Calculate limits as distributions

Well done!In summary, the conversation discusses how to calculate limits when they are viewed as distributions. The conversation covers using a test function to calculate the limits and integrating with the sifting property. The process involves using the Fourier transform to show that the limit on the left of the equation has the sifting property, leading to the conclusion that the limit is equal to the product of the test function and the delta distribution. The summary concludes that the demonstration provided in the conversation is correct.
  • #1
Haorong Wu
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TL;DR Summary
How to calculate the following limits, when viewed as distributions?
Hi, there. I am reading this thesis. On page 146, it reads that

when viewed as distributions, one can show that the following limits holds:
$$\lim_{r\rightarrow \infty}\frac {\sin ((Q-Q')r)}{Q-Q'}=\pi \delta(Q-Q') ,$$
$$\lim_{r\rightarrow \infty}\frac {\cos ((Q+Q')r)}{Q+Q'}=0 .$$

I do not know how to calculate the limits when they are viewed as distributions. I am trying to integrate a test function with the limits. So I try (##Q## is defined as ##Q>0##) $$\lim_ {r\rightarrow \infty} \int_{0}^\infty dQ \cos ((Q-Q')r )\frac {\sin ((Q-Q')r)}{Q-Q'}=\frac \pi 2,$$ while ##\int_{-\infty}^\infty dQ \cos ((Q-Q')r ) \delta (Q-Q')=1##. Then I only have ##\lim_{r\rightarrow \infty}\frac {\sin ((Q-Q')r)}{Q-Q'}=\pi \delta(Q-Q') /2##. Is this wrong? Thanks.
 
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  • #2
Haorong Wu said:
TL;DR Summary: How to calculate the following limits, when viewed as distributions?

Hi, there. I am reading this thesis. On page 146, it reads that
I do not know how to calculate the limits when they are viewed as distributions. I am trying to integrate a test function with the limits. So I try (##Q## is defined as ##Q>0##) $$\lim_ {r\rightarrow \infty} \int_{0}^\infty dQ \cos ((Q-Q')r )\frac {\sin ((Q-Q')r)}{Q-Q'}=\frac \pi 2,$$
I have no real answer for you, just a comment.
You need to show that the limit on the left of your equation has the sifting property just like a delta distribution has.
So the integral you state should give cos(Q') if you put cos(Q) (rather than cos(Q-Q')) into the integrand.
You ought to show this in general though: f(Q) is turned into f(Q') by the sifting integral.
 
  • #3
I would expand [tex]
\sin r(Q - Q') = \frac{e^{ir(Q-Q')} -e^{-ir(Q-Q')}}{2i}[/tex] and express the integral [tex]
\int_{-\infty}^\infty f(Q) \frac{\sin(r(Q-Q'))}{Q-Q'}\,dQ[/tex] as a sum of fourier transforms.
 
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  • #4
Thanks, @Philip Koeck and @pasmith. I will try to demonstrate the first expression.

Suppose ##F(\omega)## is the Fourier transform of ##f(Q)##, i.e., ##f(Q)=(2\pi)^{-1/2} \int d\omega F(\omega) e^{-i\omega Q}##. Then the integral \begin{align}
&~~\lim_{r\rightarrow \infty} \int_0^\infty dQ f(Q) \frac {\sin ((Q-Q')r)}{Q-Q'} \nonumber \\
&=\lim_{r\rightarrow \infty} \int_0^\infty dQ (2\pi)^{-1/2} \int d\omega F(\omega) e^{-i\omega Q} \frac {\sin ((Q-Q')r)}{Q-Q'} \nonumber \\
&=(2\pi)^{-1/2} \int d\omega F(\omega) e^{-i\omega Q' } \lim_{r\rightarrow \infty} \int_0^\infty dQ e^{-i\omega (Q-Q')} \frac {\sin ((Q-Q')r)}{Q-Q'}.\nonumber
\end{align} Letting ##x=Q-Q'##, we have ##\lim_{r\rightarrow \infty} \int_0^\infty dQ e^{-i\omega (Q-Q')} \frac {\sin ((Q-Q')r)}{Q-Q'}=\lim_{r\rightarrow \infty}\int_{-Q'}^\infty dx e^{-i\omega x}\frac {\sin (xr)}{x}##. Further, setting ##y=xr##. it becomes \begin{align}&~~\lim_{r\rightarrow \infty}\int_{-\infty}^\infty dy e^{-i\omega y/r}\frac {\sin (y)}{y} \nonumber \\
&=\lim_{r\rightarrow \infty}\int_0^\infty dy (e^{-i\omega y/r}\frac {\sin (y)}{y}+e^{i\omega y/r}\frac {\sin (y)}{y})\nonumber \\
&=\lim_{r\rightarrow \infty}\int_0^\infty dy 2\cos(\frac {\omega y}{r})\frac {\sin (y)}{y}\nonumber \\ &=\int_0^\infty dy 2\frac {\sin (y)}{y}=\pi. \nonumber\end{align}
Therefore, ##\lim_{r\rightarrow \infty} \int_0^\infty dQ f(Q) \frac {\sin ((Q-Q')r)}{Q-Q'}=\pi (2\pi)^{1/2} \int d\omega F(\omega) e^{-i\omega Q' }=\pi f(Q')##. Hence ##\lim_{r\rightarrow \infty} \frac {\sin ((Q-Q')r)}{Q-Q'}=\pi \delta(Q-Q')##.

Is the demonstration correct?
 
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  • #5
Haorong Wu said:
Thanks, @Philip Koeck and @pasmith. I will try to demonstrate the first expression.

Suppose ##F(\omega)## is the Fourier transform of ##f(Q)##, i.e., ##f(Q)=(2\pi)^{-1/2} \int d\omega F(\omega) e^{-i\omega Q}##. Then the integral \begin{align}
&~~\lim_{r\rightarrow \infty} \int_0^\infty dQ f(Q) \frac {\sin ((Q-Q')r)}{Q-Q'} \nonumber \\
&=\lim_{r\rightarrow \infty} \int_0^\infty dQ (2\pi)^{-1/2} \int d\omega F(\omega) e^{-i\omega Q} \frac {\sin ((Q-Q')r)}{Q-Q'} \nonumber \\
&=(2\pi)^{-1/2} \int d\omega F(\omega) e^{-i\omega Q' } \lim_{r\rightarrow \infty} \int_0^\infty dQ e^{-i\omega (Q-Q')} \frac {\sin ((Q-Q')r)}{Q-Q'}.\nonumber
\end{align} Letting ##x=Q-Q'##, we have ##\lim_{r\rightarrow \infty} \int_0^\infty dQ e^{-i\omega (Q-Q')} \frac {\sin ((Q-Q')r)}{Q-Q'}=\lim_{r\rightarrow \infty}\int_{-Q'}^\infty dx e^{-i\omega x}\frac {\sin (xr)}{x}##. Further, setting ##y=xr##. it becomes \begin{align}&~~\lim_{r\rightarrow \infty}\int_{-\infty}^\infty dy e^{-i\omega y/r}\frac {\sin (y)}{y} \nonumber \\
&=\lim_{r\rightarrow \infty}\int_0^\infty dy (e^{-i\omega y/r}\frac {\sin (y)}{y}+e^{i\omega y/r}\frac {\sin (y)}{y})\nonumber \\
&=\lim_{r\rightarrow \infty}\int_0^\infty dy 2\cos(\frac {\omega y}{r})\frac {\sin (y)}{y}\nonumber \\ &=\int_0^\infty dy 2\frac {\sin (y)}{y}=\pi. \nonumber\end{align}
Therefore, ##\lim_{r\rightarrow \infty} \int_0^\infty dQ f(Q) \frac {\sin ((Q-Q')r)}{Q-Q'}=\pi (2\pi)^{1/2} \int d\omega F(\omega) e^{-i\omega Q' }=\pi f(Q')##. Hence ##\lim_{r\rightarrow \infty} \frac {\sin ((Q-Q')r)}{Q-Q'}=\pi \delta(Q-Q')##.

Is the demonstration correct?
Looks good to me.
 
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FAQ: Calculate limits as distributions

What does it mean to calculate limits as distributions?

Calculating limits as distributions involves extending the concept of limits from classical analysis to the framework of distribution theory. In this context, we treat functions not just as pointwise defined entities but as distributions (or generalized functions) that can capture more nuanced behaviors, especially when dealing with singularities or points of discontinuity.

How do you define a distribution for a given function?

A distribution is defined as a continuous linear functional on the space of test functions (smooth functions with compact support). To define a distribution for a given function, we typically express it in terms of its action on a test function via an integral, which may involve regularization techniques if the function is not well-defined in the classical sense.

What are some common techniques for calculating limits as distributions?

Common techniques include regularization methods (such as mollification), using sequences of functions that converge to the desired limit, and employing properties of distributions like linearity and continuity. Additionally, one might use the theory of weak convergence to establish the limit in the distributional sense.

Can you provide an example of calculating a limit as a distribution?

One classic example is the limit of the sequence of functions \( f_n(x) = \frac{\sin(nx)}{n} \) as \( n \to \infty \). Pointwise, this converges to 0 for all \( x \), but in the distributional sense, it converges to the distribution defined by the Dirac delta function \( \delta'(x) \) due to its oscillatory nature. The limit can be shown by testing against a smooth function and evaluating the resulting integrals.

What is the significance of calculating limits as distributions in mathematical analysis?

Calculating limits as distributions is significant because it allows for the rigorous treatment of singularities and discontinuities in analysis. It provides a powerful framework for dealing with problems in partial differential equations, quantum mechanics, and signal processing, where traditional limit definitions may fail or be insufficient to capture the behavior of functions at critical points.

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