Overview of DARPA’s liquid mirror telescope program, and open-source liquid optic modeling tools
Bibliographic record
Abstract
Astronomy and Space Domain Awareness are limited by the size of available telescope optics, the cost for which scales steeply due to the exquisitely ground and polished primary mirrors, typically made of glass or other light-weight substrates. Liquid mirrors (LMs) may break this unfavorable cost scaling. When rotated at a constant angular velocity, it has been shown that fluid surfaces take the form of a paraboloid, which can function as a primary mirror. However, current LMs cannot slew or tilt off-zenith due to gravity, greatly limiting the viewing area in the sky. To overcome these limitations while also enabling low-cost, very-large-aperture telescopes, DARPA launched the Zenith program. Zenith is developing entirely new LM design-for-build approaches that can create large optical surfaces and maintain optical quality during tilt and slew by correcting transient liquid surface aberrations in real time. The development of these new designs is being supported by multi-physics models, materials, surface and field controls, and structures. This paper discusses key and fundamental aspects of four new design and modeling approaches for this new class of LMs. The software and simulation tools developed by the Zenith program to design tiltable and size-scalable liquid mirrors are also available to the astronomical community as an open-source repository.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.023 | 0.015 |
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".