From RATs to riches: mitigating anthropogenic and synanthropic noise in atom interferometer searches for ultra-light dark matter

TL;DR

This paper investigates noise sources from human and animal activity affecting atom interferometers used for ultra-light dark matter detection, proposing a data cleaning method to improve sensitivity despite environmental noise.

Contribution

It characterizes anthropogenic and synanthropic noise sources and develops a data masking framework to mitigate their impact on ULDM searches with atom interferometers.

Findings

Noise sources can reduce sensitivity by up to 90%.

The proposed framework restores sensitivity to 10-40% of shot noise limit.

Robust noise mitigation strategies are essential for ULDM detection.

Abstract

Atom interferometers offer promising new avenues for detecting ultra-light dark matter (ULDM). The exceptional sensitivity of atom interferometers to fluctuations in the local gravitational potential exposes them to sources of noise from human (anthropogenic) and animal (synanthropic) activity, which may obscure signals from ULDM. We characterise potential anthropogenic and synanthropic noise sources and examine their influence on a year-long measurement campaign by AION-10, an upcoming atom interferometer experiment that will be located at the University of Oxford. We propose a data cleaning framework that identifies and then masks anthropogenic and synanthropic noise. With this framework, we demonstrate that even in noisy conditions, the sensitivity to ULDM can be restored to within between 10% and 40% of an atom shot noise-limited experiment, depending on the specific composition of the anthropogenic and synanthropic noise. This work provides an important step towards creating robust noise reduction analysis strategies in the pursuit of ULDM detection with atom interferometers.