Black-hole spectroscopy from a giant quantum vortex
Bibliographic record
Abstract
Black-hole spectroscopy aims to infer the fundamental properties of black holes by analysing the spectrum of gravitational waves emitted as they settle into equilibrium. These resonances, known as quasinormal modes (QNMs), decay rapidly, which limits the time-domain analysis of gravitational-wave data or numerical simulations to the longest-lived mode, except for a particularly loud event. Owing to the analogy between fields in curved spacetime and waves propagating in a flowing medium, QNMs can be equally excited in a laboratory. In these finite-sized systems, the QNM spectrum is expected to alter: compared to their counterparts in unbounded settings, the real frequencies of QNMs shift while their damping rates (imaginary frequencies) reduce, thereby enhancing their detectability. Here we show that multiple QNMs can be extracted from noise-driven interface waves surrounding a giant quantum vortex in superfluid helium-4, which emulates a spacetime geometry indicative of a rotating black hole. By resolving waves with different azimuthal periodicity, we find that both fundamental modes and their higher-frequency overtones are excited, and oscillate at frequencies given by the size of our system. Since similar effects may arise in astrophysical scenarios due to the interstellar medium or dark matter, gravity simulators now complement numerical and observational approaches to black-hole spectroscopy.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 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".