Bibliographic record
Abstract
Context. The Spitzer Survey for Stellar Structure in Galaxies (S4G) and its more recently approved extension will lead to a set of 3.6 and 4.5 μm images for 2829 galaxies, which can be used to study many different aspects of the structure and evolution of local galaxies. Aims. We have collected and re-reduced optical images of 1768 of the survey galaxies, aiming to make these available to the community as ready-to-use FITS files to be used in conjunction with the mid-IR images. Our sky-subtraction and mosaicking procedures were optimised for imaging large galaxies. We also produce false-colour images of some of these galaxies to be used for illustrative and public outreach purposes. Methods. We collected and re-processed images in five bands from the Sloan Digital Sky Survey for 1657 galaxies, which are publicly released with the publication of this paper. We observed, in only the g-band, an additional 111 S4G galaxies in the northern hemisphere with the 2.5 m Liverpool Telescope, so that optical imaging is released for 1768 galaxies, or for 62% of the S4G sample. We visually checked all images. We noted interactions and close companions in our optical data set and in the S4G sample, confirming them by determining the galaxies’ radial velocities and magnitudes in the NASA-IPAC Extragalactic Database. Results. We find that 17% of the S4G galaxies (21% of those brighter than 13.5 mag) have a close companion (within a radius of five times the diameter of the sample galaxy, a recession velocity within ± 200 km s-1 and not more than 3 mag fainter) and that around 5% of the bright part of the S4G sample show significant morphological evidence of an ongoing interaction. This confirms and further supports previous estimates of these fractions. Conclusions. The over 8000 science images described in this paper, the re-processed Sloan Digital Sky Survey ones, the new Liverpool Telescope images, the set of 29 false-colour pictures, and the catalogue of companion and interacting galaxies, are all publicly released for general use for scientific, illustrative, or public outreach purposes.
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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 | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.293 | 0.231 |
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".