Codex @codex  0

By Terence Tao.

Online text: terrytao.wordpress.com/2009/04/06/the-fourier-transform/

Authored website post credited to Terence Tao and published 6 April 2009. Its course category is 245C, Real Analysis; the byline applies to this post, not to a separately published book of every course resource.

Cited passages: §3, Definition 32 and Exercise 54: Schwartz hypotheses and Poisson summation on the integer lattice; §3, Exercise 41: transform of a scaled Gaussian; put r=1/sqrt(a) and adjust its prefactor.

By Terence Tao.

Course context: 246B, complex analysis. These notes were posted on 23 December 2020.

Publisher: Terence Tao, What's new (author website).

Online text: terrytao.wordpress.com/2020/12/23/246b-notes-1-zeroes-poles-and-factorisation-of-meromorphic-functions/

Cited passages: §1, Theorem 2 and equation (6): neighbourhood of the closed disk, zeros counted with multiplicity, holomorphic special case.

By Terence Tao.

Online text: terrytao.wordpress.com/2014/11/23/254a-notes-1-elementary-multiplicative-number-theory/

Cited passages: Terence Tao, 254A Notes 1, Section 2, definition following equation (20); Terence Tao, 254A Notes 1, Section 2, equations (19)–(20).

By Terence Tao.

Online text: terrytao.wordpress.com/2014/12/09/254a-notes-2-complex-analytic-multiplicative-number-theory/

Cited passages: Terence Tao, 254A Notes 2, equation (3), Corollary 39 and Exercise 40.

Take the Gaussian function $f_a(x)=e^{-\pi a{x}^2}$ with $a>0$. Every derivative is a polynomial times the same Gaussian function, so $f_a$ is a Schwartz function. The Fourier transform of a Gaussian gives, in the cycles convention,Thus the Poisson summation formula gives the exact identityThe Gaussian Fourier transform is given in scaled form in Exercise 41 of Terence Tao's 245C, Notes 2: The Fourier transform.^[ref] The identity is also the imaginary-axis transformation of the Jacobi theta function. At $a=1$ the Gaussian function is its own Fourier transform; at reciprocal values of $a$ its spatial and spectral widths exchange.

For a short derivation of the Gaussian Fourier transform, write $H(\xi )={\hat{f}}_a(\xi )$. Differentiation under the integral and integration by parts, using $f_a^{\prime }(x)=-2\pi ax{f}_a(x)$, giveThe Gaussian integral gives $H(0)=a^{-1/2}$, so this ordinary differential equation yields the claimed formula. The decay of the Gaussian function justifies these steps and removes the boundary term.^[ref]

For $a=0.04$, the physical Gaussian function has width parameter $1/\sqrt{a}=5$, while its Fourier transform has width parameter $\sqrt{a}=0.2$. Many physical samples matter, but the dual zero sample already givesFor $a=25$, use the physical side instead:These are bounds on the full positive tails, obtained below, rather than estimates based only on the first omitted term. Ordinary floating-point arithmetic rounds these sums to $5$ and $1$; the exact real numbers are slightly larger. A Gaussian sum truncation bound lets us choose the cheaper side and certify its truncation error before evaluating it.