The characterization of water ice in debris discs: implications for <i>JWST</i> scattered light observations
Bibliographic record
Abstract
ABSTRACT Water ice plays a crucial role throughout the different stages of planetary evolution and is abundant in the Universe. However, its presence and nature in debris discs of exoplanetary systems are not yet strongly established observationally. In this study, we quantify and discuss the impact of ice parameters such as volume fraction ${\mathcal {F}}_{\rm ice}$, blow-out grain size, size distribution, and its phase on the observational appearance of debris discs, considering the diverse nature of these systems around stellar spectral types ranging from A to M. Our findings reveal that the prominent ice features at approximately 2.7 and 3.3 μm depend on both the water ice fraction ${\mathcal {F}}_{\rm ice}$ and the scattering angle, with backscattering geometries yielding the most prominent signatures. When the phase function is considered and data are not background limited, strong forward and backward scattering (near edge-on discs) are expected to yield the strongest detections in images/spectra for A or F-type stars, while scattering angle matters less for later type stars. The Fresnel peak at 3.1 μm serves as a viable discriminant for the transitional phase (crystalline/amorphous), while simultaneously constraining the water ice temperature. For JWST imaging, we find that the F356W and F444W filter combination is most effective for constraining the grain size distribution, while the F356W and F277W filter combination provides better constraints on the ice fraction ${\mathcal {F}}_{\rm ice}$ in debris discs. However, degeneracy between the grain size distribution and ice fraction when using photometric flux ratios means that obtaining robust constraints will likely require more than two filters, or spectroscopic data.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".