Poisson distribution (radioactive decay)

In summary, the conversation discusses a problem with fitting data to a Poisson distribution in MATLAB for a lab about radioactive decay. The poster has created an Excel chart and tried different values for lambda, but the graph does not curve as expected. Upon further examination, it was discovered that there was a mistake in the code causing the issue.
  • #1
steamyoshi
5
0

Homework Statement


I am a freshman in physics, just done a lab about radioactive decay.
I've measured the # of beta particles per second 400 times and got the frequency of each number K using Excel.
I'm supposed to take the data and fit it to a puason distribution in MATlab.
The data points themselves seem to be on a nice curve.
The problem I'm having is that the equation for the probabilty doesn't seem to curve at all.

Homework Equations


Puason distribution in radioactive decay: the chance that K beta particles will be detected in 1 second is
P(k) = e^-λ * λ^K/K!


The Attempt at a Solution


I've made an excel chart which shows λ^K/K! for many different K values, and then tweaked the lambda value.
For all the values of λ I've tried, λ^K/K! always increases for increasing values of K, meaning the graph never curves back down. What am I doing wrong?
 
Last edited:
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  • #2
Puason distribution in radioactive decay: the chance that K beta particles will be detected in 1 second is
P(k) = e^-λ * λ^K/K!
... that would be poisson distribution, like the fish.
Careful to keep the same variables through your expressions.

$$P(k)=\frac{\lambda^k e^{-k}}{k!}$$

I've made an excel chart which shows λ^K/K! for many different K values, and then tweaked the lambda value.
For all the values of λ I've tried, λ^K/K! always increases for increasing values of K, meaning the graph never curves back down. What am I doing wrong?
##\lambda^k/k!## does increase for positive k, if ##\lambda > 1##but the poisson distribution function has a negative exponential in it which makes it converge for large k.
 
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  • #3
I'm sorry, I don't understand why e is ^-k, all the formulae I've found have e^-λ, which is a constant
thanks for the reply
 
  • #4
That's because I'm an idiot... I should know better than to answer questions at 2am.
concentrating:

When I plot P vs k, I get a decreasing exponential for small values of lambda, and an approximate gaussian for large values of lambda.

You probably have a mistake in your code.
Check - sounds like a misplaced minus sign.

Time for bed.
 
  • #5
Thanks, it WAS a code problem.
 
  • #6
No worries.
And I got to demonstrate not to be afraid of making dumb mistakes too :)
 

Related to Poisson distribution (radioactive decay)

What is the Poisson distribution?

The Poisson distribution is a probability distribution that describes the likelihood of a certain number of events occurring within a specific time period. It is often used to model the number of radioactive decay events in a sample.

How is the Poisson distribution different from other probability distributions?

The Poisson distribution is unique because it assumes that the events occur independently of each other and at a constant rate. This means that the probability of an event happening in one time interval is not affected by the occurrence of an event in another time interval.

What is the formula for calculating the Poisson distribution?

The Poisson distribution formula is P(x) = (e^-λ * λ^x) / x!, where P(x) represents the probability of x events occurring, e is the mathematical constant 2.71828, λ is the average number of events per time interval, and x is the number of events.

How is the Poisson distribution used in radioactive decay studies?

The Poisson distribution is often used to predict the number of radioactive decay events that will occur in a sample over a specific time period. This can help scientists estimate the half-life of a radioactive substance and make predictions about its decay rate.

What are some limitations of using the Poisson distribution in radioactive decay studies?

The Poisson distribution assumes that events occur independently and at a constant rate, which may not always be the case in real-world situations. It also does not take into account external factors that may affect the decay rate, such as temperature or pressure.

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