1. Saturn neutral particle modeling Overview of Enceladus/Titan research with possible application to Mercury Johns Hopkins University Applied Physics Laboratory H. Todd Smith

2.  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 Introduction

3.  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 Current Research

4. Predicted nitrogen source - Titan - Dense atmosphere (~95% Nitrogen) - Larger than Mercury - No intrinsic magnetic field Anticipated nitrogen source (Pre-Cassini)

5.  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 Model predictions Computational Model Overview

6.  Neutral densities too low for direct detection (must detect ionization products – CAPS)  Titan could produce N + in inner magentosphere (6-10 Rs)  N 2 shows same basic trend but with lower densities Modeling Predicted Titan-Generated Nitrogen Tori

7. Nitrogen detected using CAPS! (…but not where anticipated)   Analysis indicated source at Titan’s orbit CAPS N+ data

8. Things are not as expected - Mainly H2O ice - Geologically young surface - New images indicate source of E-ring Credit: NASA/JPL/Space Science Institute Dominant nitrogen source in vicinity of Enceladus orbit

9. Enceladus observations concur   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 N 2 ) could explain plume activity (controversial) (despite large efforts, no previous detections of ammonia) Credit: NASA/JPL/Space Science Institute

10. What is the source species for N +   N 2 Enceladus source (if present) could produce observed N + CAPS N+ data

11. Ammonia detected Figure 5. Upper limit for N 2 + and NH x + based on CAPS LEF observations. Results shown as the upper limit N 2 + (red bars) and NH x + (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).

12. Using modeling to understand Enceladus source mechanisms Column Narrow torus Scattered torus OH Observations * Johnson et al., The Enceladus and OH Tori at Saturn, ApJ Letters, 644:L137-L139, 2006

13. 3-D neutral particle distributions assisting with field data interpretation Io Enceladus

14. 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 10 27 /sec)

15. 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?

16.  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 Possible research