Weak signal extraction using matrix decomposition, with application to ultra high energy neutrino detection

TL;DR

This paper presents a matrix decomposition technique for extracting weak signals from noisy backgrounds, demonstrated in a high-energy neutrino detection experiment at SLAC, especially effective when signal characteristics are unknown.

Contribution

The paper introduces a novel matrix decomposition method for sensitive signal extraction in noisy environments, applicable to high-energy neutrino detection experiments.

Findings

Successfully extracted signals at 1% of background noise

Effective in scenarios with unknown signal characteristics

Demonstrated in SLAC neutrino detection test-beam experiment

Abstract

In radio-based physics experiments, sensitive analysis techniques are often required to extract signals at or below the level of noise. For a recent experiment at the SLAC National Accelerator Laboratory to test a radar-based detection scheme for high energy neutrino cascades, such a sensitive analysis was employed to dig down into a spurious background and extract a putative signal. In this technique, the backgrounds are decomposed into an orthonormal basis, into which individual data vectors (signal + background) can be expanded. This expansion is a filter that can extract signals with amplitudes $\sim$1 % of the background. This analysis technique is particularly useful for applications when the exact signal characteristics (spectral content, duration) are not known. In this proceeding we briefly present the results of this analysis in the context of test-beam experiment 576 (T576) at SLAC.