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Record W2172090778 · doi:10.1117/12.669994

A Lunar Liquid Mirror Telescope (LLMT) for deep-field infrared observations near the lunar pole

2006· article· en· W2172090778 on OpenAlexafffund
Roger Angel, D. Eisenstein, Suresh Sivanandam, S. Pete Worden, Jim Burge, E. F. Borra, Clément Gosselin, Omar Seddiki, Paul Hickson, Ki Bui, Bernard Foing, Jean‐Luc Josset, Simon Thibault, Paul van Susante

Bibliographic record

VenueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2006
Typearticle
Languageen
FieldPhysics and Astronomy
TopicPlanetary Science and Exploration
Canadian institutionsUniversity of British ColumbiaUniversité Laval
FundersCanadian Space AgencyNational Aeronautics and Space Administration
KeywordsTelescopePhysicsAstronomyPrimary mirrorStarsInfraredField of viewInfrared astronomyInfrared telescopeReflecting telescopeRemote sensingAstrobiologyOpticsGeology

Abstract

fetched live from OpenAlex

We have studied the feasibility and scientific potential of a 20 - 100 m aperture astronomical telescope at the lunar pole, with its primary mirror made of spinning liquid at less than 100K. Such a telescope, equipped with imaging and multiplexed spectroscopic instruments for a deep infrared survey, would be revolutionary in its power to study the distant universe, including the formation of the first stars and their assembly into galaxies. The LLMT could be used to follow up discoveries made with the 6 m James Webb Space Telescope, with more detailed images and spectroscopic studies, as well as to detect objects 100 times fainter, such as the first, high-red shift stars in the early universe. Our preliminary analysis based on SMART-1 AMIE images shows ridges and crater rims within 0.5° of the North Pole are illuminated for at least some sun angles during lunar winter. Locations near these points may prove to be ideal for the LLMT. Lunar dust deposited on the optics or in a thin atmosphere could be problematic. An in-situ site survey appears necessary to resolve the dust questions.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.608
Threshold uncertainty score0.741

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.013
GPT teacher head0.220
Teacher spread0.207 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2006
Admission routes2
Has abstractyes

Explore more

Same venueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIESame topicPlanetary Science and ExplorationFrench-language works237,207