SHIMM as an atmospheric profiler on the Nickel telescope
Bibliographic record
Abstract
Optimal atmospheric conditions are beneficial for detecting exoplanets via High Contrast Imaging (HCI), as speckles from Adaptive Optics’ (AO’s) residuals can make it difficult to identify exoplanets. While AO systems greatly improve our image quality, having access to real-time estimates of atmospheric conditions could also help astronomers use their telescope time more efficiently in the search for exoplanets as well as aid in the data reduction process. The Shack-Hartmann Imaging Motion Monitor (SHIMM) is an atmospheric profiler that utilizes a Shack-Hartmann wavefront sensor to create spot images of a single star in order to reconstruct important atmospheric parameters such as the Fried parameter (r<sub>0</sub>), C<sup>2</sup><sub>n</sub> profile and coherence time. Due to its simplicity, the SHIMM can be directly used on a telescope to get in situ measurements while observing. We present our implementation of the Nickel-SHIMM design for the one meter Nickel Telescope at Lick Observatory. We utilize an HCIPy simulation of turbulence propagating across a telescope aperture to verify the SHIMM data reduction pipeline as we begin on-sky testing. We also used on-sky data from the AO system on the Shane Telescope to further validate our analysis, finding that both our simulation and data reduction pipeline are consistent with previously determined results for the Fried parameter at the Lick Observatory. Finally, we present first light results from commissioning of the Nickel-SHIMM.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".