Lunar ephemeris at sub microarcsecond accuracy (LESMA) leads to sub-millimeter positional accuracy of the moon
Bibliographic record
Abstract
The most accurate LLR ( l unar l aser r anging) initiative, named APOLLO ( a pache p oint o bservatory l unar l aser-ranging o peration) demonstrated millimeter-range positional accuracy in 2009, thus improving LLR by one order-of-magnitude. Since, LLR is a foundational technique in studying gravity, Murphy (principal investigator of APOLLO) stated in 2009, that with this millimeter-range accuracy, the simulation model has been found to be the limiting-factor in extracting the theoretical science results, and hence, we should: (1) develop the science case and expand our ability to model LLR for a new regime of high precision, (2) develop the theoretical tools for honing the science case for submillimeter LLR, and (3) explore which model/code is worth putting our efforts into. (4) Since millimeter-quality data are a recent development, the model effort lags. (5) Finally, we will code-in new physics so that we may simulate sensitivities. In connection with simulation model/code, Murphy stated in 2013, that among the four available LLR simulation models : JPL ( j et p ropulsion l aboratory), CfA (the Harvard-Smithsonian c enter f or a strophysics), LU ( l eibniz U niversity, Hannover, Germany), and IMCCE ( I nstitut de M ecanique c eleste et de c alcul des E phemerides, France), the JPL model currently produces weighted RMS (root-mean-square) residuals at ∼18 mm, which is about half of the other models; so, clearly a gap exists from millimeter ranging-precision of APOLLO. Hence, the CfA, LU, and IMCCE are engaged, since 2013, in a stepwise comparative streamlining effort to identify the model-differences, errors, and shortcomings. All the four available LLR simulation models can be classified as GR (general relativity)-astronomers model; they are basically similar. Professor Douglas Currie of the University of Maryland, College Park, NASA Lunar Science Institute, stated in a Conference presentation, in 2012, that Ground stations, that is, the lunar observatories, have improved by a factor of 200, but the agreement between observations and fitted theory has plateaued at ∼2 cm over the past two decades. However, no substantial progress on improving the fit has been reported in the published literature, till date. Based on about a quarter-century of experience in doing high-precision numerical simulation of celestial orbits, the authors have developed LESMA ( l unar E phemeris at s ub M icroarcsecond a ccuracy) utilizing the methodology of evolved general relativity (EGR) that has incorporated the following two concepts: (1) Relativistic time for integration and (2) methodology of conservation of magnitude of the angular momentum, M Φ , for Φ -rotation (in addition to the θ -rotation that leads to the rosetting ellipse) of the orbital plane. Incorporation of the two above-mentioned concepts has led to three orders-of-magnitude accuracy-improvement of the computed (1) precession (compared to JPL's DE405) of Lunar orbit, as verified using three independent methods and (2) radial position (compared to JPL's DE430/431) of the Moon. LESMA will enable scientists to make efficient use of research-funds from NASA, etc., for production of new science results from APOLLO. LESMA will also be useful for getting better science results (than Folkner reported {in 2014} submeter accurate Position of the Moon) from the GRAIL ( g ravity r ecovery a nd I nterior l aboratory) mission (costing 500 million USD), by spending a little more for revisiting the computations, utilizing LESMA data.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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 teacher head, 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".