Methods and prospects for gravitational wave searches targeting ultralight vector boson clouds around known black holes
Bibliographic record
Abstract
Ultralight bosons are predicted in many extensions to the Standard Model and are popular dark matter candidates. The black hole superradiance mechanism allows for these particles to be probed using only their gravitational interaction. In this scenario, an ultralight boson cloud may form spontaneously around a spinning black hole and extract a non-negligible fraction of the black hole's mass. These oscillating clouds produce quasi-monochromatic, long-duration gravitational waves that may be detectable by ground-based or space-based gravitational wave detectors. We discuss the capability of a new long-duration signal tracking method, based on a hidden Markov model, to detect gravitational wave signals generated by ultralight vector boson clouds, including cases where the signal frequency evolution timescale is much shorter than that of a typical continuous wave signal. We quantify the detection horizon distances for vector boson clouds with current- and next-generation ground-based detectors. We demonstrate that vector clouds hosted by black holes with mass $\gtrsim 60 M_{\odot}$ and spin $\gtrsim 0.6$ are within the reach of current-generation detectors up to a luminosity distance of $\sim 1$ Gpc. This search method enables one to target vector boson clouds around remnant black holes from compact binary mergers detected by gravitational-wave detectors. We discuss the impact of the sky localization of the merger events and demonstrate that a typical remnant black hole reasonably well-localized by the current generation detector network is accessible in a follow-up search.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".