CALIFA, the Calar Alto Legacy Integral Field Area survey
Bibliographic record
Abstract
We present the first public data release (DR1) of the Calar Alto Legacy Integral Field Area (CALIFA) survey. It consists of science-grade optical datacubes for the first 100 of eventually 600 nearby (0.005 < z < 0.03) galaxies, obtained with the integral-field spectrograph PMAS/PPak mounted on the 3.5 m telescope at the Calar Alto observatory. The galaxies in DR1 already cover a wide range of properties in color–magnitude space, morphological type, stellar mass, and gas ionization conditions. This offers the potential to tackle a variety of open questions in galaxy evolution using spatially resolved spectroscopy. Two different spectral setups are available for each galaxy, (i) a low-resolution V500 setup covering the nominal wavelength range 3745–7500 Å with a spectral resolution of 6.0 Å (FWHM), and (ii) a medium-resolution V1200 setup covering the nominal wavelength range 3650–4840 Å with a spectral resolution of 2.3 Å (FWHM). We present the characteristics and data structure of the CALIFA datasets that should be taken into account for scientific exploitation of the data, in particular the effects of vignetting, bad pixels and spatially correlated noise. The data quality test for all 100 galaxies showed that we reach a median limiting continuum sensitivity of 1.0 × 10-18 erg s-1 cm-2 Å-1 arcsec-2 at 5635 Å and 2.2 × 10-18 erg s-1 cm-2 Å-1 arcsec-2 at 4500 Å for the V500 and V1200 setup respectively, which corresponds to limiting r and g band surface brightnesses of 23.6 mag arcsec-2 and 23.4 mag arcsec-2, or an unresolved emission-line flux detection limit of roughly 1 × 10-17 erg s-1 cm-2 arcsec-2 and 0.6 × 10-17 erg s-1 cm-2 arcsec-2, respectively. The median spatial resolution is 37, and the absolute spectrophotometric calibration is better than 15% (1σ). We also describe the available interfaces and tools that allow easy access to this first publicCALIFA data at http://califa.caha.es/DR1.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.008 |
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".