VVV DR1: The first data release of the Milky Way bulge and southern plane from the near-infrared ESO public survey VISTA variables in the Vía Láctea
Bibliographic record
Abstract
Context. The ESO public survey VISTA variables in the Vía Láctea (VVV) started in 2010. VVV targets 562 sq. deg in the Galactic bulge and an adjacent plane region and is expected to run for about five years.Aims. We describe the progress of the survey observations in the first observing season, the observing strategy, and quality of the data obtained. Methods. The observations are carried out on the 4-m VISTA telescope in the ZYJHKs filters. In addition to the multi-band imaging the variability monitoring campaign in the Ks filter has started. Data reduction is carried out using the pipeline at the Cambridge Astronomical Survey Unit. The photometric and astrometric calibration is performed via the numerous 2MASS sources observed in each pointing.Results. The first data release contains the aperture photometry and astrometric catalogues for 348 individual pointings in the ZYJHKs filters taken in the 2010 observing season. The typical image quality is ~ . The stringent photometric and image quality requirements of the survey are satisfied in 100% of the JHKs images in the disk area and 90% of the JHKs images in the bulge area. The completeness in the Z and Y images is 84% in the disk, and 40% in the bulge. The first season catalogues contain 1.28 × 108 stellar sources in the bulge and 1.68 × 108 in the disk area detected in at least one of the photometric bands. The combined, multi-band catalogues contain more than 1.63 × 108 stellar sources. About 10% of these are double detections because of overlapping adjacent pointings. These overlapping multiple detections are used to characterise the quality of the data. The images in the JHKs bands extend typically ~4 mag deeper than 2MASS. The magnitude limit and photometric quality depend strongly on crowding in the inner Galactic regions. The astrometry for Ks = 15−18 mag has rms ~35−175 mas. Conclusions. The VVV Survey data products offer a unique dataset to map the stellar populations in the Galactic bulge and the adjacent plane and provide an exciting new tool for the study of the structure, content, and star-formation history of our Galaxy, as well as for investigations of the newly discovered star clusters, star-forming regions in the disk, high proper motion stars, asteroids, planetary nebulae, and other interesting objects.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.019 |
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".