High-frequency radio observations of two magnetars, PSR J1622 − 4950 and 1E 1547.0 − 5408
Bibliographic record
Abstract
ABSTRACT We investigated the radio spectra of two magnetars, PSR J1622 − 4950 and 1E 1547.0 − 5408, using observations from the Australia Telescope Compact Array and the Atacama Large Millimeter/submillimeter Array obtained in 2017. Our observations of PSR J1622 − 4950 show a steep spectrum with a spectral index of −1.3 ± 0.2 in the range of 5.5–45 GHz during its reactivating X-ray outburst in 2017. By comparing the data taken in different epochs, we found a significant enhancement in the radio flux density. The spectrum of 1E 1547.0 − 5408 was inverted in the range of 43–95 GHz, suggesting a spectral peak at a few hundred gigahertz. Moreover, we obtained X-ray and radio data of the radio magnetars PSR J1622 − 4950 and SGR J1745 − 2900 from the literature and found two interesting properties. First, radio emission is known to be associated with X-ray outbursts but has a different evolution; furthermore, we found that the rise time of the radio emission is much longer than that of the X-ray emission during the outburst. Second, radio magnetars may have double-peak spectra at a few GHz and a few hundred GHz. This could indicate that the emission mechanism is different in the cm and the submm bands. These two phenomena could provide a hint towards an understanding of the origin of radio emission and its connection with X-ray properties.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".