Lunar ephemeris at sub microarcsecond accuracy (LESMA) leads to sub-millimeter positional accuracy of the moon
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».