Use a mixture of DY, J/psi, and single muon tracks for background tracks (something like 5% DY+J/psi and 95% single muon tracks).
The generated muons should be weighted based on the following Z distribution, which shows the approximate distribution of muons that will be generated in the events.
There should also be partial tracks that traverse part-way through the detector array. These tracks can go through anywhere between 1 and 5 planes of a station.
These should be generated to roughly match the occupancy that we see in Seaquest docdb 9631-v3 in slides 26 and 27 (DC occupancy). The plots show that the occupancy is lower at higher Z (station 1 has higher occupancy than station 3). This means that we should weigh the partial tracks so that there are more going through the first two stations. We are trying to match the occupancy after out-of-time cuts.
Once the full tracks and partial tracks are added, we can add sources of noise: Electronic Noise, Delta Rays, and Cell-Edge Hits. These are not as important for training, because they can mostly be removed by if statements.
We will also use E906 data to compare the generated MC to real experimental data. If the neural networks we train are able to perform equally well on E906 data and MC data, then we know we have good agreement.