Bibliographic record
Abstract
Abstract The bulge of M31 is of interest regarding the nature of galactic bulges and how their structure relates to bulge formation mechanisms and their subsequent evolution. With the UVIT instrument on AstroSat, we have observed the bulge of M31 in five far-ultraviolet (FUV) and near-ultraviolet (NUV) filters at 1″ spatial resolution. Models for the luminosity distribution of the bulge are constructed using the UVIT data and the galaxy image fitting algorithm GALFIT. We fit the bulge without the nuclear region with a Sérsic function for the five images and find Sérsic indices (≃2.1–2.5) similar to previous studies but smaller R e values (≃0.5–0.6 kpc). When fitting the images including the nuclear region, a multicomponent model is used. We use an eight-component model for the FUV 148 nm image, which has the highest sensitivity. The other images (169–279 nm) are fit with four-component models. The dust lanes in the bulge region are recovered in the residual images, which have subtraction of the bright bulge light using the multicomponent models. The dust lanes show that M31's nuclear spiral is visible in absorption at NUV and FUV wavelengths. The bulge images show boxy contours in all five UVIT wave bands, which is confirmed by fitting using GALFIT. The Sérsic indices of ∼2.1–2.5 are intermediate between the expected values for a classical bulge and for a pseudobulge. The boxiness of the bulge provides further evidence that M31's bulge has contributions from a classical bulge and a pseudobulge.
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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 | 0.001 | 0.000 |
| 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".