A machine learning algorithm for direct detection of axion-like particle domain walls

TL;DR

This paper introduces a machine learning-based method to detect axion-like particle domain walls using data from a global network of atomic magnetometers, aiming to identify dark matter signatures.

Contribution

It proposes a novel stochastic optimization machine learning approach for detecting ALP domain walls in GNOME data, enhancing search sensitivity.

Findings

Validated the method with binary classification tests.

Projected improved sensitivity for ALP domain-wall detection.

Demonstrated potential for real-time dark matter searches.

Abstract

The Global Network of Optical Magnetometers for Exotic physics searches (GNOME) conducts an experimental search for certain forms of dark matter based on their spatiotemporal signatures imprinted on a global array of synchronized atomic magnetometers. The experiment described here looks for a gradient coupling of axion-like particles (ALPs) with proton spins as a signature of locally dense dark matter objects such as domain walls. In this work, stochastic optimization with machine learning is proposed for use in a search for ALP domain walls based on GNOME data. The validity and reliability of this method were verified using binary classification. The projected sensitivity of this new analysis method for ALP domain-wall crossing events is presented.