The detection of signals buried in noise

TL;DR

This paper explores Bayesian methods for detecting signals hidden in noise, focusing on nuclear activation analysis, and aims to improve hypothesis testing and signal quantification in noisy data.

Contribution

It introduces a Bayesian inference approach for signal detection and hypothesis testing in noisy environments, specifically applied to nuclear activation analysis.

Findings

Bayesian methods effectively distinguish signal from noise.

The approach quantifies detection limits and signal amplitudes.

Results demonstrate improved detection accuracy in noisy data.

Abstract

This paper examines signal detection in the presence of noise, with a particular emphasis to the nuclear activation analysis. The problem is to decide what between the signal-plus-background and no-signal hypotheses fits better the data and to quantify the relevant signal amplitude or detection limit. Our solution is based on the use of Bayesian inferences to test the different hypotheses.