MEASUREMENT OF THE RADIUS OF NEUTRON STARS WITH HIGH SIGNAL-TO-NOISE QUIESCENT LOW-MASS X-RAY BINARIES IN GLOBULAR CLUSTERS
Bibliographic record
Abstract
This paper presents the measurement of the neutron star (NS) radius using the thermal spectra from quiescent low-mass X-ray binaries (qLMXBs) inside globular clusters (GCs). Recent observations of NSs have presented evidence that cold ultra dense matter—present in the core of NSs—is best described by "normal matter" equations of state (EoSs). Such EoSs predict that the radii of NSs, R NS , are quasi-constant (within measurement errors, of ∼10%) for astrophysically relevant masses ( M NS >0.5 M ☉ ). The present work adopts this theoretical prediction as an assumption, and uses it to constrain a single R NS value from five qLMXB targets with available high signal-to-noise X-ray spectroscopic data. Employing a Markov chain Monte-Carlo approach, we produce the marginalized posterior distribution for R NS , constrained to be the same value for all five NSs in the sample. An effort was made to include all quantifiable sources of uncertainty into the uncertainty of the quoted radius measurement. These include the uncertainties in the distances to the GCs, the uncertainties due to the Galactic absorption in the direction of the GCs, and the possibility of a hard power-law spectral component for count excesses at high photon energy, which are observed in some qLMXBs in the Galactic plane. Using conservative assumptions, we found that the radius, common to the five qLMXBs and constant for a wide range of masses, lies in the low range of possible NS radii, (90%-confidence). Such a value is consistent with low- R NS equations of state. We compare this result with previous radius measurements of NSs from various analyses of different types of systems. In addition, we compare the spectral analyses of individual qLMXBs to previous works.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".