Directed searches for gravitational waves from ultralight bosons
Bibliographic record
Abstract
Gravitational-wave detectors can be used to search for yet-undiscovered ultralight bosons, including those conjectured to solve problems in particle physics, high-energy theory, and cosmology. In particular, ground-based instruments could probe boson masses between ${10}^{\ensuremath{-}15}\text{ }\text{ }\mathrm{eV}$ and ${10}^{\ensuremath{-}11}\text{ }\text{ }\mathrm{eV}$, which are largely inaccessible to other experiments. In this paper, we explore the prospect of searching for the continuous gravitational waves generated by boson clouds around known black holes. We carefully study the predicted waveforms and use the latest-available numerical results to model signals for different black-hole and boson parameters. We then demonstrate the suitability of a specific method (hidden Markov model tracking) to efficiently search for such signals, even when the source parameters are not perfectly known as well as allowing for some uncertainty in theoretical predictions. We empirically study this method's sensitivity and computational cost in the context of boson signals, finding that it will be possible to target remnants from compact-binary mergers localized with at least three instruments. For signals from scalar clouds, we also compute detection horizons for future detectors (Advanced LIGO, LIGO Voyager, Cosmic Explorer, and the Einstein Telescope). Among other results, we find that, after one year of observation, an Advanced LIGO detector at design sensitivity could detect these sources up to over 100 Mpc, while Cosmic Explorer could reach over $1{0}^{4}\text{ }\text{ }\mathrm{Mpc}$. These projections offer a more complete picture than previous estimates based on analytic approximations to the signal power or idealized search strategies. Finally, we discuss specific implications for the follow-up of compact-binary coalescences and black holes in x-ray binaries. Along the way, we review the basic physics of bosons around black holes, in the hope of providing a bridge between the theory and data-analysis literatures.
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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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".