A GALEX/Spitzer survey of the Cl 0016+16 supercluster at z = 0.55: acceleration of the onset of star formation in satellite groups
Bibliographic record
Abstract
We present the results of a panoramic (15 Mpc scale) survey of the Cl 0016+16 (z= 0.55) supercluster using Spitzer Space Telescope Multiband Infrared Photometer (MIPS) 24-μm and Galaxy Evolution Explorer near-ultraviolet (2500 Å; NUV) imaging. The supercluster regions probed are characterized by several dense nodes connected by a pronounced intermediate-density filamentary structure. We have studied the mid-infrared and NUV properties of potential cluster members within a Δz= 0.1 photometric redshift slice compared to an identical blank field selection. We have two main findings: (i) the star formation rates of individual star-forming galaxies throughout the cluster are not significantly different from identically selected field galaxies, and (ii) the cluster harbours pockets of ‘accelerated’ activity where galaxies have an enhanced probability of undergoing star formation. This observation could be explained in a simple model of ‘pre-processing’ of galaxies during cluster infall: galaxies in satellite groups have an increased chance of having star formation triggered via gravitational tidal interactions compared to their counterparts in the field, but there is no environmental mechanism boosting the individual star formation rates of galaxies. We estimate a lower limit for the total star formation rate of galaxies in the supercluster as ∼850 M⊙ yr−1 (field corrected). If this rate is maintained over the typical infall time of a few Gyr, then the infall population could contribute ∼1–2 × 1012 M⊙ of stellar mass to the structure.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".