Monte Carlo Tools Frank Krauss Institute for Particle Physics - PowerPoint PPT Presentation

Orientation ME corrections MC@NLO CKKW New Showers Underlying Event Monte Carlo Tools Frank Krauss Institute for Particle Physics Phenomenology Durham University GGI, 24.&26.9.2007 F. Krauss IPPP Monte Carlo Tools

Orientation ME corrections MC@NLO CKKW New Showers Underlying Event Topics of the lectures Lecture 1: Tour through Event Generators 1 Hard physics simulation: Parton Level event generation Dressing the partons: Parton Showers Soft physics simulation: Hadronization Beyond factorization: Underlying Event Lecture 2: Higher Orders in Monte Carlos 2 Some nomenclature: Anatomy of HO calculations Merging vs. Matching Thanks to the other Sherpas: T.Gleisberg, S.H¨ oche, S.Schumann, F.Siegert, M.Sch¨ onherr, J.Winter; other MC authors: S.Gieseke, K.Hamilton, L.Lonnblad, F.Maltoni, M.Mangano, P.Richardson, M.Seymour, T.Sjostrand, B.Webber, . . . . F. Krauss IPPP Monte Carlo Tools

Orientation ME corrections MC@NLO CKKW New Showers Underlying Event Simulation’s paradigm Basic strategy Sketch of an event Divide event into stages, separated by different scales. �� Signal/background: �� Exact matrix elements. �� QCD-Bremsstrahlung: �� Parton showers (also in initial state). �� Multiple interactions: �� Beyond factorization: Modeling. �� Hadronization: �� Non-perturbative QCD: Modeling. F. Krauss IPPP Monte Carlo Tools

Orientation ME corrections MC@NLO CKKW New Showers Underlying Event Today’s lecture: Higher Orders in Monte Carlos Which higher orders? Some anatomy First attempts: ME corrections Higher orders in rate: MC@NLO Higher orders through extra emission: Merging A new shower formulation F. Krauss IPPP Monte Carlo Tools

Orientation ME corrections MC@NLO CKKW New Showers Underlying Event Higher Orders Nomenclature Specifying higher-order corrections: γ ∗ → hadrons In general: N n LO ↔ O ( α n s ) But: only for inclusive quantities (e.g.: total xsecs like γ ∗ → hadrons) . Counter-example: thrust distribution In general, distributions are HO. Distinguish real & virtual emissions: Real emissions → mainly distributions, virtual emissions → mainly normalization. F. Krauss IPPP Monte Carlo Tools

Orientation ME corrections MC@NLO CKKW New Showers Underlying Event Higher Orders Nomenclature Anatomy of HO calculations: Virtual and real corrections NLO corrections: O ( α s ) Virtual corrections = extra loops Real corrections = extra legs UV-divergences in virtual graphs → renormalization But also: IR-divergences in real & virtual contributions Must cancel each other, non-trivial to see: N vs. N + 1 particle FS, divergence in PS vs. loop F. Krauss IPPP Monte Carlo Tools

Orientation ME corrections MC@NLO CKKW New Showers Underlying Event Higher Orders Nomenclature Cancelling the IR divergences: Subtraction method Total NLO xsec: � d D k |M| 2 � d 4 k |M| 2 σ NLO = σ Born + V + R IR div. in real piece → regularize: d 4 k |M| 2 d D k |M| 2 � � R → R Construct subtraction term with same IR structure: d D k ( |M| 2 R − |M| 2 d 4 k |M| 2 � � S ) = RS = finite. d D k | ˜ S| 2 , universal | ˜ � d D k |M| 2 � S| 2 . Possible: S = σ Born S| 2 = finite (analytical) d D k | ˜ � d D k |M| 2 � V + σ Born F. Krauss IPPP Monte Carlo Tools

Monte Carlo Tools Frank Krauss Institute for Particle Physics - PowerPoint PPT Presentation

Orientation ME corrections MC@NLO CKKW New Showers Underlying Event Monte Carlo Tools Frank Krauss Institute for Particle Physics Phenomenology Durham University GGI, 24.&26.9.2007 F. Krauss IPPP Monte Carlo Tools Orientation ME

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Monte Carlo Tools Frank Krauss Institute for Particle Physics - PowerPoint PPT Presentation

Orientation ME corrections MC@NLO CKKW New Showers Underlying Event Monte Carlo Tools Frank Krauss Institute for Particle Physics Phenomenology Durham University GGI, 24.&26.9.2007 F. Krauss IPPP Monte Carlo Tools Orientation ME

Monte Carlo Approximation of Monte Carlo Filters Adam M. Johansen et al. Collaborators Include:

Monte Carlo Tools Frank Krauss Institute for Particle Physics Phenomenology Durham University

Monte Carlo Methods An introduction to Monte Carlo (MC) methods How to use MC methods

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QUASI-EQUILIBRIUM MONTE-CARLO: OFF-LATTICE KINETIC MONTE CARLO SIMULATION OF HETEROEPITAXY

Draft 1 Density estimation by Monte Carlo and randomized quasi-Monte Carlo (RQMC) Pierre

Chapter 5: Monte Carlo Methods Monte Carlo methods are learning methods Experience

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Monte Carlo Integration Monte Carlo methods use random numbers for sampling Although

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Monte Carlo Control CMPUT 366: Intelligent Systems S&amp;B 5.3-5.5, 5.7 Lecture Outline 1.

Monte Carlo Generators Monte Carlo Generators Monte Carlo Generators QCD Lecture III P .

Linear Regression: Prior Linear Regression: Posterior 4 4 2 2 0 0 2 2 Prior P ( )

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Monte Carlo Simulation technique S. B. Santra Department of Physics Indian Institute of

Monte Carlo Generators for International Linear Collider Physics Stefano Moretti NExT Institute

Sensitivity Estimates Using a Toy Monte Carlo Dave Waters, University College London with Sean

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Techniques in Artificial Intelligence - Part I Todd W. Neller Gettysburg College Monte Carlo

Overview of Monte Carlo Generators John Campbell, Fermilab Monte Carlo overview: history

Monte Carlo Simulations and PcNaive Heino Bohn Nielsen 1 of 21 Monte Carlo Simulations MC

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