Winds in collision: high energy particles in massive binary systems
Bibliographic record
Abstract
High-resolution radio observations have revealed that non-thermal radio emission in Wolf-Rayet (WR) stars arises where the stellar wind of the WR star collides with that of a binary companion.These colliding-wind binary (CWB) systems offer an important laboratory for investigating the underlying physics of particle acceleration.Hydrodynamic models of the binary stellar winds and the wind-collision region (WCR) that account for the evolution of the electron energy spectrum, largely due to inverse Compton cooling, are now available.Radiometry and imaging obtained with the VLA, MERLIN, EVN and VLBA provide essential constraints to these models.Models of the radio emission from WR146 and WR147 are shown, though these very wide systems do not have defined orbits and hence lack a number of important model parameters.Multi-epoch VLBI imaging of the archetype WR+O star binary WR140 through a part of its 7.9-year orbit has been used to define the orbit inclination, distance and the luminosity of the companion star to enable the best constraints for any radio emitting CWB system.Models of the spatial distribution of relativistic electrons and ions, and the magnetic energy density are used to model the radio emission, and also to predict the high energy emission at X-ray and γ-ray energies.It is clear that high-energy facilities e.g.GLAST and VERITAS, will be important for constraining particle acceleration parameters such as the spectral index of the energy spectrum and the acceleration efficiency of both ions and electrons, and in turn, identify unique models for the radio spectra.This will be especially important in future attempts to model the spectra of WR140 throughout its complete orbit.A WCR origin for the synchrotron emission in O-type stars, the progenitors of WR stars, is illustrated by observations of Cyg OB2 #9.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".