The power spectrum of Poisson noise

AI Thread Summary
The discussion focuses on the power spectrum of Poisson noise with an expected value of λ=2, highlighting concerns about high power at zero frequency due to a significant DC offset in the data. Participants suggest averaging the data to eliminate this offset and recommend increasing the number of data points and pulses for better detail. They also advise using an FFT for computation, ideally starting with 1024 points for optimal results. The conversation emphasizes the importance of understanding the input data structure and suggests plotting multiple spectra to identify patterns. Overall, the thread seeks to clarify the correct shape of the power spectrum and improve analysis techniques.
arcTomato
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Poisson noise
Dear all.
I have made the power spectrum of Poisson noise(expected value ##λ=2##), and it becomes like this
1575036752663.png

I think this is not good. I don't know why the power is so high when Random variable(x axis) is 0.
I tried another expected value version ,but the result didn't change.
so I would like to know the correct shape of the power spectrum of Poisson noise.
If you can help me, please🙇‍♂️
Thank you.
 
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There is a huge DC offset in your data.
It needs to average close to zero to eliminate the zero frequency power.
 
Thanks @Baluncore!
Can I just replace the power with zero?like this?
1575038809288.png
 
Yes.
I would expect a flat spectrum.
 
Thank you @Baluncore!
If you don't mind, Could you teach me why does it happen? or show me some links?
 
You use auto-scale, so the huge DC offset attenuated the data.
I do not know the structure of your input data. I expect it is randomly located positive pulses?

You should increase the number of points and the number of pulses to get better detail.
You might plot several on the same graph to see any repeated pattern.
You might accumulate the power spectrum to average out the structure of the spectrum.

Google “shot noise spectrum”.
 
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yes I used this data.
1575040102032.png

I appreciate for your kindness,@Baluncore !
 
If you use an FFT to compute the spectrum you should have 2integer input data cells to get the optimum speed of computation. I would start with 1024 points, giving 512 frequencies.
 
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