Modelling grain alignment by radiative torques and hydrogen formation torques in reflection nebula
Bibliographic record
Abstract
Reflection nebulae – dense cores – illuminated by surrounding stars offer a unique opportunity to directly test our quantitative model of grain alignment based on radiative torques (RATs) and to explore new effects arising from additional torques. In this paper, we first perform detailed modelling of grain alignment by RATs for the IC 63 reflection nebula illuminated both by a nearby γ Cas star and the diffuse interstellar radiation field. We calculate linear polarization pλ of background stars by radiatively aligned grains and explore the variation of fractional polarization (pλ/AV) with visual extinction AV across the cloud. Our results show that the variation of pV/AV versus AV from the dayside of IC 63 to its centre can be represented by a power law (|$p_{\rm V}/A_{\rm V}\propto A_{\rm V}^{\eta }$|) with different slopes depending on AV. We find a shallow slope η ∼ −0.1 for AV < 3 and a very steep slope η ∼ −2 for AV > 4. We then consider the effects of additional torques due to H2 formation and model grain alignment by joint action of RATs and H2 torques. We find that pV/AV tends to increase with an increasing magnitude of H2 torques. In particular, the theoretical predictions obtained for pV/AV and peak wavelength λmax in this case show an improved agreement with the observational data. Our results reinforce the predictive power of the RAT alignment mechanism in a broad range of environmental conditions and show the effect of pinwheel torques in environments with efficient H2 formation. Physical parameters involved in H2 formation may be constrained using detailed modelling of grain alignment combined with observational data. In addition, we discuss implications of our modelling for interpreting latest observational data by Planck and other ground-based instruments.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".