Constraints on the alignment of galaxies in galaxy clusters from ~14 000 spectroscopic members
Bibliographic record
Abstract
Torques acting on galaxies lead to physical alignments, but the resulting ellipticity correlations are difficult to predict. As they constitute a major contaminant for cosmic shear studies, it is important to constrain the intrinsic alignment signal observationally. We measured the alignments of satellite galaxies within 90 massive galaxy clusters in the redshift range 0.05 < z < 0.55 and quantified their impact on the cosmic shear signal. We combined a sample of 38 104 galaxies with spectroscopic redshifts with high-quality data from the Canada-France-Hawaii Telescope. We used phase-space information to select 14 576 cluster members, 14 250 of which have shape measurements and measured three different types of alignment: the radial alignment of satellite galaxies toward the brightest cluster galaxies (BCGs), the common orientations of satellite galaxies and BCGs, and the radial alignments of satellites with each other. Residual systematic effects are much smaller than the statistical uncertainties. We detect no galaxy alignment of any kind out to at least 3r 200 . The signal is consistent with zero for both blue and red galaxies, bright and faint ones, and also for subsamples of clusters based on redshift, dynamical mass, and dynamical state. These conclusions are unchanged if we expand the sample with bright cluster members from the red sequence. We augment our constraints with those from the literature to estimate the importance of the intrinsic alignments of satellites compared to those of central galaxies, for which the alignments are described by the linear alignment model. Comparison of the alignment signals to the expected uncertainties of current surveys such as the Kilo-Degree Survey suggests that the linear alignment model is an adequate treatment of intrinsic alignments, but it is not clear whether this will be the case for larger surveys.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 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 teacher head, 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".