Thermalizing a telescope in Antarctica – analysis of ASTEP observations
Bibliographic record
Abstract
Abstract The installation and operation of a telescope in Antarctica represent particular challenges, in particular the requirement to operate at extremely cold temperatures, to cope with rapid temperature fluctuations and to prevent frosting. Heating of electronic subsystems is a necessity, but solutions must be found to avoid the turbulence induced by temperature fluctuations on the optical paths. ASTEP 400 is a 40cm Newton telescope installed at the Concordia station, Dome C since 2010 for photometric observations of fields of stars and their exoplanets. While the telescope is designed to spread star light on several pixels to maximize photometric stability, we show that it is nonetheless sensitive to the extreme variations of the seeing at the ground level (between about 0′′.1 and 5′′) and to temperature fluctuations between –30°C and –80 °C. We analyze both day‐time and night‐time observations and obtain the magnitude of the seeing caused by the mirrors, dome and camera. The most important effect arises from the heating of the primary mirror which gives rise to a mirror seeing of 0′′.23 K–1. We propose solutions to mitigate these effects. (© 2015 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".