A Uniform Analysis of Directly Imaged Debris Disks with the Gemini Planet Imager
Bibliographic record
Abstract
Spatially resolved images of debris disks are necessary to determine disk geometric properties and the scattering phase function (SPF) which quantifies the brightness of the scattered light as a function of phase angle. The combination of high contrast and high angular resolution imaging is required to obtain these unique and important probes of debris disks, and we report on an adaptive optics 1.25-2.2 micron imaging survey of 17 high infrared excess debris disks observed with a Gemini Large and Long Program (LLP) utilizing the Gemini Planet Imager (GPI). The sample is composed of stars ranging in ages of 10-500 Myr and spectral types A-G. We explore quantitatively whether most debris disks are characterized by a similar SPF as suggested by previous studies. The SPF depends on important dust properties such as grain size distribution and porosity that are linked to the frequency of collisions that generate small grains and promote compactification of grains. To-date, only a very limited number of SPF measurements have been made, and there has been no uniform study modeling the geometry and SPFs of even a modest sample of debris disks using a consistent methodology. Additionally, we investigate and compare geometric properties of these systems such as radial extent and vertical thickness. By determining the best-fitting models for these systems, the disk scale height, SPFs, and dust properties can be estimated and compared between host stars of different ages and masses, as well as examples from the Solar System.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.004 | 0.002 |
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".