Emission Measures and Emission-measure-weighted Temperatures of Shocked Interstellar Medium and Ejecta in Supernova Remnants
Bibliographic record
Abstract
Abstract A goal of supernova remnant (SNR) evolution models is to relate fundamental parameters of a supernova (SN) explosion and progenitor star to the current state of its SNR. The SNR hot plasma is characterized by its observed X-ray spectrum, which yields electron temperature, emission measure (EM), and abundances. Depending on their brightness, the properties of the plasmas heated by the SNR forward shock, reverse shock, or both can be measured. The current work uses models that are spherically symmetric. One-dimensional hydrodynamic simulations are carried out for SNR evolution prior to onset of radiative losses. From these, we derive dimensionless EMs and EM-weighted temperatures, and we present fitting formulae for these quantities as functions of scaled SNR time. These models allow one to infer SNR explosion energy, circumstellar medium density, age, ejecta mass, and ejecta density profile from SNR observations. The new results are incorporated into the SNR modeling code SNRPy. The code is demonstrated with application to three historical SNRs: Kepler , Tycho, and SN1006.
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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 | 0.000 | 0.000 |
| 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) | 0.001 | 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".