Articles Citing

Transformers for Charged Particle Track Reconstruction in High-Energy Physics

Samuel Van Stroud ^1,*, Philippa Duckett ^1,†, Max Hart ^1,‡, Nikita Pond ^1,§, Sébastien Rettie ^1,2,∥, Gabriel Facini ^1,¶, and Tim Scanlon ^1,**

¹Centre for Data Intensive Science and Industry, Department of Physics and Astronomy, University College London, London, United Kingdom
²CERN, European Organization for Nuclear Research, Geneva, Switzerland

^*Contact author: sam.vanstroud.18@ucl.ac.uk
^†Contact author: philippa.duckett.21@ucl.ac.uk
^‡Contact author: max.hart.22@ucl.ac.uk
^§Contact author: nikita.pond.18@ucl.ac.uk
^∥Contact author: sebastien.rettie@cern.ch
^¶Contact author: g.facini@ucl.ac.uk
^**Contact author: timothy.scanlon@ucl.ac.uk

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Articles Citing

Transformers for Charged Particle Track Reconstruction in High-Energy Physics

Generating particle physics Lagrangians with transformers

DCTracks: a dataset for machine learning-based drift chamber track reconstruction

Leveraging reinforcement learning, genetic algorithms and transformers for background determination in particle physics

Better queries, cheaper attention: adapting transformers for efficient sparse reconstruction

dN/dx reconstruction with deep learning for high-granularity TPCs

Full event interpretation with machine-learning-based particle-flow reconstruction in the CMS detector

Deep Learning Methods as a Tool for Overcoming the Crisis of Particle Tracking in High Luminosity HEP Experiments

Progress in ${\cal CP}$ violating top-Higgs coupling at the LHC with machine learning

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