I have a couple of questions regarding Geant4’s operational principles:
Firstly, in real-world scenarios, when a target material is irradiated by a particle source, multiple particles emitted simultaneously interact with the target concurrently. However, in Geant4 simulations, the next particle begins processing only after the current particle’s life cycle concludes. Although parallelization can be employed, parallel simulations still model each particle independently without mutual interference. What underlying assumption or approximation justifies Geant4’s sequential treatment of particles?
Secondly, after particles interact with a material, the material’s properties may undergo changes to some extent. In Geant4, material state information is reset between interactions with different particles. Is this approach based on the assumption that the impact of material property changes induced by one particle on subsequent particle interactions is negligible? If so, why is this assumption valid?
Geant4 is mostly for handling the physics part of radiation transport. This is as “close to the metal” as you can get for modelling any system involving radiation. It is optimized to make that workflow a fully realizable tradeoff between accuracy, computational cost, and physics. The number of applications for its output are far too numerous to be coherently mapped into one simulations package.
That said, you can modify its open source code for your particular application generally by feeding either the input or the output to some other module. This is frequently done. More typical is to join in to other simulation packages that are optimized for other regimes or application spaces. For example: heat modeling packages, particle accelerator beam optics codes, nuclear power radiation transport steady state codes, etc. The more specialized a code package the less generalizable it is.
Monte Carlo calculations yield steady-state results. However, in Geant4, GlobalTime can be used to record time. Is there an intrinsic connection between this time and the steady state? If so, what is the nature of that connection?
Quite the contrary. Methods like finite element solvers do find steady state solutions with imposed boundary conditions. These are fast but typically miss or don’t calculate outlier behaviors. Monte Carlo codes such as Geant fold that back in with probabilistic modelling of a toy model of the world most relevant to the radiation detection. You can bootstrap ensemble averages but that typically requires far more simulation than is reasonable on time or CPU scale. This is the tradeoff between the methods.
Time in Geant is meant merely to correlate events/hits in sensitive elements to underlying radiation related physics processes of interest. The only “boundary” condition comes in the form of the particle gun.
I think you are correct that “the impact of material property changes induced by one particle on subsequent particle interactions is negligible” is the right way to think about the assumption at the basis of GEANT4 and similar codes. You might imagine that if a truly huge number of particles are hitting a detector (or a model of an organism, or whatever else you are simulating) that ionization or nuclear interactions will significantly change the medium. But for most applications, the huge number of atoms in your medium (detector, organism, etc.) means that the probability of any two high-energy particles interacting with the same atom or set of atoms in the medium is extremely small. So in most cases it’s a good approximation.
Here is a very over-simplified example. A piece of silicon has a spacing of about 0.235nm between atoms. Let’s say you have a slab of silicon of area one square centimeter and particles are coming down vertically onto the slab. In 1cm2, there are about 1.8e15 different atoms you might begin by hitting. Therefore if your radiation field contains << 1e15 particles/cm2, each particle track would be hitting an independent set of atoms on the way through the medium.
One example of where it can’t do everything for you: Some of my collaborators use GEANT4 as part of a simulation of an electrical discharge in air. They put in an electric field, and let some high energy electrons get accelerated, making a big avalanche of other electrons as they collide with atoms. At some point, all these currents are going to change the electric field you started with. For this reason, they run GEANT4 for a little bit, then calculate how the electric field would be reconfigured, then run GEANT4 a little more, etc. This, I think, is another example of the sort of complicated applications that jrellin is describing.
GEANT’s timing of the particles is very useful when you want to see what the response of your detector is as a function of time when a primary particle makes a lot of secondary particles that arrive at different times.
Thank you for your reply!Geant4 employs the Monte Carlo method and calculates steady-state results. I would like to know whether the time recorded by Geant4 represents transient results. Additionally, during my simulation of particle decay, the half-life of particles is fixed. Why isn’t the generation time of their secondary particles fixed?
What exactly do you mean by “transient results”? The time recorded by Geant4 in a track is the time of a single interaction (more generally, it is the time at the end of a given step). That’s not a “result”, that is part of the Monte Carlo random procedure. For “results,” you presumably need to integrate over many events, each one of which will be different in detail.
Because that’s how radioactive decay works in real life! The half-life is a cumulative property of an isotope: it specifies how long it will take for half of the atoms in your sample to decay. It doesn’t tell you how long any individual atom will take.
Indeed, as you can trivially see, half of the atoms in your sample are guaranteed to take less than the half-life to decay, while half the atoms are guaranteed to take more than the half-life to decay. Which ones are which? Roll the dice.
I like mkelsey’s answer, it’s very clear. I wanted to add one more thing: the way particles/nuclei decay in nature (and GEANT) is based on the underlying physics that an unstable particle or nucleus has no “memory” of how old it is. If it has survived until a certain point in time, its odds of decaying in the next tiny interval of time is the same as it would have been at any other time. Mathematically, if you start with a large number of particles, this results in the number you have left decaying according to an exponential function. Because it would be very inefficient for GEANT’s program to keep rolling the dice in every infinitely small interval of time, GEANT takes advantage of this exponential property by deciding how long the unstable particle will take to decay by sampling a number from the exponential function just once.
Could you tell me which physical process in the G4NEUTRONXS database is called to generate 104mRh? I’ve tried the following two processes but they didn’t produce it:
G4NEUTRONXS >20 MeV G4NeutronElasticXS
G4NEUTRONXS >20 MeV G4NeutronInelasticXS