Gravitational Waves from F-modes Excited by the Inspiral of Highly Eccentric Neutron Star Binaries
Bibliographic record
Abstract
Abstract As gravitational wave instrumentation becomes more sensitive, it is interesting to speculate about subtle effects that could be analyzed using upcoming generations of detectors. One such effect that has great potential for revealing the properties of very dense matter is fluid oscillations of neutron stars. These have been found in numerical simulations of the hypermassive remnants of double neutron star mergers and of highly eccentric neutron star orbits. Here we focus on the latter and sketch out some ideas for the production, gravitational-wave detection, and analysis of neutron star oscillations. These events will be rare (perhaps up to several tens per year could be detected using third-generation detectors such as the Einstein Telescope or the Cosmic Explorer), but they would have unique diagnostic power for the analysis of cold, catalyzed, dense matter. Furthermore, these systems are unusual in that analysis of the tidally excited f-modes of the stars could yield simultaneous measurements of their masses, moments of inertia, and tidal Love numbers, using the frequency, damping time, and amplitude of the modes. They would thus present a nearly unique opportunity to test the I-Love-Q relation observationally. The analysis of such events will require significant further work in nuclear physics and general relativistic nonlinear mode coupling, and thus we discuss further directions that will need to be pursued. For example, we note that for nearly grazing encounters, numerical simulations show that the energy delivered to the f-modes may be up to two orders of magnitude greater than predicted in the linear theory.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".