A Multi-Messenger Search for Exotic Field Emission with a Global Magnetometer Network

TL;DR

This work develops a data-driven, multi-messenger framework to search for exotic low-mass fields (ELFs) using a global network of magnetometers (GNOME) coincident with astrophysical triggers. It models ELF propagation and detector responses, then implements a two-stage pipeline with adaptive spectrogram tiling, excess-power cuts, likelihood-ratio tests, and Feldman-Cousins confidence belts to set upper limits on ELF amplitude and couplings. Applying the method to the binary black hole merger GW200311_115853, the study finds no significant ELF signal but places the first laboratory constraints on combinations of ELF production energy, initial burst duration, and spin-coupling energy scales, with results that can be extended to future events and advanced sensors. The framework provides a general template for quantum-sensor networks in multi-messenger astronomy, offering a pathway to probe ELF production mechanisms in extreme astrophysical environments and to enhance sensitivity with next-generation comagnetometer-based GNOME.

Abstract

Quantum sensor networks in combination with traditional astronomical observations are emerging as a novel modality for multi-messenger astronomy. Here we develop a generic analysis framework that uses a data-driven approach to model the sensitivity of a quantum sensor network to astrophysical signals as a consequence of beyond-the-Standard Model (BSM) physics. The analysis method evaluates correlations between sensors to search for BSM signals coincident with astrophysical triggers such as black hole mergers, supernovae, or fast radio bursts. Complementary to astroparticle approaches that search for particlelike signals (e.g. WIMPs), quantum sensors are sensitive to wavelike signals from exotic quantum fields. This analysis method can be applied to networks of different types of quantum sensors, such as atomic clocks, matter-wave interferometers, and nuclear clocks, which can probe many types of interactions between BSM fields and standard model particles. We use this analysis method to carry out the first direct search utilizing a terrestrial network of precision quantum sensors for BSM fields emitted during a black hole merger. Specifically we use the Global Network of Optical Magnetometers for Exotic physics (GNOME) to perform a search for exotic low-mass field (ELF) bursts generated in coincidence with a gravitational wave signal from a binary black hole merger (GW200311 115853) detected by LIGO/Virgo on the 11th of March 2020. The associated gravitational wave heralds the arrival of the ELF burst that interacts with the spins of fermions in the magnetometers. This enables GNOME to serve as a tool for multi-messenger astronomy. Our search found no significant events, and consequently we place the first lab-based limits on combinations of ELF production and coupling parameters.

Figures (12)

• Figure 1: ELF propagation and signal characteristics. (a) An ELF wave packet, $\phi(r,t)$, emitted from a source at position $r=0$ and time $t=0$ with amplitude $A_0$ and pulse duration $\tau_0$ disperses as it propagates to the sensor through the distance $R$ and arrival time $t_s$ with group velocity $v_g$. At the sensor, the ELF wave packet has amplitude $A$ and pulse duration $\tau$. (b) Examples of ELF signal spectrograms with two possible central frequencies, $\omega_0^{(1)}$ and $\omega_0^{(2)}$, and two possible delay times, $\delta t_1$ and $\delta t_2$, with respect to the arrival of the GW/EM trigger. Dispersion leads to a frequency chirp $d\omega / dt$ of the signal depending on the arrival time $\delta t$ and the central frequency $\omega_0$ [(Eq. \ref{['Eq:Slope']}].
• Figure 2: (a) Cartoon picture of the GNOME network with arrows representing GNOME detectors and their sensitive axes. The arrival of a GW/EM trigger heralds the ELF pulse. The projection of the ELF on detector axes is modulated by Earth's rotation. (b) Sensitivity product for each station which is the product of the directional sensitivity $\hat{m}^i_j\cdot \hat{R}$, frequency response $\beta^i_k$, and ratio of proton-spin coupling and the Lande g-factor $\sigma^i / g^i_F$ [Eq. \ref{['Eq:Bj to Bp, more indices']}] across the foreground window for the BBH merger search target considered. We update the directional sensitivity at one minute intervals and the frequency response at 1 Hz intervals.
• Figure 3: Flowchart illustrating the analysis pipeline. A search is triggered by some GW or EM observation. Time-series measurements from the network in the foreground and background windows are represented in the time-frequency plane by spectrograms with tile dimensions chosen to match the ELF chirp rate. The EP cut passes network-wide, relatively high power tiles to the next stage as candidate tiles. The LRT statistic $\lambda$ is determined for each candidate tile and is compared to a signal-free distribution of $\lambda$ to determine the significance. The test statistic is translated to a pseudomagnetic amplitude $\mathcal{B}_{\text{p}}$ with the Feldman-Cousins construction and ELF limits are calculated.
• Figure 4: (a) Map of the adaptive tiling used to match the spectrogram tile dimensions to the chirp rate. The searched time-frequency space (foreground window), $\delta t \in [0,10]\,\text{hr} \times f_0 \in [0,100]$ Hz, is partitioned into "boxes" with boundaries marked by black lines. The box color indicates the tile dimension used that approximates the chirp rate given by Eq. \ref{['Eq:Slope']}. (b) Spectrogram of Lewisburg data with tile dimension $1\,\text{s}\times 1\,\text{Hz}$ contained within the box spanning $f_0\in[1,10]\,\text{Hz}$ and $\delta t \in [1,10]\,\text{s}$. The relative color of each tile represents the measured power in the tile. (c) Spectrogram of Lewisburg data with tile dimension $5\,\text{s}\times 0.2\,\text{Hz}$ contained within the box adjacent to (b) spanning $f_0\in[1,10]\,\text{Hz}$ and $\delta t \in [10,60]\,\text{s}$.
• Figure 5: Whitening of spectrogram of $P^i_{jk}$ and corresponding EP statistic $\varepsilon^i_{jk}$ of Lewisburg data [Eq. \ref{['Eq: Excess Power']}]. The relative color of each tile in each plot represents the measured power (on the left) and EP (on the right) in the tile. Note that the constant higher power bands at 22 Hz and 25 Hz are "whitened" to average EP values. The loud spikes in the 29 Hz--30 Hz range are mellowed due to their repetition while the quiet regions are especially suppressed due to the larger average noise. The features in the 29 Hz--30 Hz range indicate non-stationarity which is not suppressed by taking the EP.