Self-gravity effects of ultralight boson clouds formed by black hole superradiance
Bibliographic record
Abstract
Oscillating clouds of ultralight bosons can grow around spinning black holes through superradiance, extracting energy and angular momentum, and eventually dissipating through gravitational radiation. Gravitational wave detectors like LIGO, Virgo, KAGRA, and LISA can thus probe the existence of ultralight bosons. In this study, we use fully general-relativistic solutions of the black hole-boson cloud systems to study the self-gravity effects of scalar and vector boson clouds, making only the simplifying assumption that the spacetime is axisymmetric (essentially corresponding to taking an oscillation average). We calculate the self-gravity shift in the cloud oscillation frequency, which determines the frequency evolution of the gravitational wave signal, finding that this effect can be up to twice as large in the relativistic regime compared to nonrelativistic estimates. We use this to improve the superrad waveform model, and estimate that this reduces the theoretical phase error to a few cycles over the characteristic timescale of the gravitational wave emission timescale for the louder vector boson signals. We also perform an analysis of the spacetime geometry of these systems, calculating how the cloud changes the innermost stable circular orbit and light ring around the black hole.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".