The Talbot effect's impact on the high contrast imaging modes of METIS
Bibliographic record
Abstract
The ELT, Europe’s Extremely Large Telescope, with its 39m main mirror will be the largest optical/infrared telescope in the world, able to work at the diffraction limit. METIS is one of its first light instruments with powerful imaging and spectroscopic capabilities in the thermal wavelengths. It contains several high contrast imaging (HCI) modes, which allow it to detect and characterize exoplanets amongst others. The HCI performance is highly dependent on pupil stabilization mechanisms and a closed loop compensation of non-common path aberrations degrading the wavefront error of the instrument. The Talbot effect is a near-field effect on collimated light, where spatial frequencies of the wavefront are re-imaged periodically along the optical path. The periodicity is known as the Talbot length, which is a function of the wavelength and the wavefront’s spatial frequencies with the latter being a result of the wavefront errors caused by the surface form errors of optical elements. The aberrations oscillate from amplitude to phase, in the spatial scale of one Talbot length, which can have an impact on the performance of the HCI modes. We evaluate the impact of the Talbot effect with respect to the METIS phase aberration budget by assuming representative power spectral density profile for the surface form error of each optical surface. We propagate the errors to the subsequent pupil plane and finally investigate the resulting point spread function profile. Simulations are fed back into the HCI error budget and if necessary, the specifications regarding instrument surface form are adjusted.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".