The <i>Gaia</i> spectrophotometric standard stars survey – IV. Results of the absolute photometry campaign
Bibliographic record
Abstract
ABSTRACT We present Johnson–Kron–Cousins BVRI photometry of 228 candidate spectrophotometric standard stars for the external (absolute) flux calibration of Gaia data. The data were gathered as part of a 10-yr observing campaign with the goal of building the external grid of flux standards for Gaia and we obtained absolute photometry, relative photometry for constancy monitoring, and spectrophotometry. Preliminary releases of the flux tables were used to calibrate the first two Gaia releases. This paper focuses on the imaging frames observed in good sky conditions (about 9100). The photometry will be used to validate the ground-based flux tables of the Gaia spectrophotometric standard stars and to correct the spectra obtained in non-perfectly photometric observing conditions for small zero-point variations. The absolute photometry presented here is tied to the Landolt standard stars system to ≃1 per cent or better, depending on the photometric band. Extensive comparisons with various literature sources show an overall ≃1 per cent agreement, which appears to be the current limit in the accuracy of flux calibrations across various samples and techniques in the literature. The Gaia photometric precision is presently of the order of 0.1 per cent or better, thus various ideas for the improvement of photometric calibration accuracy are discussed.
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 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".