The anisotropic distribution of satellite galaxies
Bibliographic record
Abstract
We identify satellites of isolated galaxies in the Sloan Digital Sky Survey and examine their angular distribution. Using mock catalogues generated from cosmological N-body simulations, we demonstrate that the selection criteria used to select isolated galaxies and their satellites in large galaxy redshift surveys must be very strict in order to correctly identify systems in which the primary galaxy dominates its environment. We demonstrate that the criteria used in many previous studies instead select predominantly group members. We refine a set of selection criteria for which the group contamination is estimated to be less than 7 per cent and present a catalogue of the resulting sample. The angular distribution of satellites about their host is biased towards the major axes for spheroidal galaxies and probably also for red disc galaxies (the ‘intermediate’ class of Bailin & Harris), but is isotropic for blue disc galaxies, i.e. it is the colour of the host that determines the distribution of its satellites rather than its morphology. The similar anisotropy measured in this study to studies that were dominated by groups implies that group-specific processes are not responsible for the angular distribution. Satellites that are most likely to have been recently accreted, late-type galaxies at large projected radii, show a tendency to lie along the same axis as the surrounding large-scale structure. The orientations of isolated early- and intermediate-type galaxies also align with the surrounding large-scale structures. We discuss the origin of the anisotropic satellite distribution and consider the implications of our results, critically assessing the respective roles played by the orientation of the visible galaxy within its dark matter halo, anisotropic accretion of satellites from the larger scale environment, and the biased nature of satellites as tracers of the underlying dark matter subhalo population.
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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 | 0.000 | 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".