Large Interstellar Polarisation Survey (LIPS)
Bibliographic record
Abstract
Polarimetric studies of light transmitted through interstellar clouds may give constraints on the properties of the interstellar dust grains. Traditionally, broadband linear polarisation (BBLP) measurements have been considered an important diagnostic tool for the study of the interstellar dust, while comparatively less attention has been paid to spectropolarimetric measurements. However, spectropolarimetry may offer stronger constraints than BBLP, for example by revealing narrowband features, and by allowing us to distinguish the contribution of dust from the contribution of interstellar gas. Therefore, we have decided to carry out a Large Interstellar Polarisation Survey (LIPS) using spectropolarimetric facilities in both hemispheres. Here we present the results obtained in the Southern Hemisphere with the FORS2 instrument of the ESO Very Large Telescope. Our spectra cover the wavelength range 380–950 nm at a spectral resolving power of about 880. We have produced a publicly available catalogue of 127 linear polarisation spectra of 101 targets. We also provide the Serkowski-curve parameters, as well as the wavelength gradient of the polarisation position angle for the interstellar polarisation along 76 different lines of sight. In agreement with previous literature, we found that the best-fit parameters of the Serkowski-curve are not independent of each other. However, the relationships that we obtained are not always consistent with what has been found in previous studies.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".