Planes of Satellites around Simulated Disk Galaxies. I. Finding High-quality Planar Configurations from Positional Information and Their Comparison to MW/M31 Data
Bibliographic record
Abstract
Abstract We address the “plane of satellites problem” by studying planar configurations around two disk galaxies with no late major mergers, formed in zoom-in hydro-simulations. Due to the current lack of good-quality kinematic data for M31 satellites, we use only positional information. So far, positional analyses of simulations are unable to find planes as thin and populated as the observed ones. We follow a novel systematic and detailed plane searching technique to study the properties and quality of planes of satellites, in both simulations or real data. In particular, (i) we extend the four-galaxy-normal density plot method (Pawlowski et al. 2013) in a way designed to efficiently identify high-quality planes (i.e., thin and populated) without imposing extra constraints on their properties, and (ii), we apply it for the first time to simulations. Using zoom-in simulations allows us to mimic Milky Way/M31-like systems regarding the number of satellites involved as well as galactic disk effects. In both simulations, we find satellite planar configurations that are compatible, along given time intervals, with all of the spatial characteristics of observed planes identified using the same methodology. During most of these periods, planes are approximately perpendicular to the galactic disk. However, the fraction of co-orbiting satellites within them is, in general, low, suggesting time-varying satellite membership. We conclude that high-quality positional planes of satellites could be not infrequent in ΛCDM-formed disk galaxies with a quiet assembly history. Detecting kinematically coherent, time-persistent planes demands considering the full six-dimensional phase-space information of satellites.
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| 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".