  A feed-forward neural network is demonstrated to efficiently unfold the
energy distribution of protons and alpha particles passing through passive
material. This model-independent approach works with unbinned data and does not
require regularization. The training dataset was produced with the same Monte
Carlo simulation framework used by the AlCap experiment. The common problem of
designing a network is also addressed by performing a hyperparameter space scan
to find the best network geometry possible within reasonable computation time.
Finally, a comparison with other unfolding methods such as the iterative
d'Agostini Bayesian unfolding, and Singular Value Decomposition (SVD) are
shown.
