Multimessenger Search for Exotic Field Emission with a Global Magnetometer Network
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".