Emission profile variability in hot star winds
Bibliographic record
Abstract
We present theoretical calculations of emission line profile variability based on hot star wind structure calculated numerically using radiation hydrodynamics simulations. A principal goal is to examine how well short-time-scale variations observed in wind emission lines can be modelled by wind structure arising from small-scale instabilities intrinsic to the line-driving of these winds. The simulations here use a new implementation of the Smooth Source Function formalism for line-driving within a one-dimensional (1D) operation of the standard hydrodynamics code ZEUS–2D. As in previous wind instability simulations, the restriction to 1D is necessitated by the computational costs of non–local integrations needed for the line-driving force; but we find that naive application of such simulations within an explicit assumption of spherically symmetric structure leads to an unobserved strong concentration of profile variability toward the line wings. We thus introduce a new “patch method” for mimicking a full 3D wind structure by collecting random sequences of 1D simulations to represent the structure evolution along radial rays that extend over a selectable patch-size of solid angle. We provide illustrative results for a selection of patch sizes applied to a simulation with standard assumptions that govern the details of instability-generated wind structure, and show in particular that a typical model with a patch size of about $3~\deg$ can qualitatively reproduce the fundamental properties of observed profile variations. We conclude with a discussion of prospects for extending the simulation method to optically thick winds of Wolf-Rayet (WR) stars, and for thereby applying our “patch method” to dynamical modelling of the extensive variability observed in wind emission lines from these WR stars.
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 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 | 0.001 |
| 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".