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This open access book bridges a gap between introductory Quantum Field Theory (QFT) courses and state-of-the-art research in scattering amplitudes. It covers the path from basic definitions of QFT to amplitudes, which are relevant for processes in the Standard Model of particle physics. The book begins with a concise yet self-contained introduction to QFT, including perturbative quantum gravity. It then presents modern methods for calculating scattering amplitudes, focusing on tree-level amplitudes, loop-level integrands and loop integration techniques. These methods help to reveal intriguing relations between gauge and gravity amplitudes and are of increasing importance for obtaining high-precision predictions for collider experiments, such as those at the Large Hadron Collider, as well as for foundational mathematical physics studies in QFT, including recent applications to gravitational wave physics.These course-tested lecture notes include numerous exercises with solutions. Requiring only minimal knowledge of QFT, they are well-suited for MSc and PhD students as a preparation for research projects in theoretical particle physics. They can be used as a one-semester graduate level course, or as a self-study guide for researchers interested in fundamental aspects of quantum field theory.

Bryce DeWitt, a student of Nobel Laureate Julian Schwinger, was himself one of the towering figures in 20th century physics, particularly renowned for his seminal contributions to quantum field theory, numerical relativity and quantum gravity.

physics

The spin degree of freedom is an intrinsically quantum-mechanical phenomenon, leading to both intriguing applications and unsolved fundamental issues (such as "where does the proton spin come from").

Quantum dots, sometimes called artificial atoms, are exquisite tools by which quantum behavior can be probed on a larger scale than the atomic, namely on the nanometer scale. While the physics exhibited by these devices is closer to classical than atomic physics, quantum dots are still sufficiently small to clearly exhibit quantum phenomena.

This book provides a comprehensive overview of developments in the field of holographic entanglement entropy. In the first part, the concept of entanglement, and methods for computing it, in quantum field theories is reviewed.

This book focuses on one mechanism in black hole physics which has proven to be universal, multifaceted and with a rich phenomenology: rotational superradiance.

This book presents a self-contained introduction to techniques from field theory applied to stochastic and collective dynamics in neuronal networks.

This book provides an introduction to topics in non-equilibrium quantum statistical physics for both mathematicians and theoretical physicists. The quantum kinetic theory developed in the second part is an extension of Boltzmann's classical (non-quantum) kinetic theory of a dilute gas of quantum bosons.

Lectures on LHC Physics

Aside from the statement that it should be capable of unifying general relativity and quantum field theory, little is known about the nature of quantum gravity. This book covers non-commutative geometry, space-time discretization and more.

The recent discovery of a type II supernova in the Large Magellanic Cloud provides a rare chance to compare models of stellar evolution and nucleosynthesis directly with observations.

Introduction to Material Modeling.- Kernel Methods for Quantum Chemistry.- Introduction to Neural Networks.- Building nonparametric n-body force fields using Gaussian process regression.- Machine-learning of atomic-scale properties based on physical principles.- Quantum Machine Learning with Response Operators in Chemical Compound Space.- Physical extrapolation of quantum observables by generalization with Gaussian Processes.- Message Passing Neural Networks.- Learning representations of molecules and materials with atomistic neural networks.- Molecular Dynamics with Neural Network Potentials.- High-Dimensional Neural Network Potentials for Atomistic Simulations.- Construction of Machine Learned Force Fields with Quantum Chemical Accuracy: Applications and Chemical Insights.- Active learning and Uncertainty Estimation.- Machine Learning for Molecular Dynamics on Long Timescales.- Database-driven High-Throughput Calculations and Machine Learning Models for Materials Design.- Polymer Genome: A polymer informatics platform to accelerate polymer discovery.- Bayesian Optimization in Materials Science.- Recommender Systems for Materials Discovery.- Generative Models for Automatic Chemical Design.

This book provides an accessible yet comprehensive description of the application methods of group analysis to integro-differential equations. It offers both fundamental theoretical and algorithmic aspects of these methods and includes instructive examples.

This book is based on Valery Zagrebaev's original papers and lecture materials on nuclear physics with heavy ions, which he prepared and extended through many years for the students of nuclear physics specialties.Th¿ book outlines the main experimental facts on nuclear reactions involving heavy ions at low energies. It focuses on discussions of nuclear physics processes that are a subject of active, modern research and it gives illustrative explanations of these phenomena in the framework of up-to-date theoretical concepts.This textbook is intended for students in physics who have completed a standard course of quantum mechanics and have basic ideas of nuclear physics processes.It is designed as a kind of lifeboat that, at the end of the course, will allow students to navigate the modern scientific literature and to understand the goals and objectives of current, on-going research.

This volume provides an introduction to the state-of-the-art of controlled nanoscale motion in biological and artificial systems. Coverage includes the control and function of protein motors, the physics of non-equilibrium Brownian motion, and the physics and fabrication of synthetic molecular motors.