The highly miniaturised radiation monitor Edward Mitchell * On behalf of the HMRM collaboration (STFC Rutherford Appleton Laboratory & Imperial College London) Geant4 Space Users Workshop August 2010, Seattle *Imperial College London, UK 1

HMRM context Contract awarded by ESA for a phase A-B study General purpose/low resolution particle monitor Low mass and power Primarily for MEO (2000 km ~ km) Particle identification (energy and species) and dose measurement capabilities 2 (Diagram from ESA; timeline is approximate)

Particle environments in earth orbit Reference orbits chosen for detailed analysis: Maximum, minimum and orbital variation from SPENVIS Consider contributions from: – Trapped protons and electrons – Solar protons – GCR protons – Ions 3 orbittypealtitudeinclinationperiodcomment ALEO700 km98 o 1.65 hrsSSO; weather, remote sensing BMEO4,000 km83 o 2.19 hrsElliptical; space science CMEO10,000 km0o0o 5.79 hrsEarth observation DMEO23,222 km56 o hrsNavigation EGEO35,786 km0o0o hrsCommunications, meteorology

Particle environments in earth orbit 4 Example: orbit D (23000 km, 56 o MEO) – trapped electrons with E > 0.1 MeV e-e- p

Energy deposits in silicon HMRM identifies particles by measuring the ionisation response in silicon Mean energy loss rates are similar for different species (when rest mass normalised)  Degeneracy in a single sensor Path lengths are greater for electrons (Coulomb scattering)  Multiple sensors allow sampling of the energy loss curve at multiple points  Greater particle discrimination 5 e-e- p

HMRM architecture Telescopic configuration of four sensors Inter-sensor shielding to aid particle discrimination Casing and aperture designed to restrict exposure to particles Field-programmable gate array (FPGA) for data processing 6 2 cm

Sensors CMOS Monolithic Active Pixel Sensors (MAPS) with a 50x50 pixel array (1 mm 2 ) Pixel size 20x20x12 µm System-on-chip, radiation tolerant, fast read-out, low power Low noise: ENC ~ 10e - rms per pixel Damaged pixels may be individually masked Ionisation charge is collected during a variable integration period, chosen to suit the radiation environment 7

Design methodology Need to optimise the parameters of the chosen architecture: – Sensor locations – Casing thickness, material – Aperture size and shape – Inter-sensor shield thickness, material These should maximise particle identification ability (subject to mass, volume, power, manufacturing and other constraints) Problems: many design possibilities, stochastic processes, results depend on radiation environment  Propose a baseline design, then simulate individual changes and evaluate according to a set of benchmarks 8

GEANT4 model A simple HMRM model ( ~ 15 volumes) was used to simulate the baseline design This geometry has been optimised to improve particle detection The full electromechanical design will be implemented next 9

Physics list Physics list: – EM: G4EmStandardPhysics (STD EM/0) – Hadronic: BiC + QGSP The monitor’s particle detection algorithms rely on accurate simulation results, particularly for low energy EM losses A cut length of 10 µm was chosen to best model the energy loss fluctuations in thin silicon layers Validation required, especially important for small energy loss fluctuations and low energy electron scattering 10

Simulation method 11 A homogeneous, isotropic particle flux (typically 10 8 particles per run) is generated from a sphere, giving ~ 500,000 sensor hit events Efficiency increased 3x by selectively killing low energy, off-acceptance particles Initially use E -1 differential spectrum (equal numbers of primaries for logarithmic bins)  later use SPENVIS spectra

Performance benchmarks Candidate monitor designs were evaluated according to three criteria: – Geometric acceptance: typically ~ cm 2 (effective area) – Identification efficiency: the fraction of particles detected – Identification (im)purity: the fraction misidentified Energy binning scheme: seven bins for each species (protons and electrons) covering: – Electrons: 0.05 – 6.0 MeV – Protons: 1.3 – 300 MeV Problems: – MIP deposit degeneracy – “Cross-talk” between some energy bins 12

Sensor onset energies 13

Energy spectrum reconstruction The HMRM returns a particle count for each species/energy bin Total dose and species dose are also measured Particle energy spectra are reconstructed offline (in ground segment) Particle count is scaled  This accounts for ID efficiency and effective area Misidentification between bins can be described statistically  Use this to reconstruct the incident spectrum 14 E -1 spectra reconstructed with E -1 calibration data

Summary and future work HMRM features: – Low energy detection thresholds – Large particle energy range – Low mass and power (current estimates < 30 g, <200 mW) – Targeted at broad range of earth orbits Future work: – Implement full GEANT4 geometry model – Investigate orbital flux variability and anisotropy – Beam and radioisotope tests – Physics model validation 15

HMRM Collaboration STFC Rutherford Appleton Laboratory, Didcot, UK – D. Griffin – R. Turchetta – N. Guerrini – O. Poyntz-Wright – A. Marshall – M. Hapgood – C. Perry Imperial College London, UK – H. Araújo – E. Mitchell ESA – A. Menicucci – E. Daly 16