Neutron star crust cooling in the Terzan 5 X-ray transient Swift J174805.3–244637
Bibliographic record
Abstract
When neutron stars reside in transient X-ray binaries, their crustal layers become heated during accretion outbursts and subsequently cool in quiescence. Observing and modelling this thermal response has yielded valuable insight into the physics of neutron star crusts. However, one unresolved problem is the evidence in several neutron stars for an extra energy source, located at shallow depth in the crust, that is not accounted for by standard heating models. Its origin remains puzzling, and it is currently unclear whether this additional heating occurs in all neutron stars, and if the magnitude is always the same. Here, we report on Chandra observations that cover two years after the 2012 outburst of the transient neutron star X-ray binary Swift J174805.3–244637 in the globular cluster Terzan 5. The temperature of the neutron star was elevated during the first two months following its ≃8 week accretion episode, but had decayed to the pre-outburst level within ≃100 d. Interpreting this as rapid cooling of the accretion-heated crust, we model the observed temperature curve with a thermal evolution code. We find that there is no need to invoke shallow heating for this neutron star, although an extra energy release up to ≃1.4 MeV nucleon−1 is allowed by the current data (2σ confidence). We also present two new data points on the crust-cooling curve of the 11-Hz X-ray pulsar IGR J17480–2446 in Terzan 5, which was active in 2010. The temperature of this neutron star remains significantly above the pre-outburst level, but we detect no change in the thermal emission since the previous measurements of 2013 February. This is consistent with the slow crust cooling expected several years post-outburst.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".