Population Analysis of Supernova Remnants in the Galaxy Using Evolutionary Models
Bibliographic record
Abstract
Abstract One aim of supernova remnant (SNR) evolution models is to deduce fundamental properties of a supernova (SN) explosion from the current state of its SNR. The SNR hot plasma is characterized by its observed X-ray spectrum: electron temperature, emission measure and abundances. This plasma is heated by the SNR forward shock and reverse shocks. The state of the plasma is also predicted by SNR models. We have developed spherically symmetric models (Leahy & Williams 2017; Leahy et al. 2019) and have applied these to observations, e.g., for LMC SNRs (Leahy 2017) and for inner Galaxy SNRs (Leahy & Ranasinghe 2018). The models allow inference of SNR explosion energy, circumstellar medium density, age, ejecta mass and ejecta density profile. We obtain new results by including Galactic SNRs that have adequate observations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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) | 0.000 | 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 teacher head, 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".