DRIFTS

Modern particle collider detectors generate data at petabyte-per-second rates, far exceeding current storage capabilities. To handle this immense data flow, fast trigger algorithms perform real-time reconstruction and event filtering. With the next generation of the High Energy Physics experiments such as upcoming High Luminosity upgrade of the Large Hadron Collider and the introduction of advanced detectors like the High Granularity Calorimeter in the CMS experiment, data volumes will surge, necessitating more efficient processing techniques. This project proposes an innovative architecture for triggering systems by implementing distributed deep learning (DL) across all layers of the trigger system. Integrated within low-latency, high-speed Field Programmable Gate Arrays (FPGAs) and Application-Specific Integrated Circuits (ASICs), this approach aims to utilize on-detector DL-based encoded data directly. Additionally, it seeks to enable end-to-end reconstruction of physics objects, such as electrons, photons, and jets, directly from raw sensor data, eliminating the need for intermediate steps like clustering. By deploying a single, optimally distributed DL algorithm, the project expects to outperform traditional multi-stage processing methods while operating within existing hardware constraints. This unified approach simplifies system architecture and leverages the evolving capabilities of FPGAs. The methodology encompasses four key work packages: first, developing a refined simulation framework that provides realistic detector responses, including accurate representations of noise, efficiency, and electronics behavior; second, creating and optimizing distributed DL algorithms using graph neural networks and transformer-based attention mechanisms with specialized loss functions targeting both reconstruction accuracy and enhanced data interpretability; third, implementing these optimized algorithms on non-uniform hardware components such as FPGAs and ASICs with constrained resources, utilizing quantization and pruning strategies for efficient deployment; and fourth, performing comprehensive validation and performance evaluation, including physics-object reconstruction quality, latency compliance, throughput, scalability, and overall data rate reduction. This final validation ensures that the system meets stringent real-time requirements, demonstrating its feasibility and superiority compared to current trigger approaches. The proposed advancement directly enhances the potential for groundbreaking discoveries and the observation of rare processes in future collider experiments.