Lessons from the early LHC data for MC tuning

Transcription

1 Lessons from the early LHC data for MC tuning P. S k a n d s ( C E R N ) Multiple Partonic Interactions at LHC, November 211, Hamburg

2 A Factorized View 1. Where is the energy going? Note: only linearized Sphericity is IR safe Sum(pT) densities, event shapes, mini-jet rates, ctrl&fwd energy flow, energy correlations sensitive to pqcd + pmpi IR Safe 2. How many tracks is it divided onto? Ntracks, dntracks/dpt, Associated track densities, track correlations sensitive to hadronization + soft MPI 3. Are there gaps in it? Created by diffraction (and color reconnections?). Destroyed by UE. 4. What kind of tracks? Strangeness per track, baryons per track, baryon asymmetry, hadron-hadron correlations sensitive to details of hadronization + collective effects (+Quarkonium sensitive to color reconnections?) IR Sensitive More IR Sensitive 2

3 PYTHIA Models LHC data pt-ordered PYTHIA 6 Tune S Tune SA S -Pro ATLAS MC9 Perugia (+ Variations) AMBT1 Z1, Z2 Perugia 21 AUET2B? Perugia 211 (+ Variations) Q-ordered PYTHIA 6 Tune A DW(T) (default) D6(T) D -Pro Pro-Q2O Q2-LHC? pt-ordered PYTHIA 8 Tune 1 2C 2M 4C, 4Cx A1, AU1 A2, AU2 Note: tunes differ significantly in which data sets they include LEP fragmentation parameters Level of Underlying Event & Minimum-bias Tails Soft part of Drell-Yan pt spectrum 3

4 PYTHIA Models LHC data pt-ordered PYTHIA 6 Tune S Tune SA S -Pro ATLAS MC9 Perugia (+ Variations) AMBT1 Z1, Z2 Perugia 21 AUET2B? Perugia 211 (+ Variations) Q-ordered PYTHIA 6 Tune A DW(T) (default) D6(T) D -Pro Pro-Q2O Q2-LHC? pt-ordered PYTHIA 8 Main Data Sets included in each Tune (no guarantee that all subsets ok) A DW, D6,... S, SA MC9(c) Pro-, Perugia, Tune 1, 2C, 2M Tune 1 AMBT1 Perugia 21 Perugia 211 2C 2M 4C, 4Cx A1, AU1 A2, AU2 Z1, Z2 4C, 4Cx AUET2B, A2, AU2 LEP TeV MB ( )? TeV UE ( )? TeV DY LHC MB? LHC UE 4

5 PYTHIA Models LHC data pt-ordered PYTHIA 6 Tune S Tune SA S -Pro ATLAS MC9 Perugia (+ Variations) AMBT1 Z1, Z2 Perugia 21 AUET2B? Perugia 211 (+ Variations) Q-ordered PYTHIA 6 Tune A DW(T) (default) D6(T) D -Pro Pro-Q2O Q2-LHC? pt-ordered PYTHIA 8 Main Data Sets included in each Tune (no guarantee that all subsets ok) A (default) DW, D6,... S, SA MC9(c) Pro-, Perugia, Tune 1, 2C, 2M Tune 1 AMBT1 Perugia 21 Perugia 211 2C 2M 4C, 4Cx A1, AU1 A2, AU2 Z1, Z2 4C, 4Cx AUET2B, A2, AU2 LEP TeV MB ( )? TeV UE ( )? TeV DY LHC MB? LHC UE 5

6 What Works * *) if you use an up-to-date tune. Here comparing to PY6 default (~ Tune A) to show changes. Underlying Event & Jet Shapes UE ΣpT (TRNS) φ ptlead > 5 GeV Jet Shape 3 < pt < 4, All y (softest jet bin available) PS: yes, we should update the PYTHIA 6 defaults... Plots from mcplots.cern.ch 6

7 What Works * *) if you use an up-to-date tune. Here comparing to PY6 default (~ Tune A) to show changes. Drell-Yan pt (Normalized to Unity) Apologies: we don t have DY measurements from LHC on the mcplots site yet dσ (no K-factor) dσ/σ (norm to unity) φ * (norm to unity) PS: yes, we should update the PYTHIA 6 defaults... Plots from mcplots.cern.ch 7

8 What Kind of Works * *) if you use an up-to-date tune. Here comparing to PY6 default (~ Tune A) to show changes. Minimum-Bias Multiplicities (here showing as inclusive as possible) Charged Multiplicity Distribution η distribution Forward-Backward Correlation (UA5) Hoping for LHC measurements soon See Wraight + PS, EPJC71(211)1628 Central LHC Detectors ALICE FMD PS: yes, we should update the PYTHIA 6 defaults... Plots from mcplots.cern.ch 8

9 What Doesn t Work pt Spectra (in particular mass dependence) Strange and baryon production Structure of very soft events Very high-multiplicity events (CMS ridge) (No time to address here, plus no good model yet) Diffraction and forward energy (will return to diffraction on Friday) 9

10 Organized Tuning Can we be more general than thistune-does-this, that-tune-does-that? Yes Schulz & PS, Eur.Phys.J. C71 (211) 1644 The new automated tuning tools can be used to generate unbiased optimizations for different observable regions Same parameters consistent model (not just best tune ) Critical for this task (take home message): Need comparable observable sets for each region Example: test ENERGY SCALING of MB: use different collider energies as regions (Other complementary data sets could be used to test other model aspects) P. Skands 1

11 .5.5 Tuning vs Testing Models 1 3 (c) PARP(82) vs s, N ch (d) PARP(82) vs s, N ch 6 PARP(83) TEST models Evolution of PARP(83) with s PARP(83) Tune parameters in several complementary regions PARP(83) Evolution of PARP(83) with s PARP(83) 63 GeV 9 GeV 18 & 196 GeV Pythia 6 7 TeV Gauss Perugia 1.5 Consistent model same parameters Model breakdown nonuniversal parameters Transverse Mass Distribution 1 3 Exponential PARP(82) Evolution of PARP(82) with s PARP(82) Exp=.25 dˆσ dp 2 p 4 63 GeV ndence 9 GeV p (E CM )=p ref See also Rick Field s talk, p.31 (e) PARP(83) vs s, N ch 1 18 & 196 GeV E ref CM ( ECM ) ɛ Perugia IR Regularization mpact-parameter space which determines leve ts Pythia 6.5 PARP(78) Figure 1: Evolution of parameters with energy.. 7 TeV 1 PARP(78) (f) PARP(83) vs s, N ch 6 Evolution of PARP(78) with s 63 GeV 9 GeV Color Reconnection Strength 18 & 196 GeV Pythia 6 Perugia 7 TeV Energy Scaling of MB Tunes, H. Schulz + PS, Eur.Phys.J. C71 (211) 1644 ( ) 6 ( ) 6 11

12 .5.5 Tuning vs Testing Models 1 3 (c) PARP(82) vs s, N ch (d) PARP(82) vs s, N ch 6 PARP(83) TEST models Evolution of PARP(83) with s PARP(83) Tune parameters in several complementary regions PARP(83) Evolution of PARP(83) with s PARP(83) 63 GeV 9 GeV 18 & 196 GeV Pythia 6 7 TeV Gauss Perugia 1.5 Consistent model same parameters Model breakdown nonuniversal parameters Transverse Mass Distribution 1 3 Exponential PARP(82) Evolution of PARP(82) with s PARP(82) Exp=.25 dˆσ dp 2 p 4 63 GeV ndence 9 GeV p (E CM )=p ref See also Rick Field s talk, p.31 (e) PARP(83) vs s, N ch 1 18 & 196 GeV E ref CM ( ECM ) ɛ Perugia IR Regularization mpact-parameter space which determines leve ts Pythia 6.5 PARP(78) Figure 1: Evolution of parameters with energy.. 7 TeV 1 PARP(78) (f) PARP(83) vs s, N ch 6 Evolution of PARP(78) with s 63 GeV 9 GeV Color Reconnection Strength 18 & 196 GeV Pythia 6 Perugia 7 TeV Energy Scaling of MB Tunes, H. Schulz + PS, Eur.Phys.J. C71 (211) 1644 ( ) 6 ( ) 6 11

13 pt Spectra / Mass Dependence Must be compared with LEP SOFT SOFT STAR: 2 GeV OPAL all charged ~ pions HARD ALEPH Λ baryons HARD STAR measurement Average pt versus particle mass Model predict too hard Pions and too soft massive particles Pions can only be made harder Massive particles can only be made softer! So: tuning problem? or physics problem? Will return on Friday Plots from mcplots.cern.ch 12

14 Strangeness and Baryons Tried to learn from early data, but still not there Λ/K Again, quite difficult to adjust flavor parameters while remaining within LEP bounds Plots from mcplots.cern.ch 13

15 Very Soft Structure Minimum-Bias too lumpy? Underlying Event ok? ptlead > 1 ptlead > 5 Plots from mcplots.cern.ch 14

16 Summary How did the models fare? Lots could be said Bottom line: Not too bad on averages See also talks by Rick Field and others E.g., UE level underpredicted by ~ 1-2% relative to Tevatron tunes (I won my bet!) Significant discrepancies on more exclusive physics Strangeness, Baryons, and Baryon Transport pt spectra High-multiplicity tail (+ridge!) needs more study! Forward measurements and Diffraction LEP More tuning? or new physics? No single model/tune does it all (game still open) 15

17 Diffraction Framework needs testing and tuning E.g., interplay between non-diffractive and diffractive components + LEP tuning used directly for diffractive modeling Hadronization preceded by shower at LEP, but not in diffraction dedicated diffraction tuning of fragmentation pars? Study this bump + Room for new models, e.g., KMR (SHERPA) Others? P. Skands 16