A UNIFIED MODEL OF GRAIN ALIGNMENT: RADIATIVE ALIGNMENT OF INTERSTELLAR GRAINS WITH MAGNETIC INCLUSIONS
Bibliographic record
Abstract
ABSTRACT The radiative torque (RAT) alignment of interstellar grains with ordinary paramagnetic susceptibilities has been supported by earlier studies. The alignment of such grains depends on the so-called RAT parameter q max , which is determined by the grain shape. In this paper, we elaborate on our model of RAT alignment for grains with enhanced magnetic susceptibility due to iron inclusions, such that RAT alignment is magnetically enhanced, which we term the MRAT mechanism. Such grains can be aligned with high angular momentum at the so-called high- J attractor points, achieving a high degree of alignment. Using our analytical model of RATs, we derive the critical value of the magnetic relaxation parameter δ m to produce high- J attractor points as functions of q max and the anisotropic radiation angle relative to the magnetic field ψ . We find that if about 10% of the total iron abundance present in silicate grains is forming iron clusters, this is sufficient to produce high- J attractor points for all reasonable values of q max . To calculate the degree of grain alignment, we carry out numerical simulations of MRAT alignment by including stochastic excitations from gas collisions and magnetic fluctuations. We show that large grains can achieve perfect alignment when the high- J attractor point is present, regardless of the values of q max . Our obtained results pave the way for the physical modeling of polarized thermal dust emission as well as magnetic dipole emission. We also find that millimeter-sized grains in accretion disks may be aligned with the magnetic field if they are incorporated with iron nanoparticles.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".