5 yr of BRITE-Constellation photometry of the luminous blue variable P Cygni: properties of the stochastic low-frequency variability
Bibliographic record
Abstract
ABSTRACT Luminous Blue Variables (LBVs) are massive stars that are likely to be a transitionary phase between O stars and hydrogen-free classical Wolf–Rayet stars. The variability of these stars has been an area of study for both professional and amateur astronomers for more than a century. In this paper, we present 5 yr of precision photometry of the classical LBV P Cygni taken with the BRITE-Constellation nanosatellites. We have analyzed these data with Fourier analysis to search for periodicities that could elucidate the drivers of variability for these stars. These data show some long-time-scale variability over the course of all six calendar years of observations, but the frequencies needed to reproduce the individual light curves are not consistent from 1 yr to the next. These results likely show that there is no periodic phenomenon present for P Cygni, meaning that the variability is largely stochastic. We interpret the data as being caused by internal gravity waves similar to those seen in other massive stars, with P Cygni exhibiting a larger amplitude and lower characteristic frequency than the main-sequence or blue supergiant stars previously studied. These results show evidence that LBVs may be an extrapolation of the blue supergiants, which have previously been shown to be an extension of main-sequence stars in the context of the stochastic low-frequency photometric variability.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 | 0.000 |
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".