Why Do Causal Dynamical Triangulations Utilize a Partition Function?

In summary, Causal Dynamical Triangulations uses a partition function to describe the dynamics of the theory, which is related to statistical mechanics. This is because of Feynman's path integral, which allows for a weighted sum over paths in both quantum mechanics and statistical mechanics. The Wick rotation in quantum mechanics is equivalent to a transformation to a partition function, making the diffusion process more tractable for computational purposes. While this argument cannot be made rigorous in QFT, it works well in CDT with a discrete PI.
  • #1
Schreiberdk
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I just want to ask why Causal Dynamical Triangulations use a partition function for describing the dynamics of the whole theory. Does the theory have some deep relation to statistical mechanics because of this formulation of the theory? Or is the partition function also a usual terminology to use in QFT?
 
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  • #2
QFT and classical statistical mechanics are related, so QFT sometimes uses stat mech terminology (partition function), just as stat mech sometimes uses QFT terminology (Feynman diagrams). This is due to Feynman's path integral in which quantum mechanics is a weighted sum over paths, just as statistical mechanics is a Boltzmann weighted sum over microstates.

http://arxiv.org/abs/hep-lat/9807028, p21-22

http://arxiv.org/abs/1009.5966 (example of QFT terminology in random processes)
 
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  • #3
For quantum mechanics one can show that the so-called Wick rotation from t to it (i = imaginary unit) of the path integral is equivalent to a transformation to a partition function. The unitary time evolution of QM is replaced by a diffusion process.

The argument cannot be made rigorous in QFT but in CDT with a discrete PI it works rather nicely. For computational purposes (Monte-Carlo and importance sampling) the diffusion process is much more tractable due to the exponential damping exp(-S) instead of the oscillations coming from exp(iS).
 

Related to Why Do Causal Dynamical Triangulations Utilize a Partition Function?

What is the CDT approach in statistical mechanics?

The CDT (causal dynamical triangulation) approach is a method used in statistical mechanics to study the properties and behavior of physical systems. It involves representing a system as a collection of discrete, interconnected elements and analyzing the evolution of the system over time using causal relationships between these elements.

How does CDT differ from other approaches in statistical mechanics?

CDT differs from other approaches in statistical mechanics in that it takes into account the causal structure of a system, rather than just the statistical properties of its individual elements. This allows for a more detailed and accurate understanding of how a system behaves and changes over time.

What are some applications of CDT in statistical mechanics?

CDT has been used to study a wide range of physical systems, including the behavior of particles in a gas, the dynamics of biological networks, and the evolution of the universe. It has also been applied to problems in computer science, such as analyzing the efficiency of algorithms and network routing protocols.

What are the main challenges in using CDT in statistical mechanics?

One of the main challenges of using CDT in statistical mechanics is the complexity of the calculations involved. Since CDT takes into account the causal relationships between elements, it often requires more computational power and time compared to other approaches. Additionally, there are still many open questions and areas of research in CDT, making it a constantly evolving field.

What are the potential future developments in CDT and statistical mechanics?

Some potential future developments in CDT and statistical mechanics include further advancements in computational techniques to handle more complex systems, as well as the development of new theoretical frameworks to better understand the behavior of physical systems. Additionally, there is ongoing research in applying CDT to interdisciplinary fields, such as economics and social sciences.

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