Techniques for Higher Order Corrections and Their Application to LHC Phenomenology
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Numerical Solution of Ordinary Differential Equations
NUMERICAL SOLUTION OF ORDINARY DIFFERENTIAL EQUATIONS Kendall Atkinson, Weimin Han, David Stewart University of Iowa Iowa City, Iowa A JOHN WILEY & SONS, INC., PUBLICATION Copyright c 2009 by John Wiley & Sons, Inc. All rights reserved. Published by John Wiley & Sons, Inc., Hoboken, New Jersey. Published simultaneously in Canada. No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, scanning, or otherwise, except as permitted under Section 107 or 108 of the 1976 United States Copyright Act, without either the prior written permission of the Publisher, or authorization through payment of the appropriate per-copy fee to the Copyright Clearance Center, Inc., 222 Rosewood Drive, Danvers, MA 01923, (978) 750-8400, fax (978) 646-8600, or on the web at www.copyright.com. Requests to the Publisher for permission should be addressed to the Permissions Department, John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, (201) 748-6011, fax (201) 748-6008. Limit of Liability/Disclaimer of Warranty: While the publisher and author have used their best efforts in preparing this book, they make no representations or warranties with respect to the accuracy or completeness of the contents of this book and specifically disclaim any implied warranties of merchantability or fitness for a particular purpose. No warranty may be created ore extended by sales representatives or written sales materials. The advice and strategies contained herin may not be suitable for your situation. You should consult with a professional where appropriate. Neither the publisher nor author shall be liable for any loss of profit or any other commercial damages, including but not limited to special, incidental, consequential, or other damages. -
Numerical Integration
Chapter 12 Numerical Integration Numerical differentiation methods compute approximations to the derivative of a function from known values of the function. Numerical integration uses the same information to compute numerical approximations to the integral of the function. An important use of both types of methods is estimation of derivatives and integrals for functions that are only known at isolated points, as is the case with for example measurement data. An important difference between differen- tiation and integration is that for most functions it is not possible to determine the integral via symbolic methods, but we can still compute numerical approx- imations to virtually any definite integral. Numerical integration methods are therefore more useful than numerical differentiation methods, and are essential in many practical situations. We use the same general strategy for deriving numerical integration meth- ods as we did for numerical differentiation methods: We find the polynomial that interpolates the function at some suitable points, and use the integral of the polynomial as an approximation to the function. This means that the truncation error can be analysed in basically the same way as for numerical differentiation. However, when it comes to round-off error, integration behaves differently from differentiation: Numerical integration is very insensitive to round-off errors, so we will ignore round-off in our analysis. The mathematical definition of the integral is basically via a numerical in- tegration method, and we therefore start by reviewing this definition. We then derive the simplest numerical integration method, and see how its error can be analysed. We then derive two other methods that are more accurate, but for these we just indicate how the error analysis can be done. -
Persistence in Algebraic Specifications
Persistence in Algebraic Specifications Freek Wiedijk Persistence in Algebraic Specifications Persistence in Algebraic Specifications Academisch Proefschrift ter verkrijging van de graad van doctor aan de Universiteit van Amsterdam, op gezag van de Rector Magnificus prof. dr. P.W.M. de Meijer, in het openbaar te verdedigen in de Aula der Universiteit (Oude Lutherse Kerk, ingang Singel 411, hoek Spui), op donderdag 12 december 1991 te 12.00 uur door Frederik Wiedijk geboren te Haarlem Promotores: prof. dr. P. Klint & prof. dr. J.A. Bergstra Faculteit Wiskunde en Informatica Het hier beschreven onderzoek werd mede mogelijk gemaakt door steun van de Ne- derlandse organisatie voor Wetenschappelijk Onderzoek (voorheen de nederlandse organisatie voor Zuiver-Wetenschappelijk Onderzoek) binnen project nummer 612- 317-013 getiteld Executeerbaar maken van algebraïsche specificaties. voor Jan Truijens Contents Contents Acknowledgements 1 Introduction 1 1.1 Motivation and general overview 2 1.2 Examples of erroneous specifications 13 1.3 Background and related work 22 2. Theory 25 2.1 Basic notions 27 2.1.1. Equational specifications 28 2.1.2 Term rewriting systems 29 2.2 Termination 30 2.2.1 Weak and strong termination 33 2.2.2 Path orderings 34 2.2.3 Decidability 40 2.3 Confluence 41 2.3.1 Weak and strong confluence 42 2.3.2 Overlapping 44 2.3.3 Decidability 45 2.4 Reduction strategies 46 2.5 Persistence 48 2.5.1 Weak and strong persistence 52 2.5.2 Term approximations and bases 54 2.5.3 Normal form analysis 56 2.5.4 Decidability 60 2.6 Primitive recursive -
Perturbation Theory and Exact Solutions
PERTURBATION THEORY AND EXACT SOLUTIONS by J J. LODDER R|nhtdnn Report 76~96 DISSIPATIVE MOTION PERTURBATION THEORY AND EXACT SOLUTIONS J J. LODOER ASSOCIATIE EURATOM-FOM Jun»»76 FOM-INST1TUUT VOOR PLASMAFYSICA RUNHUIZEN - JUTPHAAS - NEDERLAND DISSIPATIVE MOTION PERTURBATION THEORY AND EXACT SOLUTIONS by JJ LODDER R^nhuizen Report 76-95 Thisworkwat performed at part of th«r«Mvchprogmmncof thcHMCiattofiafrccmentof EnratoniOTd th« Stichting voor FundtmenteelOiutereoek der Matctk" (FOM) wtihnnmcWMppoft from the Nederhmdie Organiutic voor Zuiver Wetemchap- pcigk Onderzoek (ZWO) and Evntom It it abo pabHtfMd w a the* of Ac Univenrty of Utrecht CONTENTS page SUMMARY iii I. INTRODUCTION 1 II. GENERALIZED FUNCTIONS DEFINED ON DISCONTINUOUS TEST FUNC TIONS AND THEIR FOURIER, LAPLACE, AND HILBERT TRANSFORMS 1. Introduction 4 2. Discontinuous test functions 5 3. Differentiation 7 4. Powers of x. The partie finie 10 5. Fourier transforms 16 6. Laplace transforms 20 7. Hubert transforms 20 8. Dispersion relations 21 III. PERTURBATION THEORY 1. Introduction 24 2. Arbitrary potential, momentum coupling 24 3. Dissipative equation of motion 31 4. Expectation values 32 5. Matrix elements, transition probabilities 33 6. Harmonic oscillator 36 7. Classical mechanics and quantum corrections 36 8. Discussion of the Pu strength function 38 IV. EXACTLY SOLVABLE MODELS FOR DISSIPATIVE MOTION 1. Introduction 40 2. General quadratic Kami1tonians 41 3. Differential equations 46 4. Classical mechanics and quantum corrections 49 5. Equation of motion for observables 51 V. SPECIAL QUADRATIC HAMILTONIANS 1. Introduction 53 2. Hamiltcnians with coordinate coupling 53 3. Double coordinate coupled Hamiltonians 62 4. Symmetric Hamiltonians 63 i page VI. DISCUSSION 1. Introduction 66 ?. -
Chapter 8 Stability Theory
Chapter 8 Stability theory We discuss properties of solutions of a first order two dimensional system, and stability theory for a special class of linear systems. We denote the independent variable by ‘t’ in place of ‘x’, and x,y denote dependent variables. Let I ⊆ R be an interval, and Ω ⊆ R2 be a domain. Let us consider the system dx = F (t, x, y), dt (8.1) dy = G(t, x, y), dt where the functions are defined on I × Ω, and are locally Lipschitz w.r.t. variable (x, y) ∈ Ω. Definition 8.1 (Autonomous system) A system of ODE having the form (8.1) is called an autonomous system if the functions F (t, x, y) and G(t, x, y) are constant w.r.t. variable t. That is, dx = F (x, y), dt (8.2) dy = G(x, y), dt Definition 8.2 A point (x0, y0) ∈ Ω is said to be a critical point of the autonomous system (8.2) if F (x0, y0) = G(x0, y0) = 0. (8.3) A critical point is also called an equilibrium point, a rest point. Definition 8.3 Let (x(t), y(t)) be a solution of a two-dimensional (planar) autonomous system (8.2). The trace of (x(t), y(t)) as t varies is a curve in the plane. This curve is called trajectory. Remark 8.4 (On solutions of autonomous systems) (i) Two different solutions may represent the same trajectory. For, (1) If (x1(t), y1(t)) defined on an interval J is a solution of the autonomous system (8.2), then the pair of functions (x2(t), y2(t)) defined by (x2(t), y2(t)) := (x1(t − s), y1(t − s)), for t ∈ s + J (8.4) is a solution on interval s + J, for every arbitrary but fixed s ∈ R. -
On the Numerical Solution of Equations Involving Differential Operators with Constant Coefficients 1
ON THE NUMERICAL SOLUTION OF EQUATIONS 219 The author acknowledges with thanks the aid of Dolores Ufford, who assisted in the calculations. Yudell L. Luke Midwest Research Institute Kansas City 2, Missouri 1 W. E. Milne, "The remainder in linear methods of approximation," NBS, Jn. of Research, v. 43, 1949, p. 501-511. 2W. E. Milne, Numerical Calculus, p. 108-116. 3 M. Bates, On the Development of Some New Formulas for Numerical Integration. Stanford University, June, 1929. 4 M. E. Youngberg, Formulas for Mechanical Quadrature of Irrational Functions. Oregon State College, June, 1937. (The author is indebted to the referee for references 3 and 4.) 6 E. L. Kaplan, "Numerical integration near a singularity," Jn. Math. Phys., v. 26, April, 1952, p. 1-28. On the Numerical Solution of Equations Involving Differential Operators with Constant Coefficients 1. The General Linear Differential Operator. Consider the differential equation of order n (1) Ly + Fiy, x) = 0, where the operator L is defined by j» dky **£**»%- and the functions Pk(x) and Fiy, x) are such that a solution y and its first m derivatives exist in 0 < x < X. In the special case when (1) is linear the solution can be completely determined by the well known method of varia- tion of parameters when n independent solutions of the associated homo- geneous equations are known. Thus for the case when Fiy, x) is independent of y, the solution of the non-homogeneous equation can be obtained by mere quadratures, rather than by laborious stepwise integrations. It does not seem to have been observed, however, that even when Fiy, x) involves the dependent variable y, the numerical integrations can be so arranged that the contributions to the integral from the upper limit at each step of the integration, at the time when y is still unknown at the upper limit, drop out. -
The Original Euler's Calculus-Of-Variations Method: Key
Submitted to EJP 1 Jozef Hanc, [email protected] The original Euler’s calculus-of-variations method: Key to Lagrangian mechanics for beginners Jozef Hanca) Technical University, Vysokoskolska 4, 042 00 Kosice, Slovakia Leonhard Euler's original version of the calculus of variations (1744) used elementary mathematics and was intuitive, geometric, and easily visualized. In 1755 Euler (1707-1783) abandoned his version and adopted instead the more rigorous and formal algebraic method of Lagrange. Lagrange’s elegant technique of variations not only bypassed the need for Euler’s intuitive use of a limit-taking process leading to the Euler-Lagrange equation but also eliminated Euler’s geometrical insight. More recently Euler's method has been resurrected, shown to be rigorous, and applied as one of the direct variational methods important in analysis and in computer solutions of physical processes. In our classrooms, however, the study of advanced mechanics is still dominated by Lagrange's analytic method, which students often apply uncritically using "variational recipes" because they have difficulty understanding it intuitively. The present paper describes an adaptation of Euler's method that restores intuition and geometric visualization. This adaptation can be used as an introductory variational treatment in almost all of undergraduate physics and is especially powerful in modern physics. Finally, we present Euler's method as a natural introduction to computer-executed numerical analysis of boundary value problems and the finite element method. I. INTRODUCTION In his pioneering 1744 work The method of finding plane curves that show some property of maximum and minimum,1 Leonhard Euler introduced a general mathematical procedure or method for the systematic investigation of variational problems. -
Improving Numerical Integration and Event Generation with Normalizing Flows — HET Brown Bag Seminar, University of Michigan —
Improving Numerical Integration and Event Generation with Normalizing Flows | HET Brown Bag Seminar, University of Michigan | Claudius Krause Fermi National Accelerator Laboratory September 25, 2019 In collaboration with: Christina Gao, Stefan H¨oche,Joshua Isaacson arXiv: 191x.abcde Claudius Krause (Fermilab) Machine Learning Phase Space September 25, 2019 1 / 27 Monte Carlo Simulations are increasingly important. https://twiki.cern.ch/twiki/bin/view/AtlasPublic/ComputingandSoftwarePublicResults MC event generation is needed for signal and background predictions. ) The required CPU time will increase in the next years. ) Claudius Krause (Fermilab) Machine Learning Phase Space September 25, 2019 2 / 27 Monte Carlo Simulations are increasingly important. 106 3 10− parton level W+0j 105 particle level W+1j 10 4 W+2j particle level − W+3j 104 WTA (> 6j) W+4j 5 10− W+5j 3 W+6j 10 Sherpa MC @ NERSC Mevt W+7j / 6 Sherpa / Pythia + DIY @ NERSC 10− W+8j 2 10 Frequency W+9j CPUh 7 10− 101 8 10− + 100 W +jets, LHC@14TeV pT,j > 20GeV, ηj < 6 9 | | 10− 1 10− 0 50000 100000 150000 200000 250000 300000 0 1 2 3 4 5 6 7 8 9 Ntrials Njet Stefan H¨oche,Stefan Prestel, Holger Schulz [1905.05120;PRD] The bottlenecks for evaluating large final state multiplicities are a slow evaluation of the matrix element a low unweighting efficiency Claudius Krause (Fermilab) Machine Learning Phase Space September 25, 2019 3 / 27 Monte Carlo Simulations are increasingly important. 106 3 10− parton level W+0j 105 particle level W+1j 10 4 W+2j particle level − W+3j 104 WTA (> -
ATTRACTORS: STRANGE and OTHERWISE Attractor - in Mathematics, an Attractor Is a Region of Phase Space That "Attracts" All Nearby Points As Time Passes
ATTRACTORS: STRANGE AND OTHERWISE Attractor - In mathematics, an attractor is a region of phase space that "attracts" all nearby points as time passes. That is, the changing values have a trajectory which moves across the phase space toward the attractor, like a ball rolling down a hilly landscape toward the valley it is attracted to. PHASE SPACE - imagine a standard graph with an x-y axis; it is a phase space. We plot the position of an x-y variable on the graph as a point. That single point summarizes all the information about x and y. If the values of x and/or y change systematically a series of points will plot as a curve or trajectory moving across the phase space. Phase space turns numbers into pictures. There are as many phase space dimensions as there are variables. The strange attractor below has 3 dimensions. LIMIT CYCLE (OR PERIODIC) ATTRACTOR STRANGE (OR COMPLEX) ATTRACTOR A system which repeats itself exactly, continuously, like A strange (or chaotic) attractor is one in which the - + a clock pendulum (left) Position (right) trajectory of the points circle around a region of phase space, but never exactly repeat their path. That is, they do have a predictable overall form, but the form is made up of unpredictable details. Velocity More important, the trajectory of nearby points diverge 0 rapidly reflecting sensitive dependence. Many different strange attractors exist, including the Lorenz, Julian, and Henon, each generated by a Velocity Y different equation. 0 Z The attractors + (right) exhibit fractal X geometry. Velocity 0 ATTRACTORS IN GENERAL We can generalize an attractor as any state toward which a Velocity system naturally evolves. -
Polarization Fields and Phase Space Densities in Storage Rings: Stroboscopic Averaging and the Ergodic Theorem
Physica D 234 (2007) 131–149 www.elsevier.com/locate/physd Polarization fields and phase space densities in storage rings: Stroboscopic averaging and the ergodic theorem✩ James A. Ellison∗, Klaus Heinemann Department of Mathematics and Statistics, University of New Mexico, Albuquerque, NM 87131, United States Received 1 May 2007; received in revised form 6 July 2007; accepted 9 July 2007 Available online 14 July 2007 Communicated by C.K.R.T. Jones Abstract A class of orbital motions with volume preserving flows and with vector fields periodic in the “time” parameter θ is defined. Spin motion coupled to the orbital dynamics is then defined, resulting in a class of spin–orbit motions which are important for storage rings. Phase space densities and polarization fields are introduced. It is important, in the context of storage rings, to understand the behavior of periodic polarization fields and phase space densities. Due to the 2π time periodicity of the spin–orbit equations of motion the polarization field, taken at a sequence of increasing time values θ,θ 2π,θ 4π,... , gives a sequence of polarization fields, called the stroboscopic sequence. We show, by using the + + Birkhoff ergodic theorem, that under very general conditions the Cesaro` averages of that sequence converge almost everywhere on phase space to a polarization field which is 2π-periodic in time. This fulfills the main aim of this paper in that it demonstrates that the tracking algorithm for stroboscopic averaging, encoded in the program SPRINT and used in the study of spin motion in storage rings, is mathematically well-founded. -
Computations in Algebraic Geometry with Macaulay 2
Computations in algebraic geometry with Macaulay 2 Editors: D. Eisenbud, D. Grayson, M. Stillman, and B. Sturmfels Preface Systems of polynomial equations arise throughout mathematics, science, and engineering. Algebraic geometry provides powerful theoretical techniques for studying the qualitative and quantitative features of their solution sets. Re- cently developed algorithms have made theoretical aspects of the subject accessible to a broad range of mathematicians and scientists. The algorith- mic approach to the subject has two principal aims: developing new tools for research within mathematics, and providing new tools for modeling and solv- ing problems that arise in the sciences and engineering. A healthy synergy emerges, as new theorems yield new algorithms and emerging applications lead to new theoretical questions. This book presents algorithmic tools for algebraic geometry and experi- mental applications of them. It also introduces a software system in which the tools have been implemented and with which the experiments can be carried out. Macaulay 2 is a computer algebra system devoted to supporting research in algebraic geometry, commutative algebra, and their applications. The reader of this book will encounter Macaulay 2 in the context of concrete applications and practical computations in algebraic geometry. The expositions of the algorithmic tools presented here are designed to serve as a useful guide for those wishing to bring such tools to bear on their own problems. A wide range of mathematical scientists should find these expositions valuable. This includes both the users of other programs similar to Macaulay 2 (for example, Singular and CoCoA) and those who are not interested in explicit machine computations at all. -
Phase Plane Methods
Chapter 10 Phase Plane Methods Contents 10.1 Planar Autonomous Systems . 680 10.2 Planar Constant Linear Systems . 694 10.3 Planar Almost Linear Systems . 705 10.4 Biological Models . 715 10.5 Mechanical Models . 730 Studied here are planar autonomous systems of differential equations. The topics: Planar Autonomous Systems: Phase Portraits, Stability. Planar Constant Linear Systems: Classification of isolated equilib- ria, Phase portraits. Planar Almost Linear Systems: Phase portraits, Nonlinear classi- fications of equilibria. Biological Models: Predator-prey models, Competition models, Survival of one species, Co-existence, Alligators, doomsday and extinction. Mechanical Models: Nonlinear spring-mass system, Soft and hard springs, Energy conservation, Phase plane and scenes. 680 Phase Plane Methods 10.1 Planar Autonomous Systems A set of two scalar differential equations of the form x0(t) = f(x(t); y(t)); (1) y0(t) = g(x(t); y(t)): is called a planar autonomous system. The term autonomous means self-governing, justified by the absence of the time variable t in the functions f(x; y), g(x; y). ! ! x(t) f(x; y) To obtain the vector form, let ~u(t) = , F~ (x; y) = y(t) g(x; y) and write (1) as the first order vector-matrix system d (2) ~u(t) = F~ (~u(t)): dt It is assumed that f, g are continuously differentiable in some region D in the xy-plane. This assumption makes F~ continuously differentiable in D and guarantees that Picard's existence-uniqueness theorem for initial d ~ value problems applies to the initial value problem dt ~u(t) = F (~u(t)), ~u(0) = ~u0.