Interpreting Correlation Functions: (1) vs (2)

In summary, correlation functions can be viewed as either the product of two fields or the difference between the product and the average of the two fields. In field theory, they can also be interpreted as Green's functions and the amplitude to go from one point to another. However, in the case of broken symmetry, a shifted field must be used to calculate time-ordered correlations, resulting in the second formula with an additional T.
  • #1
RedX
970
3
Should one view correlation functions as:

[tex](1) \; \langle T{\phi(x)\phi(y)}\rangle [/tex]

or

[tex](2) \; \langle T{\phi(x)\phi(y)}\rangle - \langle \phi(x)\rangle \langle\phi(y)}\rangle [/tex]

with the second term being zero?

(2) makes more sense as it really measures whether the two fields are independent (the correlation), while (1) just measures the product and that's it.

Yet in field theory, 2-point correlation functions have the interpretation as Green's functions (i.e., Huygen wavelets). Therefore correlation functions also have the interpretation as the amplitude to go from x to y. That implies that (1) makes better sense.

Also, in Ising's model of a ferromagnet, you can calculate spin correlation functions. The partition function is:

[tex]Z=\exp[-\beta(\Sigma_{ij} \epsilon s_is_j)] [/tex]

Can the spin correlation functions [tex]\langle s_l s_m\rangle [/tex] be interpreted as the Green's function of a spin wave? But to be interpreted as such, doesn't there need to be a kinetic term for the spins (or spin field), similar to [tex]\partial_\mu \phi \partial^\mu \phi[/tex] for scalar fields?
 
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  • #2
RedX said:
Should one view correlation functions as:

[tex](1) \; \langle T{\phi(x)\phi(y)}\rangle [/tex]

or

[tex](2) \; \langle T{\phi(x)\phi(y)}\rangle - \langle \phi(x)\rangle \langle\phi(y)}\rangle [/tex]

with the second term being zero?

(2) makes more sense as it really measures whether the two fields are independent (the correlation), while (1) just measures the product and that's it.
Usually, one takes the expectations in the vacuum state; then <phi(x)>=0. So both are the same. In case of broken symmetry, one introduces a shifted field H(x)=phi(x)-<phi(x)>, and then looks at the time-ordered correlations for that, and gets your second formula when undoing the shift transformation (except that you missed a second T in your formula).
 

1. What is a correlation function?

A correlation function is a statistical measure that examines the relationship between two variables. It allows us to determine how closely related two variables are and whether they have a positive, negative, or no relationship at all.

2. What is the difference between "Interpreting Correlation Functions: (1) vs (2)"?

The difference between "Interpreting Correlation Functions: (1) vs (2)" refers to the method used to calculate the correlation function. (1) refers to the Pearson correlation coefficient, which measures the linear relationship between two continuous variables. (2) refers to the Spearman correlation coefficient, which measures the relationship between two ranked variables.

3. How do you interpret a correlation function?

A correlation function can be interpreted by looking at the value of the coefficient. A value close to 1 indicates a strong positive relationship, a value close to -1 indicates a strong negative relationship, and a value close to 0 indicates no relationship. Additionally, the direction of the relationship can be determined by the sign of the coefficient (positive or negative).

4. Can correlation imply causation?

No, correlation does not imply causation. A correlation only shows a relationship between two variables, but it does not necessarily mean that one variable causes the other. There may be other factors at play that influence both variables.

5. How is a correlation function used in scientific research?

Correlation functions are commonly used in scientific research to analyze the relationship between variables and to identify patterns and trends. They can also be used to make predictions and inform decision-making in various fields such as psychology, economics, and biology.

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