LIMITS ON THE NUMBER OF GALACTIC YOUNG SUPERNOVA REMNANTS EMITTING IN THE DECAY LINES OF<sup>44</sup>Ti
Bibliographic record
Abstract
We revise the assumptions of the parameters involved in predicting the number of supernova remnants detectable in the nuclear lines of the decay chain of 44 Ti. Specifically, we consider the distribution of the supernova progenitors, the supernova rate in the Galaxy, the ratios of supernova types, the Galactic production of 44 Ti, and the 44 Ti yield from supernovae of different types to derive credible bounds on the expected number of detectable remnants. We find that, within 1σ uncertainty, the Galaxy should contain an average of remnants detectable to a survey with a 44 Ti decay line flux limit of 10 −5 photons cm −2 s −1 , with a probability of detecting a single remnant of , and an expected number of detections between two and nine remnants, making the single detection of Cas A unlikely but consistent with our models. Our results show that the probability of detecting the brightest 44 Ti flux source at the high absolute Galactic longitude of Cas A or above is ∼10%. Using the detected flux of Cas A, we attempt to constrain the Galactic supernova rate and Galactic production of 44 Ti, but find the detection to be only weakly informative. We conclude that even future surveys having 200 times more sensitivity than state-of-the-art surveys can be guaranteed to detect only a few new remnants, with an expected number of detections between 8 and 21 at a limiting 44 Ti decay flux of 10 −7 photons cm −2 s −1 .
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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 | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".