Quasi-simultaneous radio and X-ray observations of Aql X-1 : probing low luminosities
Bibliographic record
Abstract
ABSTRACT Aql X-1 is one of the best-studied neutron star low-mass X-ray binaries. It was previously targeted using quasi-simultaneous radio and X-ray observations during at least seven different accretion outbursts. Such observations allow us to probe the interplay between accretion inflow (X-ray) and jet outflow (radio). Thus far, these combined observations have only covered one order of magnitude in radio and X-ray luminosity range; this means that any potential radio–X-ray luminosity correlation, LR ∝ LXβ, is not well constrained (β ≈ 0.4–0.9, based on various studies) or understood. Here we present quasi-simultaneous Very Large Array and Swift-XRT observations of Aql X-1’s 2016 outburst, with which we probe one order of magnitude fainter in radio and X-ray luminosity compared to previous studies (6 × 1034 erg s−1 < LX <3 × 1035 erg s−1, i.e. the intermediate to low-luminosity regime between outburst peak and quiescence). The resulting radio non-detections indicate that Aql X-1’s radio emission decays more rapidly at low X-ray luminosities than previously assumed – at least during the 2016 outburst. Assuming similar behaviour between outbursts, and combining all available data in the hard X-ray state, this can be modelled as a steep β =$1.17^{+0.30}_{-0.21}$ power-law index or as a sharp radio cut-off at LX ≲ 5 × 1035 erg s−1 (given our deep radio upper limits at X-ray luminosities below this value). We discuss these results in the context of other similar studies.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".