Ice giant magnetospheres

The ice giant planets provide some of the most interesting natural laboratories for studying the influence of large obliquities, rapid rotation, highly asymmetric magnetic fields and wide-ranging Alfvénic and sonic Mach numbers on magnetospheric processes. The geometries of the solar wind-magnetosphere interaction at the ice giants vary dramatically on diurnal timescales due to the large tilt of the magnetic axis relative to each planet's rotational axis and the apparent off-centred nature of the magnetic field. There is also a seasonal effect on this interaction geometry due to the large obliquity of each planet (especially Uranus). With in situ observations at Uranus and Neptune limited to a single encounter by the Voyager 2 spacecraft, a growing number of analytical and numerical models have been put forward to characterize these unique magnetospheres and test hypotheses related to the magnetic structures and the distribution of plasma observed. Yet many questions regarding magnetospheric structure and dynamics, magnetospheric coupling to the ionosphere and atmosphere, and potential interactions with orbiting satellites remain unanswered. Continuing to study and explore ice giant magnetospheres is important for comparative planetology as they represent critical benchmarks on a broad spectrum of planetary magnetospheric interactions, and provide insight beyond the scope of our own Solar System with implications for exoplanet magnetospheres and magnetic reversals. This article is part of a discussion meeting issue 'Future exploration of ice giant systems'.

Incorporating Physical Knowledge Into Machine Learning for Planetary Space Physics

Recent improvements in data collection volume from planetary and space physics missions have allowed the application of novel data science techniques. The Cassini mission for example collected over 600 gigabytes of scientific data from 2004 to 2017. This represents a surge of data on the Saturn system. In comparison, the previous mission to Saturn, Voyager over 20 years earlier, had onboard a ~70 kB 8-track storage ability. Machine learning can help scientists work with data on this larger scale. Unlike many applications of machine learning, a primary use in planetary space physics applications is to infer behavior about the system itself. This raises three concerns: first, the performance of the machine learning model, second, the need for interpretable applications to answer scientific questions, and third, how characteristics of spacecraft data change these applications. In comparison to these concerns, uses of "black box" or un-interpretable machine learning methods tend toward evaluations of performance only either ignoring the underlying physical process or, less often, providing misleading explanations for it. The present work uses Cassini data as a case study as these data are similar to space physics and planetary missions at Earth and other solar system objects. We build off a previous effort applying a semi-supervised physics-based classification of plasma instabilities in Saturn's magnetic environment, or magnetosphere. We then use this previous effort in comparison to other machine learning classifiers with varying data size access, and physical information access. We show that incorporating knowledge of these orbiting spacecraft data characteristics improves the performance and interpretability of machine leaning methods, which is essential for deriving scientific meaning. Building on these findings, we present a framework on incorporating physics knowledge into machine learning problems targeting semi-supervised classification for space physics data in planetary environments. These findings present a path forward for incorporating physical knowledge into space physics and planetary mission data analyses for scientific discovery.

Electron Acceleration to MeV Energies at Jupiter and Saturn

The radiation belts and magnetospheres of Jupiter and Saturn show significant intensities of relativistic electrons with energies up to tens of megaelectronvolts (MeV). To date, the question on how the electrons reach such high energies is not fully answered. This is largely due to the lack of high-quality electron spectra in the MeV energy range that models could be fit to. We reprocess data throughout the Galileo orbiter mission in order to derive Jupiter's electron spectra up to tens of MeV. In the case of Saturn, the spectra from the Cassini orbiter are readily available and we provide a systematic analysis aiming to study their acceleration mechanisms. Our analysis focuses on the magnetospheres of these planets, at distances of L > 20 and L > 4 for Jupiter and Saturn, respectively, where electron intensities are not yet at radiation belt levels. We find no support that MeV electrons are dominantly accelerated by wave-particle interactions in the magnetospheres of both planets at these distances. Instead, electron acceleration is consistent with adiabatic transport. While this is a common assumption, confirmation of this fact is important since many studies on sources, losses, and transport of energetic particles rely on it. Adiabatic heating can be driven through various radial transport mechanisms, for example, injections driven by the interchange instability or radial diffusion. We cannot distinguish these processes at Saturn with our technique. For Jupiter, we suggest that the dominating acceleration process is radial diffusion because injections are never observed at MeV energies.