How easily can neural networks learn relativity?

Relativistic invariants are key variables in high energy physics and are believed to be learned implicitly by deep learning approaches. We investigate the minimum network complexity needed to accurately extract such invariants. Doing so will help us understand how complex a neural network needs to be to obtain certain functions. We find that neural networks do well with predicting transverse momentum of a collision which illustrates the fact that non-linear functions can be learned. On the other hand, invariant mass was much more difficult to predict. Further work will be done to learn the reason why. However the non-linearity of the function can be ruled out as the sole reason.