Why Does Particle Emission Follow a Poisson Distro?

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In summary, particle emission can be modeled as a Poisson process if the probability of the event occurring is constant over time. This is derived from the binomial distribution and is often observed in radioactive decay due to the large sample size.
  • #1
marc.morcos
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Why does particle emission follow a poisson?

Thanks in advance
 
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  • #2
marc.morcos said:
Why does particle emission follow a poisson?

Thanks in advance

This is a vague question.

What particle? And what property exactly that follows a poisson distribution? I can show you the energy spectrum of beta decay that follows nowhere near a poisson distribution.

If you wish to get some degree of a rational response, you should make some effort into presenting a clear question.

Zz.
 
  • #3
He probably means that particle emission follows a Poisson process.

If an event has a constant probability per unit time of occurring (i.e., the probability of X happening in the next 10 seconds is always a constant [itex]\lambda[/itex]), then that event can be modeled as a Poisson process. Google it to find a derivation.
 
  • #4
thanks a lot, sorry about the lack of detail, i was trying to type it before my laptop battery died... what i meant was what ben said. i was looking for the derivation in specific.
 
  • #5
marc.morcos said:
thanks a lot, sorry about the lack of detail, i was trying to type it before my laptop battery died... what i meant was what ben said. i was looking for the derivation in specific.


The possion distribution is derived from the binomial distribution in with two limits. i. the probablity for one event goes to zero and ii. the number of trials goes to infinty. I don't remember it 100%, so better look it up on google or in a reference in introductory statistics. And that is also why one can say (as Ben Niehoff) that the probablity is constant [itex]\lambda[/itex]
and so on.

The reason for WHY radioactive decay follow poisson is that is a probibalistic process, and that you have a large sample.
 

1. Why is the Poisson distribution used to model particle emission?

The Poisson distribution is used because it is a probability distribution that is commonly used to model the number of events that occur within a specific time interval or space. In the case of particle emission, the occurrence of particles follows a random and independent process, making the Poisson distribution a suitable model.

2. How does the Poisson distribution relate to particle emission?

The Poisson distribution relates to particle emission by predicting the probability of a certain number of particles being emitted within a specific time interval or space. It allows scientists to understand the expected number of particles that will be emitted and the likelihood of observing a particular number of particles.

3. What factors influence the shape of the Poisson distribution for particle emission?

The shape of the Poisson distribution for particle emission is influenced by two main factors: the average rate of particle emission and the size of the time interval or space being observed. As the average rate of emission increases, the distribution becomes more spread out and skewed to the right. Similarly, a larger time interval or space will result in a wider and flatter distribution.

4. Is the Poisson distribution a good fit for all types of particle emission?

No, the Poisson distribution may not always be the best fit for all types of particle emission. It assumes that the emission process is random and independent, which may not always be the case. Other factors such as interactions between particles or external influences may need to be considered when choosing a distribution to model particle emission.

5. How can the Poisson distribution be used to make predictions about particle emission?

The Poisson distribution can be used to make predictions about particle emission by calculating the probability of observing a certain number of particles within a given time interval or space. This information can then be used to estimate the expected number of particles that will be emitted, as well as the likelihood of observing a specific number of particles.

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