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arXiv · 2607.13118

Profile-Likelihood and Baseline-Sensitivity Diagnostics for Digitized Radiation-Sensor Decay Datasets

Victor V. Golovko
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Abstract

Accurate interpretation of radiation-sensor decay data is important for environmental monitoring, site remediation, radiation metrology, detector quality assurance, and nuclear data evaluation. When the original gamma-spectrometry records are unavailable, a published decay plot may be the only source that can be reanalyzed independently. This study presents a reproducible reduced-data workflow for testing half-life estimates from a digitized 198-Au decay dataset. A weighted exponential fit to the digitized data points reproduces the published room-temperature half-life, indicating that the main decay scale is retained in the figure-level dataset. The analysis then tests how the fitted result changes under plausible figure-level effects, including baseline-like offsets, time-axis reconstruction, finite-window leverage, and ratio-based robustness checks using pairwise summaries and Steiner's most frequent value statistics. The no-offset fit is locally well constrained, but small constant offsets can shift the fitted half-life because the normalization, decay constant, and residual baseline are partly degenerate over the limited time window. Toy Monte Carlo diagnostics show that some estimator shifts are expected for finite-window exponential data. This study does not revise recommended nuclear data or replace the original experiment. Instead, it shows how published radiation-sensor decay data can be tested for reproducibility, identifiability, and sensitivity to analysis choices when only reduced or figure-level information is available.

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Victor V. Golovko. 2026-08-11. Profile-Likelihood and Baseline-Sensitivity Diagnostics for Digitized Radiation-Sensor Decay Datasets. https://doi.org/10.3390/s26165056

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