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Quantum Noise in Gravitation and Cosmology

  • B. L. Hu
  • A. Matacz
Part of the Institute for Nonlinear Science book series (INLS)

Abstract

We begin by enumerating the many processes in gravitation and cosmology where quantum noise and fluctuations play an active role such as particle creation, galaxy formation, and entropy generation. Using the influence functional, we first explain the origin and nature of noise in quantum systems interacting with an environment at a finite temperature. With linear coupling to nonohmic baths or at low temperatures, colored noise and nonlocal dissipation would appear, and for nonlinear coupling multiplicative noise is generally expected. We derive a generalized fluctuation-dissipation relation for these systems. Then, using a model of quantum Brownian motion in a bath of parametric oscillators, we show how noise and dissipation can be related to the Bogolubov coefficients of parametric amplification, which in the second-quantized sense depict cosmological particle creation in a dynamic background. We then calculate the influence functional and study the noise characteristics of quantum fields as probed by a particle detector. As examples, we show that a uniformly accelerated observer in flat space or an inertial observer in an exponentially expanding (de Sitter) universe would see a thermal particle spectrum, recovering the well-known results of Unruh and Gibbons and Hawking, as inspired by the Hawking effect in black holes. We show how this method can be effectively used for treating the backreaction of particle creation and other quantum field processes on the dynamics of the early universe and black holes. We also discuss the advantage of adopting the viewpoint of quantum open systems in addressing some basic issues of semiclassical gravity and quantum cosmology.

Keywords

Black Hole Entropy Generation Quantum Noise Brownian Particle Multiplicative Noise 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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© Springer-Verlag New York, Inc. 1996

Authors and Affiliations

  • B. L. Hu
  • A. Matacz

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