Gamma-ray binaries : a bridge between Be stars and high energy\n astrophysics
Bibliographic record
Abstract
Advances in X-ray and gamma-ray astronomy have opened a new window on our\nuniverse and revealed a wide variety of binaries composed of a compact object\nand a Be star. In Be X-ray binaries, a neutron star accretes the Be disk and\ntruncates it through tidal interactions. Such systems have important X-ray\noutbursts, some related to the disk structure. In other systems, strong gamma\nray emission is observed. In gamma-ray binaries, the neutron star is not\naccreting but driving a highly relativistic wind. The wind collision region\npresents similarities to colliding wind binaries composed of massive stars. The\nhigh energy emission is coming from particles being accelerated at the\nrelativistic shock. I will review the physics of X-ray and gamma-ray binaries,\nfocusing particularly on the recent developments on gamma-ray binaries. I will\ndescribe physical mechanisms such as relativistic hydrodynamics, tidal forces\nand non thermal emission. I will highlight how high energy astrophysics can\nshed a new light on Be star physics and vice-versa. A video of the talk can be\nfound at\nhttp://activebstars.iag.usp.br/index.php/talk-conference-recordings/bestars-2014-in-london-ontario/session-6/video/astrid-lamberts-interacting-binaries-be-stars-and-high-energy-astrophysics.\n
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.033 | 0.011 |
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".