<scp>gofher</scp> – which side is closer? Automatically disambiguating the tilt of disc galaxies by measuring differential reddening
Bibliographic record
Abstract
ABSTRACT As viewed from the Earth, most spiral galaxies are tilted – i.e. somewhere between edge-on and face-on – giving them an elliptical appearance. Although we can estimate the tilt by ‘de-projecting’ the ellipse back to a circle, the sign of the tilt remains ambiguous: we cannot easily determine which side of the tilted disc is closer to the Earth. In this paper, we introduce an automated method for determining which side of the disc is closer by measuring differential reddening. In particular, the side of the bulge that is behind the nearer side of the disc will suffer more dust-induced reddening than the side of the bulge that is not disc-obscured. While close manual inspection of the bulge by eye can sometimes reveal which side of the galaxy is more red, automating this process is fraught with pitfalls and potential selection effects, such as exactly how and where to split the galaxy in half. We present our method and test it against the recent ‘spin parity’ paper, which carefully determined the near side of over 500 galaxies. Our method achieves 95 per cent agreement with the confident human classifications, and 73 per cent agreement in the less confident cases.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.014 |
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".