Major merger fraction along the massive galaxy-quenching channel at 0.2 &lt; <i>z</i> &lt; 0.7
Bibliographic record
Abstract
Abstract We study the major merger fraction along the massive galaxy-quenching channel (traced with rest-frame $\mathrm{NUV}-r$ color) at z = 0.2–0.7, aiming to examine the cosmic web detachment (CWD) scenario of galaxy quenching. In this scenario, the major merger fraction is expected to be high in green-valley galaxies as compared with those in star-forming and quiescent galaxies of similar stellar mass. We used photometry in the E-COSMOS field to select 1491 (2334) massive ($M_\ast \\gt 10^{9.5}\, M_{\odot }$) galaxies with $m_i\\lt 22$ mag ($m_z\\lt 22$ mag) at z = 0.2–0.4 (z = 0.4–0.7) in the rest-frame color range of $0.8\\lt r-K_{\rm s}\\lt 1.3$. We define a major galaxy–galaxy merger as a galaxy pair of comparable angular size and luminosity with tidal tails or bridges, and we identified such major mergers through visual inspection of Subaru HSC-SSP PDR 2 i- and z-band images. We classify 92 (123) galaxies as major merger galaxies at z = 0.2–0.4 (z = 0.4–0.7). The resulting major merger fraction is 5%–6% and this fraction does not change with galaxy color along the massive galaxy-quenching channel. The result is not consistent with the expectation based of the CWD scenario as the dominant mechanism of massive galaxy quenching. However, there are some caveats such as (i) the mergers that cause quenching may lose their visible merger signatures rapidly before they enter the green valley, (ii) our method may not trace the cosmic web sufficiently well, and (iii) because of our mass limit, most of the galaxies in our sample may have already experienced CWD events at higher redshifts than those studied here. Further studies with deeper data are desirable in the future.
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) | 0.002 | 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".