Titan's cold case files - Outstanding questions after Cassini-Huygens

Abstract The entry of the Cassini-Huygens spacecraft into orbit around Saturn in July 2004 marked the start of a golden era in the exploration of Titan, Saturn's giant moon. During the Prime Mission (2004–2008), ground-breaking discoveries were made by the Cassini orbiter including the equatorial dune fields (flyby T3, 2005), northern lakes and seas (T16, 2006), and the large positive and negative ions (T16 & T18, 2006), to name a few. In 2005 the Huygens probe descended through Titan's atmosphere, taking the first close-up pictures of the surface, including large networks of dendritic channels leading to a dried-up seabed, and also obtaining detailed profiles of temperature and gas composition during the atmospheric descent. The discoveries continued through the Equinox Mission (2008–2010) and Solstice Mission (2010–2017) totaling 127 targeted flybys of Titan in all. Now at the end of the mission, we are able to look back on the high-level scientific questions from the start of the mission, and assess the progress that has been made towards answering these. At the same time, new scientific questions regarding Titan have emerged from the discoveries that have been made. In this paper we review a cross-section of important scientific questions that remain partially or completely unanswered, ranging from Titan's deep interior to the exosphere. Our intention is to help formulate the science goals for the next generation of planetary missions to Titan, and to stimulate new experimental, observational and theoretical investigations in the interim

Predicting sample size required for classification performance

<p>Abstract</p> <p>Background</p> <p>Supervised learning methods need annotated data in order to generate efficient models. Annotated data, however, is a relatively scarce resource and can be expensive to obtain. For both passive and active learning methods, there is a need to estimate the size of the annotated sample required to reach a performance target.</p> <p>Methods</p> <p>We designed and implemented a method that fits an inverse power law model to points of a given learning curve created using a small annotated training set. Fitting is carried out using nonlinear weighted least squares optimization. The fitted model is then used to predict the classifier's performance and confidence interval for larger sample sizes. For evaluation, the nonlinear weighted curve fitting method was applied to a set of learning curves generated using clinical text and waveform classification tasks with active and passive sampling methods, and predictions were validated using standard goodness of fit measures. As control we used an un-weighted fitting method.</p> <p>Results</p> <p>A total of 568 models were fitted and the model predictions were compared with the observed performances. Depending on the data set and sampling method, it took between 80 to 560 annotated samples to achieve mean average and root mean squared error below 0.01. Results also show that our weighted fitting method outperformed the baseline un-weighted method (p < 0.05).</p> <p>Conclusions</p> <p>This paper describes a simple and effective sample size prediction algorithm that conducts weighted fitting of learning curves. The algorithm outperformed an un-weighted algorithm described in previous literature. It can help researchers determine annotation sample size for supervised machine learning.</p

Scientific rationale for Uranus and Neptune <i>in situ</i> explorations

The ice giants Uranus and Neptune are the least understood class of planets in our solar system but the most frequently observed type of exoplanets. Presumed to have a small rocky core, a deep interior comprising ∼70% heavy elements surrounded by a more dilute outer envelope of H2 and He, Uranus and Neptune are fundamentally different from the better-explored gas giants Jupiter and Saturn. Because of the lack of dedicated exploration missions, our knowledge of the composition and atmospheric processes of these distant worlds is primarily derived from remote sensing from Earth-based observatories and space telescopes. As a result, Uranus's and Neptune's physical and atmospheric properties remain poorly constrained and their roles in the evolution of the Solar System not well understood. Exploration of an ice giant system is therefore a high-priority science objective as these systems (including the magnetosphere, satellites, rings, atmosphere, and interior) challenge our understanding of planetary formation and evolution. Here we describe the main scientific goals to be addressed by a future in situ exploration of an ice giant. An atmospheric entry probe targeting the 10-bar level, about 5 scale heights beneath the tropopause, would yield insight into two broad themes: i) the formation history of the ice giants and, in a broader extent, that of the Solar System, and ii) the processes at play in planetary atmospheres. The probe would descend under parachute to measure composition, structure, and dynamics, with data returned to Earth using a Carrier Relay Spacecraft as a relay station. In addition, possible mission concepts and partnerships are presented, and a strawman ice-giant probe payload is described. An ice-giant atmospheric probe could represent a significant ESA contribution to a future NASA ice-giant flagship mission