Speaker
Description
The High-Luminosity LHC (HL-LHC) will bring large increases in collision rate and pile-up. This represents a significant surge in both data quantity and complexity. In addition to excellent physics performance, a high computational efficiency is critical to fully exploit the HL-LHC datasets. In response, substantial R&D efforts in machine learning (ML) have been initiated by the ATLAS collaboration to develop faster and more efficient algorithms capable of managing this deluge of data.
Charged particle tracking is the most computationally costly aspect of the reconstruction of data from the ATLAS detector. We present the first functional prototype of an ML-based track reconstruction algorithm for the ATLAS experiment at the HL-LHC. It is fully integrated into the software stack of the ATLAS collaboration (“athena”), and can be run on heterogeneous GPU clusters via a technique we call “tracking-as-a-service”.
Charged particle reconstruction is performed using a graph neural network, combined with custom algorithms for high-throughput graph generation and graph segmentation. The functional prototype that deploys this pipeline is the result of a sustained and coordinated R&D effort over the past seven years. After a brief summary of the physics performance, we report a standardized suite of metrics.