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Fast and Precise Learned Charged-Particle Trajectory Regression at the Large Hadron Collider
We propose a training recipe that treats charged-particle trajectory parameter regression on high-energy physics detector data as a sequence-modeling task. Kalman filters and linearized least-squares fits have been the classical standard approach for this task: they are optimal estimators for sparse
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