Matter RADiation interactions SIMulations and Innovative radiation Detectors development
Improving data analysis and reconstruction capabilities of the simulation software MRADSIM and of two innovative radiation detectors
BEAMIDE is an innovative spin-off of INFN (Italian National Institute for Nuclear Physics) with core business of radiation hardness assurance services tests and software & innovative radiation detectors R&D. The pilot is about the development of a software set to simulate the radiation effects on electronic and electro-mechanical systems/subsystems (DUT). In doing that use the power of open source GEANT4 package developed for high energy physics experiments (i.e. at CERN or space-borne experiments as AMS on ISS) for particle transportation from radiation source to the DUT. MRADSIM (www.mradsim.com) is a cross-platform system using OpenCascade, Qt5, CMake etc. and it provides a user-friendly interface to the users which are not experts in computing to do their simulations in autonomy.
The same kind of support is of great interest also for our innovative radiation detectors R&D group. In particular, our two detection systems’ data acquisition and reconstructions software development may benefit the support listed above. These two detection systems are at TRL=4, and they are a personal portable active dosimeters system (PDOZ) and a gamma camera (GamCam) system for health and material search application using image reconstruction and XRF method, in health and material search applications, respectively.
MRADSIM needs CPU, GPU, disk space, but in particular training on how to implement algorithms for combined CPU and GPU usage and optimization, to be able to simulate very large number of radiation events (e.g. 10^16 particles through a big GEO satellite over 20 years of activity). Alternatively, we may think to use AI and ML techniques to run lesser number of events while approaching to the same result accuracy as in full run case.
T1=T0+1. Define the needs of BEAMIDE and do a precise scheduling of needs and implementation vs time.
T2=T0 + 2 months to achieve the familiarity with services offered by EOSC and get familiar with experts of GPU and AI/ML. Get documentation from EOSC site (printed or media) and collect data from literature.
T3= T2 + 3. Acquire know-how and develop solutions for GPU computing to improve the simulation time of large number of events. This is a milestone and the outcome is to prepare a demonstrator and compare simulation accuracy and timing performances against classical” solution. The classic means no GPU use.
T4= T2 + 4 Acquire know-how and develop solutions for AI/ML application of GamCam data and eventually also on PDOZ/GamCam data. This is a milestone and the outcome is prepare a demonstrator and compare simulation spatial resolution and image reconstruction performances against “classical” solution. The classic means no AI/ML use.
T5 = T4 +2 Benchmarking of developed solutions in T3 and T4.Benchmarking of developed solutions and comparison with published performances of best solution available on the market.
T6 = T5 +1 Conclusion, presentation of results and divulgation to a larger public.