Applying Neural Networks to Resonance Search in High Energy Physics

  • Jochen Rau
  • Berndt Müller
  • Richard G. Palmer
Conference paper
Part of the Perspectives in Neural Computing book series (PERSPECT.NEURAL)


It is the aim of many modern experiments in high energy physics to discover and investigate the properties of rare particles. However, often so few of these particles are produced in an experiment that it is very difficult to discover their decay signature against the background. There are various techniques to suppress the background and thus to enhance the relative strength of the signal, but none of the techniques currently used is optimal. In view of the future operation of even larger accelerators, such as the Superconducting Supercollider, where a signal may occur once in a billion events, and millions of events will have to be analyzed each second, it is desirable to optimize the techniques for resonance search. Recently it has been suggested that neural networks may be used for this task. It is still unclear, however, whether neural networks can perform an unbiased data analysis or whether they may produce artificial signals due to information acquired during the training process.


Neural Network Invariant Mass Invariant Mass Distribution Background Distribution Artificial Signal 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag London Limited 1992

Authors and Affiliations

  • Jochen Rau
    • 1
  • Berndt Müller
    • 1
  • Richard G. Palmer
    • 1
  1. 1.Department of PhysicsDuke UniversityDurhamUSA

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