Quantified H i morphology - I. Multi-wavelength analysis of the THINGS galaxies
Bibliographic record
Abstract
Galaxy evolution is driven to a large extent by interactions and mergers with other galaxies and the gas in galaxies is extremely sensitive to the interactions. One method to measure such interactions uses the quantified morphology of galaxy images. Well-established parameters are Concentration, Asymmetry, Smoothness, Gini and M20 of a galaxy image. Thus far, the application of this technique has mostly been restricted to rest-frame ultraviolet and optical images. However, with the new radio observatories being commissioned [South African Karoo Array Telescope (MeerKAT), Australian SKA Pathfinder (ASKAP), Extended Very Large Array (EVLA), Westerbork Synthesis Radio Telescope/APERture Tile In Focus instrument (WSRT/APERTIF) and ultimately the Square Kilometer Array (SKA)], a new window on the neutral atomic hydrogen gas (H i) morphology of large numbers of galaxies will open up. The quantified morphology of gas discs of spirals can be an alternative indicator of the level and frequency of interaction. The H i in galaxies is typically spatially more extended and more sensitive to low-mass or weak interactions. In this paper, we explore six morphological parameters calculated over the extent of the stellar (optical) disc and the extent of the gas disc for a range of wavelengths spanning ultraviolet (UV), optical, near- and far-infrared and 21 cm (H i) of 28 galaxies from The H i Nearby Galaxy Survey (THINGS). Although the THINGS sample is small and contains only a single ongoing interaction, it spans both non-interacting and post-interacting galaxies with a wealth of multi-wavelength data. We find that the choice of area for the computation of the morphological parameters is less of an issue than the wavelength at which they are measured. The signal of interaction is as good in the H i as at any of the other wavelengths at which morphology has been used to trace the interaction rate to date, mostly star formation dominated ones (near- and far-ultraviolet). The Asymmetry and M20 parameters are the ones that show the most promise as tracers of interaction in 21 cm line observations.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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".