The Atacama Cosmology Telescope: the stellar content of galaxy clusters selected using the Sunyaev–Zel'dovich effect
Bibliographic record
Abstract
We present a first measurement of the stellar mass component of galaxy clusters selected via the Sunyaev–Zel'dovich (SZ) effect, using 3.6 and 4.5 μm photometry from the Spitzer Space Telescope. Our sample consists of 14 clusters detected by the Atacama Cosmology Telescope (ACT), which span the redshift range 0.27 < z < 1.07 (median z = 0.50) and have dynamical mass measurements, accurate to about 30 per cent, with median M500 = 6.9 × 1014 M⊙. We measure the 3.6 and 4.5 μm galaxy luminosity functions, finding the characteristic magnitude (m*) and faint-end slope (α) to be similar to those for infrared-selected cluster samples. We perform the first measurements of the scaling of SZ observables (Y500 and y0) with both brightest cluster galaxy (BCG) stellar mass and total cluster stellar mass (|$M_{500}^{\rm star}$|). We find a significant correlation between BCG stellar mass and Y500 (|$E(z)^{-2/3}\,D_A^2\,Y_{500} \propto M_*^{1.2 \pm 0.6}$|), although we are not able to obtain a strong constraint on the slope of the relation due to the small sample size. Additionally, we obtain |$E(z)^{-2/3}\,D_A^2\,Y_{500} \propto M_{500}^{\rm star}\,^{1.0 \pm 0.6}$| for the scaling with total stellar mass. The mass fraction in stars spans the range 0.006–0.034, with the second ranked cluster in terms of dynamical mass (ACT-CL J0237−4939) having an unusually low total stellar mass and the lowest stellar mass fraction. For the five clusters with gas mass measurements available in the literature, we see no evidence for a shortfall of baryons relative to the cosmic mean value.
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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 | 0.001 | 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) | 0.002 | 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".