MORPHOLOGICALLY IDENTIFIED MERGING GALAXIES IN THE SWIRE FIELDS
Bibliographic record
Abstract
We investigate the evolutionary and environmental effects on star formation efficiency for more than 400 merging galaxies. The ∼400 merging systems, with photometric redshifts smaller than 0.7, are obtained from a catalog of ∼15,000 morphologically identified merging galaxies derived from observations of the Canada–France–Hawaii Telescope. We also obtained the IR data of the merging galaxies from the Spitzer Wide-area InfraRed Extragalactic Survey (SWIRE). The redshift differences Δ z between the member galaxies of these merging pairs show a large distribution with 0 < Δ z < 0.4. We divide our merging pairs into two sub-samples with Δ z < 0.05 and >0.05 for further analyses. We find a statistically significant anti-correlation between the specific star formation rate (SSFR) and the separation of the merging galaxies for both subsamples. Our analyses also show that although most of the merging systems do have enhanced star formation activity, only very rare ones display extremely high SFRs. Additionally, the SSFR of the merging galaxies also decreases when the magnitude difference between two member galaxies becomes large. However, we find that for the merging pairs with large luminosity contrast, the fainter components show higher SSFR than the brighter ones. Finally, there is a higher fraction of gas-poor mergers in galaxy clusters, and the SSFR of gas-rich mergers is reduced in cluster environments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".