I'm a scientist conducting an experiment that requires some signal processing. My expertise is not in signal processing, thus here I am. We've basically re-created an experiment conducted by other scientists, attempting to check their results. Here is a link to their paper: Ultrasensitive Inverse Weak-Value Tilt Meter
In short, a laser bounces off of some mirrors, one of which is oscillating at a controlled sinusoidal frequency, onto a quadrant detector, which outputs an electrical signal to an oscilloscope where we record it. So, you end up with a noisy record that has a tiny, known sine wave hiding in it.
My question has to do with calculating the SNR from the FFTs of our records. How many bins do I include for noise? Do I include all of the non-signal bins (besides DC and Nyquist)? Or is there some standard for this type of thing?
As a follow on question, when determining the noise floor from our spectral densities, is there a certain standard calculation, or is it more of an eyeball the plot type thing? Is the floor determined for bins near the signal, or should you look at the whole record?
Thanks in advance for any help you can provide. As a side note, I have done quite a bit of due diligence trying to figure this stuff out. But, anytime I find some source that seems authoritative that says one thing, I'll find another that says something different. I can't tell if I'm misunderstanding what they're saying, or if there are just lots of different definitions for this stuff.
Here are a couple of our PSD plots.