Overview of non-transient<i>γ</i>-ray binaries and prospects for the Cherenkov Telescope Array
Bibliographic record
Abstract
Aims.Despite recent progress in the field, there are still many open questions regardingγ-ray binaries. In this paper we provide an overview of non-transientγ-ray binaries and discuss how observations with the Cherenkov Telescope Array (CTA) will contribute to their study. Methods.We simulated the spectral behaviour of the non-transientγ-ray binaries using archival observations as a reference. With this we tested the CTA capability to measure the spectral parameters of the sources and detect variability on various timescales. Results.We review the known properties ofγ-ray binaries and the theoretical models that have been used to describe their spectral and timing characteristics. We show that the CTA is capable of studying these sources on timescales comparable to their characteristic variability timescales. For most of the binaries, the unprecedented sensitivity of the CTA will allow studying the spectral evolution on a timescale as short as 30 min. This will enable a direct comparison of the TeV and lower energy (radio to GeV) properties of these sources from simultaneous observations. We also review the source-specific questions that can be addressed with these high-accuracy CTA measurements.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".