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The point of making combinatoric background MC is to again have more control. I hope its clear that with an algorithm like ktracker it easier to get alway with using real experimental data for analysis like that but for AI its very dangerous unless there is a way get information about what is going into it and have some control of variation so that it can be used in studies and the resulting model can be studies analytically based on those inputs. The way I would make this MC is use the real data to determine the critical features. It should be clear that production of partial selection track hit pattern must not have be determinable or the AI will find it the pattern and build a bias and the degree of partial-ness in a track need not be correlated to its origin as many track can easily be confused as one and this has to be properly simulated and built in to the MC. We know we are getting there when an AI built to separate can't tell the difference between the two. In terms of getting the distributions right, this is actually the easy part as you can use the real data to produce the same distributions in a multidimensional Von Neumann rejection sampling from a large uniform distribution created with the right hit patterning from single muons of broad range of momentum. The final model should not be dependent on the shape of these distributions as each trigger makes these different so the idea would be to vary these and ensure there is no embedded bias based on distribution as it has nothing to do with the selection criterial of being an incomplete track. In any case there are AI tools to help with data matching. This could be as simple or as complex as we choose. Getting some simple but working MC of this type would be pretty quick. This was always my strong suggestion. Note sure why you didn't try. Maybe there is good reason. Here I'll offer a few options that do not strictly rely on this type of MC.
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