MEASURING THE COOLING OF THE NEUTRON STAR IN CASSIOPEIA A WITH ALL<i>CHANDRA X-RAY OBSERVATORY</i>DETECTORS
Bibliographic record
Abstract
The thermal evolution of young neutron stars (NSs) reflects the neutrino emission properties of their cores. Heinke & Ho measured a 3.6% ± 0.6% decay in the surface temperature of the Cassiopeia A (Cas A) NS between 2000 and 2009, using archival data from the Chandra X-ray Observatory ACIS-S detector in Graded mode. Page et al. and Shternin et al. attributed this decay to enhanced neutrino emission from a superfluid neutron transition in the core. Here we test this decline, combining analysis of the Cas A NS using all Chandra X-ray detectors and modes (HRC-S, HRC-I, ACIS-I, ACIS-S in Faint mode, and ACIS-S in Graded mode) and adding a 2012 May ACIS-S Graded mode observation, using the most current calibrations (CALDB 4.5.5.1). We measure the temperature changes from each detector separately and test for systematic effects due to the nearby filaments of the supernova remnant. We find a 0.92%–2.0% decay over 10 yr in the effective temperature, inferred from HRC-S data, depending on the choice of source and background extraction regions, with a best-fit decay of 1.0% ± 0.7%. In comparison, the ACIS-S Graded data indicate a temperature decay of 3.1%–5.0% over 10 yr, with a best-fit decay of 3.5% ± 0.4%. Shallower observations using the other detectors yield temperature decays of 2.6% ± 1.9% (ACIS-I), 2.1% ± 1.0% (HRC-I), and 2.1% ± 1.9% (ACIS-S Faint mode) over 10 yr. Our best estimate indicates a decline of 2.9% ± 0.5% stat ± 1.0 sys % over 10 yr. The complexity of the bright and varying supernova remnant background makes a definitive interpretation of archival Cas A Chandra observations difficult. A temperature decline of 1%–3.5% over 10 yr would indicate extraordinarily fast cooling of the NS that can be regulated by superfluidity of nucleons in the stellar core.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".