Observations of Titan liquid bodies by means of the Cassini RADAR altimeter
Notice bibliographique
Résumé
Twenty years after its great departure from Cape Canaveral the Cassini mission is going to have the “Grand Finale” it deserves. This spacecraft definitely marked a milestone in astronautical history as one of the best examples of collaboration among different space agencies (NASA-ESA-ASI) and a great model for any other future mission for planetary exploration. A discovery machine that unveiled the Titan’s lively world and that doesn’t stop giving surprises at every new observation. I’m Valerio Poggiali, phD candidate, and with my colleagues and professors of La Sapienza University of Rome I’m involved in the Cassini RADAR altimeter data processing, a task bequeathed to us by our Prof. Emeritus Giovanni Picardi who started believing in this project already at the beginning of nineties when he published a paper entitled “The Radar System for the Exploration of Titan”. It is 1992, and he states: “the knowledge of the ocean depth is particularly important and its determination is one of the main requirements for the radar instrument”. Actually he announced twenty-two years in advance our work on “The Bathymetry of a Titan Sea” in which we reported about an incredible observation made by the Cassini radar altimeter on May 23st, 2013. We were waiting for that fly-by by years as it was representing the only opportunity to test the instrument capability to plumb the bottom of a Titan sea and infer about its dielectric properties (besides proving once for all the liquid nature of those dark features). After Kraken and before Punga, the Ligeia Mare is the second greatest sea of Titan (roughly 260 x 217 miles). We have been able to track a deepest point of 180 yards and estimate its liquid to be composed by an extremely pure mixture of liquid hydrocarbons (laboratory experiments at JPL eventually confirmed components to be methane, ethane and nitrogen). The following period has been very busy for us, but also extremely rewarding. After the publication of Ligeia Mare bathymetry, the Cassini radar altimeter observed the Kraken Mare on August 21st, 2014 and the Punga Mare on January 11st, 2015. The measurements we had to perform were very challenging. Main difficulties came from the necessity to track the subsurface also in the shallowest parts of seas where we have to push the radar capabilities to the limits of its vertical resolution (nominally 33 yards but with some super-resolution algorithms improvable of a factor of about two). Another great test that we faced was in the production of the bathymetry of the largest lake (43 x 124 miles) of the southern polar area of Titan, the Ontario Lacus. The severe saturation of the receiver united to the shallowness of this liquid body (preliminary results suggested a 50 yards maximum depth along the altimeter ground track, that of course could be not the deepest point) imposed the necessity of developing a different method for determine its bathymetry and dielectric properties. Also if the Cassini radar will not observe Titan seas anymore (we will have just a last look to northern polar small lakes during Cassini’s last flyby of Titan on April 2017) we have still many analyses to perform on the already collected data, these will engage us for years to come. The more Cassini mission will come closer to its end on September 15, 2017, the more we will have to stay tuned to not miss anyone of the discoveries that will certainly follow as the spacecraft will finally approach Saturn, receiving, as if it was an engagement gift, the unique opportunity of a closer observation of its precious rings. \nIn this PhD dissertation I would like to resume the main steps we followed for analyzing the Cassini radar altimeter data from which we obtained the first bathymetries of extraterrestrial liquid bodies, starting from a brief introduction to the RADAR instrument characteristics and its capabilities. Later I will focus on the backscattering models we adopted in order to effectively interpret the radar altimeter nadiral power returns from the surface of Titan. \nA detailed description will follow of the simulator we developed in order to reconstruct as accurately as possible how the Cassini radar receiving chain processes on board the incoming signal. Then, I will describe the results we obtained on Ligeia Mare and Ontario Lacus, giving some anticipations on the work ongoing for Punga and Kraken Mare. \nThe last part will be finally dedicated to our discovery on Titan of liquid-filled canyons directly connected to seas. I will describe how the radar altimeter was able to measure their depth and infer about their nature of sea flooded valleys by means of a comparison of the levels of their liquids. \nFar from being just a library-based theoretical dissertation, this document will intentionally focus on the work we carried out and on the consequent results we obtained in the framework of the Cassini mission. The techniques described herein and, in particular, the simulator and the Bayesian method for the estimation of unknown parameters we adopted for this work are the result of the fruitful collaboration between our Dipartimento di Ingegneria Elettronica e Telecomunicazioni (DIET) of “La Sapienza” University of Rome and the Astronomy department of the Cornell University in Ithaca (NY).
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| 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 | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,001 |
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 source (Gemma direct ou Codex distillé), 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 ».