Disentangling satellite galaxy populations using orbit tracking in simulations
Bibliographic record
Abstract
Physical processes regulating star formation in satellite galaxies represent an area of ongoing research, but the projected nature of observed coordinates makes separating different populations of satellites (with different processes at work) difficult. The orbital history of a satellite galaxy leads to its present-day phase space coordinates; we can also work backwards and use these coordinates to statistically infer information about the orbital history. We use merger trees from the MultiDark Run 1 N-body simulation to compile a catalogue of the orbits of satellite haloes in cluster environments. We parametrize the orbital history by the time since crossing within 2.5 rvir of the cluster centre and use our catalogue to estimate the probability density over a range of this parameter given a set of present-day projected (i.e. observable) phase space coordinates. We show that different populations of satellite haloes, e.g. infalling, backsplash and virialized, occupy distinct regions of phase space and semidistinct regions of projected phase space. This will allow us to probabilistically determine the time since infall of a large sample of observed satellite galaxies, and ultimately to study the effect of orbital history on star formation history (the topic of a future paper). We test the accuracy of our method and find that we can reliably recover this time within ±2.58 Gyr in 68 per cent of cases by using all available phase space coordinate information, compared to ±2.64 Gyr using only position coordinates and ±3.10 Gyr guessing ‘blindly’, i.e. using no coordinate information, but with knowledge of the overall distribution of infall times. In some regions of phase space, the accuracy of the infall time estimate improves to ±1.85 Gyr. Although we focus on time since infall, our method is easily generalizable to other orbital parameters (e.g. pericentric distance and time).
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".