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Kevin J Coakley1

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Summary

This study presents a new Bayesian method for pulse shape discrimination in physics experiments. It improves event classification accuracy by analyzing pulse integrals under different noise models, enabling faster analysis.

Keywords:
Bayesian analysisGaussian processesPoisson processes, prompt fraction statisticclassificationpulse shape discriminationreceiver-operating-characteristic curve

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Area of Science:

  • Nuclear physics
  • Astroparticle physics
  • Detector physics

Background:

  • Pulse shape discrimination is crucial for distinguishing signal from background events in various physics experiments.
  • Current methods often rely on Monte Carlo simulations for analysis.
  • Accurate event classification is vital for data interpretation in fields like fast neutron spectroscopy.

Purpose of the Study:

  • To develop an analytical method for pulse shape discrimination (PSD) that reduces reliance on simulations.
  • To provide a Bayesian framework for event classification considering uncertainties in energy deposits.
  • To enable efficient generation of receiver-operating-characteristic (ROC) curves.

Main Methods:

  • Derivation of analytic expressions for the conditional distribution of early pulse integral (Xp) given the total pulse integral (Xt) and scaled energy deposit.
  • Application of a Bayesian approach to account for imperfect knowledge of the scaled energy deposit.
  • Numerical integration for determining posterior mean background acceptance probability.

Main Results:

  • Analytic expressions for Xp|Xt and scaled energy deposit derived for Gaussian and Poisson noise models.
  • A Bayesian method to calculate the posterior mean background acceptance probability as a function of Xt.
  • Demonstration that ROC curves can be determined via numerical integration, bypassing Monte Carlo simulations.

Conclusions:

  • The proposed Bayesian PSD method provides an efficient and accurate alternative to Monte Carlo simulations for specific noise models.
  • This approach enhances the ability to discriminate between signal and background events in fast neutron spectroscopy and astroparticle physics.
  • The derived analytic expressions and Bayesian framework offer a valuable tool for optimizing data analysis in particle detection experiments.