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Record W2140807895 · doi:10.1088/2041-8205/796/1/l3

REJECTING PROPOSED DENSE MATTER EQUATIONS OF STATE WITH QUIESCENT LOW-MASS X-RAY BINARIES

2014· article· en· W2140807895 on OpenAlexaff
Sébastien Guillot, Robert E. Rutledge

Bibliographic record

VenueThe Astrophysical Journal Letters · 2014
Typearticle
Languageen
FieldPhysics and Astronomy
TopicPulsars and Gravitational Waves Research
Canadian institutionsMcGill University
Fundersnot available
KeywordsX-rayState (computer science)AstrophysicsPhysicsLow MassDense matterNuclear physicsMathematicsNeutron starStarsAlgorithm

Abstract

fetched live from OpenAlex

Neutrons stars are unique laboratories to discriminate 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 \rns, assumed to be the same for all neutron stars, using a slightly expanded data set. The radius measured is R_NS = 9.4 +/-1.2 km. 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 Msun, since radii <11 km 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 states: WFF1, AP4, MPA1, PAL1, MS0, and three versions of equations of state produced through chiral effective theory. Two of those, MSO and PAL1, are rejected at the 99% confidence level, accounting for all quantifiable uncertainties, while the other cannot be excluded at >99% certainty.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.810
Threshold uncertainty score0.319

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.007
GPT teacher head0.258
Teacher spread0.251 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations105
Published2014
Admission routes1
Has abstractyes

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