Bibliographic record
Abstract
Abstract We study the formation and evolution of vortices in U(1) dark photon dark matter and dark photon clouds that arise through black hole superradiance. We show how the production of both longitudinal mode and transverse mode dark photon dark matter can lead to the formation of vortices. After vortex formation, the energy stored in the dark photon dark matter will be transformed into a large number of vortex strings, eradicating the coherent dark photon dark matter field. In the case where a dark photon magnetic field is produced, bundles of vortex strings are formed in a superheated phase transition, and evolve towards a configuration consisting of many string loops that are uncorrelated on large scales, analogous to a melting phase transition in condensed matter. In the process, they dissipate via dark photon and gravitational wave emission, offering a target for experimental searches. Vortex strings were also recently shown to form in dark photon superradiance clouds around black holes, and we discuss the dynamics and observational consequences of this phenomenon with phenomenologically motivated parameters. In that case, the string loops ejected from the superradiance cloud, apart from producing gravitational waves, are also quantised magnetic flux lines and can be looked for with magnetometers. We discuss the connection between the dynamics in these scenarios and similar vortex dynamics found in type II superconductors.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".