Algorithmic model for primary decay chains

In summary, the conversation discusses the possibility of creating a computational algorithmic model that predicts the entire decay chain of an isotope based on its A and Z values. The idea is to input the values and have the model generate a graphic or list/array of all primary isotopes and decay modes, with the potential to predict the probability of different decay chains. The speaker is unsure if this is possible or if it has been done before, and is seeking input and information on the topic. They also mention the need for experimental data to ensure accuracy in the predictions.
  • #1
Admiralibr123
14
5
TL;DR Summary
Has there been written an algorithmic model in which you put in A and Z integers and it predicts the whole decay chain of isotopes.
I have seen "radioactivedecay.py" python library which employs measured experimental data for its calculations. I have seen models that solve the system of differential equation with numerical algorithms to predict the proportion of nuclides at any given time. But I have yet to see a computational algorithmic model which predicts the whole decay chain (half lives/##\lambda## are optional). Does such a model exist? Is it possible for it to exist?

What I have in mind is a piece of code to which you input only the value of A and Z and it shows you in a graphic or as a list/array all the primary isotopes and modes of decay of the isotope. After this we could add complexity to it and try to predict the probability of all the possible decay chains. We could then try to compare it with the experimental data we have.

I have no idea if every algorithm imaginable can be turned into an equation or not but if there is a class which can and we implement it using that class and then turn it into an equation and it simplifies to something more elegant than what we have today. It would be epic. What do you think of this idea? Has it been already done? Is it impossible? Does the changing into an equation an impossible task?

Googling has resulted in inconclusive results so this is my last hope. Thank You very much.
 
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  • #2
Do you want an ab-initio prediction for all the decay modes? That won't be accurate. You need experimental data.
 

FAQ: Algorithmic model for primary decay chains

What is an algorithmic model for primary decay chains?

An algorithmic model for primary decay chains is a mathematical representation of the process by which a radioactive atom decays into a stable atom. It takes into account the probability of different types of decay and the resulting daughter products, and can be used to predict the decay chain of a given radioactive element.

How is an algorithmic model for primary decay chains created?

An algorithmic model for primary decay chains is created by analyzing the known decay patterns of different radioactive elements and using mathematical algorithms to predict the probability of each type of decay occurring. This data is then used to construct a model that can accurately predict the decay chain of a given element.

What are the benefits of using an algorithmic model for primary decay chains?

An algorithmic model for primary decay chains allows scientists to make accurate predictions about the decay patterns of radioactive elements, which can be useful in various fields such as nuclear energy, medicine, and environmental studies. It also helps in understanding the stability and half-life of different elements.

How does an algorithmic model for primary decay chains differ from a simple decay model?

An algorithmic model for primary decay chains takes into account the probability of different types of decay occurring, while a simple decay model only considers the most common type of decay. This makes the algorithmic model more accurate and comprehensive in predicting the decay chain of a radioactive element.

Can an algorithmic model for primary decay chains be used for all radioactive elements?

An algorithmic model for primary decay chains can be used for most radioactive elements, but it may not be accurate for elements with very short half-lives or those that undergo rare or unusual types of decay. In these cases, additional research and data may be needed to create a more accurate model.

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