Tackling down the low wind effect on SPHERE instrument
Bibliographic record
Abstract
SPHERE is the VLT second generation planet hunter instrument. Installed since May 2014 on UT3, the system has been commissioned and verified for more than one year now and routinely delivers unprecedented images of star surroundings, exoplanets and dust disks. The exceptional performance required for this kind of observation makes the appointment: a repeatable Strehl Ratio of 90% in H band, a rough contrast level of 10-5@0.5 arcsec, and reaches 10-6 at the same separation after differential imaging (SDI, ADI). The instrument also presents high contrast levels in the visible and an unprecedented 17mas diffraction-limited resolution at 0.65 microns wavelength. SAXO is the SPHERE XAO system, allowing the system to reach its final detectivity. Its high performance and therefore highly sensitive capacities turns a new eye on telescope environment. Even if XAO performance are reached as expected, some unexpected limitations are here described and a first work around is proposed and discussed. Spatial limitation: wave-front aberrations have been identified, deviating from kolmogorov statistics, and therefore not easily seen and compensated for by the XAO system. The impact of this limitations results in a degraded performance in some particular low wind conditions. Solutions are developed and tested on sky to propose a new operation procedure reducing this limitation. Temporal limitation: high amplitude vibrations on the low order modes have been issued, due to telescope environment and XAO behaviour. Again, a solution is developed and an assessment of its performance is dressed. The potential application of these solutions to E-ELT is proposed.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".