  Proton radiography is a technique extensively used to resolve magnetic field
structures in high energy density plasmas, revealing a whole variety of
interesting phenomena such as magnetic reconnection and collisionless shocks
found in astrophysical systems. Existing methods of analyzing proton
radiographs give mostly qualitative results or specific quantitative parameters
such as magnetic field strength, and recent work showed that the
line-integrated transverse magnetic field can be reconstructed in specific
regimes where many simplifying assumptions were needed. Using artificial neural
networks, we suggest a novel 3-D reconstruction method that works for a more
general case. A proof of concept is presented here, with mean reconstruction
errors of less than 5 percent even after introducing noise. We demonstrate that
over the long term, this approach is more computationally efficient compared to
other techniques. We also highlight the need for proton tomography because (i)
certain field structures cannot be reconstructed from a single radiograph and
(ii) errors can be further reduced when reconstruction is performed on
radiographs generated by proton beams fired in different directions.
