Far-ultraviolet to Far-infrared Spectral Energy Distribution Modeling of the Star Formation History across M31
Bibliographic record
Abstract
Abstract Our neighboring galaxy M31 has been recently surveyed at the far- and near-ultraviolet (FUV and NUV) with the UVIT telescope on AstroSat, which provides unprecedented sensitivity to young stellar populations. Here the UVIT data are supplemented with optical data, near-infrared (IR) data (Spitzer), and mid- and far-IR data (Herschel). The observations are processed to obtain the spectral energy distributions (SEDs) for 73 regions covering M31. The SEDs are modeled using the Cigale SED fitting code with old and young stellar populations. The old stellar population has an age of 12 Gyr across M31 but has longer formation times at further distances from the center. Significant dependences on the position of dust extinction, dust emission, and young stellar population properties are found. Across M31, there are regions with a low-age (≲100 Myr) young population and regions with an intermediate-age (∼1 Gyr) young population. The mass in the young population has declined by a factor of ∼10 for ages 800–100 Myr ago but has increased again for ages ≲100 Myr. This indicates that cold gas available for star formation has been changing over the past Gyr, whether it is caused by a changing merger rate, changing gas infall, or changes in the gas reservoir in M31. We find that the dust luminosity, based on far-IR observations, is driven by the youngest stars, which are primarily measured in the FUV and NUV bands.
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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 | 0.001 | 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) | 0.001 | 0.001 |
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".