Precision photometric monitoring from space of the multiple system θ Muscae including the WR binary WR48
Bibliographic record
Abstract
ABSTRACT Θ Mus = HD 113904 is a massive multiple system containing the WC5/6 + O6/7V binary WR48 in a $19.1\, \mathrm{ d}$ circular orbit. Previous attempts to constrain the variable photometric properties of this binary subsystem have been thwarted by the dominating stochastically variable light from a 10-times brighter blue supergiant (BSG), located only 46 mas away. Even now, with extensive optical space-based photometry from one of the BRITE-Constellation satellites, we were unable to beat down the intrinsic stochastic variability from the BSG enough to provide a convincing detection of a low-level atmospheric eclipse of the WC + O system, as often seen in other short-period WR + O systems. We explore the variability of the dominating BSG and find that its behaviour is similar to that of other BSGs, with a forest of low-frequency Fourier peaks likely from stochastic gravity waves reaching the stellar surface. Then, by adopting an orbital inclination from another more reliable source, we obtain a clumping-independent, linear-density-dependent upper limit of the mass-loss rate for the WR component of $(6.5 \pm 0.3) \times 10^{-5}\, \mathrm{ M}_{\odot }\,\mathrm{ yr}^{ -1}$, which is consistent with values of other WC5/6 subtypes. This corresponds to an upper limit of 5.0 ± 0.2 mmag for the depth of the atmospheric eclipse in the WR48 subsystem when observed together with the BSG.
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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) | 0.001 | 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".