Soft gamma repeaters and anomalous X-ray pulsars: magnetar candidates
Bibliographic record
Abstract
Introduction Baade and Zwicky (1934) were the first to envision the formation of neutron stars as the end product of a supernova explosion. Their forward thinking was not vindicated for another three decades, with the discovery of the first radio pulsars by Bell and Hewish (Hewish et al. 1968). What Baade and Zwiscky could not have anticipated, however, was the menagerie of astrophysical objects that are now associated with neutron stars. Today, we observe them as magnetically braking pulsars, accreting pulsars in binary systems, isolated cooling blackbodies, sources of astrophysical jets, and emitters of high-luminosity bursts of X-rays. Here, we focus on two of the most extraordinary evolutionary paths of a neutron star, namely soft gamma repeaters (SGRs) and anomalous X-ray pulsars (AXPs). Soft gamma repeaters were discovered as high-energy transient burst sources; some were later found also to be persistent X-ray pulsars, with periods of several seconds, that are spinning down rapidly. Anomalous X-ray pulsars are identified through their persistent pulsations and rapid spin down; some have also been found to emit SGR-like bursts. In spite of the differing methods of discovery, this convergence in the observed properties of the SGRs and AXPs has made it clear that they are, fundamentally, the same type of object. What distinguishes them from other neutron stars is the likely source of energy for their radiative emissions, magnetism.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".