Detection of anisotropic satellite quenching in galaxy clusters up to <i>z</i> ∼ 1
Bibliographic record
Abstract
ABSTRACT Satellite galaxies in the cluster environment are more likely to be quenched than galaxies in the general field. Recently, it has been reported that satellite galaxy quenching depends on the orientation relative to their central galaxies: satellites along the major axis of centrals are more likely to be quenched than those along the minor axis. In this paper, we report a detection of such anisotropic quenching up to z ∼ 1 based on a large optically selected cluster catalogue constructed from the Hyper Suprime-Cam Subaru Strategic Program. We calculate the quiescent satellite galaxy fraction as a function of orientation angle measured from the major axis of central galaxies and find that the quiescent fractions at 0.25 < z < 1 are reasonably fitted by sinusoidal functions with amplitudes of a few per cent. Anisotropy is clearer in inner regions ( r200m). We also confirm that the observed anisotropy cannot be explained by differences in local galaxy density or stellar mass distribution along the two axes. Quiescent fraction excesses between the two axes suggest that the quenching efficiency contributing to the anisotropy is almost independent of stellar mass, at least down to our stellar mass limit of $M_{*}=1\times 10^{10}\, {\rm M}_{\odot }$. Finally, we argue that the physical origins of the observed anisotropy should have shorter quenching time-scales than $\sim 1\, \mathrm{Gyr}$, like ram-pressure stripping, because, for anisotropic quenching to be observed, satellites must be quenched before their initial orientation angles are significantly changed.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".