Chandra reveals a possible ultrafast outflow in the super-Eddington Be/X-ray binary Swift J0243.6+6124
Bibliographic record
Abstract
Accretion at super-Eddington rates is expected to be accompanied by strong outflows. Such outflows are observed in Galactic X-ray binaries and extragalactic ultraluminous X-ray sources (ULXs). However, due to their large source distances, ULX outflows are challenging to detect and study in detail. Galactic neutron stars accreting from a Be-star companion at super-Eddington rates show many similarities to ULX pulsars, and therefore offer an alternative approach to study outflows in this accretion regime. Here, we present Chandra high-resolution spectroscopy of such a super-Eddington accreting neutron star, Swift J0243.6+6124, to search for wind outflow signatures during the peak of its 2017/2018 giant outburst. We detect narrow emission features at rest from Ne, Mg, S, Si, and Fe. In addition, we detect a collection of absorption features which can be identified in two ways: either as all Fe transitions at rest (with a possible contribution from Mg), or a combination of three blue-shifted Ne and Mg lines at ∼0.22c, while the remaining lines are at rest. The second scenario would imply an outflow with a velocity similar to those seen in ULXs, including the ULX pulsar NGC 300 ULX-1. This result would also imply that Swift J0243.6+6124 launches both a jet, detected in radio and reported previously, and an ultrafast wind outflow simultaneously at super-Eddington accretion rates.
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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".