VALIDATION OF OBSERVATIONS OBTAINED WITH A LIQUID MIRROR TELESCOPE BY COMPARISON WITH SLOAN DIGITAL SKY SURVEY OBSERVATIONS
Bibliographic record
Abstract
The results of a search for peculiar astronomical objects using very low resolution spectra obtained with the NASA Orbital Debris Observatory (NODO) 3 m diameter liquid mirror telescope (LMT) are compared with results of spectra obtained with the Sloan Digital Sky Survey (SDSS). The main purpose of this comparison is to verify whether observations taken with this novel type of telescope are reliable. This comparison is important because LMTs are an inexpensive novel type of telescope that is very useful for astronomical surveys, particularly surveys in the time domain, and validation of the data taken with an LMT by comparison with data from a classical telescope will validate their reliability. We start from a published data analysis that classified as peculiar only 206 of the 18,000 astronomical objects observed with the NODO LMT. A total of 29 of these 206 objects were found in the SDSS. The reliability of the NODO data can be seen through the results of the detailed analysis that, in practice, incorrectly identified less than 0.3% of the 18,000 spectra as peculiar objects, most likely because they are variable stars. We conclude that the LMT gave reliable observations, comparable to those that would have been obtained with a telescope using a glass mirror.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".