Functional-Integral Representation of Quantum Field Theory
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FUNCTIONAL ANALYSIS 1. Banach and Hilbert Spaces in What
FUNCTIONAL ANALYSIS PIOTR HAJLASZ 1. Banach and Hilbert spaces In what follows K will denote R of C. Definition. A normed space is a pair (X, k · k), where X is a linear space over K and k · k : X → [0, ∞) is a function, called a norm, such that (1) kx + yk ≤ kxk + kyk for all x, y ∈ X; (2) kαxk = |α|kxk for all x ∈ X and α ∈ K; (3) kxk = 0 if and only if x = 0. Since kx − yk ≤ kx − zk + kz − yk for all x, y, z ∈ X, d(x, y) = kx − yk defines a metric in a normed space. In what follows normed paces will always be regarded as metric spaces with respect to the metric d. A normed space is called a Banach space if it is complete with respect to the metric d. Definition. Let X be a linear space over K (=R or C). The inner product (scalar product) is a function h·, ·i : X × X → K such that (1) hx, xi ≥ 0; (2) hx, xi = 0 if and only if x = 0; (3) hαx, yi = αhx, yi; (4) hx1 + x2, yi = hx1, yi + hx2, yi; (5) hx, yi = hy, xi, for all x, x1, x2, y ∈ X and all α ∈ K. As an obvious corollary we obtain hx, y1 + y2i = hx, y1i + hx, y2i, hx, αyi = αhx, yi , Date: February 12, 2009. 1 2 PIOTR HAJLASZ for all x, y1, y2 ∈ X and α ∈ K. For a space with an inner product we define kxk = phx, xi . Lemma 1.1 (Schwarz inequality). -
Funaional Integration for Solving the Schroedinger Equation
Comitato Nazionale Energia Nucleare Funaional Integration for Solving the Schroedinger Equation A. BOVE. G. FANO. A.G. TEOLIS RT/FIMÀ(7$ Comitato Nazionale Energia Nucleare Funaional Integration for Solving the Schroedinger Equation A. BOVE, G.FANO, A.G.TEOLIS 3 1. INTRODUCTION Vesto pervenuto il 2 agosto 197) It ia well-known (aee e.g. tei. [lj - |3| ) that the infinite-diaeneional r«nctional integration ia a powerful tool in all the evolution processes toth of the type of the heat -tanefer or Scbroediager equation. Since the 'ieid of applications of functional integration ia rapidly increasing, it *«*ae dcairahle to study the «stood from the nuaerical point of view, at leaat in siaple caaea. Such a orograa baa been already carried out by Cria* and Scorer (ace ref. |«| - |7| ), via Monte-Carlo techniques. We use extensively an iteration aethod which coneiecs in successively squa£ ing < denaicy matrix (aee Eq.(9)), since in principle ic allows a great accuracy. This aetbod was used only tarely before (aee ref. |4| ). Fur- chersjore we give an catiaate of che error (e«e Eqs. (U),(26)) boch in Che caaa of Wiener and unleabeck-Ometein integral. Contrary to the cur rent opinion (aae ref. |13| ), we find that the last integral ia too sen sitive to the choice of the parameter appearing in the haraonic oscilla tor can, in order to be useful except in particular caaea. In this paper we study the one-particle spherically sysssetric case; an application to the three nucleon probUa is in progress (sea ref. |U| ). St»wp«wirìf^m<'oUN)p^woMG>miutoN«i»OMtop«fl'Ht>p>i«Wi*faif»,Piijl(Mi^fcri \ptt,mt><r\A, i uwh P.ei#*mià, Uffcio Eanfeai SaMfiM», " ~ Hot*, Vi.1. -
Linear Spaces
Chapter 2 Linear Spaces Contents FieldofScalars ........................................ ............. 2.2 VectorSpaces ........................................ .............. 2.3 Subspaces .......................................... .............. 2.5 Sumofsubsets........................................ .............. 2.5 Linearcombinations..................................... .............. 2.6 Linearindependence................................... ................ 2.7 BasisandDimension ..................................... ............. 2.7 Convexity ............................................ ............ 2.8 Normedlinearspaces ................................... ............... 2.9 The `p and Lp spaces ............................................. 2.10 Topologicalconcepts ................................... ............... 2.12 Opensets ............................................ ............ 2.13 Closedsets........................................... ............. 2.14 Boundedsets......................................... .............. 2.15 Convergence of sequences . ................... 2.16 Series .............................................. ............ 2.17 Cauchysequences .................................... ................ 2.18 Banachspaces....................................... ............... 2.19 Completesubsets ....................................... ............. 2.19 Transformations ...................................... ............... 2.21 Lineartransformations.................................. ................ 2.21 -
НОВОСИБИРСК Budker Institute of Nuclear Physics, 630090 Novosibirsk, Russia
ИНСТИТУТ ЯДЕРНОЙ ФИЗИКИ им. Г.И. Будкера СО РАН Sudker Institute of Nuclear Physics P.G. Silvestrcv CONSTRAINED INSTANTON AND BARYON NUMBER NONCONSERVATION AT HIGH ENERGIES BIJ'DKERINP «292 НОВОСИБИРСК Budker Institute of Nuclear Physics, 630090 Novosibirsk, Russia P.G.SUvestrov CONSTRAINED INSTANTON AND BARYON NUMBER NON-CONSERVATION AT HIGH ENERGIES BUDKERINP 92-92 NOVOSIBIRSK 1992 Constrained Instanton and Baryon Number NonConservation at High Energies P.G.Silvestrov1 Budker Institute of Nuclear Physics, 630090 Novosibirsk, Russia Abstract The total cross section for baryon number violating processes at high energies is usually parametrized as <r«,toi oe exp(—.F(e)), where 5 = Л/З/EQ EQ = у/^ттпш/tx. In the present paper the third nontrivial term of the expansion is obtain^. The unknown corrections to F{e) are expected to be of 8 3 2 the order of c ' , but have neither (mhfmw) , nor log(e) enhance ment. The .total cross section is extremely sensitive to the value of single Instanton action. The correction to Instanton action A5 ~ (mp)* log(mp)/</2 is found (p is the Instanton radius). For sufficiently heavy Higgs boson the pdependent part of the Instanton action is changed drastically, in this case even the leading contribution to F(e), responsible for a growth of cross section due to the multiple produc tion of classical Wbosons, is changed: ©Budker Institute of Nuclear Physics 'етаЦ address! PStt.VESTBOV®INP.NSK.SU 1 Introduction A few years ago it was recognized, that the total crosssection for baryon 1БЭ number violating processes, which is small like ехр(4т/а) as 10~ [1] 2 (where a = р /(4тг)) at low energies, may become unsuppressed at TeV energies [2, 3, 4]. -
General Inner Product & Fourier Series
General Inner Products 1 General Inner Product & Fourier Series Advanced Topics in Linear Algebra, Spring 2014 Cameron Braithwaite 1 General Inner Product The inner product is an algebraic operation that takes two vectors of equal length and com- putes a single number, a scalar. It introduces a geometric intuition for length and angles of vectors. The inner product is a generalization of the dot product which is the more familiar operation that's specific to the field of real numbers only. Euclidean space which is limited to 2 and 3 dimensions uses the dot product. The inner product is a structure that generalizes to vector spaces of any dimension. The utility of the inner product is very significant when proving theorems and runing computations. An important aspect that can be derived is the notion of convergence. Building on convergence we can move to represenations of functions, specifally periodic functions which show up frequently. The Fourier series is an expansion of periodic functions specific to sines and cosines. By the end of this paper we will be able to use a Fourier series to represent a wave function. To build toward this result many theorems are stated and only a few have proofs while some proofs are trivial and left for the reader to save time and space. Definition 1.11 Real Inner Product Let V be a real vector space and a; b 2 V . An inner product on V is a function h ; i : V x V ! R satisfying the following conditions: (a) hαa + α0b; ci = αha; ci + α0hb; ci (b) hc; αa + α0bi = αhc; ai + α0hc; bi (c) ha; bi = hb; ai (d) ha; aiis a positive real number for any a 6= 0 Definition 1.12 Complex Inner Product Let V be a vector space. -
Function Space Tensor Decomposition and Its Application in Sports Analytics
East Tennessee State University Digital Commons @ East Tennessee State University Electronic Theses and Dissertations Student Works 12-2019 Function Space Tensor Decomposition and its Application in Sports Analytics Justin Reising East Tennessee State University Follow this and additional works at: https://dc.etsu.edu/etd Part of the Applied Statistics Commons, Multivariate Analysis Commons, and the Other Applied Mathematics Commons Recommended Citation Reising, Justin, "Function Space Tensor Decomposition and its Application in Sports Analytics" (2019). Electronic Theses and Dissertations. Paper 3676. https://dc.etsu.edu/etd/3676 This Thesis - Open Access is brought to you for free and open access by the Student Works at Digital Commons @ East Tennessee State University. It has been accepted for inclusion in Electronic Theses and Dissertations by an authorized administrator of Digital Commons @ East Tennessee State University. For more information, please contact [email protected]. Function Space Tensor Decomposition and its Application in Sports Analytics A thesis presented to the faculty of the Department of Mathematics East Tennessee State University In partial fulfillment of the requirements for the degree Master of Science in Mathematical Sciences by Justin Reising December 2019 Jeff Knisley, Ph.D. Nicole Lewis, Ph.D. Michele Joyner, Ph.D. Keywords: Sports Analytics, PCA, Tensor Decomposition, Functional Analysis. ABSTRACT Function Space Tensor Decomposition and its Application in Sports Analytics by Justin Reising Recent advancements in sports information and technology systems have ushered in a new age of applications of both supervised and unsupervised analytical techniques in the sports domain. These automated systems capture large volumes of data points about competitors during live competition. -
Functional Determinant of the Massive Laplace Operator and the Multiplicative Anomaly
Functional Determinant of the Massive Laplace Operator and the Multiplicative Anomaly a,b c a,b Guido Cognola ∗, Emilio Elizalde †, and Sergio Zerbini ‡ (a) Dipartimento di Fisica, Universit`adi Trento via Sommarive 14, 38123 Trento, Italia (b) TIFPA (INFN), Trento, Italia (c) Consejo Superior de Investigaciones Cientficas ICE (CSIC-IEEC), UAB Campus, 08193 Bellaterra, Barcelona, Spain Abstract After a brief survey of zeta function regularization issues and of the related multiplica- tive anomaly, illustrated with a couple of basic examples, namely the harmonic oscillator and quantum field theory at finite temperature, an application of these methods to the computa- tion of functional determinants corresponding to massive Laplacians on spheres in arbitrary dimensions is presented. Explicit formulas are provided for the Laplace operator on spheres in N = 1, 2, 3, 4 dimensions and for ‘vector’ and ‘tensor’ Laplacians on the unitary sphere S4. 1 Introduction In quantum field theory (QFT), the Euclidean partition function plays a very important role. The full propagator and all other n point correlation functions can be computed by means of it. − Moreover, this tool can be extended without problem to curved space-time [1]. As a formalism this is extremely beautiful but it must be noticed that in relativistic quantum field theories an infinite number of degrees of freedom is involved and, as a consequence, ultraviolet divergences will be present, thus rendering regularization and renormalization compulsory. In the one-loop approximation, and in the external field approximation too, one may describe (scalar) quantum fields by means of a (Euclidean) path integral and express the Euclidean parti- tion function as a function of functional determinants associated with the differential operators involved. -
Using Functional Distance Measures When Calibrating Journey-To-Crime Distance Decay Algorithms
USING FUNCTIONAL DISTANCE MEASURES WHEN CALIBRATING JOURNEY-TO-CRIME DISTANCE DECAY ALGORITHMS A Thesis Submitted to the Graduate Faculty of the Louisiana State University and Agricultural and Mechanical College in partial fulfillment of the requirements for the degree of Master of Natural Sciences in The Interdepartmental Program of Natural Sciences by Joshua David Kent B.S., Louisiana State University, 1994 December 2003 ACKNOWLEDGMENTS The work reported in this research was partially supported by the efforts of Dr. James Mitchell of the Louisiana Department of Transportation and Development - Information Technology section for donating portions of the Geographic Data Technology (GDT) Dynamap®-Transportation data set. Additional thanks are paid for the thirty-five residence of East Baton Rouge Parish who graciously supplied the travel data necessary for the successful completion of this study. The author also wishes to acknowledge the support expressed by Dr. Michael Leitner, Dr. Andrew Curtis, Mr. DeWitt Braud, and Dr. Frank Cartledge - their efforts helped to make this thesis possible. Finally, the author thanks his wonderful wife and supportive family for their encouragement and tolerance. ii TABLE OF CONTENTS ACKNOWLEDGMENTS .............................................................................................................. ii LIST OF TABLES.......................................................................................................................... v LIST OF FIGURES ...................................................................................................................... -
Supersymmetric Path Integrals
Communications in Commun. Math. Phys. 108, 605-629 (1987) Mathematical Physics © Springer-Verlag 1987 Supersymmetric Path Integrals John Lott* Department of Mathematics, Harvard University, Cambridge, MA 02138, USA Abstract. The supersymmetric path integral is constructed for quantum mechanical models on flat space as a supersymmetric extension of the Wiener integral. It is then pushed forward to a compact Riemannian manifold by means of a Malliavin-type construction. The relation to index theory is discussed. Introduction An interesting new branch of mathematical physics is supersymmetry. With the advent of the theory of superstrings [1], it has become important to analyze the quantum field theory of supersymmetric maps from R2 to a manifold. This should probably be done in a supersymmetric way, that is, based on the theory of supermanifolds, and in a space-time covariant way as opposed to the Hamiltonian approach. Accordingly, one wishes to make sense of supersymmetric path integrals. As a first step we study a simpler case, that of supersymmetric maps from R1 to a manifold, which gives supersymmetric quantum mechanics. As Witten has shown, supersymmetric quantum mechanics is related to the index theory of differential operators [2]. In this particular case of a supersymmetric field theory, the Witten index, which gives a criterion for dynamical supersymmetry breaking, is the ordinary index of a differential operator. If one adds the adjoint to the operator and takes the square, one obtains the Hamiltonian of the quantum mechanical theory. These indices can be formally computed by supersymmetric path integrals. For example, the Euler characteristic of a manifold M is supposed to be given by integrating e~L, with * Research supported by an NSF postdoctoral fellowship Current address: IHES, Bures-sur-Yvette, France 606 J. -
The Uncertainty Principle: Group Theoretic Approach, Possible Minimizers and Scale-Space Properties
The Uncertainty Principle: Group Theoretic Approach, Possible Minimizers and Scale-Space Properties Chen Sagiv1, Nir A. Sochen1, and Yehoshua Y. Zeevi2 1 Department of Applied Mathematics University of Tel Aviv Ramat-Aviv, Tel-Aviv 69978, Israel [email protected],[email protected] 2 Department of Electrical engineering Technion - Israel Institute of Technology Technion City, Haifa 32000, Israel [email protected] Abstract. The uncertainty principle is a fundamental concept in the context of signal and image processing, just as much as it has been in the framework of physics and more recently in harmonic analysis. Un- certainty principles can be derived by using a group theoretic approach. This approach yields also a formalism for finding functions which are the minimizers of the uncertainty principles. A general theorem which associates an uncertainty principle with a pair of self-adjoint operators is used in finding the minimizers of the uncertainty related to various groups. This study is concerned with the uncertainty principle in the context of the Weyl-Heisenberg, the SIM(2), the Affine and the Affine-Weyl- Heisenberg groups. We explore the relationship between the two-dimensional affine group and the SIM(2) group in terms of the uncertainty minimizers. The uncertainty principle is also extended to the Affine-Weyl-Heisenberg group in one dimension. Possible minimizers related to these groups are also presented and the scale-space properties of some of the minimizers are explored. 1 Introduction Various applications in signal and image processing call for deployment of a filter bank. The latter can be used for representation, de-noising and edge en- hancement, among other applications. -
Functional Integration and the Mind
Synthese (2007) 159:315–328 DOI 10.1007/s11229-007-9240-3 Functional integration and the mind Jakob Hohwy Published online: 26 September 2007 © Springer Science+Business Media B.V. 2007 Abstract Different cognitive functions recruit a number of different, often over- lapping, areas of the brain. Theories in cognitive and computational neuroscience are beginning to take this kind of functional integration into account. The contributions to this special issue consider what functional integration tells us about various aspects of the mind such as perception, language, volition, agency, and reward. Here, I consider how and why functional integration may matter for the mind; I discuss a general the- oretical framework, based on generative models, that may unify many of the debates surrounding functional integration and the mind; and I briefly introduce each of the contributions. Keywords Functional integration · Functional segregation · Interconnectivity · Phenomenology · fMRI · Generative models · Predictive coding 1 Introduction Across the diverse subdisciplines of the mind sciences, there is a growing awareness that, in order to properly understand the brain as the basis of the mind, our theorising and experimental work must to a higher degree incorporate the fact that activity in the brain, at the level of individual neurons, over neural populations, to cortical areas, is characterised by massive interconnectivity. In brain imaging science, this represents a move away from ‘blob-ology’, with a focus on local areas of activity, and towards functional integration, with a focus on the functional and dynamic causal relations between areas of activity. Similarly, in J. Hohwy (B) Department of Philosophy, Monash University, Clayton, VIC 3800, Australia e-mail: [email protected] 123 316 Synthese (2007) 159:315–328 theoretical neuroscience there is a renewed focus on the computational significance of the interaction between bottom-up and top-down neural signals. -
Derivation: the Volume of a D-Dimensional Hypershell
Derivation: the volume of a D‐dimensional hypershell Phys 427 Yann Chemla Sept. 1, 2011 In class we worked out the multiplicity of an ideal gas by summing over volume elements in k‐space (each of which contains one quantum state) that have energy E. We showed that the surface of states that have energy E is a hypershell of radius k in D‐dimensions where . Integrating in D‐dimensions in Cartesian coordinates is easy, but how do you integrate over a hypershell in D‐ dimensions? We used dimensional arguments in class to relate a volume of k‐space in Cartesian vs. hyperspherical coordinates: Ω where Ω is the solid angle subtended by the hypershell. If we integrate over all D‐dimensional space in Cartesian vs. hyperspherical coordinates, we should get the same answer: where we integrated over the solid angle to obtain the factor gD. We would like to determine gD. The above formula is not very useful in that respect because both sides are infinite. What we want is a function that can be integrated over all of D‐dimensional space in both Cartesian and hyperspherical coordinates and give a finite answer. A Gaussian function over k1 ... kD will do the trick. We can write: where we wrote the Gaussian in terms of k on the right side of the equation. Let’s look at the left side of the equation first. The integral can be written in terms of the product of D identical 1‐dimensional integrals: / where we evaluated the Gaussian integral in the last step (look up Kittel & Kroemer Appendix A for help on evaluating Gaussian integrals).