Modelling the photometric variability of magnetic massive stars with the Analytical Dynamical Magnetosphere model
Bibliographic record
Abstract
Abstract In this paper, we investigate the photometric variability of magnetic O-type stars. Such stars possess oblique, predominantly dipolar magnetic fields that confine their winds roughly axisymmetrically about the magnetic equator, thus forming a magnetosphere. We interpret their photometric variability as phase-dependent magnetospheric occultations. For massive star winds dominated by electron scattering opacity in the optical and NIR, we can compute synthetic light curves from simply knowing the magnetosphere’s mass density distribution. We exploit the newly-developed Analytical Dynamical Magnetosphere model (ADM) in order to obtain the predicted circumstellar density structures of magnetic O-type stars. The simplicity in our light curve synthesis model allows us to readily conduct a parameter space study. For validation purposes, we first apply our algorithm to HD 191612, the prototypical Of?p star. Next, we attempt to model the photometric variability of the Of?p-type stars identified in the Magellanic Clouds using OGLE photometry. We evaluate the compatibility of the ADM predictions with the observed photometric variations, and discuss the magnetic field properties that are implied by our modelling.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".