Computing path integral with real and Grassmann variables

In summary, the conversation discusses the computation of the $Z[w]$ integral, where $w_i(x)$ is a function and $n$ is a constant. The conversation suggests using the determinants of the partial derivatives of $w_i(x)$ and provides different approaches to solving the integral, such as using partial integration and the fact that the determinant can be written as the exponential of the trace of the natural logarithm. However, it is mentioned that obtaining the exact value of the integral may not be possible and the purpose of solving it is unclear.
  • #1
Geigercounter
8
2
Homework Statement
I want to compute the following path integral
$$Z[w] = \frac{1}{(2\pi)^{n/2}}\int d^n x \: \prod_{i=1}^{n}d\overline{\theta}_id\theta \: \exp{\left(-\overline{\theta}_i \partial_j w_i(x)\theta_j -\frac{1}{2}w_i(x)w_i(x)\right)}.$$ Here $w_i(x)$ are functions of the $n$ real variables $x_i$ and $\theta_i$ and $\overline{\theta}_i$ are $n$ independent Grassmann variables.
Relevant Equations
See below.
The first step seems easy: computation of the $\theta$ and $\overline{\theta}$ integrals give
$$Z[w] = \frac{1}{(2\pi)^{n/2}}\int d^n x \: \det(\partial_j w_i(x)) \exp{\left(-\frac{1}{2}w_i(x)w_i(x)\right)}.$$

From here, I tried using that $$\det(\partial_j w_i (x)) = \det\left(\partial_j w_i \left(\frac{d}{db}\right)\right) \exp\left(b_i x_i\right)\bigg\vert_{b=0}.$$ But I don't seem to be able to apply this step.

Other ideas I had included writing out the determinant as $$det(\partial_j w_i(x)) = \frac{1}{n!}\varepsilon_{i_1...i_n}\varepsilon_{j_1...j_n} \partial_{j_1} w_{i_1}(x) ... \partial_{j_n} w_{i_n}(x)$$ to then use some kind of partial integration.
Another, similar, idea was to use the fact that $$\det = \exp(\text{Tr} \ln) $$
 
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  • #2
Performing the ##x## integration exactly for ##Z[w]## could very well not be possible. You may very well not be able to obtain the exact value even if you were just considering this integral:

\begin{align*}
\frac{1}{(2 \pi)^{n/2}} \int d^nx \exp \left( - \frac{1}{2} w_i (x) w_i (x) \right)
\end{align*}

Could you clarify what it is you are aiming to achieve exactly? Do we know what the functions ##w_i (x)## are explicitly? Are you wanting to put the integral into some nice form rather than explicitly evaluating it? Could you show us the source of the question?
 
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1. What is a path integral in computing?

A path integral in computing is a mathematical tool used to calculate the probability of a particle or system moving from one state to another. It involves summing up all possible paths that the particle or system could take between the two states, taking into account the probability of each path occurring.

2. What are real and Grassmann variables?

Real variables are numerical quantities that can take on any real value, while Grassmann variables are mathematical objects that are used in the study of supersymmetry. They are anti-commuting variables, meaning that their product is anti-symmetric and they do not commute with each other.

3. How are real and Grassmann variables used in computing path integrals?

Real and Grassmann variables are used in computing path integrals to represent the different possible paths that a particle or system could take. Real variables represent the physical coordinates of the particle, while Grassmann variables represent the superpartners of these coordinates in supersymmetric theories.

4. What are some applications of computing path integrals with real and Grassmann variables?

Computing path integrals with real and Grassmann variables has many applications in theoretical physics, particularly in the study of quantum field theory and supersymmetry. It is also used in statistical mechanics and condensed matter physics to calculate the behavior of complex systems.

5. Are there any challenges in computing path integrals with real and Grassmann variables?

Yes, there are several challenges in computing path integrals with real and Grassmann variables. One of the main challenges is dealing with the anti-commuting nature of Grassmann variables, which requires special mathematical techniques. Another challenge is the complexity of the calculations involved, which can become increasingly difficult as the number of variables and dimensions increases.

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