Algorithmic Derivation of Additive Selection Rules and Particle Families from Reaction Data
Abstract
Description
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 (Valdes-Perez 96, 94, Kocabas 91). The program searches for quantities conserved in all reactions from a given input set; given current data it rediscovers the family conservation laws: baryon#, electron#, muon# and tau#. We show that these families are uniquely determined by frequent decay data.
6 pages, 2 tables. Reason for Replacement: The algorithm and theorems are correct. However, the dataset we analyzed did not contain reactions that violate Lepton Number or Baryon Number conservation. This limits the scope of our conclusions from the dataset, which are corrected in the revised version
6 pages, 2 tables. Reason for Replacement: The algorithm and theorems are correct. However, the dataset we analyzed did not contain reactions that violate Lepton Number or Baryon Number conservation. This limits the scope of our conclusions from the dataset, which are corrected in the revised version