Comments (3)
Hi @victoriafoing,
Thanks for the questions!
Equation (20) can be derived by computing the absolute value squared of equation (4):
Then the power spectrum (their equation 6) is given as:
With the substitutions:
and
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
, then
, 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
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
and have units of [y]2[t]. The quantities Q and
can be fit to your dataset.
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"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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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?
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