REJECTING PROPOSED DENSE MATTER EQUATIONS OF STATE WITH QUIESCENT LOW-MASS X-RAY BINARIES
Bibliographic record
Abstract
Neutrons stars are unique laboratories for discriminating between the various proposed equations of state of matter at and above nuclear density. One sub-class of neutron stars—those inside quiescent low-mass X-ray binaries (qLMXBs)—produce a thermal surface emission from which the neutron star radius ( R NS ) can be measured, using the widely accepted observational scenario for qLMXBs, assuming unmagnetized H atmospheres. In a combined spectral analysis, this work first reproduces a previously published measurement of the R NS , assumed to be the same for all neutron stars, using a slightly expanded data set. The radius measured is . On the basis of spectral analysis alone, this measured value is not affected by imposing an assumption of causality in the core. However, the assumptions underlying this R NS measurement would be falsified by the observation of any neutron star with a mass >2.6 M ☉ , since radii would be rejected if causality is assumed, which would exclude most of the R NS parameter space obtained in this analysis. Finally, this work directly tests a selection of dense matter equations of state: WFF1, AP4, MPA1, PAL1, MS0, and three versions of equations of state produced through chiral effective theory. Two of those, MS0 and PAL1, are rejected at the 99% confidence level, accounting for all quantifiable uncertainties, while the other cannot be excluded at >99% certainty.
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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 | 0.008 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".