Sequential hypothesis testing for Axion Haloscopes

TL;DR

This paper introduces a likelihood-based statistical framework for axion haloscope experiments, enabling efficient data analysis, significance assessment, and corrections for multiple testing, demonstrated through an application to a HAYSTAC haloscope.

Contribution

It presents a novel inferential method that improves data analysis in axion searches by providing analytical significance and power calculations under rescanning protocols.

Findings

Analytical computation of local significance and power.

Effective correction for look-elsewhere effect.

Comparison shows improved performance over geometric probability method.

Abstract

The goal of this paper is to introduce a novel likelihood-based inferential framework for axion haloscopes which is valid under the commonly applied "rescanning" protocol. The proposed method enjoys short data acquisition times and a simple tuning of the detector configuration. Local statistical significance and power are computed analytically, avoiding the need of burdensome simulations. Adequate corrections for the look-elsewhere effect are also discussed. The performance of our inferential strategy is compared with that of a simple method which exploits the geometric probability of rescan. Finally, we exemplify the method with an application to a HAYSTAC type axion haloscope.