matched sum, old showers0-jet sample

1-jet

2-jet

3-jet

4-jet

“Old” Pythia showers “New” Pythia showers

dmatch (13 GeV) dmatch (13 GeV)

) (GeV)1

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Matched sum, new showers0-jet sample1-jet2-jet3-jet4-jet

=13 GeVmatch

Matched sum, d

Matched sum, old showers

) (GeV)1

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Sum of contributions0-jet sample1-jet2-jet3-jet4-jet

=13 GeVmatch

Matched sum, d


Differential 0→ 1 jet rate

13 / 19

MatchingME+Pythia

Johan Alwall


Jet matchingschemes







Conclusions

) (GeV)2

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Matched sum, new showers0-jet sample1-jet2-jet3-jet4-jetMatched sum, old showers

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Matched sum, old showers0-jet sample

1-jet

2-jet

3-jet

4-jet



) (GeV)2

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Matched sum, new showers0-jet sample1-jet2-jet3-jet

=13 GeVmatch

Matched sum, d

Matched sum, old showers

) (GeV)2

log(Qpar0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 2.2

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Matched sum, old shower0-jet sample1-jet2-jet3-jet4-jet

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Matched sum, d


Differential 1→ 2 jet rate

Old showers smooth for small cutoff, new showers for larger cutoff14 / 19

MatchingME+Pythia

Johan Alwall


Jet matchingschemes







Conclusions

pT (W ) at the Tevatron

(W), GeV/cTP0 5 10 15 20 25 30 35 40 45 50

pb

/GeV

10

210

Matched sum, new showers0-jet sample1-jet2-jet3-jet4-jetD0 data

(W), GeVTP0 5 10 15 20 25 30 35 40 45 50

pb

/GeV

10

210

Matched sum, old showers0-jet sample1-jet2-jet3-jet4-jetD0 data



(W), GeV/cTP0 20 40 60 80 100 120 140 160 180 200

pb

/GeV

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Matched sum, new showers0-jet sample1-jet2-jet3-jet4-jetMatched sum, old showersD0 data

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pb

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Matched sum, old showers0-jet sample1-jet2-jet3-jet4-jetD0 data


Tail well described by both, but head best by new shower

15 / 19

MatchingME+Pythia

Johan Alwall


Jet matchingschemes







Conclusions

Summary of shower impact

Different showers have very different behavior, especiallyat low kT

Strongly affects matching – need different treatment(e.g. cutoffs) for different showers

Matching stabilizes tail, but overall normalization isstrongly affected by shower=⇒ Normalizing overall cross section to e.g. NLO valuedangerous

New pythia showers seem to agree better with W/Z data– but why “step” in matching for low kT ?

16 / 19

MatchingME+Pythia

Johan Alwall


Jet matchingschemes







Conclusions

“Shower kT” schemeComparison with kT and cone jet MLM schemes

) (GeV)4

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Matched sum shower kT0-jet sample1-jet2-jet3-jet4-jetMLM kT jetsMLM cone jets

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Excellent agreement with MLM methods17 / 19

MatchingME+Pythia

Johan Alwall


Jet matchingschemes







Conclusions

Dependence on highest multiplicity

) (GeV)4

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Max mult 2j0-jet sample1-jet2-jetMax mult 4j Max mult 1j

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Surprisingly small dependence with “shower kT” method!

18 / 19

MatchingME+Pythia

Johan Alwall


Jet matchingschemes


Conclusions

Conclusions

Have presented investigation of shower and schemedependence for the benchmark case W + jets

Shower dependence larger than expected (should beequivalent for small kT !?)

Needs care with e.g. cutoff values (pT or kT )

New “shower kT” scheme presented, with many niceproperties:−→ More efficient than standard MLM since no “fiducialregion” needed−→ Agrees with MLM schemes−→ Remarkable insensitivity to highest multiplicitysample−→ No need for special treatment of e.g. top or b quarks

So far only for “new” Pythia shower

19 / 19

matching of w+jets in madevent and pythia · madgraph / madevent conclusions jet matching – me...

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