Why can the ##1##-point correlation function be made to vanish?

In summary: This is essentially what is being referred to in the statement, as the transformation ##\phi \rightarrow \phi + \phi_0## is a shift of the field by the value ##\phi_0## that makes the correlation function vanish. Therefore, the criteria for determining the value of ##\phi_0## is that it must be the vacuum expectation value of the field operator.
  • #1
spaghetti3451
1,344
34
The ##1##-point correlation function in any theory, free or interacting, can be made to vanish by a suitable rescaling of the field ##\phi##.

I would like to understand this statement.

With the above goal in mind, consider the following theory:

$$\mathcal{L} = \frac{1}{2}\left((\partial\phi)^{2}-m^{2}\phi^{2}\right)+\frac{g}{2}\phi\partial^{\mu}\phi\partial_{\mu}\phi.$$

What criteria (on the Lagrangian ##\mathcal{L}##) is used to determine the value of the field ##\phi_{0}## such that the transformation ##\phi \rightarrow \phi + \phi_{0}## leads to a vanishing ##1##-point correlation function ##\displaystyle{\langle \Omega | T\{\phi(x_{1}\phi(x_{2})\}| \Omega \rangle}##?
 
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  • #2
You should look at the saddlepoint solution for the field from the path integral. When they mean shift the field it corresponds to expanding about the different vaccua.
 
  • #5
radium said:
You should look at the saddlepoint solution for the field from the path integral. When they mean shift the field it corresponds to expanding about the different vaccua.

Right!

When you expand about the vacua, shouldn't you get a new constant term in the Lagrangian?

Doesn't this constant term affect the correlation functions?
 
  • #6
Well I think you are referring to explicitly choosing a ground state writing an effective theory in term of fluctuations about the new ground state. This would correspond to picking a phase for a complex scalar with U(1) symmetry breaking for example (an effective theory for a superfluid). Once you have chosen this vacuum you must stick with your choice since the ground state is no longer invariant under whatever symmetry (remember the action still is, the spontaneous symmetry breaking is because \langle \phi \rangle \neq 0 for certain values of the parameters.
 
  • #7
A 1-point correlation function is simply the vacuum expectation value of the field. Subtract this expectation value from the field operator, and you get a shifted (not a rescaled!) field operator whose vacuum expectation value vanishes.
 

Related to Why can the ##1##-point correlation function be made to vanish?

1. What is the ##1##-point correlation function?

The ##1##-point correlation function is a mathematical tool used in statistics and physics to measure the degree of correlation between two or more variables. It is a measure of how closely related the values of one variable are to the values of another variable.

2. Why is it important for the ##1##-point correlation function to vanish?

The vanishing of the ##1##-point correlation function indicates that there is no correlation between the variables being studied. This can be useful in determining if two variables are truly independent or if there is a hidden relationship between them.

3. How can the ##1##-point correlation function be made to vanish?

The ##1##-point correlation function can be made to vanish by either manipulating the data or by choosing variables that are known to be independent. In some cases, the data may naturally have a vanishing ##1##-point correlation function due to the nature of the variables being studied.

4. What are the limitations of the ##1##-point correlation function?

The ##1##-point correlation function only measures the linear relationship between variables and does not account for non-linear relationships. It also does not indicate causation, only correlation.

5. Can the ##1##-point correlation function be used on any type of data?

The ##1##-point correlation function can be used on any type of data, as long as the variables being studied are measurable and have a linear relationship. However, it may not be the most appropriate measure for certain types of data, such as categorical variables.

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