Galaxy populations in the most distant SPT-SZ clusters
Bibliographic record
Abstract
We present the first results from a galaxy population study in the highest redshift galaxy clusters identified in the 2500 deg 2 South Pole Telescope Sunyaev Zel’dovich effect (SPT-SZ) survey, which is sensitive to M 500 ≳ 3 × 10 14 M ⊙ clusters from z ∼ 0.2 out to the highest redshifts where such massive structures exist. The cluster selection is to first order independent of galaxy properties, making the SPT-SZ sample particularly well suited for cluster galaxy population studies. We carried out a four-band imaging campaign with the Hubble and Spitzer Space Telescopes of the five z ≳ 1.4, S / N SZE > 5 clusters, that are among the rarest most massive clusters known at this redshift. All five clusters show clear overdensities of red galaxies whose colors agree with the initial cluster redshift estimates, although one (SPT-CLJ0607–4448) shows a galaxy concentration much less prominent than the others. The highest redshift cluster in this sample, SPT-CLJ0459–4947 at z ∼ 1.72, is the most distant M 500 > 10 14 M ⊙ cluster discovered thus far through its intracluster medium, and is one of only three known clusters in this mass range at z ≳ 1.7, regardless of selection. Based on U V J -like photometric classification of quiescent and star-forming galaxies, we find that the quiescent fraction in the cluster central regions ( r / r 500 < 0.7) is higher than in the field at the same redshift, with corresponding environmental quenching efficiencies typically in the range ∼0.5 − 0.8 for stellar masses log( M / M ⊙ ) > 10.85. We have explored the impact of emission from star formation on the selection of this sample, concluding that all five clusters studied here would still have been detected with S / N SZE > 5, even if they had the same quiescent fraction as measured in the field. Our results thus point towards an efficient suppression of star formation in the central regions of the most massive clusters, occurring already earlier than z ∼ 1.5.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".