Speaker
Description
As the High-Luminosity LHC (HL-LHC) era approaches, significant improvements in reconstruction software are required to keep pace with the increased data rates and detector complexity. A persistent challenge for high-throughput GPU-based event reconstruction is the estimation of track parameters, which is traditionally performed using iterative Kalman Filter-based algorithms. While GPU-based track finding is progressing rapidly, the fitting stage remains a bottleneck. The main slowdown is coming from data movement between CPU and GPU which reduce the benefits of acceleration.
This work investigates a deep learning-based alternative using Transformer architectures for the prediction of the track parameters. The approach shows promising results on the TrackML dataset.
| Would you like to be considered for an oral presentation? | Yes |
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