MétaCan
Menu
Back to cohort
Record W4220959318 · doi:10.1093/mnras/stac506

On the recurrence times of neutron star X-ray binary transients and the nature of the Galactic Centre quiescent X-ray binaries

2022· article· en· W4220959318 on OpenAlexaff
Thomas J. Maccarone, N. Degenaar, B. E. Tetarenko, C. O. Heinke, R. Wijnands, G. R. Sivakoff

Bibliographic record

VenueMonthly Notices of the Royal Astronomical Society · 2022
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstrophysical Phenomena and Observations
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPhysicsAstrophysicsNeutron starGalaxyPulsarX-ray binaryPopulationBlack hole (networking)AstronomyMillisecond pulsarLow MassBinary numberStellar populationStarsStar formation

Abstract

fetched live from OpenAlex

ABSTRACT The presence of some X-ray sources in the Galactic Centre region which show variability, but do not show outbursts in over a decade of monitoring has been used to argue for the presence of a large population of stellar mass black holes in this region. A core element of the arguments that these objects are accreting black holes is the claim that neutron stars (NSs) in low-mass X-ray binaries (LMXBs) do not have long transient recurrence times. We demonstrate in this paper that about half of the known transient LMXBs with clear signatures for NS primaries have recurrence times in excess of a decade for outbursts at the sensitivity of MAXI. We furthermore show that, in order to reconcile the expected total population of NS LMXBs with the observed one and with the millisecond radio pulsar (MSRP) population of the Galaxy, systems with recurrence times well in excess of a century for outbursts detectable by instruments like MAXI must be the dominant population of NS LMXBs, and that few of these systems have yet been discovered.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.006
GPT teacher head0.190
Teacher spread0.184 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations10
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueMonthly Notices of the Royal Astronomical SocietySame topicAstrophysical Phenomena and ObservationsFrench-language works237,207