Bias-Free Estimation of Signals on Top of Unknown Backgrounds

TL;DR

This paper introduces a method to accurately estimate signals amidst unknown backgrounds without relying on parametric background models, demonstrated on simulated data from the MADMAX axion haloscope.

Contribution

The paper proposes a novel, model-independent approach for unbiased signal estimation in the presence of unknown backgrounds, applicable to practical experimental data.

Findings

Successfully applied to simulated MADMAX data

Achieves unbiased signal estimates without background modeling

Potentially applicable to various experimental scenarios

Abstract

We present a method for obtaining unbiased signal estimates in the presence of a significant unknown background, eliminating the need for a parametric model for the background itself. Our approach is based on a minimal set of conditions for observation and background estimators, which are typically satisfied in practical scenarios. To showcase the effectiveness of our method, we apply it to simulated data from the planned dielectric axion haloscope MADMAX.