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February 22, 2026Review of Scientific Instruments0 citations

Asymptotic inconsistency of the cumulative algorithm for laser-induced damage probability analysis

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KKK. R. P. Kafka

Key Points

  • This work investigates the effectiveness of the cumulative algorithm in estimating laser-induced damage probability.
  • Analyzed empirical data from laser tests with damage and undamaged sites.
  • Explored asymptotic behavior with large test site numbers.
  • Evaluated convergence of the cumulative algorithm towards true probability distributions.
  • Cumulative algorithm fails to converge to the true probability distribution.
  • Significantly underestimates damage probability near onset of damage.
  • Not recommended for accurate damage probability estimation.

Abstract

The “cumulative algorithm” is a data analysis method that has been proposed to provide an objective, nonparametric determination of laser-induced damage probability as a function of fluence from experimental data that contain both damaged sites and undamaged sites (i.e., 1-on-1 or S-on-1 testing protocols). In this work, the limitations of this approach are explored by considering the asymptotic limit of a large number of test sites. It is shown that the cumulative algorithm does not converge to the true probability distribution and significantly underestimates the damage probability near the damage onset. Based on the results of this work, the cumulative algorithm is not recommended for accurate estimation of damage probability.

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Cite This Study

K. R. P. Kafka (2026) studied this question.

synapsesocial.com/papers/699a9d50482488d673cd32b6https://doi.org/10.1063/5.0284314
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