VTSCat: The VERITAS Catalog of Gamma-Ray Observations
Bibliographic record
Abstract
VTSCat is the catalog of high-level data products from all publications of the [VERITAS collaboration(https://veritas.sao.arizona.edu/). The VTSCat data collection contains: - high-level data like spectral flux points, light curves, spectral fits in human - and machine-readable yaml and ecsv file format - tabled data like upper limits tables from dark matter searches or results on the extragalactic background in ecsv file format - sky maps (wherever available) in FITS file format This is a pre-release for testing and early publications. A forthcoming research note will provide more details on the catalog. Please check the README file and all documentation linked to the README. VTSCat supplements the HEASARC catalogue of VERITAS results (to be published). VTSCat is inspired and derived from [gamma-cat](https://github.com/gammapy/gamma-cat). If you are a previous VERITAS author and would like to be associated with this repository, please send an email to G. Maier. Access: - GitHub: https://github.com/VERITAS-Observatory/VERITAS-VTSCat References: - VERITAS: https://veritas.sao.arizona.edu/ - VER Dictionary of Nomenclature: https://cds.u-strasbg.fr/cgi-bin/Dic-Simbad?/17350620 - ICRC 2021 proceedings: https://arxiv.org/abs/2108.06424
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.151 | 0.161 |
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".