Reduction of detection limit and quantification uncertainty due to interferent by neural classification with abstention
This addresses the need for more reliable measurements in physical sciences by improving detection sensitivity and reducing uncertainty, though it is incremental as it builds on existing classifier methods.
The paper tackled the problem that optimal accuracy classifiers do not minimize detection limits or quantification uncertainties in counting experiments, and introduced an abstention mechanism to reduce these metrics, achieving lower detection limits and uncertainties in applications like discriminating radioactive decays and neutrons from photons.
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 counting experiment case. We also present a novel abstention mechanism to minimize the detection limit or quantification uncertainty \emph{a posteriori}. We illustrate the method on two data sets from the physical sciences, discriminating Ar-37 and Ar-39 radioactive decay from non-radioactive events in a gas proportional counter, and discriminating neutrons from photons in an inorganic scintillator and report results therefrom.