SOUTH AFRICAN THEORY AND COMPUTATIONAL SCHOOL: PAST COURSES
The South African Theory School (SATS), ran successfully as a pilot programme in 2021. A total of 36 students attended from 12 South African universities. The courses presented were Nonlinear Dynamics, General Relativity, Cosmology, Machine Learning, Quantum Field Theory, and Advanced Mathematical Methods.
A year later, the programme was upgraded to the South African Theory and Computational School (SATACS) and 11 courses were presented. In 2023 the programme grew further and 13 courses were offered. Students from outside South Africa were also permitted to attend.
Here are the details of the past courses:
SATACS COURSES – 2023
Differential Geometry  Jeff Murugan
Outline:
Part I: Structures on manifolds
 Vectors
 Tensors
 Tensor products (with applications to quantum mechanics)
 Symmetric and skewsymmetric tensors (with applications to bosons and fermions)
 Cartesian tensors (with applications to Maxwell electrodynamics)
Part II: Differential calculus on manifolds
 Vector and convector fields
 Differentiating tensors – the Lie bracket and Lie derivative
 Exterior calculus
 Applications to Maxwell electrodynamics and Hamiltonian mechanics
 Covariant derivatives
Part III: Integration on manifolds
 Manifolds – charts, atlases, coverings etc.
 Pform integration
 Stokes’ theorem
 Spin textures
 Homotopy and the Hopf map.
 An application to molecular folding – twists and writhes.
Skills outcome:
Geometry is the language of physics, from quantum mechanics, to modern gauge theory to general relativity. This course will give a semirigorous introduction and overview of some of the basics of differential and integral calculus on generally curved manifolds. By the end of the course, students will have a working understanding of notions such as Lie derivatives, exterior calculus, integration on manifolds and covariant derivatives. This should provide a good platform for subsequent courses such as general relativity, cosmology and Lie group theory.
Prerequisites:
Vector calculus is essential. Some quantum mechanics would be recommended but I will cover anything required in the course.
Dates:
Roughly 13 February – 19 May 2023. More details will be available in the first lecture.
Lecture format:
The course will run over 2 days a week for 1.5 hours per lecture.
As this will be running at UCT, it will be in hybrid format. All lectures will be recorded inperson and uploaded to YouTube.
Method of evaluation:
Weekly problem sets and a final takehome exam.
Lecturer biography:
Jeff Murugan is Professor of Mathematical Physics and head of the Laboratory for Quantum Gravity & Strings at the University of Cape Town. He received a PhD in Noncommutative Geometry in String Theory from UCT and Oxford in 2004. He has held a postdoctoral position at Brown University from 20042006 and was a member at the Institute for Advanced Study in Princeton in 20162017. His research interests lie primarily in understanding emergent phenomena, from condensed matter to neurophysics. His recent focus has been on lowdimensional quantum field theory, topological quantum matter and quantum chaos in disordered systems.
E: jeffmurugan @ gmail.com  W: Lecturer’s personal website  Publications
Trustworthy Machine Learning  Makhamisa Senekane
Outline:
 Introduction to Machine Learning (ML)
 Introduction to trustworthy Machine Learning
 Privacy Enhancing Technologies (PETs)
 Differential Privacy
 Federated Learning
 Explainability in Machine Learning
 Machine Learning robustness
 Fairness and Bias in Machine Learning.
Skills outcome:
At the end of the course, the students are expected to:
 Appreciate the need to build trustworthy ML models
 Have a firm understanding and application of PETs such as Differential Privacy and Federated Learning
 Understand and be able to use the ML explainability frameworks
 Be well equipped to the ways of building robustness in Machine Learning models
 Appreciate the value of fairness and the dangers of bias in ML models.
Prerequisites:
Linear Algebra; Probability Theory; Calculus; Programming basics, especially with Python.
Dates:
13 February – 30 April 2023
Lecture format:
Synchronous virtual weekly lecture sessions (2 hours) and tutorial sessions (2 hours). All the sessions will be recorded.
Method of evaluation:
Four assignments, each worth 25% of the total mark.
Lecturer biography:
Makhamisa Senekane is a Senior Researcher in the Institute for Intelligent Systems at the University of Johannesburg. Prior to that, he lectured in the Department of Physics and Electronics at the National University of Lesotho. He was also a Senior Lecturer in the Faculty of Information and Communication Technology at Limkwokwing University of Creative Technology (Lesotho). Further, he lectured in the Faculty of Computing at Botho University (Maseru Campus). He has a PhD in Physics from the University of KwaZuluNatal, MSc.Eng in Electrical Engineering from the University of Cape Town, and B.Eng in Electronics Engineering from the National University of Lesotho. His research interests include data science, data security, data privacy, artificial intelligence (machine learning and natural language processing), and quantum information processing (quantum cryptography, quantum computing, and quantum machine learning).
E: smakhamisa @ uj.ac.za  W: Lecturer’s personal website  Publications
Software Architecture  Fritz Solms
Outline:
 Context
 the role & responsibilities of a software architect
 what is software architecture, including
 software architecture vs application design
 Software Architecture Requirements
 specifying, verifying and quantifying software architecture requirements including:
 quantified quality requirements
 making appropriate quality requirement tradeoff decisions
 integration and access requirements and
 architectural constraints
 quantified quality requirements
 specifying, verifying and quantifying software architecture requirements including:
 Elements of Software Architecture Design
 architectural patterns
 architectural tactics
 integration patterns
 reference architecture and frameworks
 Systematic Method for Software Architecture Design (SyMAD)
 Software architecture description
 ISO/IEEE standard for a software architecture description
 Overview of Software Architecture description frameworks
 SyMAD Model based description
 Generating text based descriptions from models
 Software Architecture Recovery
 Why?
 Overview of architecture recovery methods
 Manually recovering a software architecture description from code
 Software Architecture Analysis
 Why?
 Overview of analysis methods
 The Attribute TradeOff Analysis Method (ATAM)
Skills outcome:
A solid understanding of software architecture versus application design, the concepts and methods and standards applicable to software architecture design.
Prerequisites:
3rd year Computer Science or Software Engineering.
Dates:
7 March – 1 August 2023.
Lecture format:
20 weeks with 1 synchronous 60minute lecture per week on Tuesdays from 18h0019h00.
Method of evaluation:
Group projects and 1 final exam.
Lecturer biography:
After completing a PhD and a postdoc in Theoretical Physics, Fritz was a senior lecturer at Applied Mathematics at the University of Johannesburg. Here he founded with Prof Steeb the International School of Scientific Computing. After having been three years a quantitative analyst and software architect at Standard Corporate and Merchant Bank, Fritz formed a training and consulting company, Solms Training & Consulting (SolmsTC.com). The company provides training and consulting services around software application and software architecture design, as well as a range of software development technologies.
In 2011 he joined the Computer Science Department of the University of Pretoria where he founded and led the Software Architecture and Software Engineering Research (SESAr) Group. Subsequently he was employed as lead software architect at SPLANE Automation. Here he developed an application server for realtime safetycritical systems, as well as infrastructure for modelbased development.
He has currently shifted his focus back onto SolmsTC.com. Since 2017, Fritz is also a research associate of the School of Data Science and Computational Thinking at the University of Stellenbosch, where he supervises some postgraduate students.
E: fritz @ solmstc.com  W: SolmsTC.com; FritzSolms.me  Publications: see lecturer’s websites
Logics for Artificial Intelligence  Tommie Meyer
Outline:
This course will introduce students to logics used in the area of Knowledge Representation and Reasoning – a subarea of Artificial Intelligence.
Skills outcome:
Logic plays a central role in many areas of Artificial Intelligence. This course will introduce students to Propositional Logic, as well as Description Logics, a family of logics frequently used in the area of Knowledge Representation and Reasoning. Description Logics are frequently used to represent formal ontologies.
Prerequisites:
Familiarity with basic discrete mathematics, a basic understanding of algorithms and the analysis of algorithms.
Dates:
11 April to 2 June 2023.
Lecture format:
The course will be presented inperson at the University of Cape Town. All lectures and tutorials will be broadcast synchronously; the lectures and tutorials will also be recorded inperson and made available online. There are 2 onehour lectures per week.
Method of evaluation:
Two assignments and a takehome exam.
Lecturer biography:
Tommie Meyer is a professor in Computer Science at UCT and codirector of the Centre for Artificial Intelligence Research (CAIR). Prior to this he held positions at the CSIR in Pretoria, National ICT Australia, the University of New South Wales in Australia, the University of Pretoria, and the University of South Africa. He is recognised internationally as an expert in Knowledge Representation and Reasoning. He is one of only three South African Computer Scientists to have obtained an Arating from the NRF. He is a member of SAICSIT, AAAI, ACM, and ASSAf.
E: tommie.meyer @ uct.ac.za  W: https://tommiemeyer.org.za
Publications: https://tommiemeyer.org.za/publications
Symmetries in Physics  An Introduction to Group Theory  Jaco van Zyl
Outline:
 Some elementary group theory – definitions, finite groups, products of groups
 Continuous groups – orthogonal and rotation groups, SO(3), The Lorentz group, SU(2) and SL(2,C)
 Representation theory – finite dimensional reps., infinitedimensional reps, SO(2), irreducible representations of SO(3), characters
 Group representations in quantum mechanics – SU(2) and SL(2,C) representations, spinors.
Skills outcome:
Group theory and representations of said groups are foundational to modern mathematical and theoretical physics, from quantum mechanics to cosmology. Students will exit this course with a working knowledge of group and representation theory at the postgraduate level.
Prerequisites:
Dates:
26 July – 31 October 2023.
Lecture format:
Fully online.
Method of evaluation:
Problem sets and a final project.
Lecturer biography:
Jaco van Zyl is a postdoctoral fellow in the Laboratory for Quantum Gravity & Strings at the University of Cape Town. After completing his PhD in 2015 at Stellenbosch University he joined the Mandelstam Institute for Theoretical Physics at the University of the Witwatersrand as a postdoctoral fellow from 20162020 and the Department of Physics as a sessional lecturer in 2021.
His research interests include holography, conformal field theory, quantum chaos and quantum complexity. His recent work is funded by the ‘Quantum Technologies for Sustainable Development‘ grant from NITheCS, with the primary aim of developing means of quantum energy storage.
E: hjrvanzyl @ gmail.com  W: INSPIREHEP profile  Publications
Special Topics in Category Theory  Zurab Janelidze
Outline:
In this course we will explore various topics in category theory. The choice of topics can range from basic to advanced and will depend on the existing knowledge of the subject among the participants. Category theory provides a unifying language for conceptualising phenomena across different disciplines, including subjects within pure mathematics, as well as some aspects of quantum physics, computer science, biology, and others. Visit the course website.
Skills outcome:
This course introduces students to basic concepts of category theory, which are useful when applying category theory as a language of conceptualisation in various disciplines. Upon completing the course, you would have gained the skills of making sense of, working with and applying these concepts.
Prerequisites:
Experience with mathematical thinking and working with a symbolic language (for example, experience with mathematical formalisms). Anyone interested in the course is advised to look through the notes and videos.
Dates:
29 July – 4 November 2023 (Saturdays 10:0012:00). Join via this Zoom link.
Method of evaluation:
Students will be assessed based on assignments and presentations.
Lecturer biography:
Zurab Janelidze is a professor of mathematics at Stellenbosch University. He is an Associate of NITheCS and a principal investigator in one of the research programmes at NITheCS. He serves on the editorial boards of two international journals in his field of expertise, category theory, and recently joined the editorial board of Afrika Matematika, a journal of the African Mathematical Union. Prof Janelidze is currently the president of the South African Mathematical Society. He is passionate about discovering and teaching mathematics, as well as looking for mathematical structures in other art forms.
E: zurab @ sun.ac.za  W: Lecturer’s personal website  Publications
Cosmology  Amare Abebe
Outline:
 The Cosmological Principle
 Cosmological Models
 Inflationary Cosmology
 Cosmological Perturbation Theory
 Largescale Structure Formation
Skills outcome:
The course will offer hot topics in modern cosmology.
At the end of the course, students are expected to:
 understand the assumptions in cosmology that led to the formulation of the standard cosmological model
 derive the cosmological field equations and analyse their solutions
 demonstrate length and time scales of the universe
 apply the specialised and integrated knowledge of general relativity and cosmology to critically analyse the shortcomings of the Big Bang Model, and the need to introduce inflation, dark energy, and dark matter
 understand the physical processes and mechanisms that lead to largescale structure formation
 critically analyse the standard cosmological model and understand the need to look at new paradigms beyond the standard model.
Prerequisites:
Introduction to General Relativity.
Dates:
17 July — 30 October 2023
Lecture format:
Weekly synchronous lecture videos and tutorials, with the possibility of inperson discussions.
Method of evaluation:
Weekly assignments, 23 projects that test both theoretical and computational skills, and an exitassessment exam.
Lecturer biography:
Amare Abebe received his PhD in cosmology from the University of Cape Town in 2013. He held a postdoc position at the NorthWest University from 2014 to 2015 after which he joined the faculty at this same institution. He is currently a Professor of Physics and his broad research interests lie in gravitation and cosmology.
E: amare.abebe @ nithecs.ac.za  W: Lecturer’s personal website  Publications
Integrable Systems  Konstantinos Zoubos
Outline:
This course will introduce classical and quantum integrable systems. We will start with integrability in classical dynamical systems, introducing concepts such as Lax pairs, the inverse scattering method and solitons. Afterwards we will focus on the quantum case, covering integrable spin chains and the Bethe ansatz, a brief introduction to quantum groups, and statistical systems such as the Ising, RSOS and percolation models. Prerequisites for the course include lagrangian mechanics, quantum mechanics at Honours level, and group theory. Some necessary aspects from statistical mechanics will be reviewed.
Skills outcome:
Students will develop a useful background for the understanding of topics such as critical phenomena, 2d CFT and many body scattering. Some technical aspects that will need to be introduced (quantum groups, Jacobi theta functions and many more) play important roles in many areas of physics and mathematics.
Prerequisites:
Classical Mechanics and Quantum Mechanics at Honours level, as well as Lie algebras.
Dates:
24 July – 8 September 2023.
Lecture format:
Online lectures (perhaps 3 hours a week) plus a weekly tutorial.
Method of evaluation:
Weekly homework assignments, for most of which students will need to use Sagemath.
Lecturer biography:
Konstantinos Zoubos is Associate Professor at the Physics Department of the University of Pretoria. His research interests are in supersymmetric Quantum Field Theory and String Theory, with an emphasis on integrable structures and the tools to analyse them, such as quantum groups.
E: konstantinos.zoubos @ up.ac.za  W: Lecturer’s personal website
Quantum Field Theory I  W. A. Horowitz
Outline:
 Postulates of QM and SR
 Quantizing the free scalar field
 Interpreting the results
 Connecting to experiments; in and out states; LSZ reduction
 LehmanKallen representation; GellMann–Low theorem; cross sections
 Feynman rules for scalar fields
 Introduction to QED, QED Feynman rules, and trace technology for cross sections.
Skills outcome:
Students will leave the course with a deep understanding of 1) free scalar quantum field theory and 2) Feynman calculus for computing cross sections involving scalar particles. Students should also have a good facility for computing Feynman diagrams and cross sections related to QED processes.
Prerequisites:
A course in advanced quantum mechanics and a course in which special relativity was treated in some detail.
Dates:
25 July — 2 September 2023.
Lecture format:
Synchronous lecture videos four times per week, one synchronous tutorial per week.
Method of evaluation:
Weekly problem sets and a project.
Lecturer biography:
Associate Professor W. A. Horowitz received his PhD in Physics from Columbia University in 2008. He held a postdoctoral research position at the Ohio State University from 2008 to 2010 before joining the faculty at the University of Cape Town. Prof Horowitz is an expert in the use of perturbative quantum field theory and AdS/CFT methods in phenomenological highenergy quantum chromodynamics applications.
E: wa.horowitz @ gmail.com  W: Lecturer’s personal website  Publications
Adversarial Artificial Intelligence  Makhamisa Senekane
Outline:
 Introduction to Artificial Intelligence (AI)
 Categories of Adversarial attacks
 Adversarial attacks in Machine Learning (ML)
 Adversarial attacks in Natural Language Processing (NLP) – Adversarial attacks in Computer Vision (CV)
 Defense strategies against the adversarial attacks in AI.
Skills outcome:
At the end of the course, the students are expected to:
 Have an understanding of various subfields of Artificial Intelligence
 Be familiar with the adversarial attacks on AI systems
 Have a firm understanding of defense strategies against adversarial attacks on AI systems.
Prerequisites:
Linear Algebra; Probability Theory; Calculus; Basics of cryptography; Programming basics, especially with Python.
Dates:
7 August — 29 October 2023.
Lecture format:
Synchronous virtual weekly lecture sessions (2 hours) and tutorial sessions (2 hours). All the sessions will be recorded.
Method of evaluation:
Four assignments, each worth 25% of the total mark.
Lecturer biography:
Makhamisa Senekane is a Senior Researcher in the Institute for Intelligent Systems at the University of Johannesburg. Prior to that, he lectured in the Department of Physics and Electronics at the National University of Lesotho. He was also a Senior Lecturer in the Faculty of Information and Communication Technology at Limkwokwing University of Creative Technology (Lesotho). Further, he lectured in the Faculty of Computing at Botho University (Maseru Campus). He has a PhD in Physics from the University of KwaZuluNatal, MSc.Eng in Electrical Engineering from the University of Cape Town, and B.Eng in Electronics Engineering from the National University of Lesotho. His research interests include data science, data security, data privacy, artificial intelligence (machine learning and natural language processing), and quantum information processing (quantum cryptography, quantum computing, and quantum machine learning).
E: smakhamisa @ uj.ac.za  W: Lecturer’s personal website  Publications
Machine Learning: from Linear Regression to Deep Learning  Jonathan Shock
Outline:

 Intro to machine learning
 Statistical theory and naive bases
 Regression tests and training ML models
 Gradient free optimisation methods
 Classification tasks and model evaluation
 Ensemble modeling
 Unsupervised learning: Clustering
 Unsupervised learning: Dimensionality Reduction
 Reinforcement Learning
 Intro to Neural Networks
 Pytorch
 Convolutional Neural Networks
 Recurrent Neural Networks
PLUS… a project based on the above topics on a research area of the student’s choice
Skills outcome:
A practical, working knowledge of a wide variety of machine learning techniques including supervised, unsupervised and reinforcement learning.
Prerequisites:
Python, including familiarity with object oriented coding + calculus and linear algebra.
Dates:
 14 August – 10 November 2023 (semester 1)
 8 January – 29 March 2024 (semester 2)
Lecture format:
Weekly discussion sessions based on guided, asynchronous video lectures.
Method of evaluation:
Weekly assignments and a project at the end.
Lecturer biography:
Jonathan Shock is an Associate Professor at the University of Cape Town. He has a PhD in theoretical physics from the University of Southampton, focusing on string theory. He continues to work in this field along with researching in machine learning, neuroscience and medical data analysis.
E: jon.shock @ gmail.com  W: www.shocklab.net  Publications
Ordinary Differential Equations  Laure Gouba
Outline:
 Introduction
 First order differential equations
 Second order differential equations; Preliminaries
 Integral and differential operators
 Generalized Green’s identity
 Green’s identity and adjoint boundary conditions
 Second order self adjoint operators
 Green’s functions
 Properties and construction of Green’s function
 Generalized Green’s function
 Second order differential equations with inhomogeneous boundary conditions, initial value problem
 The Sturm Liouville problem
 Series representation of the Green’s function
 Preliminaries to special functions
 The hypergeometric functions
 The confluent hypergeometric functions.
Prerequisites:
Elementary differential and integral calculus, vector analysis, theory of systems of algebraic equations.
Dates:
21 August – 20 October 2023
Lecture format:
Synchronous virtual lecture videos.
Method of evaluation:
Weekly problem sets and a final exam.
Lecturer biography:
Dr Laure Gouba is a mathematical physicist in visit at the Abdus Salam International Centre for Theoretical Physics (ICTP), Trieste, Italy. She is a visiting lecturer at the African Institute for Mathematical Sciences (AIMS) and a member of the UNESCO International Chair of Mathematical Physics and Applications (ICMPA), Université Abomey Calavi (UAC), Cotonou, Benin. Dr Gouba has been involved in various teaching activities in Africa: GeoNet School, Benin, 2019; CIMPA School, Burundi, 2021; East African Institute for Fundamental Research (EAIFR), Rwanda, 2019, 2021; NITheCS MiniSchool, 2022.
Her current research interests include new trends in quantization procedures, PTsymmetry in quantum mechanics, coherent states, noncommutative quantum mechanics, quantum correlations and quantum cosmology. Dr Gouba is a referee for many international journal in mathematical physics. On January 2020, she has obtained the Italian National Scientific Qualification (ASN) for the title of Associate Professor in Mathematical Physics.
Dr Gouba is native from BurkinaFaso, where she was born, and currently a dual Italian citizen. She studied Mathematics at the University of Ouagadougou (now Université Joseph KiZerbo), where she obtained a DEA (Research Master) in Mathematics in 1999. She enrolled in a PhD programme in 2001 at Institut de Mathématiques et de Sciences Physiques (IMSP), PortoNovo, Benin, and obtained a PhD degree in Mathematical Physics in 2005. Dr Gouba worked for AIMS as a senior tutor from 2006 to 2008. She was a postdoctoral fellow at NITheP (now NITheCS) from 2008 to 2010. Dr. Laure Gouba is an alumna and a full member of the Organization for Women in Sciences for the Developing World.
Quantum Field Theory II  W. A. Horowitz
Outline:
 Brief introduction to group theory and representations and their importance in quantum state space and constraining potential Lagrangians
 Nonrelativistic quantum rotations and spin
 Irreducible representations of the Lorentz group SO(3,1)
 Free 2D Weyl spinor fields
 Interacting 2D Weyl spinor fields
 4D Majorana and Dirac fields
 Free spin1 gauge fields. BRST gauge fixing. Nonabelian gauge theory
 Spinor helicity techniques. BCFW recursion.
Skills outcome:
Students should have a thorough understanding of quantum field theories for particles up to spin1.
Prerequisites:
Quantum Field Theory I.
Dates:
12 September — 21 October 2023.
Lecture format:
Synchronous lecture videos twice per week, one synchronous tutorial per week.
Method of evaluation:
Weekly problem sets and a project.
Lecturer biography:
Associate Professor W. A. Horowitz received his PhD in Physics from Columbia University in 2008. He held a postdoctoral research position at the Ohio State University from 2008 to 2010 before joining the faculty at the University of Cape Town. Prof Horowitz is an expert in the use of perturbative quantum field theory and AdS/CFT methods in phenomenological highenergy quantum chromodynamics applications.
E: wa.horowitz @ gmail.com  W: Lecturer’s personal website  Publications
SATACS COURSES – 2022
Machine Learning for Theoretical Physicists
Outline:

 Intro to machine learning
 Statistical theory and naive bases
 Regression tests and training ML models
 Gradient free optimisation methods
 Classification tasks and model evaluation
 Ensemble modeling
 Unsupervised learning: Clustering
 Unsupervised learning: Dimensionality Reduction
 Reinforcement Learning
 Intro to Neural Networks
 Pytorch
 Convolutional Neural Networks
 Recurrent Neural Networks
PLUS… a project based on the above topics on a research area of the student’s choice
Skills outcome:
At the end of the course, students will have an ability to choose and implement modern machine learning techniques using Python with appropriate training/validation and testing pipelines, hyperparameter tuning etc.
Prerequisites:
Python, including familiarity with object oriented coding + calculus and linear algebra.
Dates:
 Semester 1: 16 August – 11 November 2022
 Semester 2: January – March 2023 (dates to be confirmed).
Lesson format:
Synchronous and asynchronous video lecture delivery.
Method of evaluation:
Coding problems throughout applied to unseen datasets and a project.
Lecturer biography:
Jonathan Shock is an Associate Professor at the University of Cape Town. He has a PhD in theoretical physics from the University of Southampton, focusing on string theory, and continues to work in this field along with researching in machine learning, neuroscience and medical data analysis.
E: jon.shock @ gmail.com  W: www.shocklab.net  Publications
Differential Geometry
1. Preliminaries
 Maps
 Vector spaces and linear algebra
 Multilinear algebra and tensors
 Topological spaces
 Neighbourhoods and Hausdorff spaces
2. Differentiable Manifold
 Differentiable manifold
 Calculus on manifolds
 Flows and Lie Derivatives
3. Affine Manifolds
 Parallel transport
 Affine Connection
 Covariant derivatives
 Curvature and Torsion
4. Riemannian Geometry
 Riemann and pseudoRiemann manifolds
 Metric connection
 LeviCivita connection
 Applications
5. Symmetries
 Lie group and Lie Algebra
 Action of Lie groups on Manifolds
 Isometries and Conformal Transformations
 Killing and Conformal Killing Vector Fields
6. Differential Forms and Exterior Calculus: TBD, if time permits
Prerequisites:
Linear Algebra, Multivariable Calculus.
Dates:
14 February – 19 May 2022
Lesson format:
Synchronous lectures with a mixture of facetoface and synchronous tutorial/discussion sessions.
Method of evaluation:
Problem sets, project and a final exam.
Lecturer biography:
Shajid Haque received his PhD in Theoretical Physics from UWMadison in 2011. He did postdoctoral research at Wits University from 2012 to 2015 and at UCT from 2015 to 2017. He is interested in the applications of quantum information theory in quantum many body system, quantum field theory, holography and cosmology.
E: shajid.haque @ uct.ac.za  Publications
Introduction to General Relativity
Outline:
 Lie groups and symmetries
 Review of special relativity
 Tensor calculus
 Differential forms
 Review of differential geometry
 Geodesics
 Curvature and the Riemann tensor
 Killing vectors
 Maximally symmetric spaces
 Einstein’s equations
 The EinsteinHilbert action
 Spherically symmetric solutions
 Basics of black hole physics.
Skills outcome:
At the end of the course the student is expected to:
 show familiarity with the basic tools of GR such as the use of tensors, the metric, the meaning of curvature and the various curvature tensors.
 be able to study the motion of free particles on curved spaces by use of the geodesic equation.
 understand the mathematical description of symmetry via the concept of Killing vectors
 reflect on the difficulty of solving Einstein’s equations and provide examples of techniques for doing so.
 discuss the main features of static black holes such as event horizons, and (qualitatively) the notion of temperature and entropy for black holes and the paradoxes it leads to.
Prerequisites:
Students are encouraged to have a background in differential geometry (e.g. from the SATACS Differential Geometry course).
Special relativity, familiarity with coordinate systems for Euclidean space (Cartesian, spherical etc). Some of the exercises will require symbolic manipulation software, preferably SageMath, but the choice is up to the student.
Dates:
2nd quarter, 4 April – 20 May, 2022; asynchronous recorded lectures with two weekly live tutorial/discussion sessions.
Method of evaluation:
Weekly assignments and a final project.
Lecturer biography:
Konstantinos Zoubos is Associate Professor at the Physics Department of the University of Pretoria. His research interests are in supersymmetric Quantum Field Theory and String Theory, with an emphasis on integrable structures and the tools to analyse them, such as quantum groups.
E: konstantinos.zoubos @ up.ac.za  W: Lecturer’s personal website
Introduction to Cosmology
Outline:
 The Cosmological Principle
 Cosmological Models
 Inflationary Cosmology
 Cosmic Acceleration
 Largescale Structure Formation
 [Some Advanced Topics] Beyond Standard Cosmology.
Skills outcome:
The course will offer hot topics in modern cosmology.
At the end of the course, the students are expected to:
 Understand the assumptions in cosmology that led to the formulation of the standard cosmological model
 Derive the cosmological field equations and analyse their solutions
 Demonstrate length and time scales of the universe
 Apply the specialized and integrated knowledge of general relativity and cosmology to critically analyse the shortcomings of the Big Bang Model, and the need to introduce inflation, dark energy, and dark matter
 Understand the physical processes and mechanisms that lead to largescale structure formation
 Critically analyse the standard cosmological model and understand the need to look at new paradigms beyond the standard model.
Prerequisites:
Introduction to General Relativity.
Dates:
1 semester, 1 August — 4 November, 2022; weekly synchronous lecture videos and tutorials.
Method of evaluation:
Weekly assignments, projects and an exitassessment exam.
Lecturer biography:
Amare Abebe received his PhD in cosmology from the University of Cape Town in 2013. He held a postdoc position at the NorthWest University from 2014 to 2015 after which he joined the faculty at this same institution. He is currently a Professor of Physics and his broad research interests lie in gravitation and cosmology.
E: amare.abbebe @ gmail.com  W: Lecturer’s personal website  Publications
Extreme Gravity or An Introduction to Black Holes and Gravitational Waves
Outline:
 The Schwarzschild solution
 Black Holes
 Penrose Diagrams
 Gravitational waves
Skills outcome:
These are key modern areas of current research in cosmology/astrophysics and gravity and yet the maths and physics underlying these areas is quite old. Nonetheless, the maths and physics aren’t taught at most places in the country, if anywhere. This course will prepare students to enter these fields if they want to – by the end of the course, students will be beautifully trained for the future. And should they not choose these fields, the work will skill them for other related fields.
Prerequisites:
Introduction to General Relativity.
Dates:
25 July — 4 November 2022; in person and synchronous lecture videos.
Method of evaluation:
Weekly assignments, a project and an exam.
Lecturer biography:
Amanda Weltman received her PhD in Physics from Columbia University in 2007. She held a postdoctoral researcher position at the University of Cambridge from 20072009 before joining the University of Cape Town as a Senior Lecturer. She is currently a Professor at UCT with research interests in astrophysics, fundamental physics, cosmology, and gravity.
E: amanda.weltman @ uct.ac.za  W: Lecturer’s personal website  Publications
Introduction to PrivacyPreserving Schemes for Applications in Artificial Intelligence
Outline:
The course will provide an introduction to various privacy tools that can be used in order to enable privacypreserving Artificial Intelligence (with focus on Machine Learning and Natural Language Processing subfields of Artificial Intelligence).
Skills outcome:
The developments in Data Science bring with them the challenge of dealing with sensitive datasets; where the need to preserve the privacy of the participants in the datasets is of utmost importance. This challenge warrants the need to explore privacypreserving schemes that can be used in order to ensure the privacy of the participants in the datasets. This course is intended to introduce these privacypreserving schemes to the students, and demonstrate how such schemes can be applied in Machine Learning and Natural Language Processing subfields of Artificial Intelligence.
At the end of the course, students would have acquired the skills on implementing Data Privacy tools such as Differential Privacy and Federated Learning in Artificial Intelligence tasks. These acquired skills would then make it possible for students to appropriately deal with sensitive datasets such as healthcare datasets, in order to design privacypreserving Artificial Intelligence techniques.
Prerequisites:
Python programming basics, Linear Algebra, Calculus, and Probability Theory.
Dates:
1 semester, 4 April — 24 June, 2022; synchronous lecture videos.
Method of evaluation:
Weekly problem sets and an exam.
Lecturer biography:
Makhamisa Senekane is a Senior Researcher in the Institute for Intelligent Systems at the University of Johannesburg. Prior to that, he worked as a Lecturer in the Department of Physics and Electronics at the National University of Lesotho, as a Senior Lecturer in the Faculty of Information and Communication Technology at Limkwokwing University of Creative Technology (Lesotho), and as a Lecturer in the Faculty of Computing at Botho University (Maseru Campus). He has obtained his PhD in Physics from the University of KwaZuluNatal, his MSc.Eng in Electrical Engineering from the University of Cape Town, and his B.Eng in Electronics Engineering from the National University of Lesotho. His research interests include data science, data security, data privacy, artificial intelligence (machine learning and natural language processing), and quantum information processing (quantum cryptography, quantum computing, and quantum machine learning).
Mathematical Structures
Outline:
The course will present a survey of and interconnections between various mathematical structures that arise on the overlap of abstract mathematics, physics, and computer science. Just to give an example: Hilbert spaces arise in quantum mechanics, their subspaces lead one to quantum logic, while the structure of subspaces of a Hilbert space is that of a “lattice” from order theory in abstract mathematics, which is a generalization of a Boolean algebra, relevant to computer science. The methodology for comparing mathematical structures in this course will rely on ideas from set theory, which enable description of mathematical structures via “elements” (or “points”), and category theory, which looks at networks of mathematical structured formed by structure preserving functions between them (e.g., linear maps in the case of vector spaces).
Skills outcome:
The goal of the course is for the students to see the bigger picture behind modern abstract mathematics and its links to other sciences, while at the same time acquiring research skills in mathematics (thanks to the problemsolving based nature of the course). The students will leave with an intuition for “how abstract mathematics works”. They will also be able to see how the mathematics that they knew previously fits into a broader realm of mathematical structures. Such course is not offered anywhere in South Africa. It will be an excellent complement to existing specialized courses in universities.
Dates:
1 August — 4 November 2022; synchronous lectures over Zoom.
Method of evaluation:
Group work assignments. Each group work will consist of problems that the students must work together on. Marks will be awarded per group. Some of the group work will be to record a joint video presentation explaining their solutions to given problems.
Lecturers:
This course will be presented by a team of investigators in a 2022 NITheCS research programme in Mathematical Structures. Course convenors are the Principal Investigators (listed alphabetically): Partha Pratim Ghosh (UNISA), Yorick Hardy (WITS), Zurab Janelidze (SU), and Cerene Rathilal (UJ).
E: zurab @ sun.ac.za  W: Lecturer’s personal website  Publications
Advanced Methods for Mathematical Physics
Outline:
 Winding number in complex analysis and the generalised Cauchy theorem
 Integral representations and differential equations
 Advanced method for Green’s functions – resolvents and operator methods
 Spectral analysis in quantum mechanics
 Integral equations
Skills outcome:
The course is designed as one in advanced mathematical methods, aimed at theoretical/mathematical physics students. Material is drawn from various sources, including Arfken & Weber and Morse & Feshbach. Students will leave the course with a working vocabulary in complex analysis, spectral analysis, and some more mathematical aspects of quantum mechanics. These topics are foundational for a number of topics in theoretical and mathematical physics across the spectrum of the South African theory community.
Prerequisites:
Basic knowledge of complex analysis (contour integrals, Cauchy’s theorem etc), differential equations (mostly ordinary); some quantum mechanics is useful but not essential.
Dates:
25 July — 4 November 2022; meeting twice a week for 2 hours per lecture.
Method of evaluation:
Weekly problem sets.
Lecturer biography:
Jeff Murugan is Professor of Mathematical Physics and head of the Laboratory for Quantum Gravity & Strings at the University of Cape Town. He received a PhD in Noncommutative Geometry in String Theory from UCT and Oxford in 2004. He has held a postdoctoral position at Brown University from 20042006 and was a member at the Institute for Advanced Study in Princeton in 20162017. His research interests lie primarily in understanding emergent phenomena, from condensed matter to neurophysics. His recent focus has been on lowdimensional quantum field theory, topological quantum matter and quantum chaos in disordered systems.
E: jeffmurugan @ gmail.com  W: Lecturer’s personal website  Publications
Nonlinear Hamiltonian Dynamics and Chaos
uOutline:
Nonlinear Hamiltonian dynamics is used to study the behavior of systems coming from a wide variety of scientific fields, the most important of them being classical mechanics, astronomy, optics, electromagnetism, solid state physics, quantum mechanics, and statistical mechanics. An important phenomenon appearing in nonlinear systems is chaos, which is attributed to the sensitive dependence of a system’s dynamical evolution on its initial conditions.
In this course we will implement several modern numerical techniques to investigate and quantify the chaotic behavior of lowdimensional Hamiltonian systems and area preserving symplectic maps. In particular, we will discuss the following topics:
 Chaos
 Autonomous Hamiltonian systems and symplectic mappings
 Numerical integration of Hamilton equations of motion
 Poincaré surface of section
 Integrals of motion
 Symplectic integrators
 Variational equations
 Tangent Map Method
 Maximum Lyapunov exponent
 Spectrum of Lyapunov exponents
 Chaos indicators
Skills outcome:
Students will learn basic concepts of nonlinear dynamics (particularly in the framework of Hamiltonian mechanics), will be introduced into various traditional as well as modern techniques of analysing chaotic systems and will also implement these methods by themselves in studying some prototypical dynamical models. Thus, students will acquire and practice some basic tools of nonlinear dynamics, which can be implemented in a wide spectrum of problems coming from various scientific fields
Prerequisites:
Basic knowledge of 2nd year classical mechanics.
Dates:
1 semester, 1 August — 4 November, 2022; synchronous lecture videos.
Method of evaluation:
Students will be asked to do 4 assignments of the form of implementing some numerical methods to a particular dynamical system. Then they will have to submit for evaluation at predefined dates their report for each assignment. Depending on practical constrains they might be requested to orally present their assignment (brief presentation of at most 20 minutes). At the beginning of week 6 each student will get a final project concerning the theory and application of a modern chaos technique which he/she will have to finalize by the end of the course, similarly to the 4 assignments. This assignment will be more demanding as it will also require some bibliographical research from the students along with the more applied aspect of the project (numerical application of the chaos indicators to some dynamical systems).
The course’s final mark (FM) will be defined at a 50% level by your mean mark of your 4 assignments (AS1, AS2, AS3, AS4) and 50% by your final project (FP) mark: FM=(AS1+AS2+AS3+AS4)/4 +FP/2.
Lecturer biography:
Charalampos (Haris) Skokos received his PhD in Nonlinear Dynamical Systems from the University of Athens, Greece. After a number of prestigious appointments, Prof Skokos joined the faculty of the University of Cape Town in 2013; he is now Associate Professor of Mathematics and Applied Mathematics and Deputy Head of Department. His research interests are in nonlinear dynamical systems, chaotic dynamics and Hamiltonian systems.
E: haris.skokos @ uct.ac.za  W: Lecturer’s personal website  Publications
Quantum Field Theory I
Outline:
 Postulates of QM and SR
 Quantizing the free scalar field
 Interpreting the results
 Connecting to experiments; in and out states; LSZ reduction
 LehmanKallen representation; GellMann–Low theorem; cross sections
 Feynman rules for scalar fields
 Introduction to QED, QED Feynman rules, and trace technology for cross sections
Skills outcome:
Students will leave the course with a deep understanding of 1) free scalar quantum field theory and 2) Feynman calculus for computing cross sections involving scalar particles. Students should also have a good facility for computing Feynman diagrams and cross sections related to QED processes.
Prerequisites:
A course in advanced quantum mechanics and a course in which special relativity was treated in some detail.
Dates:
25 July — 2 September 2022; synchronous lecture videos twice a week, one synchronous tutorial per week.
Method of evaluation:
Weekly problem sets and a project.
Lecturer biography:
Associate Professor W. A. Horowitz received his PhD in Physics from Columbia University in 2008. He held a postdoctoral research position at the Ohio State University from 2008 to 2010, and then joined the faculty at the University of Cape Town. Prof Horowitz is an expert in the use of perturbative quantum field theory and AdS/CFT methods in phenomenological highenergy quantum chromodynamics applications.
E: wa.horowitz @ gmail.com  W: Lecturer’s personal website  Publications
Quantum Field Theory II
Outline:
 Brief introduction to group theory and representations and their importance in quantum state space and constraining potential Lagrangians
 Nonrelativistic quantum rotations and spin
 Irreducible representations of the Lorentz group SO(3,1)
 Free 2D Weyl spinor fields
 Interacting 2D Weyl spinor fields
 4D Majorana and Dirac fields
 Free spin1 gauge fields. BRST gauge fixing. Nonabelian gauge theory
 Spinor helicity techniques. BCFW recursion.
Skills outcome:
Students should have a thorough understanding of quantum field theories for particles up to spin1.
Prerequisites:
Quantum Field Theory I.
Dates:
One quarter: 12 September — 21 October 2022; synchronous lecture videos twice per week, one synchronous tutorial per week.
Method of evaluation:
Weekly problem sets and a project.
Lecturer biography:
Associate Professor W. A. Horowitz received his PhD in Physics from Columbia University in 2008. He held a postdoctoral research position at the Ohio State University from 2008 to 2010, and then joined the faculty at the University of Cape Town. Prof Horowitz is an expert in the use of perturbative quantum field theory and AdS/CFT methods in phenomenological highenergy quantum chromodynamics applications.
E: wa.horowitz @ gmail.com  W: Lecturer’s personal website  Publications
SATS COURSES – 2021
Advanced Methods for Mathematical Physics
Outline:
 Winding number in complex analysis and the generalised Cauchy theorem
 Integral representations and differential equations
 Advanced method for Green’s functions – resolvents and operator methods
 Spectral analysis in quantum mechanics
 Integral equations.
Skills outcome:
At the end of the course, the students are expected to have a working knowledge of:
 complex analysis
 spectral analysis
 some more mathematical aspects of quantum mechanics.
Prerequisites:
Basic knowledge of complex analysis (contour integrals, Cauchy’s theorem etc), differential equations (mostly ordinary); some quantum mechanics is useful but not essential.
Dates:
1 semester, 2 August — 1 November, 2021; meeting twice a week for 2 hours per lecture.
Method of evaluation:
Weekly problem sets.
Lecturer biography:
Jeff Murugan is Professor of Mathematical Physics and head of the Laboratory for Quantum Gravity & Strings at the University of Cape Town. He received a PhD in Noncommutative Geometry in String Theory from UCT and Oxford in 2004. He has held a postdoctoral position at Brown University from 2004 to 2006 and was a member at the Institute for Advanced Study in Princeton in 2016 – 2017. His research interests lie primarily in understanding emergent phenomena, from condensed matter to neurophysics. His recent focus has been on lowdimensional quantum field theory, topological quantum matter and quantum chaos in disordered systems.
E: jeffmurugan @ gmail.com  W: Lecturer’s personal website  Publications
Advanced Methods for Mathematical Physics
Outline:
 Winding number in complex analysis and the generalised Cauchy theorem
 Integral representations and differential equations
 Advanced method for Green’s functions – resolvents and operator methods
 Spectral analysis in quantum mechanics
 Integral equations.
Skills outcome:
At the end of the course, the students are expected to have a working knowledge of:
 complex analysis
 spectral analysis
 some more mathematical aspects of quantum mechanics.
Prerequisites:
Basic knowledge of complex analysis (contour integrals, Cauchy’s theorem etc), differential equations (mostly ordinary); some quantum mechanics is useful but not essential.
Dates:
1 semester, 2 August — 1 November, 2021; meeting twice a week for 2 hours per lecture.
Method of evaluation:
Weekly problem sets.
Lecturer biography:
Jeff Murugan is Professor of Mathematical Physics and head of the Laboratory for Quantum Gravity & Strings at the University of Cape Town. He received a PhD in Noncommutative Geometry in String Theory from UCT and Oxford in 2004. He has held a postdoctoral position at Brown University from 2004 to 2006 and was a member at the Institute for Advanced Study in Princeton in 2016 – 2017. His research interests lie primarily in understanding emergent phenomena, from condensed matter to neurophysics. His recent focus has been on lowdimensional quantum field theory, topological quantum matter and quantum chaos in disordered systems.
E: jeffmurugan @ gmail.com  W: Lecturer’s personal website  Publications
Nonlinear Hamiltonian Dynamics and Chaos
 Chaos
 Autonomous Hamiltonian systems and symplectic mappings
 Numerical integration of Hamilton equations of motion
 Poincaré surface of section
 Integrals of motion
 Symplectic integrators
 Variational equations
 Tangent Map Method
 Maximum Lyapunov exponent
 Spectrum of Lyapunov exponents
 Chaos indicators.
Skills outcome:
Students will learn basic concepts of nonlinear dynamics, particularly in the framework of Hamiltonian systems. At the end of the course, they will be able to implement by themselves several modern numerical techniques to investigate and quantify the chaotic behaviour of systems coming from a wide variety of scientific fields, like for example classical mechanics, astronomy, optics, electromagnetism, solid state physics, as well as quantum and statistical mechanics.
Prerequisites:
A good background on Applied Mathematics, Mathematical Modelling, Mathematical Physics and Applied Computing (e.g. numerical analysis, numerical methods, ordinary differential equations, dynamical systems, Newtonian, Lagrangian and Hamiltoninan mechanics) is requited.
Good computational and programming skills in some computational environment and/or computer language (e.g. C, Python, Matlab, Mathematica) is essential.
Dates:
1 semester, 16 August — 12 November 2021; synchronous lecture videos.
Method of evaluation:
During the run of the course: Four (4) assignments (each one followed by a written report) related to the implementation of some numerical methods to particular dynamical systems.
At the end of the course: a final project (in the form of a written report) concerning the theory and applications of a modern chaos detection technique. Students will be working on their final project during the whole second half of the course’s duration.
Lecturer biography:
Associate Professor Haris Skokos acquired a PhD in PhysicsNonlinear Dynamics from the University of Athens (Greece). Over the years, he worked at several research institutes in Europe: the University of Athens (Greece), the Academy of Athens (Greece), the University of Patras (Greece), the Observatory of Paris (France), the Max Planck Institute for the Physics of Complex Systems in Dresden (Germany) and the Aristotle University of Thessaloniki (Greece), and then joined the University of Cape Town in 2013. His research activity belongs to the field of applied mathematics and computational physics, in particular, to nonlinear dynamical systems and chaotic dynamics.
E: haris.skokos @ gmail.com  W: Lecturer’s personal website  Publications
Introduction to Machine Learning for Theoretical Physicists
Outline:
PART 1: A broad sweep of ML (around 10 lectures)
 Introduction to Machine Learning
 Naïve Bayes and Probability
 Decision Trees
 Linear Regression
 Logistic Regression
 A brief overview of Neural Networks
 Kmeans Clustering
 Practical Application of ML Methods
 Principal Components Analysis
 A brief overview of Reinforcement Learning
PART 2: A deeper dive into deep learning (around 15 lectures)
 An introduction to Pytorch
 A fully connected feedforward NN
 Convolutional NN’s
 Recurrent NN’s
PART 3: Energy Based Models (around 5 lectures)
 Restricted Boltzmann Machines
 Hopfield Networks
 Selfsupervised learning and modern energy based models
Skills outcome:
Students will come away with a broad overview of machine learning ideas and techniques and will be able to implement them for themselves using Python, and Pytorch in particular. They will understand and know where to apply the three main branches of ML to different datasets and use cases.
Prerequisites:
Python, including familiarity with object oriented coding + calculus and linear algebra.
Dates:
1 semester, 16 August — 12 November 2021; asynchronous video lecture delivery.
Method of evaluation:
Weekly coding challenges and an essay at the end on a subject in ML chosen by the student (and guided by the lecturer/tutor).
Lecturer biography:
Jonathan Shock is a senior lecturer at the University of Cape Town. He has a PhD in theoretical physics from the University of Southampton, focusing on string theory, and continues to work in this field along with researching in machine learning, neuroscience and medical data analysis.
E: jon.shock @ gmail.com  W: Lecturer’s personal website  Publications
Introduction to Cosmology
Outline:
 The Cosmological Principle
 Cosmological Models
 Inflationary Cosmology
 Cosmic Acceleration
 Largescale Structure.
Skills outcome:
At the end of the course, students are expected to:
 Understand the assumptions in cosmology that led to the formulation of the standard cosmological model
 Derive the cosmological field equations and analyse their solutions
 Demonstrate length and time scales of the universe
 Apply the specialised and integrated knowledge of general relativity and cosmology to critically analyse the shortcomings of the Big Bang Model, and the need to introduce inflation, dark energy and dark matter
 Understand the physical processes and mechanisms that lead to largescale structure formation.
Prerequisites:
Introduction to General Relativity.
Dates:
4th quarter, 20 September — 12 November; synchronous lecture videos.
Method of evaluation:
Weekly assignments and an exam.
Lecturer biography:
Amare Abebe received his PhD in cosmology from the University of Cape Town in 2013. He held a postdoc position at the NorthWest University from 2014 to 2015 after which he joined the faculty at this same institution. He is currently an Associate Professor of Physics and his research interests lie in gravitation and cosmology.
E: amare.abbebe @ gmail.com  W: Lecturer’s personal website  Publications
Introduction to General Relativity
Outline:
 Lie groups and symmetries
 Review of special relativity
 Tensor calculus
 Differential forms
 Manifolds
 Geodesics
 Curvature and the Riemann tensor
 Killing vectors
 Maximally symmetric spaces
 Einstein’s equations
 Spherically symmetric solutions
 Basics of black hole physics.
Skills outcome:
At the end of the course the student is expected to:
 show familiarity with the basic tools of GR such as the use of tensors, the metric, the meaning of curvature and the various curvature tensors.
 be able to study the motion of free particles on curved spaces by use of the geodesic equation.
 understand the mathematical description of symmetry via the concept of Killing vectors
 reflect on the difficulty of solving Einstein’s equations and provide examples of techniques for doing so.
 discuss the main features of static black holes such as event horizons, and (qualitatively) the notion of temperature and entropy for black holes and the paradoxes it leads to.
Dates:
3rd quarter, 2 August – 17 September; asynchronous recorded lectures with a live weekly tutorial/discussion session.
Method of evaluation:
Weekly assignments and a final project.
Prerequisites:
Special relativity, familiarity with coordinate systems for Euclidean space (Cartesian, spherical etc). Some of the exercises will require symbolic manipulation software, preferably SageMath but the choice is up to the student.
Lecturer biography:
Konstantinos Zoubos is Associate Professor at the Physics Department of the University of Pretoria. His research interests are in supersymmetric Quantum Field Theory and String Theory, with an emphasis on integrable structures and the tools to analyse them, such as quantum groups.
E: konstantinos.zoubos@up.ac.za  W: Lecturer’s personal website
Quantum Field Theory I
Outline:
 Postulates of QM and SR
 Quantizing the free scalar field
 Interpreting the results
 Connecting to experiments; in and out states; LSZ reduction
 LehmanKallen representation; GellMann–Low theorem; cross sections
 Feynman rules for scalar fields
 Introduction to QED, QED Feynman rules, and trace technology for cross sections.
Skills outcome:
Students will leave the course with a deep understanding of 1) free scalar quantum field theory and 2) Feynman calculus for computing cross sections involving scalar particles. Students should also have a good facility for computing Feynman diagrams and cross sections related to QED processes.
Dates:
One quarter: 2 August – 17 September 2021.
Synchronous lecture videos twice per week, one synchronous tutorial per week.
Method of evaluation:
Weekly problem sets and an exam.
Lecturer biography:
Associate Professor W. A. Horowitz received his PhD in Physics from Columbia University in 2008. He held a postdoctoral research position at the Ohio State University from 2008 to 2010, and then joined the faculty at the University of Cape Town. Prof Horowitz is an expert in the use of perturbative quantum field theory and AdS/CFT methods in phenomenological highenergy quantum chromodynamics applications.
E: wa.horowitz @ gmail.com  W: Lecturer’s personal website  Publications
Quantum Field Theory II
Outline:
 Brief introduction to group theory and representations and their importance in quantum state space and constraining potential Lagrangians
 Nonrelativistic quantum rotations and spin
 Irreducible representations of the Lorentz group SO(3,1)
 Free 2D Weyl spinor fields
 Interacting 2D Weyl spinor fields
 4D Majorana and Dirac fields
 Free spin1 gauge fields. BRST gauge fixing. Nonabelian gauge theory
 Spinor helicity techniques. BCFW recursion.
Skills outcome:
At the end of the course, the students are expected to have a thorough understanding of quantum field theories for particles up to spin1.
Prerequisites:
Quantum Field Theory I.
Dates:
One quarter: 27 September — 1 November 2021;
Synchronous lecture videos twice per week, one synchronous tutorial per week.
Method of evaluation:
Weekly problem sets and a project.
Lecturer biography:
Associate Professor W. A. Horowitz received his PhD in Physics from Columbia University in 2008. He held a postdoctoral research position at the Ohio State University from 2008 to 2010, and then joined the faculty at the University of Cape Town. Prof Horowitz is an expert in the use of perturbative quantum field theory and AdS/CFT methods in phenomenological highenergy quantum chromodynamics applications.
E: wa.horowitz @ gmail.com  W: Lecturer’s personal website  Publications