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

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

2006-02-01

arxiv.org/abs/hep-ph/0602011v2

Physics

High Energy Physics

High Energy Physics - Phenomenology

6 pages, 2 tables. Reason for Replacement: The algorithm and theorems are correct. However, the dataset we analyzed did not co

Scientific paper

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.

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