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Conveners: Fabio Maltoni (UC Louvain) Jon Butterworth (UC London) Peter Skands (Fermilab) Tools and Monte Carlos Session 1: SM issues.

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Presentation on theme: "Conveners: Fabio Maltoni (UC Louvain) Jon Butterworth (UC London) Peter Skands (Fermilab) Tools and Monte Carlos Session 1: SM issues."— Presentation transcript:

1 Conveners: Fabio Maltoni (UC Louvain) Jon Butterworth (UC London) Peter Skands (Fermilab) Tools and Monte Carlos Session 1: SM issues

2 Session 1: SM Issues 1.Tuning 2.Model (In)-dependence in Data/Theory Comparisons 3.Matching 4.Parton Densitites 5.Jet Physics 6.?

3 General Procedure Kick-Offs: brainstorm sessions today + tomorrow –IMPORTANT to attend –Later, further storms may be scheduled as needed Conveners have a list of possible interesting topics to use as a baseline. Not exhaustive nor exclusive. –Declare your interests –Well collect a list of possible projects with at least one name attached to each one –Well put these on the wiki and add/remove as necessary. Each will have its own wiki page proceedings contribution

4 Schedule on Wiki 14:00 16:00 17:00 09:00 11:00 14:00 16:00 After first few days, organization will be more dynamical up to you Think something needs to be discussed? Book a room!

5 Tuning – The Issue UA5 @ 540 GeV, single pp, charged multiplicity in minimum-bias events Simple physics models ~ Poisson Can tune to get average right, but much too small fluctuations inadequate physics model More Physics: Multiple interactions + impact-parameter dependence Moral: 1)It is not possible to tune anything better than the underlying physics model allows 2)Failure of a physically motivated model usually points to more physics (interesting) 3)Failure of a fit not as interesting

6 Tuning – Current Issues Automation Validation Uncertainties (of tunes, and of MCs in general) Impact of Tunes on Exp. Calibrations –Cf Model (in)-dependence of TH/EX comparisons Tuning in the presence of matching –Cf Matching Extrapolations to the LHC –What to do with first data and what we get from it

7 Tuning – Organization Tuning Brainstorm Session Anything connected with precision of Monte Carlos, uncertainties, tuning, systematics, fragmentation/hadronization, automation, validation, programme of LHC measurements for SM/MC validation, etc. WEDNESDAY 9:00, AUDITORIUM

8 Model Independence Covered by Jon Example: Monte Carlo Truth –What is it? –Can we even define it? –Can we define it better? –Can we define it independently of MC? –Cf Tevatron Drell-Yan pT distribution Crucial for tuning PS, arXiv: 0905.3418 [hep-ph] Buckley et al, arXiv: 0906.0075 [hep-ph]

9 Matching – The Issue A (Complete Idiots) Solution – Combine 1.[X] ME + showering 2.[X + 1 jet] ME + showering 3.… Doesnt work –[X] + shower is inclusive –[X+1] + shower is also inclusive X inclusive X+1 inclusive X+2 inclusive X exclusive X+1 exclusive X+2 inclusive Run generator for X (+ shower) Run generator for X+1 (+ shower) Run generator for … (+ shower) Combine everything into one sample What you get What you want Overlapping binsOne sample NLM + MC Overlap: Ensure consistent concepts and language

10 The Matching Problem [X] ME + shower already contains sing LL { [X + n jet] ME } –Adding full [X + n jet] ME is overkill LL singular terms are double-counted Solution 1: work out the difference and correct by that amount add shower-subtracted matrix elements –Correction events with weights w n = [X + n jet] ME – Shower{w n-1,2,3,.. } –I call these matching approaches additive Solution 2: work out the ratio between PS and ME multiply shower kernels by that ratio (< 1 if shower is an overestimate) –Correction factor on nth emission P n = [X + n jet] ME / Shower{[X+n-1 jet] ME } –I call these matching approaches multiplicative

11 Matching – Tree-Level Benchmarks –Important Processes (Pheno) W + jets QCD jets, Top, Bottom, Higgs, … BSM benchmarks? (with session 2) –Pathological Observables (Theory Tests) Obs that are explicitly sensitive to subleading logs Changing matching different subleading logs tuning affected. This problem needs to be charted. Automation (tree-level)

12 Matching – with NLM Matching at NLO –State of the field: comparisons, tests Analytical tests in simpler theories? –Multileg matching at NLO Keep eye on Standardization discussions… Matching at NNLO? –Showers as phase space generators Matching beyond fixed order? –BFKL×DGLAP, MC@NLL, …

13 Matching – Organization Tree-level brainstorm + joint brainstorm with NLM WG MC Tuesday, 14:00, LIBRARY WG MC + NLM Tuesday, 17:00, AUDITORIUM + Spillover: Wednesday, 16:00, AUDITORIUM

14 PDFs Heavy-quark PDFs NNPDFs, MC PDFs, CTEQ, MRST, … Special Effects in PDFs –QED (MRST2004QED) –Unintegrated PDFs –PDFs for Monte Carlos (LO* and Beyond) –Correlations Brainstorm Joint with NLM WG MC + NLM Tuesday, 16:00, AUDITORIUM

15 Jets Covered by Jon –Jet algorithms, substructure, subtractions, calibrations, … Jet Calculations (of jets and jet stucture) –Jet-jet correlations, jet masses, ΔR jj Matching benchmarks? Connects to substructure –Large rapidity, definition of rapidity gaps –Jet definitions for matching –New Showers (Catani-Seymour, Antenna, Sector, …) Comparisons, Energy and color flows, coherence WG MC Wednesday, 14:00, AUDITORIUM

16 Summary QCD Phenomenology is in a state of impressive activity –Increasing move from educated guesses to precision science –Better matrix element calculators+integrators (+ more user-friendly) –Improved parton showers and improved matching to matrix elements –Improved models for underlying events / minimum bias –Upgrades of hadronization and decays Early LHC Physics: theory –At 14 TeV, everything is interesting –Even if not a dinner Chez Maxim, rediscovering the Standard Model is much more than bread and butter –Real possibilities for real surprises –Timely discussions on non-classified data, such as min-bias, dijets, Drell-Yan, etc allow rapid improvements in QCD modeling (beyond simple retunes) after startup

17 Summary Need for good tools already demonstrated Solution provided by Gudrun More conventional, but got the job done

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