Sirius – Like Systems in the Gaia DR3
Bibliographic record
Abstract
Evidence of hot white dwarf (WD) components in 722 binary stars with main sequence (MS) primaries was examined by calculating an absolute magnitude excess in the Galaxy Evolution Explorer (GALEX) far-UV (FUV) band. The binary stars selected for this study were characterized by the Gaia Data Processing and Analysis Consortium (DPAC) as having a faint and compact companion in accordance with the Astrometric Mass Ratio Function (AMRF) algorithm of Shahaf et al (2019). The excess calculation was taken as the difference between the absolute FUV magnitude of the collective binary system, and a calculation of the absolute FUV magnitude of the MS component. The former quantity was calculated from the GALEX GR6+7 release of the photometric data, and was scaled to an absolute context using parallax data from the Gaia Data Release 3 (DR3). The MS component FUV magnitudes were calculated from Kurucz (2003) stellar models. For 16 of the binary systems, the astrophysical parameters that characterize the Kurucz models were taken as the results from the Gaia DR3 Astrophysical Parameters Inference System (APSIS). For 13 of the binary systems, astrophysical parameters were obtained from a chi square best of Kurucz stellar models to spectral energy distributions (SEDs), that were constructed from Gaia synthetic optical photometry bands. For these 13systems, the effective temperature, surface gravity, stellar radius and metallicity of the MS component was estimated. In total, 28 binary systems were found to have an FUV excess greater than zero, with 24/28 having an excess of >10 and a maximum FUV excess of ~23. From the FUV excess results, the absolute FUV magnitudes of the possible WD components and the corresponding effective temperatures were estimated from a linear interpolation of WD photometry tables of the state-of-the-art Montréal white dwarf models.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".