KAHIBARO
21.4. Reproducing an Event
Table of Contents
Saving random states
To reproduce a specific Monte Carlo event in Geant4, you must control and record the state of the random number generator. Every random choice in your simulation, such as interaction position, scattering angle, and secondary production, depends on this state. If you can restore it, you can recreate the exact same sequence of random numbers, and therefore the same simulation history, provided nothing else in the code or environment has changed.
Geant4 provides several ways to access and store the random engine state. The most direct method is to use the CLHEP random interface, which Geant4 relies on internally. You typically interact through the global G4Random wrapper. The key operations are to save the current state to a file, and to restore from that file before rerunning.
You can save the random state at well defined points, for example at the beginning of each event or each run. The usual pattern in user code is to call a function that writes the current engine state into a text file with a unique name that encodes the run and event identifiers. When you later want to reproduce the event, you run the same executable, restore the state from that file immediately before starting that event, and then process it again.
In a typical workflow you use the Geant4 macro command /random/setSavingFlag or you implement your own saving in a user action, usually RunAction or EventAction. If you use the macro support, Geant4 can automatically write files containing random states at the beginning of runs or events, depending on your configuration, so you do not have to write C++ code for file handling. When you use your own code, you have full control over file naming and when states are captured.
It is important to understand that restoring a random state reproduces results only if everything that uses random numbers is called in the same order as before. This means the geometry, physics list, cuts, user actions, and any conditions that affect which code paths are executed must be identical between the original and the reproduction run. Even an extra debug print that causes no branching is fine, but if you add or remove logic that calls random functions, you will no longer follow the same sequence.
Furthermore, in multithreaded simulations each worker thread has its own random engine and state. Reproducing a single event from a multithreaded run requires that you know which thread processed that event and you restore the state in the same threading configuration, or you rerun in a controlled single threaded mode with a state that corresponds to that event. For beginners, it is usually easier to record and reproduce events in single threaded runs until you are fully comfortable with how random numbers are managed.
To reproduce an event, you must:
- Save the random engine state at a known point such as the start of the event.
- Use the same executable, geometry, physics, and configuration.
- Restore exactly that saved state before rerunning the event.
Any change in the order or number of random calls will break reproducibility.
Debugging simulations
Saving and restoring random states is extremely powerful for debugging, because it lets you examine a problematic event repeatedly, step by step, without relying on chance to see it again. Instead of hoping that a rare error or suspicious feature will reappear in a long run, you capture the event when it first occurs, store its random state and identifiers, then switch into a special debug mode where only that event is replayed.
A common strategy is to detect unusual conditions during the simulation itself, for example an energy leak, an unexpected track termination, or a hit in an unphysical region. When your user code notices such a condition, it can trigger saving of the current random state to a file and print the run and event number. After the original run finishes, you start a dedicated debugging run where you restore that specific state and process only that event. You can now increase verbose levels, enable detailed tracking and stepping output, or send the event to a visualizer to inspect tracks and interactions.
In practice, you combine random state control with Geant4 verbose commands such as /tracking/verbose and /step/verbose, and with your own diagnostic prints inside SteppingAction or TrackingAction. During the original large production you keep verbosity low to avoid huge output files and slow execution. Once you have a state file that corresponds to a problematic event, you rerun using that state, set high verbosity, and perhaps simplify the setup to focus on the region or detector that shows the problem.
When debugging in this way you must avoid changing anything that would modify the sequence of random calls before the event is replayed. If you add prints that do not affect control flow, you are safe. If you insert new random draws or conditional branches that depend on random results, the state will diverge and the reproduced event will no longer match the original. For this reason it is often useful to isolate debug code under compile time or runtime switches, so that you can turn it on only after the random state has been restored, for example at the start of a specific event or for a narrow subset of tracks.
Random state replay also helps with numerical investigations. If you suspect a floating point instability or platform dependent behavior, you can reproduce the same event on different machines or with different compiler settings, provided you keep the code and configuration strictly synchronized. By comparing step by step results you can locate exactly where two executions begin to diverge.
Finally, when working in multithreaded mode, debugging with event reproduction requires more care. In many cases the simplest approach is to reproduce the problematic event in single threaded mode after capturing its random state. This avoids any thread scheduling differences that could reorder events. You save the state from the worker that processed the event, then write a small single threaded driver that restores this state and runs just one event in an otherwise identical configuration. This way you gain deterministic control, which makes deep debugging and analysis much more straightforward.
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