Unveiling the nature of polar-ring galaxies from deep imaging
Bibliographic record
Abstract
General structural properties and low surface brightness tidal features hold important clues to the formation of galaxies. In this paper, we study a sample of polar-ring galaxies (PRGs) based on optical imaging from the Sloan Digital Sky Survey (SDSS) Stripe82 and other deep surveys. We investigate the deepest images of candidates for PRGs to date. We carry out photometric decomposition on the host galaxies and associated polar structures that allows us to derive the structural properties of both components. We are able to detect very faint tidal structures around most PRGs in our sample. For several galaxies, we can directly observe the formation of the polar ring due to merging, which is manifested in debris of the victim galaxy and an arc-like polar structure made up of its material. In a few cases, we can discern signs of tidal accretion. The results obtained indicate that the gravitational interaction and merging of galaxies are the most plausible mechanisms for the formation of polar-ring galaxies.
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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 | 0.001 |
| 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 | 0.001 |
| 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".