Ground-based Mid-IR Direct Imaging: The Origin of the Thermal Background on the Keck II Telescope and Correcting Instrumental Systematics
Bibliographic record
Abstract
Abstract Mid-IR wavelengths are of particular interest to exoplanet science due to the fact they can extend the searchable parameter space to planets that are older and/or colder. However, a significant source of uncertainty at mid-IR wavelengths on ground-based telescopes is the thermal background. This background comes from blackbody radiation in the atmosphere and telescope and is therefore dependent on instrument design and atmospheric conditions. When performing imaging observations, this background manifests as a slowly varying, inhomogeneous signal throughout the image, underlying our data. Photometry at mid-IR can greatly constrain atmospheric models but existing data are usually scarce or have significant error bars due to the difficulty of subtracting the background. Using M-band direct imaging observing sequences on NIRC2, we evaluate the thermal background of the Keck II telescope and attempt to subtract the background in a more comprehensive way. For our primary science target, the forming protoplanet AB Aur b, we present a contrast upper limit of 2 × 10−4 in M-band and address the limiting factors in our observation due to the thermal background. We determine that the origin of the systematic components of the thermal background comes from the K-mirror and find that the thermal background is also strongly influenced by emission from the secondary spiders on Keck II.
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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 | 0.002 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".