An «XMM» search for quiescient low -mass x-ray binaries in globular cluster using x-ray spectral identification
Bibliographic record
Abstract
Low-mass X-ray binaries (LMXBs) are now routinely used for neutron star (NS) radius measurements. Observations of these systems in their quiescent stage (qLMXB) is one method leading to precise measurements. However, the dominant source of uncertainty on the NS radii remains the distance to the binary systems. Globular clusters (GCs) are therefore the places to look for more qLMXBs, due to their known or measurable distances and their abundance of binary systems. This thesis reports the discovery of seven candidate qLMXBs in six GCs, using observations from the XMM-Newton satellite, based on X-ray spectral consistency with NS hydrogen atmosphere models. The goal of this program of observations is to increase the population of known GC qLMXBs, for which longer follow-up exposures will permit high precision radius measurements. Nine candidates were initially identified based on their X-ray spectra with signal-to-noise ratio S/N > 3. Two candidates in NGC 6304 were tentatively confirmed, with consistent best-fit parameters, in a follow-up Chandra X-ray observatory observation. One low-S/N candidate in NGC 6540 (S/N = 7) was subsequently excluded by a deeper, higher S/N (S/N = 17) observation. One other candidate was also excluded on the basis of the spatial velocity of the associated companion star, precluding cluster membership. Thus, the present work has added seven new qLMXB candidates to the eleven previously known.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".