STD of Poisson distributed particle intensity

In summary, the conversation discusses a detector that detects alpha-particles in a decay process and the standard deviation of the intensity of detected particles per second. The Poisson distribution is used to calculate the expectation value and standard deviation of the intensity, and the standard deviation of ln I is derived using propagation of error. The formula for calculating standard deviation for non-linear combinations is referenced as a resource for this derivation.
  • #1
theorem
4
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Homework Statement


Say I have a detector that detects alpha-particles of some decay process. Let N be the amount of particles detected and let N be Poisson distributed. If I now define the intensity to be the amount of detected particles per second I = N/t, what would the standard deviation of I be? Also derive the standard deviation of ln I using propagation of error (error in t can be disregarded)..

Homework Equations


Poisson distribution

The Attempt at a Solution


If I let the amount of particles detected in a time interval t be n = μ/t, the probability of detecting x particles is given by the Poisson probability function with mean (parameter) μ = nt. The expectation value is therefore just μ and the std is sqrt(μ) = sqrt(nt). This means that the std for the intensity is sqrt(nt)/t = sqrt(n/t). That's the first part done.

As for the std of ln I, I don't really know what is meant by deriving it using propagation of error or maybe I'm just too tired. Any help is appreciated.
 
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  • #2
theorem said:
let the amount of particles detected in a time interval t be n = μ/t
Shouldn't this be n = µt?

As for the std of ln I, I don't really know what is meant by deriving it using propagation of error or maybe I'm just too tired. Any help is appreciated.
https://en.wikipedia.org/wiki/Propagation_of_uncertainty#Non-linear_combinations

Instead of defining a new variable µ in part 1, you should try applying the formula above to I = N/t in order to calculate sI in terms of N and t.
 

1. What is a Poisson distributed particle intensity?

A Poisson distributed particle intensity is a statistical concept used to describe the number of particles present in a given area or volume. It follows a Poisson distribution, which is a probability distribution that models the occurrence of rare events.

2. How is a Poisson distributed particle intensity calculated?

A Poisson distributed particle intensity is calculated by dividing the total number of particles by the total area or volume in which they are present. This results in a measure of particles per unit area or volume, which can then be used to analyze and compare different samples.

3. What are the main applications of Poisson distributed particle intensity?

Poisson distributed particle intensity is commonly used in various fields such as physics, biology, and environmental science to characterize the spatial distribution of particles. It is also used in quality control processes to monitor the number of defects in a product or system.

4. How does the Poisson distributed particle intensity relate to standard deviation?

The Poisson distribution has a mean and standard deviation that are equal, which means that the standard deviation of a Poisson distributed particle intensity is equal to the square root of its mean value. This relationship is important for understanding the variability in particle intensity measurements.

5. Can a Poisson distributed particle intensity be used to model other types of data?

Yes, a Poisson distributed particle intensity can be used to model other discrete data that follows a similar pattern of rare events occurring randomly. This includes occurrences of accidents, defects, and rare diseases.

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