Star formation efficiency across large-scale galactic environments
Bibliographic record
Abstract
ABSTRACT Environmental effects on the formation and evolution of galaxies have been one of the leading questions in galaxy studies during the past few decades. In this work, we investigate the relationship between the star formation activity of galaxies and their environmental matter density using the cosmological hydrodynamic simulation Simba. The galactic star formation activity indicators that we explore include the star formation efficiency (SFE), specific star formation rate (sSFR), and molecular hydrogen mass fraction ($f^{*}_{\mathrm{H_2}}$), and the environment is considered as the large-scale environmental matter density, calculated based on the stellar mass of nearby galaxies on a 1 h−1 Mpc grid using the cloud in cell method. Our sample includes galaxies with $9\lt \log \frac{M_*}{{\rm M}_{\odot }}$ at 0 < z < 4, divided into three stellar mass bins to disentangle the effects of stellar mass and environment on the star formation activity of galaxies. For low- to intermediate-mass galaxies at low redshifts (z < 1.5), we find that the star formation efficiency of those in high-density regions are ∼0.3 dex lower than those in low-density regions. However, there is no significant environmental dependence of the star formation efficiency for massive galaxies over all our redshift range, and low- to intermediate-mass galaxies at high redshifts (z > 1.5). We present a scaling relation for the depletion time of cold molecular hydrogen (tdepl = 1/SFE) as a function of galaxy parameters including environmental density. Our findings provide a framework for quantifying the environmental effects on the star formation activities of galaxies as a function of stellar mass and redshift. The most significant environmental dependence is seen at later cosmic times (z < 1.5) and towards lower stellar masses ($9\lt \log \frac{M_*}{{\rm M}_{\odot }}\lt 10$). Future large galaxy surveys can use this framework to look for the environmental dependence of the star formation activity and examine our predictions.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".