Learning Tree Structures from Leaves For Particle Decay Reconstruction. (arXiv:2208.14924v2 [physics.comp-ph] UPDATED)

In this work, we present a neural approach to reconstructing rooted tree
graphs describing hierarchical interactions, using a novel representation we
term the Lowest Common Ancestor Generations (LCAG) matrix. This compact
formulation is equivalent to the adjacency matrix, but enables learning a
tree's structure from its leaves alone without the prior assumptions required
if using the adjacency matrix directly. Employing the LCAG therefore enables
the first end-to-end trainable solution which learns the hierarchical structure
of varying tree sizes directly, using …

arxiv learning physics tree