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The history of astronomy has shown that advances in sensing methods open up new windows to the Universe and often lead to unexpected discoveries. Quantum sensor networks in combination with traditional astronomical observations are emerging as a novel modality for multimessenger 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., weakly interacting massive particles), 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 March 11, 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 multimessenger astronomy. Our search found no significant events and, consequently, we place the first lab-based limits on combinations of ELF production and coupling parameters. 

Abstract The QCD axion is a particle postulated to exist since the 1970s to explain the strong-CP problem in particle physics. It could also account for all of the observed dark matter in the Universe. The axion resonant interaction detection experiment (ARIADNE) intends to detect the QCD axion by sensing the fictitious ‘magnetic field’ created by its coupling to spin. Short-range axion-mediated interactions can occur between a sample of laser-polarized³He nuclear spins and an unpolarized source-mass sprocket. The experiment must be sensitive to magnetic fields below the 10⁻¹⁹T level to achieve its design sensitivity, necessitating tight control of the experiment’s magnetic environment. We describe a method for controlling three aspects of that environment which would otherwise limit the experimental sensitivity. Firstly, a system of superconducting magnetic shielding is described to screen ordinary magnetic noise from the sample volume at the 10⁸level, which should be sufficient to reduce the contribution of Johnson noise in the sprocket-shaped source mass, expected to be at the 10⁻¹²T $/\sqrt{\mathrm{H}\mathrm{z}}$level, to below the threshold for signal detection. Secondly, a method for reducing magnetic field gradients within the sample up to 10²times is described, using a simple and cost-effective design geometry. Thirdly, a novel coil design is introduced which allows the generation of fields similar to those produced by Helmholtz coils in regions directly abutting superconducting boundaries. This method allows the nuclear Larmor frequency of the sample to be tuned to match the axion field modulation frequency set by the sprocket rotation. Finally, we experimentally investigate the magnetic shielding factor of sputtered thin-film superconducting niobium on quartz substrates for various geometries and film thicknesses relevant for the ARIADNE axion experiment using SQUID magnetometry. The methods may be generally useful for magnetic field control near superconducting boundaries in other experiments where similar considerations apply. 

We present details on a new measurement of the muon magnetic anomaly, $a_{\mu }=(g_{\mu }-2)/2$. The result is based on positive muon data taken at Fermilab’s Muon Campus during the 2019 and 2020 accelerator runs. The measurement uses $3.1\mathrm{GeV}/c$polarized muons stored in a 7.1-m-radius storage ring with a 1.45 T uniform magnetic field. The value of $a_{\mu }$is determined from the measured difference between the muon spin precession frequency and its cyclotron frequency. This difference is normalized to the strength of the magnetic field, measured using nuclear magnetic resonance. The ratio is then corrected for small contributions from beam motion, beam dispersion, and transient magnetic fields. We measure $a_{\mu }=116592057\left(25\right)\times {10}^{-11}$(0.21 ppm). This is the world’s most precise measurement of this quantity and represents a factor of 2.2 improvement over our previous result based on the 2018 dataset. In combination, the two datasets yield $a_{\mu }\left(\mathrm{FNAL}\right)=116592055\left(24\right)\times {10}^{-11}$(0.20 ppm). Combining this with the measurements from Brookhaven National Laboratory for both positive and negative muons, the new world average is $a_{\mu }\left(\exp \right)=116592059\left(22\right)\times {10}^{-11}$(0.19 ppm). Published by the American Physical Society2024 

We present a new measurement of the positive muon magnetic anomaly, 𝑎𝜇≡(𝑔𝜇−2)/2, from the Fermilab Muon 𝑔−2 Experiment using data collected in 2019 and 2020. We have analyzed more than 4 times the number of positrons from muon decay than in our previous result from 2018 data. The systematic error is reduced by more than a factor of 2 due to better running conditions, a more stable beam, and improved knowledge of the magnetic field weighted by the muon distribution, 𝜔𝑝, and of the anomalous precession frequency corrected for beam dynamics effects, 𝜔𝑎. From the ratio 𝜔𝑎/𝜔𝑝, together with precisely determined external parameters, we determine 𝑎𝜇=116 592 057⁢(25)×10−11 (0.21 ppm). Combining this result with our previous result from the 2018 data, we obtain 𝑎𝜇⁡(FNAL)=116 592 055⁢(24)×10−11 (0.20 ppm). The new experimental world average is 𝑎𝜇⁡(exp)=116 592 059⁢(22)×10−11 (0.19 ppm), which represents a factor of 2 improvement in precision.