Quantified H i morphology - III. Merger visibility times from H i in galaxy simulations
Bibliographic record
Abstract
Major mergers of disc galaxies are thought to be a substantial driver in galaxy evolution. To trace the fraction and rate of galaxy mergers over cosmic times, several observational techniques have been developed over the last decade, including parametrized morphological selection. We apply this morphological selection of mergers to 21 cm radio emission line (H i) column density images of spiral galaxies in nearby surveys. In this paper, we investigate how long a 1:1 merger is visible in H i from N-body simulations. We evaluate the merger visibility times for selection criteria based on four parameters: Concentration, Asymmetry, M20 and the Gini parameter of the second-order moment of the flux distribution (GM). Of three selection criteria used in the literature, one based on Concentration and M20 works well for the H i perspective with a merger time-scale of 0.4 Gyr. Of the three selection criteria defined in our previous paper, the GM performs well and cleanly selects mergers for 0.69 Gyr. The other two criteria (Asymmetry–M20 and Concentration–M20) select isolated discs as well, but perform best for face-on, gas-rich discs (Tmgr∼ 1 Gyr). The different visibility scales can be combined with the selected fractions of galaxies in any large H i survey to obtain merger rates in the nearby Universe. All-sky surveys such as the Widefield ASKAP L-band Legacy All-sky Blind surveY (WALLABY) with the Australian SKA Pathfinder (ASKAP) and the Medium Deep Survey with the APERture Tile In Focus (APERTIF) instrument on Westerbork are set to revolutionize our perspective on neutral hydrogen and will provide an accurate measure of the merger fraction and rate of the present epoch.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".