The power spectrum of Poisson noise

In summary, the speaker has made a power spectrum of Poisson noise with an expected value of λ=2, but is concerned about the high power at x=0. They have tried a different expected value but the result did not change. They are looking for guidance on the correct shape of the power spectrum and are interested in learning more about why this is happening. They have been advised to average the data close to zero to eliminate the zero frequency power and to increase the number of points and pulses for better detail. They have also been recommended to plot multiple spectra and accumulate the power spectrum for better understanding. The speaker has used this data and is grateful for the advice.
  • #1
arcTomato
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27
TL;DR Summary
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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  • #2
There is a huge DC offset in your data.
It needs to average close to zero to eliminate the zero frequency power.
 
  • #3
Thanks @Baluncore!
Can I just replace the power with zero?like this?
1575038809288.png
 
  • #4
Yes.
I would expect a flat spectrum.
 
  • #5
Thank you @Baluncore!
If you don't mind, Could you teach me why does it happen? or show me some links?
 
  • #6
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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  • #7
yes I used this data.
1575040102032.png

I appreciate for your kindness,@Baluncore !
 
  • #8
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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1. What is Poisson noise?

Poisson noise is a type of statistical noise that occurs in many natural phenomena, such as radioactive decay, photon counting, and astronomical observations. It is characterized by random fluctuations in the number of events occurring over a given time or space.

2. How is Poisson noise different from other types of noise?

Poisson noise differs from other types of noise, such as Gaussian noise, in that it follows a specific probability distribution known as the Poisson distribution. This distribution describes the probability of a certain number of events occurring in a given time or space, assuming a constant rate of occurrence.

3. What is the power spectrum of Poisson noise?

The power spectrum of Poisson noise is a way of analyzing the frequency components of the noise. It represents the distribution of power as a function of frequency, and can be used to identify any periodicities or patterns in the noise.

4. How is the power spectrum of Poisson noise calculated?

The power spectrum of Poisson noise is typically calculated using a Fourier transform, which converts the noise signal from the time or space domain to the frequency domain. The resulting power spectrum can then be visualized and analyzed to better understand the characteristics of the noise.

5. How is the power spectrum of Poisson noise used in scientific research?

The power spectrum of Poisson noise is commonly used in fields such as astronomy, physics, and engineering to analyze and filter out unwanted noise from data. It can also be used to study the underlying processes that produce the noise and make predictions about future observations.

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