Evolutionary Models for 43 Galactic Supernova Remnants with Distances and X-Ray Spectra
Bibliographic record
Abstract
Abstract The X-ray emission from a supernova remnant (SNR) is a powerful diagnostic of the state of the shocked plasma. The temperature ( kT ) and the emission measure (EM) of the shocked gas are related to the energy of the explosion, the age of the SNR, and the density of the surrounding medium. Progress in X-ray observations of SNRs has resulted in a significant sample of Galactic SNRs with measured kT and EM values. We apply spherically symmetric SNR evolution models to a new set of 43 SNRs to estimate ages, explosion energies, and circumstellar medium densities. The distribution of ages yields an SNR birth rate. The energies and densities are well fit with lognormal distributions, with wide dispersions. SNRs with two emission components are used to distinguish between SNR models with uniform interstellar medium and with stellar wind environment. We find Type Ia SNRs to be consistent with a stellar wind environment. Inclusion of stellar wind SNR models has a significant effect on estimated lifetimes and explosion energies of SNRs. This reduces the discrepancy between the estimated SNR birth rate and the SN rate of the Galaxy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".