I have a white noise generator circuit which involves a Zener diode meant to undergo breakdown across a resistor $R1$, and am trying to compare power spectral densities and autocorrelation of the output I get. I recorded the data on an oscilloscope with microsecond, milisecond, and half second timescales.
First, I tried looking at what sample rates that I chose from was going to best characterise the noise. I figured a lower sampling time would be better, sort of arbitrarily, but the noise histograms I got for lower sampling rates tended to look more Poissonian in nature (well, most of all Gaussian looking):
I guess I should take it that longer sampling rates tend to characterize the shot noise better?
As I say, Zener diode breakdown noise is shot noise, so I was ideally trying to see which combination of Zener diode voltage and $R1$ I could use to generate the "whitest" looking noise. That is, that could generate a flat power spectral density and most random-looking autocorrelation (all lags except for $1$ near $0$).
However, I have some contradicting graphs when I plot them. For instance, for a Zener diode voltage of $12 V$ and $R1 = 100 k \Omega$ my PSD looks like this (using
which is somewhat flat? However, the autocorrelation certainly disagrees with this being perfectly random (the axes are the canonical ones - lags and ACF):
which, if I am to assume the PSD shows flatness, contradicts it. Meanwhile, for a milisecond time between samples with the same Zener voltage and $R1$ resistance..
which again seems to contradict itself, but now in the other direction -- the near 0 autocorrelation supports the idea that noise is random, yet the PSD is not flat.
Meanwhile, with a half-second time between samples..
my data seems to be the most self-consistent here, if I can say that the PSD is more-or-less flat. Is this essentially telling me that the proper time between samples should've been one half-second? If not, what's going on here?
I'm very shaky to the idea that the faster sampling rate failed to capture the noise distribution as well as a slower one.