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Ratio-Based Pulse Shape Discrimination: Analytic Results for Gaussian and Poisson Noise Models

Published

Author(s)

Kevin J. Coakley

Abstract

In experiments in a range of fields including fast neutron spectroscopy and astroparticle physics, one can discriminate events of interest from background events based on the shapes of electronic pulses produced by energy deposits in a detector. Here, I focus on a well-known pulse shape discrimination method based on the ratio of the temporal integral of the pulse over an early interval Xp and the temporal integral over the entire pulse Xt. For both event classes, for both a Gaussian noise model and a Poisson noise model, I present analytic expressions for the conditional distribution of Xp given knowledge of the observed value of Xt and a scaled energy deposit corresponding to the product of the full energy deposit and a relative yield factor. I assume that the energy-dependent theoretical prompt fraction for both classes are known exactly. With a Bayesian approach that accounts for imperfect knowledge of the scaled energy deposit, I determine the posterior mean background acceptance probability given the target signal acceptance probability as a function of the observed value of Xt . My method enables one to determine receiver-operating-characteristic curves by numerical integration rather than by Monte Carlo simulation for these two noise models.
Citation
Journal of Research (NIST JRES) -
Volume
126

Keywords

Bayesian analysis, classifcation, Gaussian processes, Poisson processes, prompt fraction statistic, pulse shape discrimination, receiver-operating-characteristic curve

Citation

Coakley, K. (2021), Ratio-Based Pulse Shape Discrimination: Analytic Results for Gaussian and Poisson Noise Models, Journal of Research (NIST JRES), National Institute of Standards and Technology, Gaithersburg, MD, [online], https://doi.org/10.6028/jres.126.032, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=913346 (Accessed May 26, 2024)

Issues

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Created November 9, 2021, Updated December 16, 2021