FAST-ASKAP Synergy: Quantifying Coexistent Tidal and Ram Pressure Strippings in the NGC 4636 Group
Bibliographic record
Abstract
Abstract Combining new H i data from a synergetic survey of Australian Square Kilometre Array Pathfinder (ASKAP) Widefield ASKAP L -band Legacy All-sky Blind surveY and Five-hundred-meter Aperture Spherical radio Telescope with the Arecibo Legacy Fast ALFA data, we study the effect of ram pressure and tidal interactions in the NGC 4636 group. We develop two parameters to quantify and disentangle these two effects on gas stripping in H i -bearing galaxies: the strength of external forces at the optical-disk edge, and the outside-in extents of H i -disk stripping. We find that gas stripping is widespread in this group, affecting 80% of H i -detected nonmerging galaxies, and that 41% are experiencing both types of stripping. Among the galaxies experiencing both effects, the two types of strengths are independent, while two H i -stripping extents moderately anticorrelate with each other. Both strengths are correlated with H i -disk shrinkage. The tidal strength is related to a rather uniform reddening of low-mass galaxies ( M * < 10 9 M ☉ ) when tidal stripping is the dominating effect. In contrast, ram pressure is not clearly linked to the color-changing patterns of galaxies in the group. Combining these two stripping extents, we estimate the total stripping extent, and put forward an empirical model that can describe the decrease of H i richness as galaxies fall toward the group center. The stripping timescale we derived decreases with distance to the center, from ∼1 Gyr beyond R 200 to ≲10 Myr near the center. Gas depletion happens ∼3 Gyr since crossing 2 R 200 for H i -rich galaxies, but much quicker for H i -poor ones. Our results quantify in a physically motivated way the details and processes of environmental-effects-driven galaxy evolution, and might assist in analyzing hydrodynamic simulations in an observational way.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".