Examining partial ergodicity as a predictor of star formation departures from the galactic main sequence in isolated galaxies
Bibliographic record
Abstract
Lacking the ability to follow individual galaxies on cosmological timescales, our understanding of individual galaxy evolution is broadly inferred from population trends and behaviours. In its most prohibitive form, this approach assumes that galactic star formation properties exhibit ergodicity, so that individual galaxy evolution can be statistically inferred via ensemble behaviours. The validity of this assumption is tested through the use of observationally motivated simulations of isolated galaxies. The suite of simulated galaxies is statistically constructed to match observed galaxy properties by using kernel density estimation to create structural parameter distributions, augmented by theoretical relationships where necessary. We also test the impact of different physical processes, such as stellar winds or the presence of halo substructure on the star formation behaviour. We consider the subtleties involved in constraining ergodic properties, such as the distinction between stationarity imposed by stellar wind feedback and truly ergodic behaviour. However, without sufficient variability in star formation properties, individual galaxies are unable to explore the full parameter space. While, as expected, full ergodicity appears to be ruled out, we find reasonable evidence for partial ergodicity, where averaging over mass-selected subsets of galaxies more broadly resembles time averages, where the average largest deviation across physical scenarios is 0.20 dex. As far as we are aware, this the first time partial ergodicity has been considered in an astronomical context, and provides a promising statistical concept. Despite morphological changes introduced by close encounters with dark matter substructure, subhaloes are not found to significantly increase deviations from ergodic assumptions.
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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 | 0.001 | 0.008 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".