LoCuSS: THE SLOW QUENCHING OF STAR FORMATION IN CLUSTER GALAXIES AND THE NEED FOR PRE-PROCESSING
Bibliographic record
Abstract
We present a study of the spatial distribution and kinematics of star-forming galaxies in 30 massive clusters at 0.15 < z < 0.30, combining wide-field Spitzer 24 μ m and GALEX near-ultraviolet imaging with highly complete spectroscopy of cluster members. The fraction ( f SF ) of star-forming cluster galaxies rises steadily with cluster-centric radius, increasing fivefold by 2 r 200 , but remains well below field values even at 3 r 200 . This suppression of star formation at large radii cannot be reproduced by models in which star formation is quenched in infalling field galaxies only once they pass within r 200 of the cluster, but is consistent with some of them being first pre-processed within galaxy groups. Despite the increasing f SF -radius trend, the surface density of star-forming galaxies actually declines steadily with radius, falling ∼15 × from the core to 2 r 200 . This requires star formation to survive within recently accreted spirals for 2–3 Gyr to build up the apparent over-density of star-forming galaxies within clusters. The velocity dispersion profile of the star-forming galaxy population shows a sharp peak of 1.44 σ ν at 0.3 r 500 , and is 10%–35% higher than that of the inactive cluster members at all cluster-centric radii, while their velocity distribution shows a flat, top-hat profile within r 500 . All of these results are consistent with star-forming cluster galaxies being an infalling population, but one that must also survive ∼0.5–2 Gyr beyond passing within r 200 . By comparing the observed distribution of star-forming galaxies in the stacked caustic diagram with predictions from the Millennium simulation, we obtain a best-fit model in which star formation rates decline exponentially on quenching timescales of 1.73 ± 0.25 Gyr upon accretion into the cluster.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".