Bibliographic record
Abstract
Context. Low metallicities, high gas-to-star mass ratios, and blue colors of most low surface brightness (LSB) galaxies imply that these systems may be younger than their high surface brightness counterparts.Aims. We look for observational signatures that can help to constrain the age of blue LSB galaxies.Methods. We use numerical hydrodynamic modelling to study the long-term (∼13 Gyr) dynamical and chemical evolution of blue LSB galaxies, adopting a sporadic scenario of star formation. Our model galaxy consists of a thin gas disk, a stellar disk with a developed spiral structure and a spherical dark matter halo. Our models utilize various rates of star formation and different shapes of the initial mass function (IMF). We complement hydrodynamic modelling with population synthesis modelling to produce the integrated colors and Hα equivalent widths.Results. We find that the mean oxygen abundances, colors, Hα equivalent widths, and the radial fluctuations in the oxygen abundance, when considered altogether, can be used to constrain the age of blue LSB galaxies if some independent knowledge of the IMF is available. Our modelling strongly suggests the existence of a minimum age for blue LSB galaxies. Model colors and mean oxygen abundances set a tentative minimum age at 1.5–3.0 Gyr, whereas model Hα equivalent widths suggest a larger value of the order of 5–6 Gyr. The latter value may decrease somewhat if blue LSB galaxies host IMFs with a truncated upper mass limit. We found no firm evidence that the age of blue LSB galaxies is significantly lower than 13 Gyr. Conclusions. The age of blue LSB galaxies may vary between 1.5–6 Gyr and 13 Gyr, depending on the physical conditions in the disk that control the form of the IMF and the rate of star formation. A failure to observationally detect large radial fluctuations in the oxygen abundance of the order of 0.5–1.0 dex, which, according to our modelling, are characteristic of (1–2)-Gyr-old galaxies, will argue in favour of the more evolved nature of blue LSB 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 | 0.000 | 0.000 |
| 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 | 0.000 |
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".