Astrophysical constraints on neutron star <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mi>f</mml:mi> </mml:math> -modes with a nonparametric equation of state representation
Bibliographic record
Abstract
We constrain the fundamental-mode ($f$-mode) oscillation frequencies of nonrotating neutron stars using a phenomenological Gaussian process model for the unknown dense-matter equation of state conditioned on a suite of gravitational-wave, radio and x-ray observations. We infer the quadrupolar $f$-mode frequency preferred by the astronomical data as a function of neutron star mass, with error estimates that quantify the impact of equation of state uncertainty, and compare it to the contact frequency for inspiraling neutron-star binaries, finding that resonance with the orbital frequency can be achieved for the coalescences with the most unequal mass ratio. For an optimally configured binary neutron star merger, we estimate the gravitational waveform's tidal phasing due to $f$-mode dynamical tides as ${7}_{\ensuremath{-}3}^{+2}\text{ }\text{ }\mathrm{rad}$ at merger. We assess prospects for distinguishing $f$-mode dynamical tides with current and future-generation gravitational-wave observatories.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".