Effect of the local and large-scale environment on the star formation histories of galaxies
Bibliographic record
Abstract
Context. The specific environment of galaxies may play a key role in their evolution. Large extragalactic surveys make it possible to study galaxies not only within their local environment, but also within the large-scale structure of the Universe. Aims. We aim to investigate how the local environment influences the star formation history (SFH) of galaxies across a range of large-scale environments. Methods. We categorised a sample of 9384 galaxies into the three primary large-scale structures (voids, walls and filaments, and clusters). We further classified them based on their local environment (as either ‘singlets’ or group members) through a search of companion galaxies within sky-projected distances of Δrp < 0.45 Mpc and velocity differences of Δv < 160 km s−1. Subsequently, we explored these subsamples using SFH data from previous works. Throughout this study, we divided galaxies into long-timescale SFH galaxies (LT-SFH), which assemble their mass steadily along cosmic time, and short-timescale SFH galaxies (ST-SFH), which form their stars early on. We then compared their characteristic mass assembly look-back times. Results. The distributions of mass assembly look-back times in ST-SFH galaxies are statistically different for singlets and groups. These differences are only found in LT-SFH galaxies when studying these distributions in stellar mass bins. Our results indicate that the large-scale environment is related to a delay in mass assembly of up to ∼2 Gyr, while this delay is < 1 Gyr in the case of local environment. The effects of both types of environment are more significant in less massive galaxies and in LT-SFHs. Conclusions. Our results are consistent with galaxies in groups assembling their stellar mass earlier than in singlets, especially in voids and lower mass galaxies. Local environment plays a relevant role in stellar mass assembly times, although we find that large-scale structures also cause a delay in mass assembly, and all the more so in the case of cluster galaxies.
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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 | 0.004 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".