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Record W2118139755 · doi:10.1088/0004-637x/775/1/48

CONTINUED NEUTRON STAR CRUST COOLING OF THE 11 Hz X-RAY PULSAR IN TERZAN 5: A CHALLENGE TO HEATING AND COOLING MODELS?

2013· article· en· W2118139755 on OpenAlexafffund

Bibliographic record

VenueThe Astrophysical Journal · 2013
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstrophysical Phenomena and Observations
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaSmithsonian Astrophysical ObservatoryNational Aeronautics and Space AdministrationSpace Telescope Science InstituteSmithsonian Institution
KeywordsNeutron starPulsarGlobular clusterCrustAccretion (finance)X-ray pulsarX-ray binaryFlux (metallurgy)

Abstract

fetched live from OpenAlex

The transient neutron star low-mass X-ray binary and 11 Hz X-ray pulsar IGR J17480-2446 in the globular cluster Terzan 5 exhibited an 11 week accretion outburst in 2010. Chandra observations performed within five months after the end of the outburst revealed evidence that the crust of the neutron star became substantially heated during the accretion episode and was subsequently cooling in quiescence. This provides the rare opportunity to probe the structure and composition of the crust. Here, we report on new Chandra observations of Terzan 5 that extend the monitoring to sime2.2 yr into quiescence. We find that the thermal flux and neutron star temperature have continued to decrease, but remain significantly above the values that were measured before the 2010 accretion phase. This suggests that the crust has not thermally relaxed yet, and may continue to cool. Such behavior is difficult to explain within our current understanding of heating and cooling of transiently accreting neutron stars. Alternatively, the quiescent emission may have settled at a higher observed equilibrium level (for the same interior temperature), in which case the neutron star crust may have fully cooled.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.594
Threshold uncertainty score0.523

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.017
GPT teacher head0.228
Teacher spread0.210 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations44
Published2013
Admission routes2
Has abstractyes

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