Radio monitoring of transient Be/X-ray binaries and the inflow–outflow coupling of strongly magnetized accreting neutron stars
Bibliographic record
Abstract
ABSTRACT Strongly magnetized (B ≥ 1012 G) accreting neutron stars (NSs) are prime targets for studying the launching of jets by objects with a solid surface; while classical jet-launching models predict that such NSs cannot launch jets, recent observations and models argue otherwise. Transient Be/X-ray binaries (BeXRBs) are critical laboratories for probing this poorly explored parameter space for jet formation. Here, we present the coordinated monitoring campaigns of three BeXRBs across four outbursts: giant outbursts of SAX 2103.5+4545, 1A 0535+262, and GRO J1008–57, as well as a Type-I outburst of the latter. We obtain radio detections of 1A 0535+262 during ten out of twenty observations, while the other targets remained undetected at typical limits of 20–50 $\mu$Jy. The radio luminosity of 1A 0535+262 positively correlates with its evolving X-ray luminosity, and inhabits a region of the LX–LR plane continuing the correlation observed previously for the BeXRB Swift J0243.6+6124. We measure a BeXRB LX–LR coupling index of β = 0.86 ± 0.06 ($L_R \propto L_X^\beta$), similar to the indices measured in NS and black hole low-mass X-ray binaries. Strikingly, the coupling’s LR normalization is ∼275 and ∼6.2 × 103 times lower than in those two comparison samples, respectively. We conclude that jet emission likely dominates during the main peak of giant outbursts, but is only detectable for close-by or super-Eddington systems at current radio sensitivities. We discuss these results in the broader context of X-ray binary radio studies, concluding that our results suggest how supergiant X-ray binaries may host a currently unidentified additional radio emission mechanism.
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 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".