SEARCHING FOR THE HR 8799 DEBRIS DISK WITH HST/STIS
Bibliographic record
Abstract
ABSTRACT We present a new algorithm for space telescope high contrast imaging of close-to-face-on planetary disks called Optimized Spatially Filtered (OSFi) normalization. This algorithm is used on HR 8799 Hubble Space Telescope (HST) Space Telescope Imaging Spectrograph (STIS) coronagraphic archival data, showing an over-luminosity after reference star point-spread function (PSF) subtraction that may be from the inner disk and/or planetesimal belt components of this system. The PSF-subtracted radial profiles in two separate epochs from 2011 and 2012 are consistent with one another, and self-subtraction shows no residual in both epochs. We explore a number of possible false-positive scenarios that could explain this residual flux, including telescope breathing, spectral differences between HR 8799 and the reference star, imaging of the known warm inner disk component, OSFi algorithm throughput and consistency with the standard spider normalization HST PSF subtraction technique, and coronagraph misalignment from pointing accuracy. In comparison to another similar STIS data set, we find that the over-luminosity is likely a result of telescope breathing and spectral difference between HR 8799 and the reference star. Thus, assuming a non-detection, we derive upper limits on the HR 8799 dust belt mass in small grains. In this scenario, we find that the flux of these micron-sized dust grains leaving the system due to radiation pressure is small enough to be consistent with measurements of other debris disk halos.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".