Properties and Alignment of Interstellar Dust Grains toward Type Ia Supernovae with Anomalous Polarization Curves
Bibliographic record
Abstract
Abstract Recent photometric and polarimetric observations of Type Ia supernovae (SNe Ia) show unusually low total-to-selective extinction ratios (R V < 2) and wavelengths of maximum polarization (λ max < 0.4 μm) for several SNe Ia, which indicates peculiar properties of interstellar (IS) dust in the SN-hosted galaxies and/or the presence of circumstellar (CS) dust. In this paper, we use an inversion technique to infer the best-fit grain size distribution and the alignment function of interstellar grains along the lines of sight toward four SNe Ia with anomalous extinction and polarization data (SN 1986G, SN 2006X, SN 2008fp, and SN 2014J). We find that to reproduce low values of R V , a significant enhancement in the mass of small grains of radius a < 0.1 μm is required. For SN 2014J, a simultaneous fit to its observed extinction and polarization is unsuccessful if all the data are attributed to IS dust (model 1), but a good fit is obtained when accounting for the contribution of CS dust (model 2). For SN 2008fp, our best-fit results for model 1 show that in order to reproduce an extreme value of λ max ∼ 0.15 μm, small silicate grains must be aligned as efficiently as big grains. For this case, we suggest that strong radiation from the SN can induce efficient alignment of small grains in a nearby intervening molecular cloud via the radiative torque (RAT) mechanism. The resulting time dependence polarization from this RAT alignment model can be tested by observing at ultraviolet wavelengths.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".