Super-radiance from a relativistic source
Bibliographic record
Abstract
Abstract Cooperative super-radiant emission from a highly relativistic multi-particle source is modeled and solved for the simple case of two particles. An existing model of a single relativistic two-level particle is used to construct a Hamiltonian describing relativistic velocity dependent multi-particle super-radiance. The standard diagrammatic framework is applied to the calculation of time evolution and density operators from this Hamiltonian, demonstrating during the process a departure from standard results and calculation methods. In particular, the so-called vertical photon result of the literature is shown to be modified by the relativistic Lorentz factor of the sample; additionally, a set of coupled differential equations describing certain propagators in the velocity-dependent small sample framework are introduced and solved numerically via a hybrid fourth order Runge–Kutta and convolution approach. The model is applied to the simple case of two highly relativistic particles travelling with slightly differing velocities simulated at varying relativistic mean sample β factors, and velocity coherence requirements for a sample to demonstrate enhanced super-radiant emission in the observer frame are evaluated. These coherence requirements are found to become increasingly restrictive at higher β factors, even in the context of standard results of relativistic velocity differential transformations.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 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".