Super-Nyquist ultralight dark matter searches with broadband atom gradiometers

TL;DR

This paper investigates how aliasing affects ultralight dark matter detection with atom gradiometers and proposes a framework for accurate parameter reconstruction despite super-Nyquist frequencies.

Contribution

It introduces a likelihood-based method to recover ULDM parameters considering aliasing effects, enabling detection of super-Nyquist signals with atom gradiometers.

Findings

Accurate ULDM parameter reconstruction is possible if frequency resolution exceeds signal linewidth.

Aliased signals form discrete islands in parameter space, aiding identification.

First comprehensive analysis of aliasing effects in dark matter detection with atom-based sensors.

Abstract

Atom gradiometers have emerged as compelling broadband probes of scalar ultralight dark matter (ULDM) candidates that oscillate with frequencies between approximately $10^{-2}$ Hz and $10^3$ Hz. ULDM signals with frequencies greater than $\sim 1$ Hz exceed the expected Nyquist frequency of atom gradiometers, and so are affected by aliasing and related phenomena, including signal folding and spectral distortion. To facilitate the discovery of super-Nyquist ULDM signals, in this work we investigate the impact of these effects on parameter reconstruction using a robust likelihood-based framework. We demonstrate that accurate reconstruction of ULDM parameters can be achieved as long as the experimental frequency resolution is larger than the ULDM signal linewidth. Notably, as ULDM candidates whose frequencies differ by integer multiples of the sampling frequency are identified at the same aliased frequency, our discovery analysis recovers discrete islands in parameter space. Our study represents the first comprehensive exploration of aliasing in the context of dark matter direct detection and paves the way for enhanced ULDM detection strategies with atom gradiometers.