Revealing the properties of void galaxies and their assembly using the <scp>eagle</scp> simulation
Bibliographic record
Abstract
ABSTRACT We explore the properties of central galaxies living in voids using the eagle cosmological hydrodynamic simulations. Based on the minimum void-centric distance, we define four galaxy samples: inner void, outer void, wall, and skeleton. We find that inner void galaxies with host halo masses $\lt 10^{12}\,\rm M_{\odot }$ have lower stellar mass and stellar mass fractions than those in denser environments, and the fraction of galaxies with star formation (SF) activity and atomic hydrogen (H i) gas decreases with increasing void-centric distance, in agreement with observations. To mitigate the influence of stellar (halo) mass, we compare inner void galaxies to subsamples of fixed stellar (halo) mass. Compared to denser environments, inner void galaxies with $M_{*}= 10^{[9.0-9.5]}\,\rm M_{\odot }$ have comparable SF activity and H i gas fractions, but the lowest quenched galaxy fraction. Inner void galaxies with $M_{*}= 10^{[9.5-10.5]}\,\rm M_{\odot }$ have the lowest H i gas fraction, the highest quenched fraction and the lowest gas metallicities. On the other hand, inner void galaxies with $M_{*}\gt 10^{10.5}\,\rm M_{\odot }$ have comparable SF activity and H i gas fractions to their analogues in denser environments. They retain the highest metallicity gas that might be linked to physical processes that act with lower efficiency in underdense regions such as AGN (active galaxy nucleus) feedback. Furthermore, inner void galaxies have the lowest fraction of positive gas-phase metallicity gradients, which are typically associated with external processes or feedback events, suggesting they have more quiet merger histories than galaxies in denser environments. Our findings shed light on how galaxies are influenced by their large-scale environment.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".