Function integration of a Gaussian integral

In summary, the partition function ##Z[J]## of the Klein-Gordon theory can be written as an integral over a functional space, with the assumption that the operator ##\partial^{2}+m^{2}## is symmetric and positive-definite. This is important for deriving the Feynman diagrammatic rules from the partition function. For an operator to be symmetric and positive-definite, it means that its matrix representation in any basis is symmetric and positive-definite, and its eigenvalues are all positive. However, in infinite-dimensional vector spaces, there may be a different definition of positive-definite operator.
  • #1
spaghetti3451
1,344
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Consider the partition function ##Z[J]## of the Klein-Gordon theory

##Z[J] =\int \mathcal{D}\phi\ e^{i\int d^{4}x\ [\frac{1}{2}(\partial\phi)^{2}-\frac{1}{2}m^{2}\phi^{2}+J\phi]}
=\int \mathcal{D}\phi\ e^{-i\int d^{4}x\ [\frac{1}{2}\phi(\partial^{2}+m^{2})\phi]}\ e^{i\int d^{4}x\ J\phi}##

If the operator ##\partial^{2}+m^{2}## is symmetric and positive-definite, then

##Z[J]=\int \mathcal{D}\phi\ e^{i\int d^{4}x\ [\frac{1}{2}\phi(-\partial^{2}-m^{2})\phi]}\ e^{-\frac{i}{2}\int d^{4}x\ J(-\partial^{2}-m^{2})^{-1}J(x)}.##The assumption of symmetry and positive-definiteness of the operator ##\partial^{2}+m^{2}## is crucial in the derivation of the Feynman diagrammatic rules of the scalar theory from the partition function of the scalar theory.What does it mean for an operator to be symmetric and positive-definite? Does it mean that its matrix representation in any basis is symmetric and positive-definite?
 
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  • #2
For an operator ##T## on a vector space ##V## to be symmetric means that, for all ##\vec u,v\in V## we have ##\vec u\cdot T\vec v=T\vec u\cdot\vec v##.

For it to be positive-definite means that, for all ##\vec v\in V## we have ##\vec v\cdot T\vec v>0##, which of course requires that the field over which the vector space is defined have an ordered subfield, and that all ##\vec v\cdot T\vec v## are in that subfield. That is the case automatically if ##V## is over ##\mathbb R##, and if the field is ##\mathbb C## then the subfield is the isomorphic copy of ##\mathbb R## that is embedded in ##\mathbb C##.

It would follow from those definitions that the matrix representation in any basis be symmetric and positive-definite. In infinite-dimensional vector spaces, the converse may not necessarily follow.

I see from this link that there is another definition of positive-definite operator (definition (i) in the link), that is not necessarily the same as the above (definition (ii) in the link) for infinite-dimensional vector spaces.
 

1. What is a Gaussian integral?

A Gaussian integral, also known as a normal integral, is a type of definite integral that involves the function e^(-x^2). It is commonly used in statistics and probability to calculate the area under a normal distribution curve.

2. Why is function integration of a Gaussian integral important?

Function integration of a Gaussian integral is important because it allows us to calculate probabilities and make predictions about normally distributed data. It is also used in various fields of science, such as physics, engineering, and economics, to solve complex problems.

3. How do you solve a Gaussian integral?

To solve a Gaussian integral, you can use various methods such as completing the square, substitution, and integration by parts. The specific method used will depend on the form of the integral. It is also possible to use numerical methods, such as Simpson's rule, to approximate the value of the integral.

4. What are the applications of Gaussian integration?

Gaussian integration has many applications in science and mathematics. It is used to model and analyze normally distributed data in statistics and probability. It is also used in physics to calculate the energy of molecules and in signal processing to filter out noise from signals. Additionally, it has applications in finance, where it is used to model stock prices and calculate risk.

5. What are the limitations of Gaussian integration?

One limitation of Gaussian integration is that it can only be applied to functions that can be integrated analytically. This means that not all functions can be integrated using this method. Additionally, it is not always possible to find a closed-form solution for the integral, so numerical approximations may be necessary. Lastly, Gaussian integration assumes that the data is normally distributed, so it may not be suitable for non-normal distributions.

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