COSMIC EVOLUTION OF STAR FORMATION ENHANCEMENT IN CLOSE MAJOR-MERGER GALAXY PAIRS SINCE<i>z</i>= 1
Bibliographic record
Abstract
The infrared (IR) emission of " M * galaxies" (10 10.4 ⩽ M star ⩽ 10 11.0 M ☉ ) in galaxy pairs, derived using data obtained in Herschel (PEP/HerMES) and Spitzer (S-COSMOS) surveys, is compared to that of single-disk galaxies in well-matched control samples to study the cosmic evolution of the star formation enhancement induced by galaxy–galaxy interaction. Both the mean IR spectral energy distribution and mean IR luminosity of star-forming galaxies (SFGs) in SFG+SFG (S+S) pairs in the redshift bin of 0.6 < z < 1 are consistent with no star formation enhancement. SFGs in S+S pairs in a lower redshift bin of 0.2 < z < 0.6 show marginal evidence for a weak star formation enhancement. Together with the significant and strong sSFR enhancement shown by SFGs in a local sample of S+S pairs (obtained using previously published Spitzer observations), our results reveal a trend for the star formation enhancement in S+S pairs to decrease with increasing redshift. Between z = 0 and z = 1, this decline of interaction-induced star formation enhancement occurs in parallel with the dramatic increase (by a factor of ∼10) of the sSFR of single SFGs, both of which can be explained by the higher gas fraction in higher- z disks. SFGs in mixed pairs (S+E pairs) do not show any significant star formation enhancement at any redshift. The difference between SFGs in S+S pairs and in S+E pairs suggests a modulation of the sSFR by the intergalactic medium (IGM) in the dark matter halos hosting these pairs.
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 | 0.000 | 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 | 0.000 |
| 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".