Introduction to Statistical Physics

The main purpose of statistical physics is to clarify the properties of matter in aggre- gate, in terms of the physical laws governing atomic motion. The present book is a textbook for advanced undergraduates. It assumes background knowledge of classical and quantum physics, on an introductory undergraduate level.
In introducing the properties of matter, one faces a dilemma: should one follow thermodynamics, and bring in entropy as that mystical object Tooming out of the second law, or should one first discuss the randomness of atomic collisions? I choose thermodynamics, because it describes everyday experience. (And it is profound, as one realizes after having absorbed the atomic view.)
This book may be divided into three parts, the classical view, the quantum view, and advanced applications.
The classical portion occupies first thirteen chapters, more than half the book. It covers the classical ensembles of statistical mechanics and stochastic processes. The latter subject includes Brownian motion, probability theory, and the Fokker-Planck and Langevin equations, with emphasis on physical understanding. To illustrate the use of statistical methods beyond the theory of matter, there are brief discussions of entropy in information theory, Brownian motion in the stock market, and the Monte Carlo method in computer simulations.
The quantum part comprises five chapters. On quantum ensembles, the discussion emphasizes what makes quantum mechanics different from classical mechanics-the quantum phase. Applications include Fermi statistics and semiconductors, and Bose statistics and Bose-Einstein condensation.
The final three chapters deal with advanced topics. A long chapter introduces what might be viewed as a major phenomenology after thermodynamics-the Ginsburg- Landau theory of the order parameter. The last two chapters are devoted to the special kind of quantum order as manifested in superfluidity and superconductivity.
The present edition is expanded from an earlier edition, which was based on a one-semester course given at MIT. I am grateful for the opportunity to interact with students who took the course, known in MIT (inge as 8.08, and people who helped me teach the course: Alexander Lomakin, Patrick Lee, and Lisa Randall.