Nova contributions to the chemical evolution of the Milky Way
Bibliographic record
Abstract
Context. The explosive burning that drives nova eruptions results in unique nucleosynthesis that heavily over-produces certain isotopes relative to the solar abundance. However, novae are often ignored when considering the chemical evolution of our Galaxy due to their low ejecta masses. Galactic chemical evolution studies including novae are rare and have previously relied upon simplified treatments for the behaviour of nova populations. Aims. In this work, we use previously computed synthetic nova populations and the galactic chemical evolution code OMEGA+ to assess the impact that novae have on the evolution of stable elemental and isotopic abundances. Methods. We combine populations of novae computed using the binary population synthesis code binary_c with the galactic chemical evolution code OMEGA+ and detailed, white dwarf mass-dependent nova yields to model the nucleosynthetic contributions of novae to the evolution of the Milky Way. We consider three different nova yield profiles, each corresponding to a different set of nova yield calculations. Results. We examine which nova sites contribute most to which isotopes. Despite novae from low-mass white dwarfs (WDs) dominating nova ejecta contributions, we find that novae occurring on massive WDs are still able to contribute significantly to many isotopes, particularly those with high mass numbers. We find that novae can produce up to 35% of the Galactic 13C and 15N mass by the time the model Galaxy reaches [Fe/H] = 0, and earlier in the evolution of the Galaxy (between [Fe/H] = −2 and −1) novae may have been the dominant source of 15N. Predictions for [13C/Fe], [15N/Fe], 12C/13C, and 14N/15N abundances ratios vary by up to 0.2 dex at [Fe/H] = 0 and by up to 0.7 dex in [15N/Fe] and 14N/15N between [Fe/H] = −2 and −1 (corresponding approximately to Galactic ages of 170 Myr and 1 Gyr in our model). The Galactic evolution of other stable isotopes (excluding Li) is not noticeably affected by including novae. For most isotopes, agreement is generally good between the three different yield profiles we consider. Isotopes where agreement is relatively poor include: 3He (especially at high MWD), 7Li, 18O, 18F, and the > 1.3 M⊙ regime of 29Si, 33S, 34S, 35Cl, and 36Ar.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".