Stellar Spectropolarimetry with Retarder Waveplate and Beam Splitter Devices
Bibliographic record
Abstract
Nighttime polarimetric measurements are often obtained very close to the limits of the instrumental capabilities. It is important to be aware of the possible sources of spurious polarization, and to adopt data reduction techniques that best compensate for the instrumental effects intrinsic to the design of the most common polarimeters adopted for nighttime observations. We define a self-consistent framework starting from the basic definitions of the Stokes parameters, and we present an analytical description of the data reduction techniques commonly used with a polarimeter (consisting of a retarder wave plate and a Wollaston prism) to explore their advantages and limitations. We first consider an ideal polarimeter in which all optical components are perfectly defined by their nominal characteristics. We then introduce deviations from the nominal behavior of the polarimetric optics, and develop an analytical model to describe the polarization of the outgoing radiation. We study and compare the results of two different data reduction methods, one based on the differences of the signals, and one based on their ratios, to evaluate the residual amount of spurious polarization. We show that data reduction techniques may fully compensate for small deviations of the polarimetric optics from their nominal values, although some important (first-order) corrections have to be adopted for linear polarization data. We include a detailed discussion of quality checking by means of null parameters. We present an application to data obtained with the FORS1 instrument of the ESO VLT, in which we have detected a significant amount of cross talk between circular and linear polarization. We show that this cross-talk effect is not due to the polarimetric optics themselves, but is most likely caused by spurious birefringence due to the instrument's collimator lens.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".