Hunting Hidden Axion Signals in Pulsar Dispersion Measurements with Machine Learning

TL;DR

This paper introduces a machine learning approach to detect subtle axion-induced dispersion signals in pulsar data, potentially improving constraints on axion properties with future radio telescope observations.

Contribution

It develops a novel machine learning method to identify axion-induced dispersion features in pulsar signals, outperforming traditional detection techniques.

Findings

Achieves 90% classification accuracy in simulations.

Demonstrates robustness against false positives.

Shows potential to significantly improve axion decay constant constraints.

Abstract

In axion models, interactions between axions and electromagnetic waves induce frequency-dependent time delays determined by the axion mass and decay constant. These small delays are difficult to detect, limiting the effectiveness of traditional methods. We compute such delays under realistic radio telescope conditions and identify a prominent dispersive feature near half the axion mass, which appears non-divergent within the limits of observational resolution. Based on this, we develop a machine learning method that achieves 90\% classification accuracy and demonstrates well performance in low signal-to-noise regimes. The method's robustness is confirmed against false positives using both simulated noisy data and real-world, known-null observations. Future improvements in optical clock precision and telescope bandwidth, particularly with instruments such as the Qitai Radio Telescope, may enhance constraints on the axion decay constant by up to four orders of magnitude in the $10^{-6} \sim 10^{-4}$ eV mass range.