Latest results with LBTI's Vortex coronagraph: real-time tip/tilt sensing, new data reduction algorithms, and YSO observations
Bibliographic record
Abstract
Vortex coronagraphs are among the most promising solutions to perform high contrast imaging at small angular separations from bright stars. They enhance the dynamic range at very small inner working angle (down to the diffraction limit of the telescope) and provide a clear 360 degree discovery space for high-contrast direct imaging of exoplanets. In 2013, we installed and commissioned an L-band coronagraph in LBTI/LMIRCam and obtained outstanding images of the four planets around HR8799 during the first hours on sky. In this presentation, we will present the results of the latest data reduction performed with the VIP software that is developed at the University of Liège and that features state-of-the-art image processing algorithms inherited from the field of background subtraction in computer vision (including machine learning algorithms and low rank modeling algorithms). We will also present the results obtained with the second L- and M-band coronagraph that was recently installed in LMIRCam to enable binocular Vortex observations. During the first observations (October 2016), we tested and validated a new real-time post-coronagraphic tip-tilt sensing technique (called QACITS) to quickly align each beam on the center of their respective Vortex coronagraph and obtained observations of a young star showing disk features near the resolution limit of each aperture. Finally, we will present some exciting prospects for the Vortex coronagraph that will be installed on VISIR and ELT/METIS.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".