The First High-Contrast Images of Near High-Mass X-Ray Binaries with Keck/NIRC2
Bibliographic record
Abstract
Although the study of X-ray binaries has led to major breakthroughs in high-energy astrophysics, their circumbinary environment at scales of $\sim$100--10,000 astronomical units has not been thoroughly investigated. In this paper, we undertake a novel and exploratory study by employing direct and high-contrast imaging techniques on a sample of X-ray binaries, using adaptive optics and the vortex coronagraph on Keck/NIRC2. High-contrast imaging opens up the possibility to search for exoplanets, brown dwarfs, circumbinary companion stars, and protoplanetary disks in these extreme systems. Here, we present the first near-infrared high-contrast images of 13 high-mass X-ray binaries located within $\sim$2--3 kpc. The key results of this campaign involve the discovery of several candidate circumbinary companions ranging from sub-stellar (brown dwarf) to stellar masses. By conducting an analysis based on galactic population models, we discriminate sources that are likely background/foreground stars and isolate those that have a high probability ($\gtrsim 60 - 99\%$) of being gravitationally bound to the X-ray binary. This publication seeks to establish a preliminary catalog for future analyses of proper motion and subsequent observations. With our preliminary results, we calculate the first estimate of the companion frequency and the multiplicity frequency for X-ray binaries: $\approx$0.6 and 1.8 $\pm$ 0.9 respectively, considering only the sources that are most likely bound to the X-ray binary. In addition to extending our comprehension of how brown dwarfs and stars can form and survive in such extreme systems, our study opens a new window to our understanding of the formation of X-ray binaries.
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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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".