Speaker
Description
Diamond detectors with laser-graphitized electrodes orthogonal to the surface are emerging as fast, full-carbon sensors for applications ranging from High Energy Physics to Nuclear Medicine. Recent advances in low-resistance electrode fabrication have enabled sub-100 ps timing performance. However, accurately modeling signal formation remains challenging due to the intertwined effects of energy deposition, carrier transport, signal propagation, and readout electronics. We present an innovative simulation approach based on an extension of the Ramo–Shockley theorem by introducing time-dependent weighting potentials. These potentials, which model propagation effects in a theoretically sound manner, are obtained by solving a third-order partial differential equation derived as a quasi-static approximation of Maxwell’s laws.
The workflow combines HTC workloads for simulating energy deposition and carrier trajectories with GPU-accelerated tasks for solving the PDE and computing induced charge. Snakemake orchestrates dependencies across heterogeneous components, while the AI_INFN Platform enables offloading to HPC resources at TeRABIT (Padova) and Tier-1 INFN CNAF, with artifacts exchanged via INFN Cloud S3 storage.
While not demanding in terms of scalability, this deployment exemplifies a shift from monolithic applications to microjob-based architectures, enabling efficient use of heterogeneous, distributed resources. This approach opens the door to next-generation detector simulations, where modular workflows and accelerator-aware computing become the new standard.