Spectral analysis of the quiescent low-mass X-ray binary in the globular cluster M30
Bibliographic record
Abstract
ABSTRACT We present a recent Chandra observation of the quiescent low-mass X-ray binary containing a neutron star (NS), located in the globular cluster M30. We fit the thermal emission from the NS to extract its mass and radius. We find no evidence of flux variability between the two observations taken in 2001 and 2017, nor between individual 2017 observations, so we analyse them together to increase the signal-to-noise ratio. We perform simultaneous spectral fits using standard light-element composition atmosphere models (hydrogen or helium), including absorption by the interstellar medium, correction for pile-up of X-ray photons on the detector, and a power law for count excesses at high photon energy. Using a Markov chain Monte Carlo approach, we extract mass and radius credible intervals for both chemical compositions of the atmosphere: $\mbox{$R_{\rm NS}$}=7.94\mbox{{$\scriptstyle ^{ + 0.76}_{- 1.21}$}}$ km and $\mbox{$M_{\rm NS}$}\lt 1.19$ M⊙ assuming pure hydrogen, and $\mbox{$R_{\rm NS}$}=10.50\mbox{{$\scriptstyle ^{ + 2.88}_{- 2.03}$}}$ km and $\mbox{$M_{\rm NS}$}\lt 1.78$ M⊙ for helium, where the uncertainties represent the 90 per cent credible regions. For H, the small radius is difficult to reconcile with most current nuclear physics models (especially for nucleonic equations of state) and with other measurements of NS radii, with recent preferred values generally in the 11–14 km range. Whereas for He, the measured radius is consistent with this range. We discuss possible sources of systematic uncertainty that may result in an underestimation of the radius, identifying the presence of surface temperature inhomogeneities as the most relevant bias. According to this, we conclude that either the atmosphere is composed of He or it is anH atmosphere with a significant contribution of hotspots to the observed radiation.
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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".