I Properties of the Fourier transform

AI Thread Summary
The discussion centers on the validity of an equation involving the Fourier transform of two functions, specifically questioning whether the manipulation of terms within the integral is accurate. It is noted that the equation may only hold point-wise in k-space, which could lead to misunderstandings. The Fourier Convolution Theorem is referenced, clarifying that the equation does not represent convolution since only one function is transformed. Participants emphasize the importance of correctly applying Fourier analysis principles to avoid confusion. Overall, the equation's utility in practical applications is questioned, highlighting the need for careful interpretation in Fourier analysis contexts.
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TL;DR Summary
Properties of the Fourier transform of two functions
I was wondering if the following is true and if not, why?

$$
\begin{split}
\hat{f}_1(\vec{k}) \hat{f}_2(\vec{k}) &= \hat{f}_1(\vec{k}) \int_{\mathbb{R}^n} f_2(\vec{x}) e^{-2 \pi i \vec{k} \cdot \vec{x}} d\vec{x}
\\
&= \int_{\mathbb{R}^n} \hat{f}_1(\vec{k}) f_2(\vec{x}) e^{-2 \pi i \vec{k} \cdot \vec{x}} d\vec{x}
\\
&= \mathscr{F}\left[\hat{f}_1(\vec{k}) f_2(\vec{x}) \right]
\end{split}
$$
where
$$
\mathscr{F} \left[ f_n(\vec{x}) \right] = \hat{f}_n(\vec{k})
$$
 
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Your LaTex isn't rendering.
 
jbergman said:
Your LaTex isn't rendering.
Fixed!
 
It looks like an identity. Am I missing something?
 
It’s not the convolution theorem in that only $$\hat{f}_2$$ is Fourier transformed.

I was told by that $$\hat{f}_1$$ cannot be moved into the integral $$\int_{-\infty}^{+\infty} dx$$ and so the equation is not accurate. I disagreed and so posted the question. It seems an identity to me too.
 
The Fourier series transform is a sum over all k, not the value for a single k.
 
redtree said:
TL;DR Summary: Properties of the Fourier transform of two functions

I was wondering if the following is true and if not, why?

$$
\begin{split}
\hat{f}_1(\vec{k}) \hat{f}_2(\vec{k}) &= \hat{f}_1(\vec{k}) \int_{\mathbb{R}^n} f_2(\vec{x}) e^{-2 \pi i \vec{k} \cdot \vec{x}} d\vec{x}
\\
&= \int_{\mathbb{R}^n} \hat{f}_1(\vec{k}) f_2(\vec{x}) e^{-2 \pi i \vec{k} \cdot \vec{x}} d\vec{x}
\\
&= \mathscr{F}\left[\hat{f}_1(\vec{k}) f_2(\vec{x}) \right]
\end{split}
$$
where
$$
\mathscr{F} \left[ f_n(\vec{x}) \right] = \hat{f}_n(\vec{k})
$$
From a certain perspective it's only true point-wise in "##\vec k##" space, so it might be misleading. I can't think of any setting off the top of my head where that equation (i.e. ##\mathscr{F}\left[\hat f_1(\vec k) f_2(\vec x)\right] = \hat f_1(\vec k)\hat f_2(\vec k)##) specifically would be useful. The identity ##\mathscr{F}\left[ f\right](\vec k) \equiv \hat f(\vec k)## can be helpful, however, when introducing Fourier analysis to the uninitiated, or improving the flow of a paper/derivation where Fourier analysis is used extensively and intermittently. In general, ##\hat f(\vec k) \hat g(\vec k) = \mathscr{F}\left[f * g\right](\vec k)##, where ##*## is the convolution operator (i.e. ##f * g(x) \equiv \int_y f(y) g(x - y)##, which can be checked with the heuristic "identity" ##\int \frac{dk}{2\pi}e^{ik\cdot x} = \delta(x)##.)
 
Last edited:
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