Poster De Conférence Année : 2025

Track parameter regression with Transformers

Résumé

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.

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Dates et versions

hal-05437583 , version 1 (01-01-2026)

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  • HAL Id : hal-05437583 , version 1

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Jeremy Couthures, Corentin Allaire, Marco Delmastro, David Rousseau, Alexis Vallier. Track parameter regression with Transformers. 7th Inter-Experimental LHC Machine Learning Workshop, May 2025, Meyrin, Switzerland. . ⟨hal-05437583⟩
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