The importance of non-photon noise in astronomical spectropolarimetry
Bibliographic record
Abstract
Abstract Stellar spectropolarimetry has become an extremely popular technique during the last decade or two, and has led to major advances in the studies of stellar magnetic fields. Many important discoveries have been made thanks to ultra-precise measurements of very small polarimetric signals, which require very stable instruments and special observing strategies. The so called beam-swapping technique is a well-known polarimetric technique capable of suppressing many spurious signals due to various instrumental effects. However, when one is interested in ultra-high signal-to-noise ratio measurements, observers start to hit various limitations introduced by the instrument, by the atmosphere, and even by the software for data-reduction. These limitations cannot be overcome by the observing strategies, and sources of errors other than photon-noise must be taken into account. Here we discuss the advantages of the beam-swapping technique, and the impact of small instrument and atmospheric instabilities, and how these issues offer an explanation for the origin of the apparently significant observed polarisation signals produced by effects other than those intrinsic to the observed target.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".