Algorithmic Derivation of Additive Selection Rules and Particle Families from Reaction Data

We describe a machine-learning system that uses linear vector-space based techniques for inference from observations to extend previous work on model construction for particle physics [10, 9, 5]. The program searches for quantities conserved in all reactions from a given input set; given data based on frequent decays it rediscovers the family conservation laws: baryon#, electron#, muon# and tau#. We show that these families are uniquely determined by frequent decay data.