Systematic generation of constituent models of particle families.

Abstract

A recurrent task in elementary particle physics involves building constituent models for particle families that can account for the particles' quantum numbers. We describe a systematic computerized approach to this task based on artificial intelligence principles, and discuss the output of two implemented programs that find constituent models of some standard families using additive quantum numbers. We also illustrate the capability to search for alternative quark models for single exotic particles in terms of the standard quarks. Although the current programs make use of very limited constraints on models, they serve as a foundation upon which more elaborate model-building programs can be designed. \textcopyright{} 1996 The American Physical Society.