MATH+X SYMPOSIUM ON MATTER UNDER EXTREME CONDITIONS IN SOLAR SYSTEM GIANT PLANETS AND EXOPLANETS, INVERSE PROBLEMS AND DEEP LEARNING

The analysis of planet formation and evolution has moved beyond an era focused on dynamics of accretion followed by secular cooling and has progressed to an era where coupling between physical processes and material properties at extreme conditions is considered. In this coupling lie opportunities, with possible underlying models, for the analysis of inverse problems and deep learning to constrain these material properties and further reveal these processes. Modeling the formation, evolution, interior structure, and magnetic fields of the diversity of exoplanets depends fundamentally on the EOS, chemistry, and optical and transport properties of the constituent materials under WDM conditions. A central role is played by liquid metallic hydrogen and ionized H2O in gas and ice giants.

While the study of the metallic phase transition remains challenging, supervised learning with deep neural networks has been employed to identify EOS (from final-state particle spectra) in QCD. Moreover, deep learning is already employed in condensed matter physics. It has been shown that deep neural networks can be integrated into, or fully replace, the Kohn-Sham density functional theory scheme for multi-electron systems in simple harmonic oscillator and random external potentials. At the same time, the physical properties and phase diagrams of the mentioned ice compounds at high pressures are still a main subject of research.

Inside our solar system, extreme conditions also exist in the interiors of ice giants, Neptune and Uranus, and gas giants, Saturn and Jupiter. In Saturn and Jupiter the mentioned metallic phase transitions occur. Not only hydrogen, but also helium, in quite recent work, has been shown to behave as a metal at least at the highest pressures encountered in Jupiter and perhaps over a wider range of pressures in the many, often much hotter, planets of Jupiter’s mass and larger that are now apparently common in the universe. Of particular relevance to gas-giant evolution, is their specific heat and hydrogen/helium immiscibility curve. These are accessible via the above mentioned theoretical and experimental techniques of HEDP studies. Recent and past measurements of the higher gravitational moments of Jupiter and Saturn via the Juno mission and the Cassini Grand Finale, provide planetary density-profile data of unprecedented character. Also, the rings of Saturn and Uranus may act as seismographs, recording gravitational perturbations associated with acoustic oscillation modes of the planet. Moreover, Doppler imaging from spacecrafts for “helio”seismology on giant planets holds great promise. These data, along with high-pressure EOS data and benchmarked theory for planetary materials from HEDP laboratory studies may provide key constraints for new models of solar systems and exoplanets.

Connecting observations for Jupiter and Saturn, Uranus and Neptune, and assimilating data from exoplanets, experimental data and theoretical formulations, directly (physical properties) and indirectly (coupling to processes), naturally belongs to the fields of (initially idealized) inverse problems and deep learning. This Symposium aims to break new ground in these connections.