1. Geant4 Simulations of Space Radiation Sensors and Environment at The Aerospace Corporation
M. D. Looper
Physical Sciences Laboratory
Space Sciences Department
April 10-12, 2017

2. Modeling sensors, shielding, penetration/secondaries…
RPS (Relativistic Proton Spectrometer) aboard Van Allen Probes
Microdosimeters aboard AeroCube-6 (1/2U CubeSat)
MagEIS (Magnetic Electron Ion Spectrometer) aboard Van Allen Probes
These are Geant4 geometry dumps of some of the specific sensors I’ve worked on at Aerospace since the last Geant4 Space Users’ Workshop in Hiroshima. RPS uses a silicon solid-state detector (SSD) stack and a MgF2 Cherenkov radiator with microchannel plate photomultiplier readout to measure trapped and interplanetary protons above 60 MeV, as well as electrons above about 8 MeV. MagEIS uses magnetic spectroscopy to study electrons from 10s of keV to a few MeV. AeroCube-6 measures dose in low Earth orbit, and CRaTER has two big chunks of tissue-equivalent plastic inside a silicon SSD stack to study effects of radiation on astronauts. I also use Geant4 to calculate the effects of shielding and to help calibrate lab test setups for applied purposes, and lately I have been doing a lot of calculations of cosmic-ray secondaries produced at the Moon.
CRaTER (Cosmic Ray Telescope for the Effects of Radiation) aboard Lunar Reconnaissance Orbiter

3. MagEIS: Refining Calibration
Iterated simulations of response with field intensity scaled by up to several percent to line up simulated “sweet spot” energy with observed values in each sensor (LOW, MED, HIGH)
Gap “structure” at upper right was container for fields in simulation of MagEIS (LOW/MED shown, LOW data at left)
Instrument paper: Blake et al., Space Sci. Rev. 2013, doi: /s
Background correction: Claudepierre et al., J. Geophys. Res. 2015, doi: /2015JA021171
Schematic and Geant4 geometry dumps of the MagEIS sensor heads, and performance parameters. Observations show energy deposits in a given pixel, including “sweet spot” and background. Recent simulation work has resulted in improved agreement between calibrated energy deposits and simulated values, giving more accurate values for actual sensors’ energy passbands. Figures are from references cited.

4. MagEIS: Defining Background
Background in the HIGH observations turned up at low energy deposits -- not reproduced in simulations of penetrating high-energy protons and electrons (and bremsstrahlung).
Simulations of 56Co gammas from activation of red parts (G4Box structures) above gave turned-up energy deposit spectra as at left.
Currently considering other nuclides with shorter half-lives.

5. CRaTER: Energy Deposit Behind Shielding
Note to approvers: the figures on this chart and the next have previously been cleared for public release as part of OTR
Purpose of CRaTER is to measure energy deposit as in astronaut’s bone marrow under tissue, including high-Z primaries and secondaries that deposit more energy (dose).

6. CRaTER: Background from Outside Field of Viewp+
High energy-deposit measurements are contaminated by background; presently performing simulations of response, including to secondaries generated on material of spacecraft.

7. CRaTER: Sensor Mounted on LRO Spacecraft
In addition to the multi-element telescope, there is a microdosimeter in its electronics box (Aerospace flies a lot of these)
Adjoint Monte Carlo methods are good for small sensitive volume(s) inside large target
Limited to electromagnetic interactions; gammas, electrons/positrons, protons

8. Adjoint Monte Carlo With “Post-Processing,” Proton Primaries
Simulation of sensitive part inside part package inside spacecraft
Geant4 simulates adjoint particles back in time outward from source surface; for those that could have come from outside, simulates forward particles inward
Post-processing: either for specific forward protons simulated or for a new population (needs another normalization step), simulate with full physics, e.g., Shielding_EMZ (my go-to physics list), including short-range, high-Z particles from nuclear interactions
This is an idea I had for how to make adjoint simulations more realistic, by adding another stage to the standard Geant4 adjoint procedures. Note to approvers: I am in the process of evaluating this idea; if I get some definite results between submitting this chart package for clearance and giving the talk, I will mention verbally that the difference is whatever percent with and without the “post-processing.” I will not stick another chart into the package unapproved.

9. MagEIS: CAD (Computer-Aided Design) Import
GDML
primitives
Geant4 can import GDML (Geometry Description Markup Language) files as tessellated solids. Various software tools can convert CAD STEP files to GDML; I am testing FASTRAD and Solveering InStep Basic. Above left is old (pre-launch) model of MagEIS LOW sensor with most structure imported from CAD file; above right is most current model built using Geant4 primitives (G4Box, G4Tubs, G4SubtractionSolid, etc.).

10. I last experimented with GDML import eight or nine years ago; my codes crashed with tracking errors. A recent comparison of the two MagEIS models on previous slide shows that the crashing has gone away; code runs much slower (especially for electrons – all those secondaries?), but it seems worth another look due to the time saved in creating geometries.

11. Lunar Albedo: Neutrons Below/At Surface
We have performed simulations of GCR albedo as on previous slide for slabs 1 mm to 10 m thick with 1% or 10% hydrogen by weight above dry regolith (and dry throughout). Here are neutrons going into bottom and coming out top of 10 cm slab with 0%, 1%, or 10% hydrogen. “Filtering” of incoming GCRs results in minor reduction of neutrons coming up from below (left graph), but a much larger fraction are stopped from going back out through the hydrated layers (right graph). Where did they go? Note to approvers: This chart and the next two have previously been cleared for public release as part of OTR
New: extensive set of simulations of hydrated layers of varying thickness and either 1% or 10% by weight hydrogen above dry regolith, bombarded by GCRs
Also evaluating 9% water by weight (8% oxygen, 1% hydrogen) and pure ice
Here are angular distributions of upgoing MeV albedo neutrons going into bottom and coming out top of 10 cm slab with 0%, 1%, or 10% hydrogen
Minimal effect at bottom of slab, significant depletion of neutrons escaping top

12. Lunar Albedo: “Tertiary” Protons
Neutrons preferentially transfer their energy to hydrogen nuclei, depleting albedo neutrons and enhancing protons
Effect saturates with thicker hydrogenated layers, as “tertiary” protons come to a stop before reaching surface
Schematic at left from Schwadron et al., Icarus 2016, doi: /j.icarus
Those missing neutrons transferred their energy to protons from the additional hydrogen: primary GCRs produce secondary (albedo) neutrons and protons, and these neutrons produce “tertiary” protons adding to the albedo. Energy range plotted is MeV, appropriate to the D4D6 observations we have made; schematic is after figure 14 of Schwadron et al. (2016).

13. Lunar Albedo: Proton Enhancement vs. H Fraction
Selecting out the parts of the previous plots that approximately correspond to our nadir and horizon observations, we see that within statistics the tertiary proton enhancement is approximately linear with hydrogen column density, independent of the concentration that makes it up, until the thickness gets great enough to range out the protons and saturate the yield. This means that very concentrated, thin layers can have the same effect as less enhanced but thicker (but not too thick) layers.
Here are fractional enhancements of proton yield from parts of previous plots near nadir (0°to 20°) and limb (70°to 90°), vs. thickness and vs. H column density.
Errors on all points are about 0.02, so points below that (including small negative numbers) are consistent with
Approximately linear response (“optically thin” filter), followed by saturation