Synthesizing Large Telescope Apertures: Adaptive Optics and Interferometry
Bibliographic record
Abstract
At sometime in every astronomer’s life they are struck with “aperture fever”, that is the desire for an ever-large telescope. Building filled aperture telescopes larger than a few 10s of meters in diameter is a costly and challenging endeavor, however, unfilled aperture telescopes can be constructed with very long baselines at considerably less cost. The adaptive optics technology available today allows novel interferometric telescopes to be constructed and such telescope could have applications for astronomers of diverse scientific interests. 2002 IAPPP-Western Wing, Inc. References and Links 1. S.W. Teare, I. Elliott, “Big or Biggest Telescope?”, Mercury (in Society Scope), 29(4) Sept/ Oct (2000). 2. R. Ellis, “Unraveling Cosmic History with Giant Ground-Based Telescopes”, Engineering and Science, California Institute of Technology, Vol. LXIV, N. 3-4, (2001). 3. E. Hecht and A. Zajac, Optics, (Addison-Wesley, Don Mills, Ontario. 1979), p 281-286. 4. D. Malacara, Optical Shop Testing, (John Wiley & Sons, New York, 1992). p 118. 5. A.A. Michelson, 6. J.W. Hardy, Adaptive Optics for Astronomical Telescopes, (New York: Oxford Univ. Press, 1998). 7. G.C. Loos, S.W. Teare, “On the Importance of Wavefront Correction for Astronomical Seeing Conditions”. Unpublished (2001). http://www.ee.nmt.edu/~teare. 8. S.K. Saha, “Modern optical astronomy: technology and impact on interferometry”, unpublished, (2002). 9. E.B. Fomalont, M.C.H. Wright, in Galactic and Extra-galactic Radio Astronomy, eds., G.L. Verschuur, K.I. Kellerman, 256.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".