Cold gas and dust: Hunting spiral-like structures in early-type galaxies
Bibliographic record
Abstract
Context. Observations of neutral hydrogen (H I) and molecular gas show that 50% of all nearby early-type galaxies contain some cold gas. Molecular gas is always found in small gas discs in the central region of the galaxy, while neutral hydrogen is often distributed in a low-column density disc or ring typically extending well beyond the stellar body. Dust is frequently found in early-type galaxies as well. Aims. The goal of our study is to understand the link between dust and cold gas in nearby early-type galaxies as a function of H I content. Methods. We analyse deep optical g − r images obtained with the MegaCam camera at the Canada-France-Hawaii Telescope for a sample of 21 H I-rich and 41 H I-poor early-type galaxies. Results. We find that all H I-rich galaxies contain dust seen as absorption. Moreover, in 57% of these H I-rich galaxies, the dust is distributed in a large-scale spiral pattern. Although the dust detection rate is relatively high in the H I-poor galaxies (∼59%), most of these systems exhibit simpler dust morphologies without any evidence of spiral structures. We find that the H I-rich galaxies possess more complex dust morphology extending to almost two times larger radii than H I-poor objects. We measured the dust content of the galaxies from the optical colour excess and find that H I-rich galaxies contain six times more dust (in mass) than H I-poor ones. In order to maintain the dust structures in the galaxies, continuous gas accretion is needed, and the substantial H I gas reservoirs in the outer regions of early-type galaxies can satisfy this need for a long time. We find that there is a good correspondence between the observed masses of the gas and dust, and it is also clear that dust is present in regions further than 3 Reff. Conclusions. Our findings indicate an essential relation between the presence of cold gas and dust in early-type galaxies and offer a way to study the interstellar medium in more detail than what is possible with H I observations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".