The inner working of event generators . . . simulation: divide et - - PowerPoint PPT Presentation

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The inner working of event generators . . . simulation: divide et - - PowerPoint PPT Presentation

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Motivation Ingredients NLO improvements M E P S @L O M E P S @N LO Conclusion Precision simulations in S HERPA Frank Krauss Institute for Particle Physics Phenomenology Durham University HP 2 , GGI Florence, 3.9.2014 F. Krauss IPPP Precision

slide-1
SLIDE 1

Motivation Ingredients NLO improvements MEPS@LO MEPS@NLO Conclusion

Precision simulations in SHERPA

Frank Krauss

Institute for Particle Physics Phenomenology Durham University

HP2, GGI Florence, 3.9.2014

  • F. Krauss

IPPP Precision simulations in SHERPA

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SLIDE 2

Motivation Ingredients NLO improvements MEPS@LO MEPS@NLO Conclusion

The inner working of event generators . . .

simulation: divide et impera hard process: fixed order perturbation theory

traditionally: Born-approximation

bremsstrahlung: resummed perturbation theory hadronisation: phenomenological models hadron decays: effective theories, data ”underlying event”: phenomenological models

  • F. Krauss

IPPP Precision simulations in SHERPA

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SLIDE 3

Motivation Ingredients NLO improvements MEPS@LO MEPS@NLO Conclusion

. . . and possible improvements

possible strategies: improving the phenomenological models:

“tuning” (fitting parameters to data) replacing by better models, based on more physics

(my hot candidate: “minimum bias” and “underlying event” simulation)

  • improving the perturbative description:

inclusion of higher order exact matrix elements and correct connection to resummation in the parton shower: “NLO-Matching” & “Multijet-Merging” systematic improvement of the parton shower: next-to leading (or higher) logs & colours

  • F. Krauss

IPPP Precision simulations in SHERPA

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SLIDE 4

Motivation Ingredients NLO improvements MEPS@LO MEPS@NLO Conclusion

Reminder: Ingredients of simulations at NLO

  • F. Krauss

IPPP Precision simulations in SHERPA

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SLIDE 5

Motivation Ingredients NLO improvements MEPS@LO MEPS@NLO Conclusion

Structure of an NLO calculation

sketch of cross section calculation dσ(NLO)

N

= dΦNBN

Born approximation

+ dΦNVN

renormalised virtual correction IR-divergent

+ dΦN+1RN+1

  • real correction

IR-divergent

= dΦN

  • BN + VN + BN ⊗ S
  • + dΦN+1
  • RN+1 − BN ⊗ dS
  • subtraction terms S (integrated) and dS:

exactly cancel IR divergence in R – process-independent structures result: terms in both brackets separately infrared finite in SHERPA: virtual parts provided by some great other codes: BLACKHAT, GOSAM, NJET, OPENLOOPS, . . . + some process-specific codes interfaced

  • F. Krauss

IPPP Precision simulations in SHERPA

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SLIDE 6

Motivation Ingredients NLO improvements MEPS@LO MEPS@NLO Conclusion

Probabilistic treatment of emissions

Sudakov form factor (no-emission probability) ∆ij,k(t, t0) = exp

  • −

t

  • t0

dΓij,k(t)

  • decay width for parton i(j) → ik(j)

(spectator j for 4-momentum conservation)

dΓij,k(t) = dt′ t′ αS(k2

⊥)

2π

z+(t′/t)

  • z−(t′/t)

dz dφ 2π Kij,k(t, z, φ)

  • splitting kernel

comparison with QT-resummation: Sudakov form factor contains terms A1, A2 and B1

(up to some power-like corrections ∝ t′/t

evolution parameter t′ defined by kinematics:

  • gen. angle (HERWIG++) or transverse momentum (PYTHIA, SHERPA)
  • F. Krauss

IPPP Precision simulations in SHERPA

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SLIDE 7

Motivation Ingredients NLO improvements MEPS@LO MEPS@NLO Conclusion

Emissions off a Born matrix element

“compound” splitting kernels Kn and Sudakov form factors ∆(K)

n

for emission off n-particle final state: Kn(Φ1) = αS 2π

  • all {ij,k}

Kij,k(Φij,k) , ∆(K)

n

(t, t0) = exp

  • −

t

  • t0

dΦ1 Kn(Φ1)

  • consider first emission only off Born configuration (N ext. particles)

dσB = dΦN BN(ΦN) ·

  • ∆(K)

N (µ2 N, t0) + µ2

N

  • t0

dΦ1

  • KN(Φ1)∆(K)

N (µ2 N, t(Φ1))

  • integrates to unity −

→ “unitarity” of parton shower

further emissions by recursion with µ2

N −

→ t of previous emission

  • F. Krauss

IPPP Precision simulations in SHERPA

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SLIDE 8

Motivation Ingredients NLO improvements MEPS@LO MEPS@NLO Conclusion

NLO improvements: Matching & Merging

  • F. Krauss

IPPP Precision simulations in SHERPA

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SLIDE 9

Motivation Ingredients NLO improvements MEPS@LO MEPS@NLO Conclusion

NLO matching: Basic idea

parton shower resums logarithms fair description of collinear/soft emissions jet evolution

(where the logs are large)

matrix elements exact at given order fair description of hard/large-angle emissions jet production

(where the logs are small)

adjust (“match”) terms:

cross section at NLO accuracy correct hardest emission in PS to exactly reproduce ME at order αS (R-part of the NLO calculation)

resummed in PS exact ME LO 5jet, but also NLO 4jet

L αn

m NLL exact ME LO 4jet

4 4 4 4 4 5 5 5 5 5 5 5

  • F. Krauss

IPPP Precision simulations in SHERPA

slide-10
SLIDE 10

Motivation Ingredients NLO improvements MEPS@LO MEPS@NLO Conclusion

Matching a la MC@NLO

(S. Frixione & B. Webber, JHEP 0602 (2002) 029) (S. Hoeche, F. Krauss, M. Schoenherr, & F. Siegert, JHEP 1209 (2012) 049)

divide RN in soft (“S”) and hard (“H”) part: RN = R(S)

N

+ R(H)

N

= BN ⊗ dS1 + HN identify subtraction terms and shower kernels dS1 ≡

{ij,k}

Kij,k ≡ KN

(modify K in 1st emission to account for colour)

dσN = dΦN

  • BN + VN + BN ⊗ S1
  • ⊗
  • ∆(K)

N (µ2 N, t0) + µ2

N

  • t0

dΦ1 KN(Φ1) ∆(K)

N (µ2 N, k2 ⊥)

  • +dΦN+1 HN
  • F. Krauss

IPPP Precision simulations in SHERPA

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SLIDE 11

Motivation Ingredients NLO improvements MEPS@LO MEPS@NLO Conclusion

Aside: impact of full colour

(S. Hoeche, J. Huang, G. Luisoni, M. Schoenherr, & J. Winter, arXiv:1306.2703 [hep-ph])

effect of full colour treatment in Sudakov form factor, MC@NLO without H-part vs. parton shower with B − → ˜ B take t¯ t production (red = full colour, blue = “PS” colours)

  • F. Krauss

IPPP Precision simulations in SHERPA

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SLIDE 12

Motivation Ingredients NLO improvements MEPS@LO MEPS@NLO Conclusion

Multijet merging @ leading order

  • F. Krauss

IPPP Precision simulations in SHERPA

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SLIDE 13

Motivation Ingredients NLO improvements MEPS@LO MEPS@NLO Conclusion

Multijet merging: basic idea

(S. Catani, F. Krauss, R. Kuhn, B. Webber, JHEP 0111 (2001) 063,

  • L. Lonnblad, JHEP 0205 (2002) 046, & F. Krauss, JHEP 0208 (2002) 015)

parton shower resums logarithms fair description of collinear/soft emissions jet evolution

(where the logs are large)

matrix elements exact at given order fair description of hard/large-angle emissions jet production

(where the logs are small)

combine (“merge”) both: result: “towers” of MEs with increasing number of jets evolved with PS

multijet cross sections at Born accuracy maintain (N)LL accuracy of parton shower

resummed in PS exact ME LO 5jet, but also NLO 4jet

L αn

m NLL exact ME LO 4jet

4 4 4 4 4 5 5 5 5 5 5 5

  • F. Krauss

IPPP Precision simulations in SHERPA

slide-14
SLIDE 14

Motivation Ingredients NLO improvements MEPS@LO MEPS@NLO Conclusion

Separating jet evolution and jet production

separate regions of jet production and jet evolution with jet measure QJ

(“truncated showering” if not identical with evolution parameter)

matrix elements populate hard regime parton showers populate soft domain

/ GeV

W

p 20 40 60 80 100 120 140 160 180 200 [ pb/GeV ]

W

/dp σ d

  • 2

10

  • 1

10 1 10

2

10 SHERPA

W

p W + 0jet W + 1jet W + 2jet W + 3jet W + 4jet D0 Data

  • F. Krauss

IPPP Precision simulations in SHERPA

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SLIDE 15

Motivation Ingredients NLO improvements MEPS@LO MEPS@NLO Conclusion

First emission(s), again

(S. Hoeche, F. Krauss, S. Schumann, F. Siegert, JHEP 0905 (2009) 053)

dσ = dΦN BN

  • ∆(K)

N (µ2 N, t0) + µ2

N

  • t0

dΦ1 KN∆(K)

N (µ2 N, tN+1)Θ(QJ − QN+1)

  • +dΦN+1 BN+1 ∆(K)

N (µ2 N+1, tN+1)Θ(QN+1 − QJ)

note: N + 1-contribution includes also N + 2, N + 3, . . .

(no Sudakov suppression below tn+1, see further slides for iterated expression)

potential occurence of different shower start scales: µN,N+1,... “unitarity violation” in square bracket: BNKN − → BN+1

(cured with UMEPS formalism, L. Lonnblad & S. Prestel, JHEP 1302 (2013) 094 &

  • S. Platzer, arXiv:1211.5467 [hep-ph] & arXiv:1307.0774 [hep-ph])
  • F. Krauss

IPPP Precision simulations in SHERPA

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SLIDE 16

Motivation Ingredients NLO improvements MEPS@LO MEPS@NLO Conclusion

Di-photons @ ATLAS: mγγ, p⊥,γγ, and ∆φγγ in showers

(arXiv:1211.1913 [hep-ex])

[pb/GeV]

γ γ

/dm σ d

  • 4

10

  • 3

10

  • 2

10

  • 1

10 1

  • 1

Ldt = 4.9 fb

∫

Data 2011, 1.2 (MRST2007) × PYTHIA MC11c 1.2 (CTEQ6L1) × SHERPA MC11c

ATLAS

= 7 TeV s

data/SHERPA 0.5 1 1.5 2 2.5 3 [GeV]

γ γ

m 100 200 300 400 500 600 700 800 data/PYTHIA 0.5 1 1.5 2 2.5 3 [pb/GeV]

γ γ T,

/dp σ d

  • 5

10

  • 4

10

  • 3

10

  • 2

10

  • 1

10 1 10

  • 1

Ldt = 4.9 fb

∫

Data 2011, 1.3 (MRST2007) × PYTHIA MC11c 1.3 (CTEQ6L1) × SHERPA MC11c

ATLAS

= 7 TeV s

data/SHERPA 0.5 1 1.5 2 2.5 3 [GeV]

γ γ T,

p 50 100 150 200 250 300 350 400 450 500 data/PYTHIA 0.5 1 1.5 2 2.5 3 [pb/rad]

γ γ

φ ∆ /d σ d 1 10

2

10

  • 1

Ldt = 4.9 fb

∫

Data 2011, 1.2 (MRST2007) × PYTHIA MC11c 1.2 (CTEQ6L1) × SHERPA MC11c

ATLAS

= 7 TeV s

data/SHERPA 0.5 1 1.5 2 2.5 3 [rad]

γ γ

φ ∆ 0.5 1 1.5 2 2.5 3 data/PYTHIA 0.5 1 1.5 2 2.5 3

  • F. Krauss

IPPP Precision simulations in SHERPA

slide-17
SLIDE 17

Motivation Ingredients NLO improvements MEPS@LO MEPS@NLO Conclusion

Aside: Comparison with higher order calculations

(arXiv:1211.1913 [hep-ex])

[pb/GeV]

γ γ

/dm σ d

  • 4

10

  • 3

10

  • 2

10

  • 1

10 1

  • 1

Ldt = 4.9 fb

∫

Data 2011, DIPHOX+GAMMA2MC (CT10) NNLO (MSTW2008) γ 2

ATLAS

= 7 TeV s

data/DIPHOX 0.5 1 1.5 2 2.5 3 [GeV]

γ γ

m 100 200 300 400 500 600 700 800 NNLO γ data/2 0.5 1 1.5 2 2.5 3 [pb/GeV]

γ γ T,

/dp σ d

  • 5

10

  • 4

10

  • 3

10

  • 2

10

  • 1

10 1 10

  • 1

Ldt = 4.9 fb

∫

Data 2011, DIPHOX+GAMMA2MC (CT10) NNLO (MSTW2008) γ 2

ATLAS

= 7 TeV s

data/DIPHOX 0.5 1 1.5 2 2.5 3 [GeV]

γ γ T,

p 50 100 150 200 250 300 350 400 450 500 NNLO γ data/2 0.5 1 1.5 2 2.5 3 [pb/rad]

γ γ

φ ∆ /d σ d 1 10

2

10

  • 1

Ldt = 4.9 fb

∫

Data 2011, DIPHOX+GAMMA2MC (CT10) NNLO (MSTW2008) γ 2

ATLAS

= 7 TeV s

data/DIPHOX 0.5 1 1.5 2 2.5 3 [rad]

γ γ

φ ∆ 0.5 1 1.5 2 2.5 3 NNLO γ data/2 0.5 1 1.5 2 2.5 3

  • F. Krauss

IPPP Precision simulations in SHERPA

slide-18
SLIDE 18

Motivation Ingredients NLO improvements MEPS@LO MEPS@NLO Conclusion

Multijet merging @ next-to leading order

  • F. Krauss

IPPP Precision simulations in SHERPA

slide-19
SLIDE 19

Motivation Ingredients NLO improvements MEPS@LO MEPS@NLO Conclusion

Multijet-merging at NLO: MEPS@NLO

(arXiv: 1207.5030, 1207.5031 [hep-ph])

basic idea like at LO: towers of MEs with increasing jet multi (but this time at NLO) combine them into one sample, remove overlap/double-counting maintain NLO and LL accuracy of ME and PS this effectively translates into a merging of MC@NLO simulations and can be further supplemented with LO simulations for even higher final state multiplicities

  • F. Krauss

IPPP Precision simulations in SHERPA

slide-20
SLIDE 20

Motivation Ingredients NLO improvements MEPS@LO MEPS@NLO Conclusion

First emission(s), once more

dσ = dΦN ˜ BN

  • ∆(K)

N (µ2 N, t0) + µ2

N

  • t0

dΦ1 KN∆(K)

N (µ2 N, tN+1)Θ(QJ − QN+1)

  • +dΦN+1 HN∆(K)

N (µ2 N, tN+1)Θ(QJ − QN+1)

+dΦN+1 ˜ BN+1

  • 1 + BN+1

˜ BN+1

µ2

N

  • tN+1

dΦ1 KN

  • Θ(QN+1 − QJ)

·∆(K)

N (µ2 N, tN+1) ·

  • ∆(K)

N+1(tN+1, t0) + tN+1

  • t0

dΦ1 KN+1∆(K)

N+1(tN+1, tN+2)

  • +dΦN+2 HN+1∆(K)

N (µ2 N, tN+1)∆(K) N+1(tN+1, tN+2)Θ(QN+1 − QJ) + . . .

  • F. Krauss

IPPP Precision simulations in SHERPA

slide-21
SLIDE 21

Motivation Ingredients NLO improvements MEPS@LO MEPS@NLO Conclusion

MEPS@NLO: validation in W +jets

(S. Hoeche, F. Krauss, M. Schoenherr & F. Siegert, JHEP 1304 (2013) 027) pjet

⊥ > 30 GeV

pjet

⊥ > 20 GeV

(×10)

b b b b b b b b b b b b

ATLAS data MePs@Nlo MePs@Nlo µ/2 . . . 2µ MEnloPS MEnloPS µ/2 . . . 2µ Mc@Nlo 1 2 3 4 5 10 1 10 2 10 3 10 4 Inclusive Jet Multiplicity Njet σ(W + ≥ Njet jets) [pb]

pjet

⊥ > 20 GeV

b b b b b

0.15 0.2 0.25 0.3 σ(≥ Njet jets)/σ(≥ Njet − 1 jets) pjet

⊥ > 30 GeV

b b b b b

1 2 3 4 5 0.1 0.15 0.2 0.25 Njet σ(≥ Njet jets)/σ(≥ Njet − 1 jets)

  • F. Krauss

IPPP Precision simulations in SHERPA

slide-22
SLIDE 22

Motivation Ingredients NLO improvements MEPS@LO MEPS@NLO Conclusion

W+ ≥ 1 jet (×1) W+ ≥ 2 jets (×0.1) W+ ≥ 3 jets (×0.01)

b b b b b b b b b b b b b b b b b b b b b b b b b b b b b b b

ATLAS data MePs@Nlo MePs@Nlo µ/2 . . . 2µ MEnloPS MEnloPS µ/2 . . . 2µ Mc@Nlo 10−4 10−3 10−2 10−1 1 10 1 10 2 10 3 First Jet p⊥ dσ/dp⊥ [pb/GeV]

b b b b b b b b b b

0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 MC/data

b b b b b b b b b b

0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 MC/data

b b b b b b b b b b

50 100 150 200 250 300 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 p⊥ [GeV] MC/data

W+ ≥ 2 jets (×1) W+ ≥ 3 jets (×0.1)

b b b b b b b b b b b b b b b

ATLAS data MePs@Nlo MePs@Nlo µ/2 . . . 2µ MEnloPS MEnloPS µ/2 . . . 2µ Mc@Nlo 10−3 10−2 10−1 1 10 1 10 2 Second Jet p⊥ dσ/dp⊥ [pb/GeV]

b b b b b b b

0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 MC/data

b b b b b b b

40 60 80 100 120 140 160 180 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 p⊥ [GeV] MC/data

W+ ≥ 3 jets

b b b b b b b

ATLAS data MePs@Nlo MePs@Nlo µ/2 . . . 2µ MEnloPS MEnloPS µ/2 . . . 2µ Mc@Nlo 10−3 10−2 10−1 1 Third Jet p⊥ dσ/dp⊥ [pb/GeV]

b b b b b b

40 60 80 100 120 140 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 p⊥ [GeV] MC/data

  • F. Krauss

IPPP Precision simulations in SHERPA

slide-23
SLIDE 23

Motivation Ingredients NLO improvements MEPS@LO MEPS@NLO Conclusion

W+ ≥ 1 jet (×1) W+ ≥ 2 jets (×0.1) W+ ≥ 3 jets (×0.01)

b b b b b b b b b b b b b b b b b b b b b b b b b b b b b b b b b b b b b

ATLAS data MePs@Nlo MePs@Nlo µ/2 . . . 2µ MEnloPS MEnloPS µ/2 . . . 2µ Mc@Nlo 100 200 300 400 500 600 700 10−4 10−3 10−2 10−1 1 10 1 10 2 10 3 HT [GeV] dσ/dHT [pb/GeV]

b b b b b b b b b b b b b

0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 MC/data

b b b b b b b b b b b b

0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 MC/data

b b b b b b b b b b b

100 200 300 400 500 600 700 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 HT [GeV] MC/data

b b b b b b b b b b b b b b b b b b b b b b b b b b b b b

ATLAS data MePs@Nlo MePs@Nlo µ/2 . . . 2µ MEnloPS MEnloPS µ/2 . . . 2µ Mc@Nlo 20 40 60 80 100 ∆R Distance of Leading Jets dσ/d∆R [pb]

b b b b b b b b b b b b b b b b b b b b b b b b b b b b

1 2 3 4 5 6 7 8 0.5 1 1.5 2 ∆R(First Jet, Second Jet) MC/data

b b b b b b b b b b b b b b b b b

ATLAS data MePs@Nlo MePs@Nlo µ/2 . . . 2µ MEnloPS MEnloPS µ/2 . . . 2µ Mc@Nlo 20 40 60 80 100 120 140 Azimuthal Distance of Leading Jets dσ/d∆φ [pb]

b b b b b b b b b b b b b b b b

0.5 1 1.5 2 2.5 3 0.5 1 1.5 2 ∆φ(First Jet, Second Jet) MC/data

  • F. Krauss

IPPP Precision simulations in SHERPA

slide-24
SLIDE 24

Motivation Ingredients NLO improvements MEPS@LO MEPS@NLO Conclusion

Inclusive observables for gg → H

note: impact of central scale choice! uncertainty bands through variation by factor of 2 on µF,R and √ 2

  • n µQ

Sherpa MePs@Nlo µR = µCKKW µR = mh µR = ˆ H′

T

10−2 10−1 1 Higgs boson transverse momentum dσ/dp⊥ [pb/GeV] 20 40 60 80 100 0.6 0.8 1 1.2 1.4 p⊥(h) [GeV] Ratio to µR = mh Sherpa MePs@Nlo µR = µCKKW µR = mh µR = ˆ H′

T

1 2 3 4 5 Higgs boson rapidity dσ/dy [pb]

  • 4
  • 3
  • 2
  • 1

1 2 3 4 0.6 0.8 1 1.2 1.4 y(h) Ratio to µR = mh

  • F. Krauss

IPPP Precision simulations in SHERPA

slide-25
SLIDE 25

Motivation Ingredients NLO improvements MEPS@LO MEPS@NLO Conclusion

Exclusive observables for gg → H

Sherpa MePs@Nlo µR = µCKKW µR = mh µR = ˆ H′

T

10−3 10−2 10−1 1 Higgs boson transverse momentum (njet = 0) dσ/dp⊥ [pb/GeV] 20 40 60 80 100 0.6 0.8 1 1.2 1.4 1.6 1.8 p⊥(h) [GeV] Ratio to µR = mh Sherpa MePs@Nlo µR = µCKKW µR = mh µR = ˆ H′

T

10−5 10−4 10−3 10−2 10−1 Transverse momentum of the H j1 system dσ/dp⊥ [pb/GeV] 50 100 150 200 250 300 0.6 0.8 1 1.2 1.4 1.6 1.8 p⊥(hj1) Ratio to µR = mh

  • F. Krauss

IPPP Precision simulations in SHERPA

slide-26
SLIDE 26

Motivation Ingredients NLO improvements MEPS@LO MEPS@NLO Conclusion

gg → H after WBF cuts

Sherpa MePs@Nlo µR = µCKKW µR = mh µR = ˆ H′

T

VBF cuts 0.0005 0.001 0.0015 0.002 Transverse momentum of the Higgs boson dσ/dp⊥ [pb/GeV] 50 100 150 200 250 300 0.6 0.8 1 1.2 1.4 1.6 1.8 p⊥(h) [GeV] Ratio to µR = mh Sherpa MePs@Nlo µR = µCKKW µR = mh µR = ˆ H′

T

VBF cuts 10−6 10−5 10−4 10−3 10−2 Transverse momentum of the H j1 j2 system dσ/dp⊥ [pb/GeV] 50 100 150 200 250 300 0.6 0.8 1 1.2 1.4 1.6 1.8 p⊥(hj1j2) Ratio to µR = mh

  • F. Krauss

IPPP Precision simulations in SHERPA

slide-27
SLIDE 27

Motivation Ingredients NLO improvements MEPS@LO MEPS@NLO Conclusion

gg → H after WBF cuts

Sherpa MePs@Nlo µR = µCKKW µR = mh µR = ˆ H′

T

VBF cuts 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 Azumuthal separation of the two leading jets dσ/d∆φ [pb] 0.5 1 1.5 2 2.5 3 0.6 0.8 1 1.2 1.4 1.6 1.8 ∆φ(j1, j2) Ratio to µR = mh Sherpa MePs@Nlo µR = µCKKW µR = mh µR = ˆ H′

T

VBF cuts 0.1 0.2 0.3 0.4 0.5 0.6 0.7 Azumuthal separation of the Higgs and the two leading jets dσ/d(π−∆φ) [pb] 0.2 0.4 0.6 0.8 1 0.6 0.8 1 1.2 1.4 1.6 1.8 π − ∆φ(h, j1j2) Ratio to µR = mh

  • F. Krauss

IPPP Precision simulations in SHERPA

slide-28
SLIDE 28

Motivation Ingredients NLO improvements MEPS@LO MEPS@NLO Conclusion

Higgs backgrounds: inclusive observables in W +W −+jets

Sherpa+OpenLoops MEPS@NLO 4ℓ + 0, 1j MC@NLO 4ℓ NLO 4ℓ NLO 4ℓ + 1j 10−4 10−3 10−2 Transverse momentum of leading jet dσ/dpT [pb/GeV] 10 1 10 2 0.6 0.8 1 1.2 1.4 pT [GeV] Ratio Sherpa+OpenLoops MEPS@NLO 4ℓ + 0, 1j MC@NLO 4ℓ NLO 4ℓ 10−8 10−7 10−6 10−5 10−4 10−3 Total transverse energy dσ/dHT [pb/GeV] 10 2 10 3 0.6 0.8 1 1.2 1.4 HT [GeV] Ratio

  • F. Krauss

IPPP Precision simulations in SHERPA

slide-29
SLIDE 29

Motivation Ingredients NLO improvements MEPS@LO MEPS@NLO Conclusion

Higgs backgrounds: jet vetoes in W +W −+jets

integrated cross sections in 0/1-jet bin in dependence on pT of jet

Sherpa+OpenLoops MEPS@NLO 4ℓ + 0, 1j MC@NLO 4ℓ NLO 4ℓ 0.05 0.1 0.15 0.2 0.25 0.3 0.35 Integrated cross section in the exclusive 0-jet bin σ(pTjet < pmax

T

) [pb] 10 20 30 40 50 60 70 80 90 100 0.85 0.9 0.95 1.0 1.05 1.1 1.15 pmax

T

[GeV] Ratio Sherpa+OpenLoops MEPS@NLO 4ℓ + 0, 1j MC@NLO 4ℓ NLO 4ℓ + 1j 0.05 0.1 0.15 0.2 0.25 Integrated cross section in the inclusive 1-jet bin σ(pTjet > pmin

T

) [pb] 10 20 30 40 50 60 70 80 90 100 0.7 0.8 0.9 1.0 1.1 1.2 1.3 pmin

T

[GeV] Ratio

  • F. Krauss

IPPP Precision simulations in SHERPA

slide-30
SLIDE 30

Motivation Ingredients NLO improvements MEPS@LO MEPS@NLO Conclusion

Higgs backgrounds: gluon-induced processes W +W −+jets

include (LO-) merged loop2 contributions of gg → VV (+1 jet)

Sherpa+OpenLoops MEPS@NLO 4ℓ + 0, 1j MEPS@LOOP2 4ℓ + 0, 1j 10−6 10−5 10−4 10−3 10−2 Transverse momentum of leading jet dσ/dpT [pb/GeV] 10 1 10 2 0.02 0.04 0.06 0.08 pT [GeV] dσ/dσMEPS@NLO Sherpa+OpenLoops MEPS@NLO 4ℓ + 0, 1j MEPS@LOOP2 4ℓ + 0, 1j 10−6 10−5 10−4 10−3 10−2 Invariant mass of oppositely charged leptons dσ/dmℓℓ [pb/GeV] 50 100 150 200 250 300 0.02 0.04 0.06 0.08 mℓℓ [GeV] dσ/dσMEPS@NLO

  • F. Krauss

IPPP Precision simulations in SHERPA

slide-31
SLIDE 31

Motivation Ingredients NLO improvements MEPS@LO MEPS@NLO Conclusion

Some fun with VH → 3ℓ

relevant observables: E /T, m123, and ∆R01 lots of backgrounds . . . , signal and dominant ones multijet-merged

Signal WH(WW) WH(ττ) ZH(WW) ZH(ττ) WH(ZZ) ZH(ZZ) Background WZ WWW WWZ ZZ WZZ ZZZ 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.1 Trilepton invariant mass after Z and top veto dσ/dm3ℓ [fb/GeV] Signal WH(WW) WH(ττ) ZH(WW) ZH(ττ) WH(ZZ) ZH(ZZ) Background WZ WWW WWZ ZZ WZZ ZZZ 0.5 1 1.5 2 2.5 Angular separation of closest opposite-sign lepton pair in Z-depleted sample d σ/d ∆R01 [fb] 50 100 150 200 250 300 0.9 1.0 1.1 Accumulated signal uncertainty Accumulated background uncertainty m3ℓ [GeV] Relative uncertainty 1 2 3 4 5 0.9 1.0 1.1 Accumulated background uncertainty Accumulated signal uncertainty ∆R01 Ratio

  • F. Krauss

IPPP Precision simulations in SHERPA

slide-32
SLIDE 32

Motivation Ingredients NLO improvements MEPS@LO MEPS@NLO Conclusion

Inclusive observables in t¯ t + jets

multijet merging for t¯ t + {0, 1, 2} jets

Sherpa+OpenLoops MEPS@NLO 1.65 × MEPS@LO S-MC@NLO 10−5 10−4 10−3 Total transverse energy dσ/dHtot

T

[pb/GeV] 200 400 600 800 1000 1200 0.5 1 1.5 Htot

T

[GeV] Ratio to MEPS@NLO Sherpa+OpenLoops MEPS@NLO 1.65 × MEPS@LO S-MC@NLO 10−6 10−5 10−4 10−3 10−2 Transverse momentum of reconstructed top quark dσ/dpT [pb/GeV] 100 200 300 400 500 600 0.5 1 1.5 pT (top) [GeV] Ratio to MEPS@NLO

  • F. Krauss

IPPP Precision simulations in SHERPA

slide-33
SLIDE 33

Motivation Ingredients NLO improvements MEPS@LO MEPS@NLO Conclusion

Light jet observables in t¯ t + jets

Sherpa+OpenLoops pl-jet

⊥

> 40 GeV pl-jet

⊥

> 60 GeV ×0.1 pl-jet

⊥

> 80 GeV ×0.01 MEPS@NLO 1.65 × MEPS@LO S-MC@NLO 10−5 10−4 10−3 10−2 10−1 1 Inclusive light jet multiplicity σ(≥ Nl-jet) [pb] pl-jet

⊥

> 40 GeV 1 1.5 2 Ratio to MEPS@NLO pl-jet

⊥

> 60 GeV 1 1.5 2 Ratio to MEPS@NLO pl-jet

⊥

> 80 GeV 1 2 3 1 1.5 2 Nl-jet Ratio to MEPS@NLO Sherpa+OpenLoops 1st jet 2nd jet 3rd jet MEPS@NLO 1.65 × MEPS@LO S-MC@NLO 10−8 10−7 10−6 10−5 10−4 10−3 Light jet transverse momenta dσ/dpT [pb/GeV] 1st jet 0.5 1 1.5 Ratio to MEPS@NLO 2nd jet 0.5 1 1.5 2 Ratio to MEPS@NLO 3rd jet 40 50 100 200 500 0.5 1 1.5 2 2.5 pT (light jet) [GeV] Ratio to MEPS@NLO

  • F. Krauss

IPPP Precision simulations in SHERPA

slide-34
SLIDE 34

Motivation Ingredients NLO improvements MEPS@LO MEPS@NLO Conclusion

Summary

Systematic improvement of event generators by including higher orders has been at the core

  • f QCD theory and developments in the past

decade:

multijet merging (“CKKW”, “MLM”) NLO matching (“MC@NLO”, “POWHEG”) MENLOPS NLO matching & merging MEPS@NLO (“SHERPA”, “UNLOPS”, “MINLO”, “FxFx”)

multijet merging an important tool for many relevant signals and backgrounds - pioneered by SHERPA at LO & NLO

(and excessively validated in a lot of different processes: V /H/VV /VH/VVV /t¯ t/t¯ tV +jets, jets alone, . . . )

complete automation of NLO calculations done − → benefit from it!

(it’s the precision and trustworthy & systematic uncertainty estimates!)

  • F. Krauss

IPPP Precision simulations in SHERPA