60Fe in core-collapse supernovae and prospects for X-ray and gamma-ray detection in supernova remnants
Bibliographic record
Abstract
Abstract We investigate 60Fe in massive stars and core-collapse supernovae focussing on uncertainties that influence its production in 15, 20, and 25 M$\odot$ stars at solar metallicity. We find that the 60Fe yield is a monotonic increasing function of the uncertain 59Fe(n, γ)60Fe cross-section and that a factor of 10 reduction in the reaction rate results in a factor of 8–10 reduction in the 60Fe yield, while a factor of 10 increase in the rate increases the yield by a factor of 4–7. We find that none of the 189 simulations we have performed are consistent with a core-collapse supernova triggering the formation of the Solar system, and that only models using 59Fe(n, γ)60Fe cross-section that is less than or equal to that from NON-SMOKER can reproduce the observed 60Fe/26Al line flux ratio in the diffuse interstellar medium. We examine the prospects of detecting old core-collapse supernova remnants (SNRs) in the Milky Way from their gamma-ray emission from the decay of 60Fe, finding that the next generation of gamma-ray missions could be able to discover up to ∼100 such old SNRs as well as measure the 60Fe yields of a handful of known Galactic SNRs. We also predict the X-ray spectrum that is produced by atomic transitions in 60Co following its ionization by internal conversion and give theoretical X-ray line fluxes as a function of remnant age as well as the Doppler and fine-structure line broadening effects. The X-ray emission presents an interesting prospect for addressing the missing SNR problem with future X-ray missions.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".