Beamline neural network reconstruction training

[simonge]

Briefly, what does this PR introduce?

Trains a neural network to reproduce the momentum of the particle at the origin based on the position and direction that it exits the B2eR magnet into the Tagger drift volume.

Produces plots which demonstrate the reconstruction limits from the beam divergence and optics without worrying about the effects of the detector reconstruction. This might introduce some systematic error in the reconstruction later.

Shares the simulation from #167
Replacing the previous onnx training #123 which relied on reconstructed tagger tracks.

Runs new simulation of only electrons from eic-pythia6 files on the xrootd server.
Uses the tracks reconstructed from the taggers.

Using perfect information from the beamline tracking layers resulted in only perfect tracks in the tagger being reconstructed well, everything else was far off. Using a simulation sample which covered the full acceptance also pulled the mean residual of the pythia sample away from 0 suggesting there might not be a clean 1 to 1 mapping - this needs some further investigation.

What kind of change does this PR introduce?

• Bug fix (issue #__)
• New feature (issue Benchmark to verify simulation beamline optics #153)
• Documentation update
• Other: __

Please check if this PR fulfills the following:

• Tests for the changes have been added
• Documentation has been added / updated
• Changes have been communicated to collaborators

Does this PR introduce breaking changes? What changes might users need to make to their code?

No

Does this PR change default behavior?

Adds a new benchmark to train a new onnx neural network to reconstruct electron momentum through beamline magnetic optics.