VLA monitoring of LS V +44 17 reveals scatter in the X-ray–radio correlation of Be/X-ray binaries
Bibliographic record
Abstract
ABSTRACT LS V +44 17 is a persistent Be/X-ray binary (BeXRB) that displayed a bright, double-peaked period of X-ray activity in late 2022/early 2023. We present a radio monitoring campaign of this outburst using the Very Large Array. Radio emission was detected, but only during the second, X-ray brightest, peak, where the radio emission followed the rise and decay of the X-ray outburst. LS V +44 17 is therefore the third neutron star BeXRB with a radio counterpart. Similar to the other two systems (Swift J0243.6+6124 and 1A 0535+262), its X-ray and radio luminosity are correlated: we measure a power-law slope $\beta = 1.25^{+0.64}_{-0.30}$ and a radio luminosity of LR = (1.6 ± 0.2) × 1026 erg s−1 at a 0.5–10 keV X-ray luminosity of 2 × 1036 erg s−1 (i.e. $\sim 1~{{\ \rm per\ cent}}$LEdd). This correlation index is slightly steeper than measured for the other two sources, while its radio luminosity is higher. We discuss the origin of the radio emission, specifically in the context of jet launching. The enhanced radio brightness compared to the other two BeXRBs is the first evidence of scatter in the giant BeXRB outburst X-ray–radio correlation, similar to the scatter observed in subclasses of low-mass X-ray binaries. While a universal explanation for such scatter is not known, we explore several options: we conclude that the three sources do not follow proposed scalings between jet power and neutron star spin or magnetic field, and instead briefly explore the effects that ambient stellar wind density may have on BeXRB jet luminosity.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".