A differentiable end-to-end model combining graph attention networks with clustering and fitting improves muon track reconstruction and pT estimation at the LHC compared to factorized approaches.
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A CNN-based hit filter is proposed to accelerate online track reconstruction by removing unnecessary hits in high-occupancy detector environments at hadron colliders.
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Learning to Reconstruct: A Differentiable Approach to Muon Tracking at the LHC
A differentiable end-to-end model combining graph attention networks with clustering and fitting improves muon track reconstruction and pT estimation at the LHC compared to factorized approaches.
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Filtering hits for speeding up online track reconstruction at hadron colliders
A CNN-based hit filter is proposed to accelerate online track reconstruction by removing unnecessary hits in high-occupancy detector environments at hadron colliders.