Machine Learning for Trigger and Data Acquisition
The Large Hadron Collider (LHC) operates at a staggering speed, producing billions of collisions per second, generating an enormous amount of raw data for experiments like CMS and ATLAS. However, it is virtually impossible to process, readout, and store all this data. To address this challenge, a sophisticated multi-tier trigger system is in place to select only the most relevant collisions for further analysis, boasting a high efficiency and low false positive rate.
The first level of the trigger system plays a crucial role in making rapid selection decisions within microseconds, given the throughput and latency constraints. At the same time, the field of Machine Learning (ML) has been rapidly evolving, with new and powerful techniques constantly emerging. Innovations in faster processors and specialized ML devices have further propelled the growth of ML, making its integration into real-time processing for trigger and data acquisition increasingly feasible and relevant.
As the LHC gears up for upgrades that will significantly increase its instantaneous luminosity in the next decade, the demand for fast ML at the edge becomes indispensable to manage and filter the vast data stream effectively. This lecture will delve into the application of ML, particularly neural networks (NN), for ultra-low latency event selection, rapid reconstruction, anomaly detection, and data reduction at LHC experiments. The implementation of real-time ML inference on GPU and FPGA devices will be explored, along with cutting-edge optimization techniques like high-level synthesis, quantization, and knowledge distillation.
In conclusion, the fusion of Machine Learning with real-time processing for trigger and data acquisition holds immense potential for streamlining operations at the LHC experiments, ensuring efficient data selection and processing amid the deluge of collisions generated by the world’s most powerful particle accelerator.
