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Saturn neutral particle modeling Overview of Enceladus/Titan research with possible application to Mercury. Johns Hopkins University Applied Physics Laboratory. H. Todd Smith. Introduction.
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Saturn neutral particle modelingOverview of Enceladus/Titan research with possible application to Mercury Johns Hopkins University Applied Physics Laboratory H. Todd Smith
Introduction • Show examples of how we used neutral particle modeling with data analysis for studying the Saturnian system • Titan and Enceladus neutral particle source investigation • Initial ground work for possible assistance with Mercury neutral particle prediction and analysis
Current Research • Investigating neutral particle sources and processes in Saturnian system • Particle distribution • Source & interaction characterization • Titan (nitrogen/methane) • Enceladus (water, nitrogen species) • Pre-Cassini arrival predictions (data limited to 3 fly-bys and Earth based observations) • Post-arrival interpretation using data analysis and modeling
Predicted nitrogen source - Titan - Dense atmosphere (~95% Nitrogen) - Larger than Mercury - No intrinsic magnetic field Anticipated nitrogen source(Pre-Cassini)
Model predictionsComputational Model Overview • 3-D neutral particle model • Multi-species, multi-resolution • Modeled aspects • All gravitational effects and collisions • Particle interactions with photons, electrons & ions • Output • 3-D Neutral particle density and topology • Ion production
Modeling Predicted Titan-Generated Nitrogen Tori • Neutral densities too low for direct detection (must detect ionization products – CAPS) • Titan could produce N+ in inner magentosphere (6-10 Rs) • N2 shows same basic trend but with lower densities
Nitrogen detected using CAPS! (…but not where anticipated) • Analysis indicated source at Titan’s orbit CAPS N+ data
Things are not as expected Dominant nitrogen source in vicinity of Enceladus orbit Credit: NASA/JPL/Space Science Institute - Mainly H2O ice - Geologically young surface - New images indicate source of E-ring
Enceladus observations concur Credit: NASA/JPL/Space Science Institute • Enceladus “plumes” detected • Tiger stripes – south pole • Possible nitrogen source (Water dominated) • Principal source of E-ring • Subsurface composition questions • Cassini Ion Neutral Mass Spectrometer (mass 28 detection ~4%) • What processes produce these plumes • Neutral particles provide clues to mechanisms • Water should remain frozen under pressure/temperature conditions • Ammonia (& possibly N2) could explain plume activity (controversial) (despite large efforts, no previous detections of ammonia) Credit: NASA/JPL/Space Science Institute Credit: NASA/JPL/Space Science Institute
What is the source species for N+ CAPS N+ data • N2 Enceladus source (if present) could produce observed N+
Ammonia detected Figure 5. Upper limit for N2+ and NHx+ based on CAPS LEF observations. Results shown as the upper limit N2+ (red bars) and NHx+ (black line) percentage of all heavy ions as a function of radial distance from Saturn in planetary radii (Rs). Error bars represent 1-sigma errors for peak widths. (Enceladus orbits at ~4 Rs while Titan is ~20Rs from Saturn).
Using modeling to understand Enceladus source mechanisms Narrow torus Scattered torus OH Observations Column * Johnson et al., The Enceladus and OH Tori at Saturn, ApJ Letters, 644:L137-L139, 2006
3-D neutral particle distributions assisting with field data interpretation Io Enceladus
Constraining Enceladus source using neutral particle data and modeling • Larger than expected ejection velocity (~750 m/s) • Ejection angle limited (< 30 degrees from pole) • Variable source rate (~3-10 x 1027 /sec)
Enceladus dominant source in Saturn’s magnetosphere…WHY?? • Possible causes and focus of latest research • Atmospheric interactions are more complex than estimates (effecting atmospheric loss) • Plasma environment more complex • Hydrodynamic methane escape?
Possible research • Modifying model for the Mercury system • Sample data in 3-D model along spacecraft trajectory • Local densities and source characterization • Global distributions • Spatial and temporal variation • Insight into interaction process • Coordinate with other modeling efforts to avoid duplication of effort • Pre-arrival predictions to optimize instrument utilization • Post-arrival modeling to help interpret observations