Astrometric excess noise in <i>Gaia</i> EDR3 and the search for X-ray binaries
Bibliographic record
Abstract
ABSTRACT Astrometric noise (ϵ) in excess of parallax and proper motion is a potential signature of orbital wobble (ω) of individual components in binary star systems. The combination of X-ray selection with astrometric noise could then be a powerful tool for robustly isolating accreting binaries in large surveys. Here, we mine the Gaia EDR3 catalogue for Galactic sources with significant values of astrometric noise over the parameter space expected for known and candidate X-ray binaries (XRBs). Cross-matching our sample with the Chandra Source Catalogue returns a primary sample of ≈6500 X-ray sources with significant ϵ. X-ray detection efficiency for objects with significant ϵ is a factor of ≈4.5 times higher than in a matched control sample exhibiting low ϵ. The primary sample branches off the main sequence much more than control objects in colour–mag space, and includes a higher fraction of known binaries, variables, and young stellar object class types. However, values of ϵ reported in the Gaia pipeline releases so far can exceed expectations for individual XRBs with known semimajor axis size and other system parameters. It is likely that other factors (possibly attitude and modelling uncertainties, as well as source variability) currently dominate the observed excess noise in such systems. Confirmation of their nature must therefore await future Gaia releases. The full X-ray matched catalogue is released here to enable legacy follow-up.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".