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ericagol avatar ericagol commented on June 3, 2024 2

Hi @victoriafoing,

Thanks for the questions!

Equation (20) can be derived by computing the absolute value squared of equation (4):

image

Then the power spectrum (their equation 6) is given as:

image

With the substitutions:

image

and

image

this yields an expression which is proportional to our equation (20). The \sqrt{2/\pi} results from the way we defined the Fourier transform (see footnote on page 3 of our paper), and the P_X(\nu) is a constant as we assume that \epsilon(t) = \omega_0^2 x(t) is white noise, and thus the Fourier transform is a constant value at all frequencies.

Then, S0 is the normalization constant of the power spectrum, which is the Fourier transform of the kernel.

I find it easiest to interpret S0 in terms of the kernel definition in our equation (23). When
image, then
image, which is the limiting amplitude of the kernel at zero time lag (which is in addition to a white-noise amplitude which is only present at zero time lag). The kernel has units of the y2 (let's say this is [y]2), and
image has units of 2\pi/[t] (using whatever "time" unit you are using, [t], or other independent vector for a general GP),
which is in units of the quantity you have a vector of measurements of, and so S0 should equal
image and have units of [y]2[t]. The quantities Q and
image can be fit to your dataset.

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dfm avatar dfm commented on June 3, 2024 2

"i" is the imaginary unit and when you take the absolute value squared of the numerator, it is equal to the denominator, so it cancels.

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victoriafoing avatar victoriafoing commented on June 3, 2024

Hi @ericagol!

Thank you for your answers. What does i represent in equation 4? How do we get rid of the numerator in equation 4 after taking the absolute value squared?
image

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