Speaker
Description
The ALICE time projection chamber (TPC) is the main tracking and particle identification device used in the ALICE experiment at CERN. With a 900 GB/s data rate and a fully GPU-based online reconstruction, the online processing is capable of handling even the densest environments of central Pb--Pb interactions at 50 kHz nominal interaction rate (Run 3) and creates an ideal environment for the application of parallelizable machine learning algorithms.
The work to be presented concerns cluster finding, with the first-ever application of neural networks in ALICE online processing. Both a classification network for noise removal and a regression network for cluster property inference can be presented. A 3D charge input to the cluster finding step marks a new approach taken for this challenge. The tuning of this algorithm for physics and computing performance is a major part of the optimizations put in place for the feasibility of deployment. On the technical aspect, design optimizations of the network architecture, floating-point quantization, custom CUDA-streamed implementation, efficient utilization of the ONNX Runtime framework and metrics from the first commissioning runs mark the cornerstones of this project. The achieved performance is a reduction in total number of clusters of up to 18% with maintained or improved physics performance, that is demonstrated on both simulated and real data. Extensions of this work demonstrate the feasibility of extracting a track direction vector before the tracking stage, using the local 3D charge information only.