Viability of Novae as Sources of Galactic Lithium
Bibliographic record
Abstract
Abstract Of all the light elements, the evolution of lithium (Li) in the Milky Way is perhaps the most difficult to explain. Li is difficult to synthesize and is easily destroyed, making most stellar sites unsuitable for producing Li in sufficient quantities to account for the protosolar abundance. For decades, novae have been proposed as a potential explanation for this “Galactic Li problem,” and the recent detection of 7Be in the ejecta of multiple nova eruptions has breathed new life into this theory. In this work, we assess the viability of novae as dominant producers of Li in the Milky Way. We present the most comprehensive treatment of novae in a galactic chemical evolution code to date, testing theoretically and observationally derived nova Li yields by integrating metallicity-dependent nova ejecta profiles computed using the binary population synthesis code binary_c with the galactic chemical evolution code OMEGA+. We find that our galactic chemical evolution models which use observationally derived Li yields account for the protosolar Li abundance very well, while models relying on theoretical nova yields cannot reproduce the protosolar observation. A brief exploration of physical uncertainties including single-stellar yields, the metallicity resolution of our nova treatment, common-envelope physics, and nova accretion efficiencies indicates that this result is robust to physical assumptions. Scatter within the observationally derived Li yields in novae is identified as the primary source of uncertainty, motivating further observations of 7Be in nova ejecta.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".