We review the key observations and theories relevant to the origin and evolution of the Galilean satellites. Key observations include: the potentially undifferentiated nature of Callisto; the increasing ice fraction with semi-major axis; the present-day existence of the Laplace resonance; the potential resurfacing of Ganymede mid-way through its evolution; and the metal-enriched nature of Jupiter’s envelope. The most widely accepted theory for the formation of the satellites is the so-called “starved disk” model, although newer alternatives including decretion disks and pebble accretion have also been proposed. Models that allow slow satellite formation in a cold disk are preferred, based on the density progression and Callisto’s apparent differentiation state. Major model uncertainties include the angular momentum distribution of the material infalling to the circumplanetary disk, the source of the solids, and the thermal and viscosity structure of the disk. We identify six outstanding questions, some of which will be answered by JUICE, Europa Clipper and Tianwen-4. A major difficulty in answering some questions is overprinting of primordial characteristics by later events.
Abstract Surveys and observations of lightning on Jupiter prior to the NASA Juno mission used night‐side imaging approaches, and a common conclusion was that the optical energy was similar to the highest energy terrestrial lightning flashes, or superbolts. We use data from the Juno Microwave Radiometer (MWR) to measure the first radio pulse power distribution of Jovian lightning. The power distribution measurement was enabled by unique meteorological conditions in Jupiter's North Equatorial Belt (NEB) in 2021–2022, as the belt transitioned from an anomalously quiescent (non‐convective) state to its more typical configuration with small moist convective plumes scattered in longitude. During this transition, convective plumes in the NEB occurred only in isolated storms we label “stealth superstorms.” The isolated nature of these storms (as lightning sources) resolved the degeneracy between pulse location and pulse strength, allowing measurement of a pulse power distribution with statistical median values ranging from 27 to 214 W over the MWR bandpass, well within the observational sensitivity range. The MWR thus measures typical pulse power in the storms, rather than high‐power outliers. Pulse power in the stealth superstorms may be comparable to terrestrial lightning radio emission, or up to a million times more powerful, depending on uncertainties in unresolved pulse duration and lightning spectral energy distributions. Future studies may determine whether the lightning pulse power in stealth superstorm is typical or anomalous of Jupiter's lightning in general.
The IMAP-Hi Energetic Neutral Atom (ENA) Imager on NASA’s Interstellar Mapping and Acceleration Probe (IMAP) mission (McComas et al. 2018a, 2025) is designed to measure ENAs from the global interaction between the heliosphere and the local interstellar medium (LISM). These ENAs are initially plasma ions of solar wind origin that are neutralized by charge exchange with the cold neutral atoms of LISM that freely flow through the heliosphere-LISM interaction region. IMAP-Hi consists of two identical single-pixel sensors, each covering the ENA spectral range from 0.44 keV to 15.6 keV over nine contiguous energy passbands and having an approximately conical field-of-view (FOV) of 4.1o full width at half maximum (FWHM). The Hi-45 sensor points 45o relative to the spacecraft spin axis from the antisunward direction; each spacecraft spin, it measures ENA intensity over a circular swath with half-cone angle 45o centered on the ecliptic plane. The Hi-90 sensor points 90o relative to the spin axis; each spacecraft spin, it measures ENA intensity over a great circle in the sky, sampling both the north and south ecliptic poles. As the IMAP spin vector is re-pointed daily toward the Sun, the ecliptic longitude of the swaths moves daily by ∼1o such that a full sky map is acquired by Hi-90 every six months and a complete low latitude (−45o to +45o) map is acquired by Hi-45 annually. The IMAP-Hi sensor design has direct heritage from the IBEX-Hi imager on the Interstellar Boundary Explorer (IBEX) mission, with substantial improvements in energy range, energy resolution, angular resolution, signal-to-noise ratio, and, for ecliptic latitudes within ±45o, temporal resolution and exposure time. The global ENA maps acquired by IMAP-Hi partially overlap in energy and viewing with the ENA maps acquired by the IMAP-Lo and IMAP-Ultra ENA imagers, which we combine to answer fundamental questions about the structure and dynamics of the interaction of the heliosphere and the LISM.
The Solar Wind Electron (SWE) instrument of the Interstellar Mapping and Acceleration Probe (IMAP) mission is designed to measure the in situ solar wind thermal and suprathermal electrons at the spacecraft. SWE contributes to the IMAP science objective to understand particle injection and acceleration processes near the Sun and in the heliosphere and heliosheath, and provides context for the IMAP measurements of energetic neutral atoms from the outer heliosphere. SWE measures the solar wind thermal and suprathermal electron distribution from 1–5000 eV in 24 log-spaced steps with energy resolution Δ E/E of 14
Deep generative models have shown immense potential in generating unseen data that has properties of real data. These models learn complex data-generating distributions starting from a smaller set of latent dimensions. However, generative models have encountered great skepticism in scientific domains due to the disconnection between generative latent vectors and scientifically relevant quantities. In this study, we integrate three types of machine learning models to generate solar magnetic patches in a physically interpretable manner and use those as a query to find matching patches in real observations. We use the magnetic field measurements from Space-weather HMI Active Region Patches (SHARPs) to train a Generative Adversarial Network (GAN). We connect the physical properties of GAN-generated images with their latent vectors to train Support Vector Machines (SVMs) that do mapping between physical and latent spaces. These produce directions in the GAN latent space along which known physical parameters of the SHARPs change. We train a self-supervised learner (SSL) to make queries with generated images and find matches from real data. We find that the GAN-SVM combination enables users to produce high-quality patches that change smoothly only with a prescribed physical quantity, making generative models physically interpretable. We also show that GAN outputs can be used to retrieve real data that shares the same physical properties as the generated query. This elevates Generative Artificial Intelligence (AI) from a means-to-produce artificial data to a novel tool for scientific data interrogation, supporting its applicability beyond the domain of heliophysics.
