Implicit correlations within phenomenological parametric models of the neutron star equation of state
Bibliographic record
Abstract
The rapid increase in the number and precision of astrophysical probes of neutron stars in recent years allows for the inference of their equation of state. Observations target different macroscopic properties of neutron stars which vary from star to star, such as mass and radius, but the equation of state allows for a common description of all neutron stars. To connect these observations and infer the properties of dense matter and neutron stars simultaneously, models for the equation of state are introduced. Parametric models rely on carefully engineered functional forms that reproduce a large array of realistic equations of state. Such models benefit from their simplicity but are limited because any finite-parameter model cannot accurately approximate all possible equations of state. Nonparametric methods overcome this by increasing model freedom at the cost of increased complexity. In this study, we compare common parametric and nonparametric models, quantify the limitations of the former, and study the impact of modeling on our current understanding of high-density physics. We show that parametric models impose strongly model-dependent, and sometimes opaque, correlations between density scales. Such interdensity correlations result in tighter constraints that are unsupported by data and can lead to biased inference of the equation of state and of individual neutron star properties.
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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 | 0.009 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| 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".