The Renormalization Group in Quantum Field Theory
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Foundations of Probability Theory and Statistical Mechanics
Chapter 6 Foundations of Probability Theory and Statistical Mechanics EDWIN T. JAYNES Department of Physics, Washington University St. Louis, Missouri 1. What Makes Theories Grow? Scientific theories are invented and cared for by people; and so have the properties of any other human institution - vigorous growth when all the factors are right; stagnation, decadence, and even retrograde progress when they are not. And the factors that determine which it will be are seldom the ones (such as the state of experimental or mathe matical techniques) that one might at first expect. Among factors that have seemed, historically, to be more important are practical considera tions, accidents of birth or personality of individual people; and above all, the general philosophical climate in which the scientist lives, which determines whether efforts in a certain direction will be approved or deprecated by the scientific community as a whole. However much the" pure" scientist may deplore it, the fact remains that military or engineering applications of science have, over and over again, provided the impetus without which a field would have remained stagnant. We know, for example, that ARCHIMEDES' work in mechanics was at the forefront of efforts to defend Syracuse against the Romans; and that RUMFORD'S experiments which led eventually to the first law of thermodynamics were performed in the course of boring cannon. The development of microwave theory and techniques during World War II, and the present high level of activity in plasma physics are more recent examples of this kind of interaction; and it is clear that the past decade of unprecedented advances in solid-state physics is not entirely unrelated to commercial applications, particularly in electronics. -
Perturbative Versus Non-Perturbative Quantum Field Theory: Tao’S Method, the Casimir Effect, and Interacting Wightman Theories
universe Review Perturbative versus Non-Perturbative Quantum Field Theory: Tao’s Method, the Casimir Effect, and Interacting Wightman Theories Walter Felipe Wreszinski Instituto de Física, Universidade de São Paulo, São Paulo 05508-090, SP, Brazil; [email protected] Abstract: We dwell upon certain points concerning the meaning of quantum field theory: the problems with the perturbative approach, and the question raised by ’t Hooft of the existence of the theory in a well-defined (rigorous) mathematical sense, as well as some of the few existent mathematically precise results on fully quantized field theories. Emphasis is brought on how the mathematical contributions help to elucidate or illuminate certain conceptual aspects of the theory when applied to real physical phenomena, in particular, the singular nature of quantum fields. In a first part, we present a comprehensive review of divergent versus asymptotic series, with qed as background example, as well as a method due to Terence Tao which conveys mathematical sense to divergent series. In a second part, we apply Tao’s method to the Casimir effect in its simplest form, consisting of perfectly conducting parallel plates, arguing that the usual theory, which makes use of the Euler-MacLaurin formula, still contains a residual infinity, which is eliminated in our approach. In the third part, we revisit the general theory of nonperturbative quantum fields, in the form of newly proposed (with Christian Jaekel) Wightman axioms for interacting field theories, with applications to “dressed” electrons in a theory with massless particles (such as qed), as well as Citation: Wreszinski, W.F. unstable particles. Various problems (mostly open) are finally discussed in connection with concrete Perturbative versus Non-Perturbative models. -
Randomized Algorithms Week 1: Probability Concepts
600.664: Randomized Algorithms Professor: Rao Kosaraju Johns Hopkins University Scribe: Your Name Randomized Algorithms Week 1: Probability Concepts Rao Kosaraju 1.1 Motivation for Randomized Algorithms In a randomized algorithm you can toss a fair coin as a step of computation. Alternatively a bit taking values of 0 and 1 with equal probabilities can be chosen in a single step. More generally, in a single step an element out of n elements can be chosen with equal probalities (uniformly at random). In such a setting, our goal can be to design an algorithm that minimizes run time. Randomized algorithms are often advantageous for several reasons. They may be faster than deterministic algorithms. They may also be simpler. In addition, there are problems that can be solved with randomization for which we cannot design efficient deterministic algorithms directly. In some of those situations, we can design attractive deterministic algoirthms by first designing randomized algorithms and then converting them into deter- ministic algorithms by applying standard derandomization techniques. Deterministic Algorithm: Worst case running time, i.e. the number of steps the algo- rithm takes on the worst input of length n. Randomized Algorithm: 1) Expected number of steps the algorithm takes on the worst input of length n. 2) w.h.p. bounds. As an example of a randomized algorithm, we now present the classic quicksort algorithm and derive its performance later on in the chapter. We are given a set S of n distinct elements and we want to sort them. Below is the randomized quicksort algorithm. Algorithm RandQuickSort(S = fa1; a2; ··· ; ang If jSj ≤ 1 then output S; else: Choose a pivot element ai uniformly at random (u.a.r.) from S Split the set S into two subsets S1 = fajjaj < aig and S2 = fajjaj > aig by comparing each aj with the chosen ai Recurse on sets S1 and S2 Output the sorted set S1 then ai and then sorted S2. -
Probability Theory Review for Machine Learning
Probability Theory Review for Machine Learning Samuel Ieong November 6, 2006 1 Basic Concepts Broadly speaking, probability theory is the mathematical study of uncertainty. It plays a central role in machine learning, as the design of learning algorithms often relies on proba- bilistic assumption of the data. This set of notes attempts to cover some basic probability theory that serves as a background for the class. 1.1 Probability Space When we speak about probability, we often refer to the probability of an event of uncertain nature taking place. For example, we speak about the probability of rain next Tuesday. Therefore, in order to discuss probability theory formally, we must first clarify what the possible events are to which we would like to attach probability. Formally, a probability space is defined by the triple (Ω, F,P ), where • Ω is the space of possible outcomes (or outcome space), • F ⊆ 2Ω (the power set of Ω) is the space of (measurable) events (or event space), • P is the probability measure (or probability distribution) that maps an event E ∈ F to a real value between 0 and 1 (think of P as a function). Given the outcome space Ω, there is some restrictions as to what subset of 2Ω can be considered an event space F: • The trivial event Ω and the empty event ∅ is in F. • The event space F is closed under (countable) union, i.e., if α, β ∈ F, then α ∪ β ∈ F. • The even space F is closed under complement, i.e., if α ∈ F, then (Ω \ α) ∈ F. -
Effective Theory of Levy and Feller Processes
The Pennsylvania State University The Graduate School Eberly College of Science EFFECTIVE THEORY OF LEVY AND FELLER PROCESSES A Dissertation in Mathematics by Adrian Maler © 2015 Adrian Maler Submitted in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy December 2015 The dissertation of Adrian Maler was reviewed and approved* by the following: Stephen G. Simpson Professor of Mathematics Dissertation Adviser Chair of Committee Jan Reimann Assistant Professor of Mathematics Manfred Denker Visiting Professor of Mathematics Bharath Sriperumbudur Assistant Professor of Statistics Yuxi Zheng Francis R. Pentz and Helen M. Pentz Professor of Science Head of the Department of Mathematics *Signatures are on file in the Graduate School. ii ABSTRACT We develop a computational framework for the study of continuous-time stochastic processes with c`adl`agsample paths, then effectivize important results from the classical theory of L´evyand Feller processes. Probability theory (including stochastic processes) is based on measure. In Chapter 2, we review computable measure theory, and, as an application to probability, effectivize the Skorokhod representation theorem. C`adl`ag(right-continuous, left-limited) functions, representing possible sample paths of a stochastic process, form a metric space called Skorokhod space. In Chapter 3, we show that Skorokhod space is a computable metric space, and establish fundamental computable properties of this space. In Chapter 4, we develop an effective theory of L´evyprocesses. L´evy processes are known to have c`adl`agmodifications, and we show that such a modification is computable from a suitable representation of the process. We also show that the L´evy-It^odecomposition is computable. -
Renormalisation Circumvents Haag's Theorem
Some no-go results & canonical quantum fields Axiomatics & Haag's theorem Renormalisation bypasses Haag's theorem Renormalisation circumvents Haag's theorem Lutz Klaczynski, HU Berlin Theory seminar DESY Zeuthen, 31.03.2016 Lutz Klaczynski, HU Berlin Renormalisation circumvents Haag's theorem Some no-go results & canonical quantum fields Axiomatics & Haag's theorem Renormalisation bypasses Haag's theorem Outline 1 Some no-go results & canonical quantum fields Sharp-spacetime quantum fields Interaction picture Theorems by Powers & Baumann (CCR/CAR) Schrader's result 2 Axiomatics & Haag's theorem Wightman axioms Haag vs. Gell-Mann & Low Haag's theorem for nontrivial spin 3 Renormalisation bypasses Haag's theorem Renormalisation Mass shift wrecks unitary equivalence Conclusion Lutz Klaczynski, HU Berlin Renormalisation circumvents Haag's theorem Sharp-spacetime quantum fields Some no-go results & canonical quantum fields Interaction picture Axiomatics & Haag's theorem Theorems by Powers & Baumann (CCR/CAR) Renormalisation bypasses Haag's theorem Schrader's result The quest for understanding canonical quantum fields 1 1 Constructive approaches by Glimm & Jaffe : d ≤ 3 2 2 Axiomatic quantum field theory by Wightman & G˚arding : axioms to construe quantum fields in terms of operator theory hard work, some results (eg PCT, spin-statistics theorem) interesting: triviality results (no-go theorems) 3 other axiomatic approaches, eg algebraic quantum field theory General form of no-go theorems φ quantum field with properties so-and-so ) φ is trivial. 3 forms -
Curriculum Vitae
Curriculum Vitae General First name: Khudoyberdiyev Surname: Abror Sex: Male Date of birth: 1985, September 19 Place of birth: Tashkent, Uzbekistan Nationality: Uzbek Citizenship: Uzbekistan Marital status: married, two children. Official address: Department of Algebra and Functional Analysis, National University of Uzbekistan, 4, Talabalar Street, Tashkent, 100174, Uzbekistan. Phone: 998-71-227-12-24, Fax: 998-71-246-02-24 Private address: 5-14, Lutfiy Street, Tashkent city, Uzbekistan, Phone: 998-97-422-00-89 (mobile), E-mail: [email protected] Education: 2008-2010 Institute of Mathematics and Information Technologies of Uzbek Academy of Sciences, Uzbekistan, PHD student; 2005-2007 National University of Uzbekistan, master student; 2001-2005 National University of Uzbekistan, bachelor student. Languages: English (good), Russian (good), Uzbek (native), 1 Academic Degrees: Doctor of Sciences in physics and mathematics 28.04.2016, Tashkent, Uzbekistan, Dissertation title: Structural theory of finite - dimensional complex Leibniz algebras and classification of nilpotent Leibniz superalgebras. Scientific adviser: Prof. Sh.A. Ayupov; Doctor of Philosophy in physics and mathematics (PhD) 28.10.2010, Tashkent, Uzbekistan, Dissertation title: The classification some nilpotent finite dimensional graded complex Leibniz algebras. Supervisor: Prof. Sh.A. Ayupov; Master of Science, National University of Uzbekistan, 05.06.2007, Tashkent, Uzbekistan, Dissertation title: The classification filiform Leibniz superalgebras with nilindex n+m. Supervisor: Prof. Sh.A. Ayupov; Bachelor in Mathematics, National University of Uzbekistan, 20.06.2005, Tashkent, Uzbekistan, Dissertation title: On the classification of low dimensional Zinbiel algebras. Supervisor: Prof. I.S. Rakhimov. Professional occupation: June of 2017 - up to Professor, department Algebra and Functional Analysis, present time National University of Uzbekistan. -
275 – Quantum Field Theory
Quantum eld theory Lectures by Richard Borcherds, notes by Søren Fuglede Jørgensen Math 275, UC Berkeley, Fall 2010 Contents 1 Introduction 1 1.1 Dening a QFT . 1 1.2 Constructing a QFT . 2 1.3 Feynman diagrams . 4 1.4 Renormalization . 4 1.5 Constructing an interacting theory . 6 1.6 Anomalies . 6 1.7 Infrared divergences . 6 1.8 Summary of problems and solutions . 7 2 Review of Wightman axioms for a QFT 7 2.1 Free eld theories . 8 2.2 Fixing the Wightman axioms . 10 2.2.1 Reformulation in terms of distributions and states . 10 2.2.2 Extension of the Wightman axioms . 12 2.3 Free eld theory revisited . 12 2.4 Propagators . 13 2.5 Review of properties of distributions . 13 2.6 Construction of a massive propagator . 14 2.7 Wave front sets . 17 Disclaimer These are notes from the rst 4 weeks of a course given by Richard Borcherds in 2010.1 They are discontinued from the 7th lecture due to time constraints. They have been written and TeX'ed during the lecture and some parts have not been completely proofread, so there's bound to be a number of typos and mistakes that should be attributed to me rather than the lecturer. Also, I've made these notes primarily to be able to look back on what happened with more ease, and to get experience with TeX'ing live. That being said, feel very free to send any comments and or corrections to [email protected]. 1st lecture, August 26th 2010 1 Introduction 1.1 Dening a QFT The aim of the course is to dene a quantum eld theory, to nd out what a quantum eld theory is, and to dene Feynman measures, renormalization, anomalies, and regularization. -
Discrete Mathematics Discrete Probability
Introduction Probability Theory Discrete Mathematics Discrete Probability Prof. Steven Evans Prof. Steven Evans Discrete Mathematics Introduction Probability Theory 7.1: An Introduction to Discrete Probability Prof. Steven Evans Discrete Mathematics Introduction Probability Theory Finite probability Definition An experiment is a procedure that yields one of a given set os possible outcomes. The sample space of the experiment is the set of possible outcomes. An event is a subset of the sample space. Laplace's definition of the probability p(E) of an event E in a sample space S with finitely many equally possible outcomes is jEj p(E) = : jSj Prof. Steven Evans Discrete Mathematics By the product rule, the number of hands containing a full house is the product of the number of ways to pick two kinds in order, the number of ways to pick three out of four for the first kind, and the number of ways to pick two out of the four for the second kind. We see that the number of hands containing a full house is P(13; 2) · C(4; 3) · C(4; 2) = 13 · 12 · 4 · 6 = 3744: Because there are C(52; 5) = 2; 598; 960 poker hands, the probability of a full house is 3744 ≈ 0:0014: 2598960 Introduction Probability Theory Finite probability Example What is the probability that a poker hand contains a full house, that is, three of one kind and two of another kind? Prof. Steven Evans Discrete Mathematics Introduction Probability Theory Finite probability Example What is the probability that a poker hand contains a full house, that is, three of one kind and two of another kind? By the product rule, the number of hands containing a full house is the product of the number of ways to pick two kinds in order, the number of ways to pick three out of four for the first kind, and the number of ways to pick two out of the four for the second kind. -
The Cosmological Probability Density Function for Bianchi Class A
December 9, 1994 The Cosmological Probability Density Function for Bianchi Class A Models in Quantum Supergravity HUGH LUCKOCK and CHRIS OLIWA School of Mathematics and Statistics University of Sydney NSW 2006 Australia Abstract Nicolai’s theorem suggests a simple stochastic interpetation for supersymmetric Eu- clidean quantum theories, without requiring any inner product to be defined on the space of states. In order to apply this idea to supergravity, we first reduce to a one-dimensional theory with local supersymmetry by the imposition of homogeneity arXiv:gr-qc/9412028v1 9 Dec 1994 conditions. We then make the supersymmetry rigid by imposing gauge conditions, and quantise to obtain the evolution equation for a time-dependent wave function. Owing to the inclusion of a certain boundary term in the classical action, and a careful treatment of the initial conditions, the evolution equation has the form of a Fokker-Planck equation. Of particular interest is the static solution, as this satisfies all the standard quantum constraints. This is naturally interpreted as a cosmolog- ical probability density function, and is found to coincide with the square of the magnitude of the conventional wave function for the wormhole state. PACS numbers: 98.80.H , 04.65 Introduction Supersymmetric theories enjoy a number of appealing properties, many of which are particularly attractive in the context of quantum cosmology. For example, su- persymmetry leads to the replacement of second-order constraints by first-order constraints, which are both easier to solve and more restrictive. A feature of supersymmetry which has not yet been fully exploited in quantum cosmology arises from a theorem proved by Nicolai in 1980 [1]. -
Probability Theory and Stochastic Processes (R18a0403)
PROBABILITY THEORY AND STOCHASTIC PROCESSES (R18A0403) LECTURE NOTES B.TECH (II YEAR – I SEM) (2020-21) Prepared by: Mrs.N.Saritha, Assistant Professor Mr.G.S. Naveen Kumar, Assoc.Professor Department of Electronics and Communication Engineering MALLA REDDY COLLEGE OF ENGINEERING & TECHNOLOGY (Autonomous Institution – UGC, Govt. of India) Recognized under 2(f) and 12 (B) of UGC ACT 1956 (Affiliated to JNTUH, Hyderabad, Approved by AICTE - Accredited by NBA & NAAC – ‘A’ Grade - ISO 9001:2015 Certified) Maisammaguda, Dhulapally (Post Via. Kompally), Secunderabad – 500100, Telangana State, India Sensitivity: Internal & Restricted MALLA REDDY COLLEGE OF ENGINEERING AND TECHNOLOGY (AUTONOMOUS INSTITUTION: UGC, GOVT. OF INDIA) ELECTRONICS AND COMMUNICATION ENGINEERING II ECE I SEM PROBABILITY THEORY AND STOCHASTIC PROCESSES CONTENTS SYLLABUS UNIT-I-PROBABILITY AND RANDOM VARIABLE UNIT-II- DISTRIBUTION AND DENSITY FUNCTIONS AND OPERATIONS ON ONE RANDOM VARIABLE UNIT-III-MULTIPLE RANDOM VARIABLES AND OPERATIONS UNIT-IV-STOCHASTIC PROCESSES-TEMPORAL CHARACTERISTICS UNIT-V- STOCHASTIC PROCESSES-SPECTRAL CHARACTERISTICS UNITWISE IMPORTANT QUESTIONS PROBABILITY THEORY AND STOCHASTIC PROCESS Course Objectives: To provide mathematical background and sufficient experience so that student can read, write and understand sentences in the language of probability theory. To introduce students to the basic methodology of “probabilistic thinking” and apply it to problems. To understand basic concepts of Probability theory and Random Variables, how to deal with multiple Random Variables. To understand the difference between time averages statistical averages. To teach students how to apply sums and integrals to compute probabilities, and expectations. UNIT I: Probability and Random Variable Probability: Set theory, Experiments and Sample Spaces, Discrete and Continuous Sample Spaces, Events, Probability Definitions and Axioms, Joint Probability, Conditional Probability, Total Probability, Bayes’ Theorem, and Independent Events, Bernoulli’s trials. -
A. Ya. Khintchine's Work in Probability Theory
A.Ya. KHINTCHINE's WORK IN PROBABILITY THEORY Sergei Rogosin1, Francesco Mainardi2 1 Department of Economics, Belarusian State University Nezavisimosti ave 4, 220030 Minsk, Belarus e-mail: [email protected] (Corresponding author) 2 Department of Physics and Astronomy, Bologna University and INFN Via Irnerio 46, I-40126 Bologna, Italy e-mail: [email protected] Abstract The paper is devoted to the contribution in the Probability Theory of the well-known Soviet mathematician Alexander Yakovlevich Khint- chine (1894{1959). Several of his results are described, in particu- lar those fundamental results on the infinitely divisible distributions. Attention is paid also to his interaction with Paul L´evy. The con- tent of the paper is related to our joint book The Legacy of A.Ya. Khintchine's Work in Probability Theory published in 2010 by Cam- bridge Scientific Publishers. It is published in Notices of the Inter- national Congress of Chinese Mathematicians (ICCM), International Press, Boston, Vol. 5, No 2, pp. 60{75 (December 2017). DOI: 10.4310/ICCM.2017.v5.n2.a6 Keywords: History of XX Century Mathematics, A.Ya.Khintchine, Probability Theory, Infinitely Divisible Distributions. AMS 2010 Mathematics Subject Classification: 01A60, 60-03, 60E07. 1. Introduction:::::::::::::::::::::::::::::::::::::::::::::::::: 2 2. Short biography of Alexander Yakovlevich Khintchine::: 3 3. First papers in Probability: 1924{1936: : : : : : : : : : : : : : : : : : ::: 5 4. The interaction with Paul L´evy:::::::::::::::::::::::::::::10 5. Infinitely divisible