To train the neural network, we broke the data down into a simple classification problem, where each isotope cluster would be classified as a discrete charge from 1 to 49. Although not implemented yet, the classification of charge 0 has been reserved for pure noise, yielding a classification vector of length 50. It is possible to extend this limit to higher charge states, but we found that it was hard to get examples of charges above 30. There were several charge states above 40 that had very few examples in our training data, with no representatives for z=46 (see Figure ). It is possible to synthesize higher charge states for training, and preliminary data indicates this is highly effective at improving model performance for charge 30 to 50 (as described below). However, the initial model presented here is primarily limited to below 30.