<i>SPITZER</i>OBSERVATIONS OF PASSIVE AND STAR-FORMING EARLY-TYPE GALAXIES: AN INFRARED COLOR–COLOR SEQUENCE
Bibliographic record
Abstract
We describe the infrared properties of a large sample of early-type galaxies, comparing data from the Spitzer archive with Ks -band emission from the Two Micron All Sky Survey. While most representations of this data result in correlations with large scatter, we find a remarkably tight relation among colors formed by ratios of luminosities in Spitzer -Multiband Imaging Photometer bands (24, 70, and 160 μm) and the Ks band. Remarkably, this correlation among E and S0 galaxies follows that of nearby normal galaxies of all morphological types. In particular, the tight infrared color–color correlation for S0 galaxies alone follows that of the entire Hubble sequence of normal galaxies, roughly in order of galaxy type from ellipticals to spirals to irregulars. The specific star formation rate (SFR) of S0 galaxies estimated from the 24 μm luminosity increases with decreasing K -band luminosity (or stellar mass) from essentially zero, as with most massive ellipticals, to rates typical of irregular galaxies. Moreover, the luminosities of the many infrared-luminous S0 galaxies can significantly exceed those of the most luminous (presumably post-merger) E galaxies. SFRs in the most infrared-luminous S0 galaxies approach 1–10 solar masses per year. Consistently, with this picture we find that while most early-type galaxies populate an infrared red sequence, about 24% of the objects (mostly S0s) are in an infrared blue cloud together with late-type galaxies. For those early-type galaxies also observed at radio frequencies, we find that the far-infrared luminosities correlate with the mass of neutral and molecular hydrogen, but the scatter is large. This scatter suggests that the star formation may be intermittent or that similar S0 galaxies with cold gaseous disks of nearly equal mass can have varying radial column density distributions that alter the local and global SFRs.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".