Multimessenger window into galactic magnetic fields and black-hole mergers with LISA
Bibliographic record
Abstract
Large-scale (i.e., $\ensuremath{\gtrsim}\mathrm{kpc}$) and micro-Gauss-scale magnetic fields have been observed throughout the Milky Way and nearby galaxies. These fields depend on the geometry and matter-energy composition, can display complicated behavior such as direction reversals, and are intimately related to the evolution of the source galaxy. Simultaneously, gravitational-wave astronomy offers a new probe into astrophysical systems; for example, the Laser Interferometer Space Antenna (LISA) will observe the mergers of massive (i.e., $M>{10}^{6}{M}_{\ensuremath{\bigodot}}$) black-hole binaries and provide extraordinary constraints on the evolution of their galactic hosts. In this work, we show how galactic, large-scale magnetic fields and their electromagnetic signatures are connected with LISA gravitational-wave observations via their common dependence on the massive black-hole binary formation scenario of hierarchical galaxy mergers. Combining existing codes, we astrophysically evolve a population of massive binaries from formation to merger and find that they are detectable by LISA with signal-to-noise ratio $\ensuremath{\sim}{10}^{3}$ which is correlated with quantities from the progenitors' phase of circumbinary disk migration such as the maximum magnetic field magnitude $|\mathbf{B}|\ensuremath{\approx}7\text{ }\text{ }\mathrm{\ensuremath{\mu}}\mathrm{G}$, polarized intensity, and Faraday rotation measure. Interesting correlations result between these observables arising from their dependencies on the black-hole binary total mass, suggesting a need for further analyses of the full parameter space. We conclude with a discussion on this new multimessenger window into galactic magnetic fields.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".