A New Distance to the Supernova Remnant DA 530 Based on H i Absorption of Polarized Emission
Bibliographic record
Abstract
Abstract Supernova remnants (SNRs) are significant contributors of matter and energy to the interstellar medium. Understanding the impact and the mechanism of this contribution requires knowledge of the physical size, energy, and expansion rate of individual SNRs, which can only come if reliable distances can be obtained. We aim to determine the distance to the SNR DA 530 (G93.3+6.9), an object of low surface brightness. To achieve this, we used the Dominion Radio Astrophysical Observatory Synthesis Telescope and the National Radio Astronomy Observatory Very Large Array to observe the absorption by intervening H i of the polarized emission from DA 530. Significant absorption was detected at velocities −28 and −67 km s−1 (relative to the local standard of rest), corresponding to distances of 4.4 and 8.3 kpc, respectively. Based on the radio and X-ray characteristics of DA 530, we conclude that the minimum distance is 4.4 − 0.2 + 0.4 kpc. At this minimum distance, the diameter of the SNR is 34 − 1 + 4 pc, and the elevation above the Galactic plane is 537 − 32 + 40 pc. The −67 km s−1 absorption likely occurs in gas whose velocity is not determined by Galactic rotation. We present a new data processing method for combining Stokes Q and U observations of the emission from an SNR into a single H i absorption spectrum, which avoids the difficulties of the noise-bias subtraction required for the calculation of polarized intensity. The polarized absorption technique can be applied to determine the distances to many more SNRs.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".