THE IMPACT OF SURFACE TEMPERATURE INHOMOGENEITIES ON QUIESCENT NEUTRON STAR RADIUS MEASUREMENTS
Bibliographic record
Abstract
ABSTRACT Fitting the thermal X-ray spectra of neutron stars (NSs) in quiescent X-ray binaries can constrain the masses and radii of NSs. The effect of undetected hot spots on the spectrum, and thus on the inferred NS mass and radius, has not yet been explored for appropriate atmospheres and spectra. A hot spot would harden the observed spectrum, so that spectral modeling tends to infer radii that are too small. However, a hot spot may also produce detectable pulsations. We simulated the effects of a hot spot on the pulsed fraction and spectrum of the quiescent NSs X5 and X7 in the globular cluster 47 Tucanae, using appropriate spectra and beaming for hydrogen atmosphere models, incorporating special and general relativistic effects, and sampling a range of system angles. We searched for pulsations in archival Chandra HRC-S observations of X5 and X7, placing 90% confidence upper limits on their pulsed fractions below 16%. We use these pulsation limits to constrain the temperature differential of any hot spots, and to then constrain the effects of possible hot spots on the X-ray spectrum and the inferred radius from spectral fitting. We find that hot spots below our pulsation limit could bias the spectroscopically inferred radius downward by up to 28%. For Cen X-4 (which has deeper published pulsation searches), an undetected hot spot could bias its inferred radius downward by up to 10%. Improving constraints on pulsations from quiescent LMXBs may be essential for progress in constraining their radii.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".