Reduction of detection limit and quantification uncertainty due to interferent by neural classification with abstention
Alex Hagen, Ken Jarman, Jesse Ward, Greg Eiden, Charles Barinaga,, Emily Mace, Craig Aalseth, Anthony Carado

TL;DR
This paper introduces a neural classification method with abstention to reduce detection limits and quantification uncertainties in counting experiments, improving discrimination between signal and background in physical science measurements.
Contribution
It derives detection limits and uncertainties for classifier-based counting experiments and proposes a novel abstention mechanism to optimize these metrics post hoc.
Findings
Abstention mechanism reduces detection limit in radioactive decay detection.
Method improves quantification accuracy in neutron-photon discrimination.
Demonstrated effectiveness on physical science datasets.
Abstract
Many measurements in the physical sciences can be cast as counting experiments, where the number of occurrences of a physical phenomenon informs the prevalence of the phenomenon's source. Often, detection of the physical phenomenon (termed signal) is difficult to distinguish from naturally occurring phenomena (termed background). In this case, the discrimination of signal events from background can be performed using classifiers, and they may range from simple, threshold-based classifiers to sophisticated neural networks. These classifiers are often trained and validated to obtain optimal accuracy, however we show that the optimal accuracy classifier does not generally coincide with a classifier that provides the lowest detection limit, nor the lowest quantification uncertainty. We present a derivation of the detection limit and quantification uncertainty in the classifier-based…
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