H<scp>i</scp>Absorption of Polarized Emission: A New Technique for Determining Kinematic Distances to Galactic Supernova Remnants
Bibliographic record
Abstract
We present a new method of determining the systemic velocity of Galactic supernova remnants (SNRs) based on H I absorption of their linearly polarized radio continuum emission. Conventional H I observations of total power emission are limited by H I emission and self-absorption along the line of sight, but since H I emission is unpolarized, the only limits on measurements of absorption of the polarized emission are noise and velocity resolution. This leads to lower uncertainties and makes it possible to obtain absorption profiles for virtually all Galactic SNRs with very precise H I column densities. To demonstrate the new technique, we have obtained H I absorption profiles from Tycho's supernova remnant (G120.1+1.4). Absorption profiles of the polarized emission are very similar to those of the total power emission. Optical depths from the polarization profiles are slightly larger because of small-scale emission features. We also observed polarization absorption profiles of the Boomerang pulsar wind nebula (part of G106.3+2.7) and the plerionic SNR DA 495 (G65.7+1.2), remnants that are so faint that absorption profiles cannot be obtained in total power.
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 | 0.000 | 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 | 0.000 | 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".